From 790768ae76af029cee39449fea35f6cb828358d1 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Wed, 17 Aug 2022 14:17:01 +0800 Subject: [PATCH 01/12] 20220817 --- Part.1.E.5.strings.ipynb | 313 ++++++------------ Part.1.E.6.containers.ipynb | 209 ++++++------ Part.1.E.7.files.ipynb | 34 +- Part.1.F.deal-with-forward-references.ipynb | 4 +- Part.2.A.clumsy-and-patience.ipynb | 8 +- Part.2.B.deliberate-practicing.ipynb | 2 +- ...2.C.why-start-from-writing-functions.ipynb | 7 +- Part.2.D.1-args.ipynb | 16 + Part.2.D.2-aargs.ipynb | 192 ++++++++++- Part.2.D.3-lambda.ipynb | 251 +++++++++++++- 10 files changed, 675 insertions(+), 361 deletions(-) diff --git a/Part.1.E.5.strings.ipynb b/Part.1.E.5.strings.ipynb index aeff7632c..5ec5bfc2a 100644 --- a/Part.1.E.5.strings.ipynb +++ b/Part.1.E.5.strings.ipynb @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -199,7 +199,7 @@ "'\\nSimple is better than complex.\\nComplex is better than complicated.\\n'" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -342,25 +342,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stdin", - "output_type": "stream", - "text": [ - "Please tell me your age: 19\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'<' not supported between instances of 'str' and 'int'", + "ename": "KeyboardInterrupt", + "evalue": "Interrupted by user", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please tell me your age: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mage\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'I can not sell you drinks...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Have a nice drink!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_14509/1388896846.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please tell me your age: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mage\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'I can not sell you drinks...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Have a nice drink!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1005\u001b[0m )\n\u001b[0;32m-> 1006\u001b[0;31m return self._input_request(\n\u001b[0m\u001b[1;32m 1007\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1008\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"shell\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m 1049\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1052\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user" ] } ], @@ -382,21 +377,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { - "name": "stdin", - "output_type": "stream", - "text": [ - "Please tell me your age: 19\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Have a nice drink!\n" + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: ''", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_14509/1650674318.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m age = int(input('''Please tell me your age: \n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0man\u001b[0m \u001b[0mint\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m22\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m '''))\n\u001b[1;32m 4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mage\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'I can not sell you drinks...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: ''" ] } ], @@ -433,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -442,7 +434,7 @@ "'\\\\'" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -453,15 +445,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "SyntaxError", - "evalue": "EOL while scanning string literal (, line 1)", + "evalue": "EOL while scanning string literal (190624804.py, line 1)", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m '\\'\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n" + "\u001b[0;36m File \u001b[0;32m\"/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_14509/190624804.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m '\\'\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n" ] } ], @@ -917,91 +909,20 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "9" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "13" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "'A'" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "请照抄一遍这个数字 3.14: 3.14\n" - ] - }, - { - "data": { - "text/plain": [ - "3" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "28.26" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.143.143.14\n" + "ename": "KeyboardInterrupt", + "evalue": "Interrupted by user", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_14509/602898412.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m65\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 与 ord() 相对的函数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'请照抄一遍这个数字 3.14: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# int(s) 这一句会报错…… 所以暂时注释掉了\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1005\u001b[0m )\n\u001b[0;32m-> 1006\u001b[0;31m return self._input_request(\n\u001b[0m\u001b[1;32m 1007\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1008\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"shell\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m 1049\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1052\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user" ] } ], @@ -1081,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1090,7 +1011,7 @@ "'NOW IS BETTER THAN NEVER.'" ] }, - "execution_count": 24, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -1100,7 +1021,7 @@ "'now is better than never.'" ] }, - "execution_count": 24, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1674,7 +1595,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1716,7 +1637,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1725,7 +1646,7 @@ "\"Special cases aren't special enough to break the rules.\"" ] }, - "execution_count": 5, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -1735,7 +1656,17 @@ "\"Special cases aren't special enough to break the rules.\"" ] }, - "execution_count": 5, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "\"Special cases aren't special enough to break the rules.\"" + ] + }, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1747,7 +1678,8 @@ "# str.expandtabs(tabsize=8)\n", "s = \"Special\\tcases\\taren't\\tspecial\\tenough\\tto\\tbreak\\tthe\\trules.\"\n", "s.expandtabs()\n", - "s.expandtabs(2)" + "s.expandtabs(2)\n", + "s.expandtabs(1)" ] }, { @@ -1823,7 +1755,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1832,27 +1764,27 @@ "'Simple is better than complex.'" ] }, - "execution_count": 4, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "'mple is better than comple'" + "'ple is better than comp'" ] }, - "execution_count": 4, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "' is better than co'" + "'mple is better than comple'" ] }, - "execution_count": 4, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1863,8 +1795,8 @@ "\n", "s = \"Simple is better than complex.\"\n", "s\n", - "s.strip('Six.p') # p 全部处理完之后，p 并不在首尾，所以原字符串中的 p 字母不受影响；\n", - "s.strip('pSix.mle') # 这一次，首尾的 p 被处理了…… 参数中的字符顺序对结果没有影响，换成 Sipx.mle 也一样……" + "s.strip('Sim.lex') # p 全部处理完之后，p 并不在首尾，所以原字符串中的 p 字母不受影响；\n", + "s.strip('Si.x') # 这一次，首尾的 p 被处理了…… 参数中的字符顺序对结果没有影响，换成 Sipx.mle 也一样……" ] }, { @@ -1876,7 +1808,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": { "button": false, "collapsed": true, @@ -1892,17 +1824,17 @@ "'Simple is better than complex.'" ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "'mple is better than complex.'" + "'ple is better than complex.'" ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, @@ -1912,7 +1844,7 @@ "' is better than complex.'" ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1924,7 +1856,7 @@ "# str.lstrip([chars])\n", "s = \"Simple is better than complex.\"\n", "s\n", - "s.lstrip('Six.p') # p 全部处理完之后，p 并不在首部，所以原字符串中的 p 字母不受影响；\n", + "s.lstrip('Sim.lex') # p 全部处理完之后，p 并不在首部，所以原字符串中的 p 字母不受影响；\n", "s.lstrip('pSix.mle') # 这一次，首部的 p 被处理了…… 参数中的字符顺序对结果没有影响，换成 Sipx.mle 也一样……" ] }, @@ -2073,7 +2005,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2082,37 +2014,7 @@ "'Mike,22,San Francisco'" ] }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "['Mike,22,San', 'Francisco']" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "['Mike', '22', 'San Francisco']" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "['Mike', '22', 'San Francisco']" - ] - }, - "execution_count": 40, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, @@ -2122,17 +2024,7 @@ "['Mike', '22,San Francisco']" ] }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "['Mike,22,San Francisco']" - ] - }, - "execution_count": 40, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, @@ -2142,7 +2034,7 @@ "['Mike', '22', 'San Francisco']" ] }, - "execution_count": 40, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2159,13 +2051,13 @@ "\n", "r = s.splitlines()[2] # 取出返回列表中索引值为 2 的那一行\n", "r\n", - "r.split() # 如果没有给 str.split() 传递参数，那么默认为用 None 分割（各种空白，比如，\\t 和 \\r 都被当作 None）\n", - "r.split(sep=',') \n", - "r.split(',') # 上一行可以这样写。\n", + "# r.split() # 如果没有给 str.split() 传递参数，那么默认为用 None 分割（各种空白，比如，\\t 和 \\r 都被当作 None）\n", + "# r.split(sep=',') \n", + "# r.split(',') # 上一行可以这样写。\n", "\n", "r.split(sep=',', maxsplit=1) # 第二个参数指定拆分几次\n", "# r.split(sep=',', 1) # 上一行不能这样写。\n", - "r.split(sep=',', maxsplit=0) # 0 次，即不拆分\n", + "# r.split(sep=',', maxsplit=0) # 0 次，即不拆分\n", "r.split(sep=',', maxsplit=-1) # 默认值是 -1，拆分全部" ] }, @@ -2243,7 +2135,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 46, "metadata": { "button": false, "new_sheet": false, @@ -2259,7 +2151,7 @@ "' Sparse Is Better Than Dense! '" ] }, - "execution_count": 42, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2269,7 +2161,7 @@ "'================Sparse Is Better Than Dense!================'" ] }, - "execution_count": 42, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2279,7 +2171,7 @@ "'Sparse Is Better Than Dense!'" ] }, - "execution_count": 42, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2289,7 +2181,7 @@ "' Sparse Is Better Than Dense!'" ] }, - "execution_count": 42, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2299,7 +2191,7 @@ "'................................Sparse Is Better Than Dense!'" ] }, - "execution_count": 42, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2338,29 +2230,29 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "001.mp3\n", - "002.mp3\n", - "003.mp3\n", - "004.mp3\n", - "005.mp3\n", - "006.mp3\n", - "007.mp3\n", - "008.mp3\n", - "009.mp3\n", - "010.mp3\n" + "0001.mp3\n", + "0002.mp3\n", + "0003.mp3\n", + "0004.mp3\n", + "0005.mp3\n", + "0006.mp3\n", + "0007.mp3\n", + "0008.mp3\n", + "0009.mp3\n", + "0010.mp3\n" ] } ], "source": [ "for i in range(1, 11):\n", - " filename = str(i).zfill(3) + '.mp3'\n", + " filename = str(i).zfill(4) + '.mp3'\n", " print(filename)" ] }, @@ -2531,16 +2423,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'25 is John years old.'" + "'John is 25 years old.'" ] }, - "execution_count": 46, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -2548,7 +2440,7 @@ "source": [ "name = 'John'\n", "age = 25\n", - "'{1} is {0} years old.'.format(name, age)" + "'{0} is {1} years old.'.format(name, age)" ] }, { @@ -2574,7 +2466,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2733,8 +2625,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -2748,7 +2643,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" }, "toc-autonumbering": true }, diff --git a/Part.1.E.6.containers.ipynb b/Part.1.E.6.containers.ipynb index 057f7d7c7..40f695fb5 100644 --- a/Part.1.E.6.containers.ipynb +++ b/Part.1.E.6.containers.ipynb @@ -227,15 +227,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "a_list comprehends 10 random numbers: [52, 34, 7, 96, 33, 79, 95, 18, 37, 46]\n", - "... and it has 5 even numbers: [52, 34, 96, 18, 46]\n" + "a_list comprehends 10 random numbers: [2, 7, 8, 8, 2, 1, 5, 7, 6, 4]\n", + "... and it has 6 even numbers: [2, 8, 8, 2, 6, 4]\n" ] } ], @@ -245,7 +245,7 @@ "n = 10 \n", "\n", "# 生成一个 n 个元素的序列，每个元素是 1~100 之间的随机数\n", - "a_list = [random.randrange(1, 100) for i in range(n)]\n", + "a_list = [random.randrange(1, 10) for i in range(n)]\n", "print(f'a_list comprehends {len(a_list)} random numbers: {a_list}')\n", "\n", "# 从 a_list 里把偶数都挑出来\n", @@ -275,16 +275,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 2, 3, 4, 5, 6, 4, 5, 6, 4, 5, 6]" + "[1, 9, 8, 4, 5, 6, 4, 5, 6, 4, 5, 6, 1, 9, 8]" ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -294,7 +294,7 @@ "True" ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -304,7 +304,7 @@ "False" ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -313,9 +313,9 @@ "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"\n", "\n", - "a_list = [1, 2, 3]\n", + "a_list = [1, 9, 8]\n", "b_list = [4, 5, 6]\n", - "c_list = a_list + b_list * 3\n", + "c_list = a_list + b_list * 3+a_list\n", "c_list\n", "7 not in c_list\n", "a_list > b_list" @@ -337,51 +337,47 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[77, 66, 79]\n", - "[77, 66, 79, 'L', 'Z', 'R', 77, 66, 79, 77, 66, 79]\n", + "[89, 81, 89]\n", + "[89, 81, 89, 'V', 'U', 'V', 89, 81, 89, 89, 81, 89]\n", "\n", - "L\n", - "[77, 66, 79, 'L', 'Z', 'R', 77, 66, 79, 77, 66, 79]\n", - "['R', 77, 66, 79, 77, 66, 79]\n", - "[77, 66, 79]\n", - "[79, 'L', 'Z', 'R']\n", + "[89, 'V', 'U', 'V']\n", "\n", - "[77, 66, 79, 'Z', 'R', 77, 66, 79, 77, 66, 79]\n", - "[77, 66, 79, 'Z', 'R', 77, 66, 79]\n", + "[89, 81, 89, 'V', 'U', 'V', 89, 81, 89, 89, 81, 89]\n", + "[89, 81, 89, 81, 89, 89, 81, 89]\n", "\n", - "[77, 'a', 79, 2, 'R', 77, 66, 79]\n" + "[89, 'a', 89, 2, 89, 89, 81, 89]\n" ] } ], "source": [ "import random\n", "n = 3 \n", - "a_list = [random.randrange(65, 91) for i in range(n)]\n", - "b_list = [chr(random.randrange(65, 91)) for i in range(n)]\n", + "a_list = [random.randrange(77, 91) for i in range(n)]\n", + "b_list = [chr(random.randrange(77, 91)) for i in range(n)]\n", "print(a_list)\n", "c_list = a_list + b_list + a_list * 2\n", "print(c_list)\n", "\n", "print()\n", "# 根据索引提取（Slicing）\n", - "print(c_list[3]) # 返回索引值为 3 的元素值\n", - "print(c_list[:]) # 相当于 c_list，返回整个列表\n", - "print(c_list[5:]) # 从索引为 5 的值开始直到末尾\n", - "print(c_list[:3]) # 从索引 0 开始，直到索引 3 之前（不包括 3）\n", + "# print(c_list[3]) # 返回索引值为 3 的元素值\n", + "# print(c_list[:]) # 相当于 c_list，返回整个列表\n", + "# print(c_list[5:]) # 从索引为 5 的值开始直到末尾\n", + "# print(c_list[:3]) # 从索引 0 开始，直到索引 3 之前（不包括 3）\n", "print(c_list[2:6]) # 从索引 2 开始，直到索引 6 之前（不包括 6）\n", "\n", "print()\n", "# 根据索引删除\n", - "del c_list[3]\n", + "# del c_list[3]\n", "print(c_list) # del 是个命令，del c_list[3] 是一个语句；不能这么写：print(del c_list[3])\n", - "del c_list[5:8] \n", + "del c_list[2:6] \n", "print(c_list)\n", "\n", "print()\n", @@ -561,7 +557,7 @@ "source": [ "import random\n", "n = 10 \n", - "a_list = [random.randrange(1, 100) for i in range(n)]\n", + "a_list = [random.randrange(1,100)for i in range(n)]\n", "print(f'a_list comprehends {len(a_list)} random numbers:\\n', a_list)\n", "\n", "a_list.sort()\n", @@ -580,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -588,18 +584,18 @@ "output_type": "stream", "text": [ "a_list comprehends 10 random string elements:\n", - " ['B', 'U', 'H', 'D', 'C', 'V', 'V', 'Q', 'U', 'P']\n", + " ['W', 'P', 'K', 'I', 'Z', 'M', 'R', 'B', 'L', 'G']\n", "the list sorted:\n", - " ['B', 'C', 'D', 'H', 'P', 'Q', 'U', 'U', 'V', 'V']\n", + " ['B', 'G', 'I', 'K', 'L', 'M', 'P', 'R', 'W', 'Z']\n", "the list sorted reversely:\n", - " ['V', 'V', 'U', 'U', 'Q', 'P', 'H', 'D', 'C', 'B']\n", + " ['Z', 'W', 'R', 'P', 'M', 'L', 'K', 'I', 'G', 'B']\n", "\n", "b_list comprehends 10 random string elements:\n", - " ['Nl', 'Mh', 'Ta', 'By', 'Ul', 'Nc', 'Gu', 'Rp', 'Pv', 'Bu']\n", + " ['Rra', 'Sym', 'Sxb', 'Izj', 'Bwt', 'Hin', 'Pso', 'Czt', 'Zen', 'Kbb']\n", "the sorted:\n", - " ['Bu', 'By', 'Gu', 'Mh', 'Nc', 'Nl', 'Pv', 'Rp', 'Ta', 'Ul']\n", + " ['Bwt', 'Czt', 'Hin', 'Izj', 'Kbb', 'Pso', 'Rra', 'Sxb', 'Sym', 'Zen']\n", "the sorted reversely:\n", - " ['Ul', 'Ta', 'Rp', 'Pv', 'Nl', 'Nc', 'Mh', 'Gu', 'By', 'Bu']\n" + " ['Zen', 'Sym', 'Sxb', 'Rra', 'Pso', 'Kbb', 'Izj', 'Hin', 'Czt', 'Bwt']\n" ] } ], @@ -620,7 +616,8 @@ "print()\n", "\n", "b_list = [chr(random.randrange(65, 91)) +\\\n", - " chr(random.randrange(97, 123))\\\n", + " chr(random.randrange(97, 123))+\\\n", + " chr(random.randrange(97, 123))\\\n", " for i in range(n)]\n", "# 可以在行末加上 \\ 符号，表示 “该行未完待续……”\n", "\n", @@ -629,7 +626,7 @@ "b_list.sort()\n", "print('the sorted:\\n', b_list)\n", "\n", - "b_list.sort(key=str.lower, reverse=True) \n", + "b_list.sort(key=str.upper, reverse=True) \n", "# key 参数，默认是 None\n", "# key=str.lower 的意思是，在比较的时候，先全都转换成小写再比较……\n", "# —— 但并不改变原有值\n", @@ -645,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -655,14 +652,15 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ma_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 这一句会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m/Users/chenqiang/Documents/the-craft-of-selfteaching/Part.1.E.6.containers.ipynb Cell 36'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m a_list \u001b[39m=\u001b[39m [\u001b[39m1\u001b[39m, \u001b[39m'\u001b[39m\u001b[39ma\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mc\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m----> 2\u001b[0m a_list\u001b[39m.\u001b[39;49msort() \u001b[39m# 这一句会报错\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mthe sorted:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m, a_list)\n", "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'" ] } ], "source": [ "a_list = [1, 'a', 'c']\n", - "a_list = a_list.sort() # 这一句会报错" + "a_list.sort() # 这一句会报错\n", + "print('the sorted:\\n', a_list)" ] }, { @@ -674,35 +672,36 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[90, 88, 73]\n", - "[90, 88, 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73]\n", - "[90, 88, 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", + "[71, 68, 75]\n", + "['R', 'Y', 'U']\n", + "[71, 68, 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75]\n", + "[71, 68, 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", "\n", - "[90, 88, 73]\n", + "[71, 68, 75]\n", "[]\n", "\n", - "[90, 88, 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", - "[90, 88, 73, 'T', 'N', 'Y', 73, 90, 88, 73, '100']\n", - "[90, 88, 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 75, 71, 68, 75, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", "\n", - "[90, 88, 73, 'T', 'N', 'Y', 88, 73, '100']\n", - "[90, 88, 73, 'T', 'N', 'Y', 88, 73, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 68, 75, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 68, 75, '100']\n", "\n", "[]\n", - "[90, 88, 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", "\n", - "[90, 88, 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", - "[90, 'example', 88, 'example', 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", + "[71, 68, 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", + "[71, 'example', 68, 'example', 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", "\n", - "[90, 'example', 88, 'example', 73, 'T', 'N', 'Y', 90, 88, 73, 90, 88, 73, '100']\n", - "['100', 73, 88, 90, 73, 88, 90, 'Y', 'N', 'T', 73, 'example', 88, 'example', 90]\n", + "[71, 'example', 68, 'example', 75, 'R', 'Y', 'U', 71, 68, 75, 71, 68, 75, '100']\n", + "['100', 75, 68, 71, 75, 68, 71, 'U', 'Y', 'R', 75, 'example', 68, 'example', 71]\n", "None\n" ] } @@ -713,6 +712,7 @@ "a_list = [random.randrange(65, 91) for i in range(n)]\n", "b_list = [chr(random.randrange(65, 91)) for i in range(n)]\n", "print(a_list)\n", + "print(b_list)\n", "c_list = a_list + b_list + a_list * 2\n", "print(c_list)\n", "\n", @@ -754,9 +754,9 @@ "a_list.insert(3, 'example') # 在索引 3 的位置插入 'example'；\n", "print(a_list)\n", "\n", - "# 排序\n", + "# # 排序\n", "\n", - "# a_list.sort() 这一句会出错，因为当前列表中的元素，是 int 和 str 混合的。\n", + "# # a_list.sort() 这一句会出错，因为当前列表中的元素，是 int 和 str 混合的。\n", "\n", "print()\n", "print(a_list)\n", @@ -775,28 +775,29 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[88, 84, 69]\n", + "[80, 87, 65]\n", "\n", + "[80, 'example', 87, 65]\n", "\n", - "[88, 'example', 84, 69]\n", - "[88, 84, 69]\n", + "[80, 'example', 87, 65]\n", + "[80, 87, 65]\n", "\n", - "[88, 84, 69]\n", - "[88, 84]\n", - "69\n", + "[80, 87, 65]\n", + "[80, 87]\n", + "65\n", "\n", - "[88, 84, 'example', 'example']\n", - "[88, 84, 'example']\n", + "[80, 87, 'example', 'example']\n", + "[80, 87, 'example']\n", "\n", "None\n", - "[88, 84]\n" + "[80, 87]\n" ] } ], @@ -809,14 +810,15 @@ "# 插入\n", "print()\n", "a_list.insert(1, 'example') # 在索引 1 的位置插入 'example'\n", + "print(a_list)\n", "\n", - "# 删除\n", + "# # 删除\n", "print()\n", "print(a_list)\n", "a_list.remove('example') # 去除 'example' 这个元素，如果有多个 'example'，只删除第一个\n", "print(a_list)\n", "\n", - "# pop() 删除并返回被删除的值\n", + "# # pop() 删除并返回被删除的值\n", "\n", "print()\n", "print(a_list)\n", @@ -824,7 +826,7 @@ "print(a_list)\n", "print(p)\n", "\n", - "# pop() 与 del，或者 remove() 的区别\n", + "# # pop() 与 del，或者 remove() 的区别\n", "print()\n", "a_list.insert(2, 'example')\n", "a_list.insert(2, 'example')\n", @@ -1423,18 +1425,19 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 38, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/chenqiang/Documents/the-craft-of-selfteaching/Part.1.E.6.containers.ipynb Cell 67'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m# 这个 cell 集合运算图示需要安装 matplotlib 和 matplotlib-venn\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39m# !pip install matplotlib\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[39m# !pip install matplotlib-venn\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmatplotlib_venn\u001b[39;00m \u001b[39mimport\u001b[39;00m venn2\n\u001b[1;32m 7\u001b[0m admins \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mMoose\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mJoker\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mJoker\u001b[39m\u001b[39m'\u001b[39m}\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -2123,7 +2126,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -2132,7 +2135,7 @@ "{'john': 9876, 'mike': 5603, 'stan': 6898, 'eric': 7898}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2142,7 +2145,7 @@ "{}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2152,7 +2155,7 @@ "{'john': 9876, 'mike': 5603, 'stan': 6898, 'eric': 7898}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2162,7 +2165,7 @@ "('zoe', 1225)" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2172,7 +2175,7 @@ "{'ann': 6585, 'bob': 8982, 'joe': 2598}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2182,7 +2185,7 @@ "3538" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2192,7 +2195,7 @@ "{'ann': 6585, 'bob': 8982, 'joe': 2598}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2202,7 +2205,7 @@ "3538" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2212,7 +2215,7 @@ "{'ann': 6585, 'bob': 8982, 'joe': 2598}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2222,7 +2225,7 @@ "3538" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, @@ -2232,7 +2235,7 @@ "{'ann': 6585, 'bob': 8982, 'joe': 2598, 'adam': 3538}" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -2343,7 +2346,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -2368,7 +2371,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2377,12 +2380,13 @@ "text": [ "0 0\n", "1 1\n", - "2 2\n" + "2 2\n", + "3 3\n" ] } ], "source": [ - "for i, v in enumerate(range(3)):\n", + "for i, v in enumerate(range(4)):\n", " print(i, v)" ] }, @@ -2411,7 +2415,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -2655,8 +2659,11 @@ } ], "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.0 64-bit", "language": "python", "name": "python3" }, @@ -2670,7 +2677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.10.0" }, "toc-autonumbering": true }, diff --git a/Part.1.E.7.files.ipynb b/Part.1.E.7.files.ipynb index 0d179ba01..b07e6a430 100644 --- a/Part.1.E.7.files.ipynb +++ b/Part.1.E.7.files.ipynb @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -156,16 +156,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "first line\n", - "second line\n", - "third line\n", + "abc\n", + "bcd\n", + "efg\n", "\n" ] } ], "source": [ "f = open('/tmp/test-file.txt', 'w')\n", - "f.write('first line\\nsecond line\\nthird line\\n')\n", + "f.write('abc\\nbcd\\nefg\\n')\n", "f.close()\n", "\n", "f = open('/tmp/test-file.txt', 'r')\n", @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -193,6 +193,8 @@ "first line\n", "\n", "second line\n", + "\n", + "third line\n", "\n" ] } @@ -207,6 +209,8 @@ "print(s)\n", "s = f.readline() # 返回的是 'second line\\n'\n", "print(s)\n", + "s = f.readline() # 返回的是 'third line\\n'\n", + "print(s)\n", "f.close()" ] }, @@ -219,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -227,13 +231,14 @@ "output_type": "stream", "text": [ "first line\n", - "second line\n" + "second line\n", + "third line\n" ] } ], "source": [ "f = open('/tmp/test-file.txt', 'w')\n", - "f.write('first line\\nsecond line\\nthird line\\n')\n", + "f.write('\\t\\t\\tfirst line\\nsecond line\\nthird line\\n')\n", "f.close()\n", "\n", "f = open('/tmp/test-file.txt', 'r')\n", @@ -241,6 +246,8 @@ "print(s)\n", "s = f.readline().strip() # 返回的是 'second line'，'\\n' 被去掉了……\n", "print(s)\n", + "s = f.readline().strip() # 返回的是 'second line'，'\\n' 被去掉了……\n", + "print(s)\n", "f.close()" ] }, @@ -372,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -9681,8 +9688,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -9696,7 +9706,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.1.F.deal-with-forward-references.ipynb b/Part.1.F.deal-with-forward-references.ipynb index e3c343877..3611a79c2 100644 --- a/Part.1.F.deal-with-forward-references.ipynb +++ b/Part.1.F.deal-with-forward-references.ipynb @@ -317,11 +317,11 @@ "\n", "现在的你，不一样了 —— 你要跳出来。养成一个习惯：\n", "\n", - "> 不管怎么样，先用起来，反正，研究透原理，不可能马上做到，需要时间漫漫。\n", + "> 不管怎么样，先用起来，反正，研究透原理，不可能马上做到，需要时间漫漫。\n", "\n", "用错了没关系，改正就好。用得不好没关系，用多了就会好。只要开始用起来，理解速度就会加快 —— 实践出真知，不是空话。\n", "\n", - "有的时候，就是因为没有犯过错，所以不可能有机会改正，于是，就从未做对过。" + "有的时候，就是因为没有犯过错，所以不可能有机会改正，于是，就从未做对过。" ] }, { diff --git a/Part.2.A.clumsy-and-patience.ipynb b/Part.2.A.clumsy-and-patience.ipynb index 0dc13a983..f558b211f 100644 --- a/Part.2.A.clumsy-and-patience.ipynb +++ b/Part.2.A.clumsy-and-patience.ipynb @@ -24,7 +24,7 @@ "\n", "第一部分的内容，基本用来展示 “学” 的过程。**学，就需要重复**，甚至很多次重复，尤其是在面对充满了 “过早引用” 现象的知识结构的时候。\n", "\n", - "反复学，最锻炼的是 “归纳整理” 的能力。而且，最有意思的，这在大多数情况下还是自动发生的 —— 只要你不断重复，你的大脑会在不自主之间把那些已经掌握的知识点与当前尚未掌握的知识点区分开来，前者处理起来轻松容易，甚至可以跳过；后者需要投入更多的注意力去仔细处理…… 在这个过程中，绝大多数的归纳整理工作自动完成了。最后再加上一点 “刻意的、收尾性的归纳总结整理工作” —— 大功告成。\n", + "反复学，最锻炼的是 “归纳整理” 的能力。而且，最有意思的，这在大多数情况下还是自动发生的 —— 只要你不断重复，你的大脑会在不自主之间把那些已经掌握的知识点与当前尚未掌握的知识点区分开来，前者处理起来轻松容易，甚至可以跳过；后者需要投入更多的注意力去仔细处理…… 在这个过程中，绝大多数的归纳整理工作自动完成了。最后再加上一点 “刻意的、收尾性的归纳总结整理工作” —— 大功告成。\n", "\n", "\n", "\n", @@ -79,13 +79,13 @@ "\n", "离开学校之后，绝大多数人很难再有 “一看一下午”、“一练一整天”、“一玩一整夜” 的本钱。又由于生活的压力越来越大，对 “能够使用” 新技能的 “需求” 越来越紧迫，于是，对任何一次自学的 “时间精力投资” 都缩手缩脚，小里小气……\n", "\n", - "**预算观念**非常重要 —— 这个观念的存在与否，成熟与否，基本上决定一个人未来的盈利能力。\n", + "**预算观念**非常重要 —— 这个观念的存在与否，成熟与否，基本上决定一个人未来的盈利能力。\n", "\n", "大多数人对此不仅不成熟，甚至干脆没有预算观念！—— 这也是为什么绝大多数人不适合创业的最根本原因。\n", "\n", "不夸张地讲，未来的你只需要恪守一个原则，就很可能会因此超越 99% 的人：\n", "\n", - "> 绝对不做预算不够的事情。\n", + "> 绝对不做预算不够的事情。\n", "\n", "说来惭愧，我是四十多岁之后，在创业和投资中经历了大量的失败，付了不可想像的学费之后，才深刻理解这条看起来过分简单的原则 —— 亏得本科还是学会计毕业的呢！我的运气在于，在自学这件事上，从来给出的预算都很多……\n", "\n", @@ -149,7 +149,7 @@ "\n", "> 因为早已习惯了投入大量时间换取新技能……\n", "\n", - "等后来真的开始用这些技能做事，不断地做其他人因为时间白过了或者因为投入的 “预算” 不够而学不会做不到的事情 —— 并且还能充分明白，这并不是自己聪明、有天分的结果；只不过是做了该做的事情，投入了该投入的 “成本” 和 “预算” 而已……\n", + "等后来真的开始用这些技能做事，不断地做其他人因为时间白过了或者因为投入的 “预算” 不够而学不会做不到的事情 —— 并且还能充分明白，这并不是自己聪明、有天分的结果；只不过是做了该做的事情，投入了该投入的 “成本” 和 “预算” 而已……\n", "\n", "于是，就真的能够理解下面这句话背后的深意：\n", "\n", diff --git a/Part.2.B.deliberate-practicing.ipynb b/Part.2.B.deliberate-practicing.ipynb index 1b5c4d64d..e708228ba 100644 --- a/Part.2.B.deliberate-practicing.ipynb +++ b/Part.2.B.deliberate-practicing.ipynb @@ -53,7 +53,7 @@ "\n", "对，所谓的 “混”，解释很简单：\n", "\n", - "> **不做刻意练习的人就是在混时间**。\n", + "> **不做刻意练习的人就是在混时间**。\n", "\n" ] }, diff --git a/Part.2.C.why-start-from-writing-functions.ipynb b/Part.2.C.why-start-from-writing-functions.ipynb index 335f534ab..f71b04a0a 100644 --- a/Part.2.C.why-start-from-writing-functions.ipynb +++ b/Part.2.C.why-start-from-writing-functions.ipynb @@ -109,8 +109,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -124,7 +127,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.2.D.1-args.ipynb b/Part.2.D.1-args.ipynb index 46c032315..2d547aec4 100644 --- a/Part.2.D.1-args.ipynb +++ b/Part.2.D.1-args.ipynb @@ -541,6 +541,22 @@ "a, b" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "这一章主要讲的是函数和参数：\n", + "- 函数：\n", + " - 函数的组成：输入和运算还有输出，输入指的是输入的值，也就是入参，运算其实就是逻辑，就是产品的加工过程，而输出其实就是加工好的结果，术语叫做返回值\n", + " - 函数命名是可以用下划线也可以用驼峰，建议是用下划线代替，数字是不能命名在开头\n", + " - 定义一个函数，开头可以声明为def，比如def do_something(): 函数后面必须要加括号，以便和变量区分\n", + "- 参数：\n", + " - 参数，即入参，也就是我们所说的变量\n", + " - 变量分全局变量和局部变量，函数内的变量算做变量，它是不会因运算而改变\n", + " - 当然需要考虑一个例外，那就是容器，当我们指定容器中的某个元素等于某个值时，那么这个元素会随之改变，比如： b[0] = 'What?!'，我们可以调整下，通过copy的方式：b1=b.copy(),b1[0]='What?!'" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.2.D.2-aargs.ipynb b/Part.2.D.2-aargs.ipynb index 40e956907..f6a1ead64 100644 --- a/Part.2.D.2-aargs.ipynb +++ b/Part.2.D.2-aargs.ipynb @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -273,7 +273,7 @@ ], "source": [ "def say_hi(**names_greetings):\n", - " for name, greeting in names_greetings.items():\n", + " for name,greeting in names_greetings.items():\n", " print(f'{greeting}, {name}!')\n", " \n", "say_hi(mike='Hello', ann='Oh, my darling', john='Hi')" @@ -288,16 +288,13 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hello, mike!\n", - "Oh, my darling, ann!\n", - "Hi, john!\n", "Hello, mike!\n", "Oh, my darling, ann!\n", "Hi, john!\n" @@ -396,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -406,7 +403,6 @@ "Hi, mike!\n", "Hi, john!\n", "Hi, zeo!\n", - "\n", "Welcome, Mike!\n", "Welcome, John!\n", "Welcome, Zeo!\n" @@ -464,31 +460,31 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "hello, mike!\n", + "hello, john!\n", + "hello, zeo!\n", "Hello, mike!\n", "Hello, john!\n", - "Hello, zeo!\n", - "Hi, mike!\n", - "Hi, john!\n", - "Hi, zeo!\n" + "Hello, zeo!\n" ] } ], "source": [ - "def say_hi(*names, greeting='Hello', capitalized=False):\n", + "def say_hi(*names,greeting='hello',capitalized=False):\n", " for name in names:\n", " if capitalized:\n", " name = name.capitalize()\n", " print(f'{greeting}, {name}!')\n", "\n", "say_hi('mike', 'john', 'zeo')\n", - "say_hi('mike', 'john', 'zeo', greeting='Hi')" + "say_hi('mike', 'john', 'zeo',greeting='Hello')" ] }, { @@ -523,6 +519,163 @@ "Python 都会认为接收到的第一个值是 Positional Argument —— 因为在定义中，`greeting` 被放到了 Arbitrary Positional Arguments 之前。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "位置参数Postional Argument\n", + "\n", + "那么我们在定义*+参数时，其实定义为“Arbitrary Positional Arguments”，就是随意变换的位置参数。\n", + "\n", + "那么这个随意变换的位置参数有什么用呢？当我们需要输入一个数组的参数或者一个字典的参数时，觉得很麻烦，那么我们就可以用“Arbitrary Positional Arguments”,比如:def do_something(*names):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['abc', '123', 'sb']\n" + ] + } + ], + "source": [ + "def do_sometings(*names):\n", + " for name in names:\n", + " print(f'{name}')\n", + "names=['abc','123','sb']\n", + "do_sometings(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以搞成使用关键字参数，比如def do_somethings(**greetings_names):" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello,mike\n", + "hi,david\n", + "welcome,andy\n" + ] + } + ], + "source": [ + "def do_sometings(**greetings_names):\n", + " for greeting,name in greetings_names.items():\n", + " print(f'{greeting},{name}')\n", + "a_dictionary={'hello':'mike','hi':'david','welcome':'andy'}\n", + "do_sometings(**a_dictionary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以换其他的方式表达" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I:mike,hello\n", + "I:david,hi\n", + "I:andy,welcome\n" + ] + } + ], + "source": [ + "def do_sometings(who='I:',**greetings_names):\n", + " for name in greetings_names:\n", + " print(f'{who}{greetings_names[name]},{name}')\n", + "a_dictionary={'hello':'mike','hi':'david','welcome':'andy'}\n", + "do_sometings(**a_dictionary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如果关键词参数和普通参数放在一起是怎么样的呢" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mikedavid\n", + "mikeandy\n" + ] + } + ], + "source": [ + "def do_sometings(who='I:',*names):\n", + " for name in names:\n", + " print(f'{who}{name}')\n", + "names=['mike','david','andy']\n", + "do_sometings(*names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们发现这样做并不能输出我们想要的函数，所以根据以下的顺序，我们要做下调整\n", + "**Order of Arguments**\n", + "> 1. Positional\n", + "> 1. Arbitrary Positional\n", + "> 1. Keyword\n", + "> 1. Arbitrary Keyword" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I:mike\n", + "I:david\n", + "I:andy\n" + ] + } + ], + "source": [ + "def do_sometings(*names,who='I:'):\n", + " for name in names:\n", + " print(f'{who}{name}')\n", + "names=['mike','david','andy']\n", + "do_sometings(*names)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -532,8 +685,11 @@ } ], "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.0 64-bit", "language": "python", "name": "python3" }, @@ -547,7 +703,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.10.0" }, "toc-autonumbering": true }, diff --git a/Part.2.D.3-lambda.ipynb b/Part.2.D.3-lambda.ipynb index bd563434f..537b1115f 100644 --- a/Part.2.D.3-lambda.ipynb +++ b/Part.2.D.3-lambda.ipynb @@ -51,7 +51,7 @@ { "data": { "text/plain": [ - "4547071648" + "4352890848" ] }, "execution_count": 2, @@ -61,7 +61,7 @@ { "data": { "text/plain": [ - "4547071648" + "4352890848" ] }, "execution_count": 2, @@ -247,26 +247,26 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "42" + "43" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "43" + "44" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +275,7 @@ "def make_incrementor(n):\n", " return lambda x: x + n\n", "\n", - "f = make_incrementor(42)\n", + "f = make_incrementor(43)\n", "f(0)\n", "\n", "f(1)" @@ -532,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ "[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]" ] }, - "execution_count": 1, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -552,6 +552,230 @@ "pairs" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "化名和匿名\n", + "- 化名：当我们给一个函数取名字，那么这个取名字的过程就是化名\n", + "- 匿名：也就是我们不想给这个名字取名，当想用这个函数的过程返回结果，那么这里就需要用到lambda表达式\n", + " - 如何用：首先还是需要举个例子，原来我们化名的例子是怎么取的，比如：def do_something(x,y): " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def do_something(x,y):\n", + " return x+y\n", + "do_something(1,2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这里我们返回的是3，x=1，y=2，x+y=1+2=3，那么改造成lambda表达式又是怎么样呢？还是举个例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "4390204560" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "4390196352" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add=lambda x,y:x+y\n", + "add(1,2)\n", + "id(add)\n", + "id(lambda x,y:x+y)\n", + "type(add)\n", + "type(lambda x,y:x+y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这个就是lambda表达式的改造，可以看出add和```lambda x,y:x+y```之间的内存地址不同，但是他们同样是函数" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### lambda的使用场景\n", + "1. 作为某函数的返回值" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "42" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "43" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def do_something(n):\n", + " return lambda x:x+n\n", + "f=do_something(42)\n", + "f(0)\n", + "f(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. 作为某函数的参数，比如我们用map()函数将原来数组翻倍" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "[2, 4, 6, 8, 10, 12]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_list=list(range(1,7))\n", + "a_list\n", + "b_list=list(map(lambda x:x*2,a_list))\n", + "b_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然也可以在字典里做一些操作比如：" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ann', 'bob', 'joe', 'zoe']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phonebook1 = {'ann':6575, 'bob':8982, 'joe':2598, 'zoe':1225}\n", + "list(map(lambda x:x,phonebook1))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -561,8 +785,11 @@ } ], "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.0 64-bit", "language": "python", "name": "python3" }, @@ -576,7 +803,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.10.0" } }, "nbformat": 4, From ce73c8e961ac291572d8b2c408408e232322093f Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Sat, 20 Aug 2022 14:33:43 +0800 Subject: [PATCH 02/12] 20220820 --- Part.2.D.3-lambda.ipynb | 10 +- Part.2.D.4-recursion.ipynb | 133 +++++++- Part.2.D.5-docstrings.ipynb | 36 +-- Part.2.D.6-modules.ipynb | 227 +++++++++++-- Part.2.D.7-tdd.ipynb | 58 +++- Part.2.D.8-main.ipynb | 497 +++++++++++++++++++++++++++-- Part.2.E.deliberate-thinking.ipynb | 14 + 7 files changed, 875 insertions(+), 100 deletions(-) diff --git a/Part.2.D.3-lambda.ipynb b/Part.2.D.3-lambda.ipynb index 537b1115f..8a734e951 100644 --- a/Part.2.D.3-lambda.ipynb +++ b/Part.2.D.3-lambda.ipynb @@ -661,15 +661,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "这个就是lambda表达式的改造，可以看出add和```lambda x,y:x+y```之间的内存地址不同，但是他们同样是函数" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "这个就是lambda表达式的改造，可以看出add和```lambda x,y:x+y```之间的内存地址不同，但是他们同样是函数\n", "### lambda的使用场景\n", "1. 作为某函数的返回值" ] diff --git a/Part.2.D.4-recursion.ipynb b/Part.2.D.4-recursion.ipynb index 159bf0912..6d4663254 100644 --- a/Part.2.D.4-recursion.ipynb +++ b/Part.2.D.4-recursion.ipynb @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -590,6 +590,130 @@ "↑Back to Content↑" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "好的递归函数需要符合三大原则：\n", + "1. 内部调用自己\n", + "2. 有一个退出条件\n", + "3. 可以逐步达到退出条件\n", + "不要因为递归而递归，递归只是让代码显得更加优雅，它是一种特殊算法，任何编程语言都可以做成递归\n", + "递归的例子n！=n*(n-1)*(n-2)*...*1\n", + "我们可以用for循环迭代也可以用递归的方式\n", + "比如" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def do_something(n):\n", + " sum=1\n", + " for i in range(n,1,-1):\n", + " sum=sum*i\n", + " return sum\n", + "do_something(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可以用递归的方式求，比如" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def do_something(n):\n", + " if n==1:\n", + " return 1\n", + " else:\n", + " return n*do_something(n-1)\n", + "do_something(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "试着加入一些输出语句看看底层的原理" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "4\n", + "3\n", + "2\n", + "1\n", + "\tn = 1 return: 1\n", + "\tn = 2 return: 2\n", + "\tn = 3 return: 6\n", + "\tn = 4 return: 24\n", + "\tn = 5 return: 120\n" + ] + }, + { + "data": { + "text/plain": [ + "120" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def do_something(n):\n", + " print(n)\n", + " if n==1:\n", + " print('\\tn =', n, 'return:', 1)\n", + " return 1\n", + " else:\n", + " r = n * do_something(n-1)\n", + " print('\\tn =', n, 'return:', r)\n", + " return r\n", + "do_something(5)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -599,8 +723,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -614,7 +741,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.2.D.5-docstrings.ipynb b/Part.2.D.5-docstrings.ipynb index cc2c7764c..e0243af24 100644 --- a/Part.2.D.5-docstrings.ipynb +++ b/Part.2.D.5-docstrings.ipynb @@ -99,32 +99,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function is_prime in module __main__:\n", - "\n", - "is_prime(n)\n", - " Return a boolean value based upon whether the argument n is a prime number.\n", - "\n", - "Return a boolean value based upon whether the argument n is a prime number.\n" - ] - }, - { - "data": { - "text/plain": [ - "'Return a boolean value based upon whether the argument n is a prime number.'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def is_prime(n):\n", " \"\"\"Return a boolean value based upon whether the argument n is a prime number.\"\"\"\n", @@ -154,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -354,8 +331,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -369,7 +349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" }, "toc-autonumbering": true }, diff --git a/Part.2.D.6-modules.ipynb b/Part.2.D.6-modules.ipynb index 5dfbefa6e..a431efa2f 100644 --- a/Part.2.D.6-modules.ipynb +++ b/Part.2.D.6-modules.ipynb @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -98,10 +98,6 @@ " :param capitalized: Whether name should be converted to capitalzed before print. False as default.\n", " :returns: None\n", "\n", - "'mycode'\n", - "\n", - "True\n", - "\n", "Hello, mike!\n", "Hello, zoe!\n" ] @@ -319,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -328,7 +324,7 @@ "True" ] }, - "execution_count": 7, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -498,9 +494,97 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'A': 'N',\n", + " 'B': 'O',\n", + " 'C': 'P',\n", + " 'D': 'Q',\n", + " 'E': 'R',\n", + " 'F': 'S',\n", + " 'G': 'T',\n", + " 'H': 'U',\n", + " 'I': 'V',\n", + " 'J': 'W',\n", + " 'K': 'X',\n", + " 'L': 'Y',\n", + " 'M': 'Z',\n", + " 'N': 'A',\n", + " 'O': 'B',\n", + " 'P': 'C',\n", + " 'Q': 'D',\n", + " 'R': 'E',\n", + " 'S': 'F',\n", + " 'T': 'G',\n", + " 'U': 'H',\n", + " 'V': 'I',\n", + " 'W': 'J',\n", + " 'X': 'K',\n", + " 'Y': 'L',\n", + " 'Z': 'M',\n", + " 'a': 'n',\n", + " 'b': 'o',\n", + " 'c': 'p',\n", + " 'd': 'q',\n", + " 'e': 'r',\n", + " 'f': 's',\n", + " 'g': 't',\n", + " 'h': 'u',\n", + " 'i': 'v',\n", + " 'j': 'w',\n", + " 'k': 'x',\n", + " 'l': 'y',\n", + " 'm': 'z',\n", + " 'n': 'a',\n", + " 'o': 'b',\n", + " 'p': 'c',\n", + " 'q': 'd',\n", + " 'r': 'e',\n", + " 's': 'f',\n", + " 't': 'g',\n", + " 'u': 'h',\n", + " 'v': 'i',\n", + " 'w': 'j',\n", + " 'x': 'k',\n", + " 'y': 'l',\n", + " 'z': 'm'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], "source": [ "s = \"\"\"Gur Mra bs Clguba, ol Gvz Crgref\n", "Ornhgvshy vf orggre guna htyl.\n", @@ -526,9 +610,14 @@ "d = {}\n", "for c in (65, 97):\n", " for i in range(26):\n", + " # print(f'i:{i}')\n", + " # print(f'c:{c}')\n", + " # print(f'i+c:{i+c}')\n", + " # print(f'chr(i+c):{chr(i+c)}')\n", + " # print(f'chr((i+13) % 26 + c):{chr((i+13) % 26 + c)}')\n", " d[chr(i+c)] = chr((i+13) % 26 + c)\n", - "\n", - "print(\"\".join([d.get(c, c) for c in s]))" + "d\n", + "print(\"\".join([d.get(c,c) for c in s]))" ] }, { @@ -540,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -602,7 +691,7 @@ " 'z': 'm'}" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -612,7 +701,7 @@ "\"Gur Mra bs Clguba, ol Gvz Crgref\\n\\nOrnhgvshy vf orggre guna htyl.\\nRkcyvpvg vf orggre guna vzcyvpvg.\\nFvzcyr vf orggre guna pbzcyrk.\\nPbzcyrk vf orggre guna pbzcyvpngrq.\\nSyng vf orggre guna arfgrq.\\nFcnefr vf orggre guna qrafr.\\nErnqnovyvgl pbhagf.\\nFcrpvny pnfrf nera'g fcrpvny rabhtu gb oernx gur ehyrf.\\nNygubhtu cenpgvpnyvgl orngf chevgl.\\nReebef fubhyq arire cnff fvyragyl.\\nHayrff rkcyvpvgyl fvyraprq.\\nVa gur snpr bs nzovthvgl, ershfr gur grzcgngvba gb thrff.\\nGurer fubhyq or bar-- naq cersrenoyl bayl bar --boivbhf jnl gb qb vg.\\nNygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.\\nAbj vf orggre guna arire.\\nNygubhtu arire vf bsgra orggre guna *evtug* abj.\\nVs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.\\nVs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.\\nAnzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!\"" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -653,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -671,14 +760,111 @@ " 'say_hi']" ] }, - "execution_count": 23, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "['__builtins__',\n", + " '__cached__',\n", + " '__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'c',\n", + " 'd',\n", + " 'i',\n", + " 's']" + ] + }, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mycode\n", - "dir(mycode)" + "import this\n", + "dir(mycode)\n", + "dir(this)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "modules：\n", + " 当我们保存好的代码块文件就是modules，中文叫模块。而这些文件在同个目录下是可以引用的，比如：\n", + "```python\n", + "import mycode\n", + "```\n", + "而我们如果只想引入模块中的某个函数或者变量的话我们可以用\n", + "```python\n", + "from mycode import is_prime\n", + "或者\n", + "import mycode.is_prime\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from mycode import is_prime\n", + "dir(is_prime)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以通过\n", + "```python\n", + "from mycode import *\n", + "``` \n", + "这样就可以导入所有的函数和变量" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from mycode import *\n", + "dir(is_prime)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然还可以这样\n", + "```python\n", + "from 目录 import *\n", + "或者\n", + "from 目录 import mycode\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "也可以使用化名\n", + "比如\n", + "```python\n", + "from mycode import mycode as myc\n", + "或者整个模块化名\n", + "import mycode as m\n", + "```" ] }, { @@ -690,8 +876,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -705,7 +894,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.2.D.7-tdd.ipynb b/Part.2.D.7-tdd.ipynb index 8dcb34e55..6ca4d8467 100644 --- a/Part.2.D.7-tdd.ipynb +++ b/Part.2.D.7-tdd.ipynb @@ -85,7 +85,7 @@ { "data": { "text/plain": [ - "False" + "True" ] }, "execution_count": 4, @@ -95,7 +95,7 @@ { "data": { "text/plain": [ - "False" + "True" ] }, "execution_count": 4, @@ -105,7 +105,7 @@ { "data": { "text/plain": [ - "False" + "True" ] }, "execution_count": 4, @@ -115,7 +115,7 @@ { "data": { "text/plain": [ - "False" + "True" ] }, "execution_count": 4, @@ -128,7 +128,13 @@ "InteractiveShell.ast_node_interactivity = \"all\"\n", "\n", "def is_leap(year):\n", - " pass\n", + " if year%4!=0:\n", + " return False\n", + " elif year%100==0 and year%400!=0:\n", + " return False\n", + " elif year%4==0:\n", + " return True\n", + " \n", "\n", "is_leap(4) is True\n", "is_leap(200) is False\n", @@ -155,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -164,7 +170,7 @@ "False" ] }, - "execution_count": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -174,7 +180,7 @@ "True" ] }, - "execution_count": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -184,7 +190,7 @@ "False" ] }, - "execution_count": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -194,7 +200,7 @@ "False" ] }, - "execution_count": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -664,6 +670,31 @@ "> * [unittest —— Unit testing framework](https://docs.python.org/3/library/unittest.html)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "我们在写函数的时候查查需要写备注写功能以及写测试\n", + "这里讲的就是异常捕获的环节\n", + "随着工程的不断壮大，代码行数变多，做这些确实是必要的\n", + "那么要怎么做呢？\n", + "异常捕获有哪些呢\n", + "```python\n", + "try:\n", + "do_something()\n", + "except error as name_of_error:\n", + " print('name_of_error')\n", + "else:#还是否有其他情况\n", + " #也可以嵌套\n", + " try:\n", + " do_something()\n", + " except error as name_of_error:\n", + " print('name_of_error')\n", + "finally:\n", + " do_something()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -673,8 +704,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -688,7 +722,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.2.D.8-main.ipynb b/Part.2.D.8-main.ipynb index da4cb340b..cf819dcbd 100644 --- a/Part.2.D.8-main.ipynb +++ b/Part.2.D.8-main.ipynb @@ -16,21 +16,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Routine 1 done.\n", - "Sub-routine 1 done.\n", - "Sub-routine 2 done.\n", - "Routine 2 done.\n", - "This is the end of the program.\n" - ] - } - ], + "outputs": [], "source": [ "def routine_1():\n", " print('Routine 1 done.')\n", @@ -122,9 +110,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], "source": [ "import this" ] @@ -138,9 +154,36 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], "source": [ "# %load that.py\n", "def main():\n", @@ -187,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -203,9 +246,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], "source": [ "%%bash\n", "python that.py" @@ -213,9 +283,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], "source": [ "%%bash\n", "python -m that" @@ -230,9 +327,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], "source": [ "import that\n", "that.main()" @@ -247,9 +371,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on module that:\n", + "\n", + "NAME\n", + " that\n", + "\n", + "FUNCTIONS\n", + " main()\n", + "\n", + "FILE\n", + " /Users/chenqiang/Documents/the-craft-of-selfteaching/that.py\n", + "\n", + "\n" + ] + } + ], "source": [ "import that\n", "help(that)" @@ -264,11 +407,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ - "#!/usr/bin/env python\n", + "# !/usr/bin/env python 'words_alpha.txt' 'results1.txt'\n", "\n", "def sum_of_word(word):\n", " sum = 0\n", @@ -455,6 +598,302 @@ "> [Python Easter Eggs](https://github.com/OrkoHunter/python-easter-eggs)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "```python\n", + "if __name__ == '__main__':\n", + " main()\n", + "```\n", + "我们在执行python文件时，会看到以上这个代码，其实就是为了判断到底是要执行的文件，还是import模块\n", + "- 当要import模块时，if 判断失败，那么就不会执行main()函数\n", + "- 当是要执行的python文件时，if判断成功，那么就会执行main()函数\n", + "举以下的例子" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "s = \"\"\"Gur Mra bs Clguba, ol Gvz Crgref\n", + "Ornhgvshy vf orggre guna htyl.\n", + "Rkcyvpvg vf orggre guna vzcyvpvg.\n", + "Fvzcyr vf orggre guna pbzcyrk.\n", + "Pbzcyrk vf orggre guna pbzcyvpngrq.\n", + "Syng vf orggre guna arfgrq.\n", + "Fcnefr vf orggre guna qrafr.\n", + "Ernqnovyvgl pbhagf.\n", + "Fcrpvny pnfrf nera'g fcrpvny rabhtu gb oernx gur ehyrf.\n", + "Nygubhtu cenpgvpnyvgl orngf chevgl.\n", + "Reebef fubhyq arire cnff fvyragyl.\n", + "Hayrff rkcyvpvgyl fvyraprq.\n", + "Va gur snpr bs nzovthvgl, ershfr gur grzcgngvba gb thrff.\n", + "Gurer fubhyq or bar-- naq cersrenoyl bayl bar --boivbhf jnl gb qb vg.\n", + "Nygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.\n", + "Abj vf orggre guna arire.\n", + "Nygubhtu arire vf bsgra orggre guna *evtug* abj.\n", + "Vs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.\n", + "Vs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.\n", + "Anzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!\"\"\"\n", + "\n", + "d = {}\n", + "for c in (65, 97):\n", + " for i in range(26):\n", + " d[chr(i+c)] = chr((i+13) % 26 + c)\n", + "\n", + "print(\"\".join([d.get(c, c) for c in s]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这个是this的代码，我们执行的话，会立马返回结果，我们做下调整" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def main():\n", + " s = \"\"\"Gur Mra bs Clguba, ol Gvz Crgref\n", + " Ornhgvshy vf orggre guna htyl.\n", + " Rkcyvpvg vf orggre guna vzcyvpvg.\n", + " Fvzcyr vf orggre guna pbzcyrk.\n", + " Pbzcyrk vf orggre guna pbzcyvpngrq.\n", + " Syng vf orggre guna arfgrq.\n", + " Fcnefr vf orggre guna qrafr.\n", + " Ernqnovyvgl pbhagf.\n", + " Fcrpvny pnfrf nera'g fcrpvny rabhtu gb oernx gur ehyrf.\n", + " Nygubhtu cenpgvpnyvgl orngf chevgl.\n", + " Reebef fubhyq arire cnff fvyragyl.\n", + " Hayrff rkcyvpvgyl fvyraprq.\n", + " Va gur snpr bs nzovthvgl, ershfr gur grzcgngvba gb thrff.\n", + " Gurer fubhyq or bar-- naq cersrenoyl bayl bar --boivbhf jnl gb qb vg.\n", + " Nygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.\n", + " Abj vf orggre guna arire.\n", + " Nygubhtu arire vf bsgra orggre guna *evtug* abj.\n", + " Vs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.\n", + " Vs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.\n", + " Anzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!\"\"\"\n", + "\n", + " d = {}\n", + " for c in (65, 97):\n", + " for i in range(26):\n", + " d[chr(i+c)] = chr((i+13) % 26 + c)\n", + "\n", + " print(\"\".join([d.get(c, c) for c in s]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这样执行是无法执行出结果的，我们再调整下" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "def main():\n", + " s = \"\"\"Gur Mra bs Clguba, ol Gvz Crgref\n", + " Ornhgvshy vf orggre guna htyl.\n", + " Rkcyvpvg vf orggre guna vzcyvpvg.\n", + " Fvzcyr vf orggre guna pbzcyrk.\n", + " Pbzcyrk vf orggre guna pbzcyvpngrq.\n", + " Syng vf orggre guna arfgrq.\n", + " Fcnefr vf orggre guna qrafr.\n", + " Ernqnovyvgl pbhagf.\n", + " Fcrpvny pnfrf nera'g fcrpvny rabhtu gb oernx gur ehyrf.\n", + " Nygubhtu cenpgvpnyvgl orngf chevgl.\n", + " Reebef fubhyq arire cnff fvyragyl.\n", + " Hayrff rkcyvpvgyl fvyraprq.\n", + " Va gur snpr bs nzovthvgl, ershfr gur grzcgngvba gb thrff.\n", + " Gurer fubhyq or bar-- naq cersrenoyl bayl bar --boivbhf jnl gb qb vg.\n", + " Nygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.\n", + " Abj vf orggre guna arire.\n", + " Nygubhtu arire vf bsgra orggre guna *evtug* abj.\n", + " Vs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.\n", + " Vs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.\n", + " Anzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!\"\"\"\n", + "\n", + " d = {}\n", + " for c in (65, 97):\n", + " for i in range(26):\n", + " d[chr(i+c)] = chr((i+13) % 26 + c)\n", + "\n", + " print(\"\".join([d.get(c, c) for c in s]))\n", + "if __name__=='__main__':\n", + " main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这样就能输出结果了，我们把这个文件保存在that.py中，试着import that.py文件" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "import that" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这样执行不出结果，可以直接执行that中的main函数" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import that\n", + "that.main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "那么试着python 下that.py文件,记得要在命令行里执行%%bash" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + " Beautiful is better than ugly.\n", + " Explicit is better than implicit.\n", + " Simple is better than complex.\n", + " Complex is better than complicated.\n", + " Flat is better than nested.\n", + " Sparse is better than dense.\n", + " Readability counts.\n", + " Special cases aren't special enough to break the rules.\n", + " Although practicality beats purity.\n", + " Errors should never pass silently.\n", + " Unless explicitly silenced.\n", + " In the face of ambiguity, refuse the temptation to guess.\n", + " There should be one-- and preferably only one --obvious way to do it.\n", + " Although that way may not be obvious at first unless you're Dutch.\n", + " Now is better than never.\n", + " Although never is often better than *right* now.\n", + " If the implementation is hard to explain, it's a bad idea.\n", + " If the implementation is easy to explain, it may be a good idea.\n", + " Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "%%bash\n", + "python -m that" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.2.E.deliberate-thinking.ipynb b/Part.2.E.deliberate-thinking.ipynb index c5e71d183..122ab312d 100644 --- a/Part.2.E.deliberate-thinking.ipynb +++ b/Part.2.E.deliberate-thinking.ipynb @@ -100,6 +100,20 @@ "所谓的 “活学活用”，所谓的 “触类旁通”，也不过如此。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "刻意思考\n", + "- 多去想想什么地方可以用到，我现在学的这块未来在哪里可以用到，这是一个非常重要的过程，我们需要刻意思考我们在哪里可以练习到这些，之前我们认为的一些傻子，说的能者多劳，其实只有多劳动才能练的更多，才能进步的更快，因此要改叫劳者多能。\n", + "- 用用MoSCoW原则\n", + " - Must have 必须要做的\n", + " - Should have 需要去做的\n", + " - Could have 看时间是否允许去做\n", + " - Won‘t have 不去做的\n" + ] + }, { "cell_type": "markdown", "metadata": {}, From f653b5e0b504c47914bc10b3e867a6c3fcc9e1f9 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Sun, 21 Aug 2022 21:19:23 +0800 Subject: [PATCH 03/12] 20220821 --- Part.3.B.1.classes-1.ipynb | 17 ++ Part.3.B.2.classes-2.ipynb | 510 +++++++++++++++++++++++++++++++------ 2 files changed, 455 insertions(+), 72 deletions(-) diff --git a/Part.3.B.1.classes-1.ipynb b/Part.3.B.1.classes-1.ipynb index f4ed75e6c..aae544aa4 100644 --- a/Part.3.B.1.classes-1.ipynb +++ b/Part.3.B.1.classes-1.ipynb @@ -144,6 +144,23 @@ "对基本概念有了一定的了解之后，再去看 Python 语言是如何实现的，就感觉没那么难了。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "> * 对象，封装，抽象\n", + "> * 界面，属性，方法\n", + "> * 继承，类，子类，实例\n", + "- 对象其实就是object，比如我们说的动物就是对象。\n", + "- 对象和类之间的区别是什么，其实对象就是类和子类的统称\n", + "- 子类是继承了类的所有属性和方法，但也可以去重写属性和方法。\n", + "- 我们创建实例就相当于是创建某个类的实例，比如我们要创建狗，狗属于哺乳类这样\n", + "- 那抽象是有些类，我们不需要写明他具体是哪个类，我们在实例化时可以具体声明\n", + "- 封装指的是我们要把不需要给使用者看的东西封装好，不被他们看到，给他们看到就是个开关\n", + "- 界面就是由属性和方法组成，就是英文里头的动词和名词" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.3.B.2.classes-2.ipynb b/Part.3.B.2.classes-2.ipynb index 111030a2f..fdfa0852a 100644 --- a/Part.3.B.2.classes-2.ipynb +++ b/Part.3.B.2.classes-2.ipynb @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "2019" + "2022" ] }, "execution_count": 2, @@ -60,7 +60,7 @@ { "data": { "text/plain": [ - ">" + ">" ] }, "execution_count": 2, @@ -202,16 +202,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - ">" + ">" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -228,17 +228,17 @@ "'Clay'" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "2019" + "2022" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -401,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -478,17 +478,17 @@ " 'say_hi']" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "{'name': 'Clay', 'built_year': 2019}" + "{'name': 'Clay', 'built_year': 2022}" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, @@ -498,7 +498,7 @@ "True" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -526,10 +526,10 @@ " print('Hey! Nice day, Huh?')\n", "\n", "rg = runningGolem('Clay')\n", - "help(rg)\n", - "dir(rg)\n", - "rg.__dict__\n", - "hasattr(rg, 'built_year')" + "help(rg)#查看帮助\n", + "dir(rg)#输出所有属性和方法\n", + "rg.__dict__#以字典的方式输出\n", + "hasattr(rg, 'name')#是否存在此属性" ] }, { @@ -562,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -571,7 +571,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -581,7 +581,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -591,7 +591,7 @@ "False" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -601,7 +601,7 @@ "False" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -611,7 +611,7 @@ "False" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -621,7 +621,7 @@ "1" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -631,7 +631,7 @@ "10" ] }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -641,39 +641,20 @@ "11" ] }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 5, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "type object 'Golem' has no attribute '__life_span'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_5015/2149860634.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGolem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mGolem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulation\u001b[0m \u001b[0;31m# 11\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mGolem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__life_span\u001b[0m \u001b[0;31m# 11\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: type object 'Golem' has no attribute '__life_span'" + ] } ], "source": [ @@ -706,7 +687,7 @@ "g = Golem()\n", "hasattr(Golem, 'population') # True\n", "hasattr(g, 'population') # True\n", - "hasattr(Golem, '__life_span') # False\n", + "hasattr(Golem, '__life_span') # False 变成了非私有变量\n", "hasattr(g, '__life_span') # False\n", "hasattr(g, '__active') # False\n", "Golem.population # 1\n", @@ -714,7 +695,9 @@ "Golem.population # 10\n", "x = Golem()\n", "Golem.population # 11\n", + "Golem.__life_span # 私有变量，在外部作用域是没法用的\n", "x.cease()\n", + "x.is_active()\n", "Golem.population # 10\n", "getattr(g, 'population') # 10\n", "g.is_active()" @@ -733,7 +716,7 @@ "```\n", "—— 本地变量 `population` 尚未赋值，就已经提前被引用…… 为什么会这样呢？因为在你所创建 `g` 之后，马上执行的是 `__init()__` 这个初始化函数，而 `population` 是在这个函数之外定义的……\n", "\n", - "如果你足够细心，你会发现这个版本中，有些变量前面有两个下划线 `__`，比如，`__life_span` 和 `self.__active`。这是 Python 的定义，变量名前面加上一个以上下划线（Underscore）`_` 的话，那么该变量是 “私有变量”（Private Variables），不能被外部引用。而按照 Python 的惯例，我们会使用两个下划线起始，去命名私有变量，如：`__life_span`。你可以回去试试，把所有的 `__life_span` 改成 `_life_span`（即，变量名开头只有一个 `_`，那么，`hasattr(Golem, '_life_span')` 和 `hasattr(g, '_life_span')` 的返回值就都变成了 `True`。\n", + "如果你足够细心，你会发现这个版本中，有些变量前面有两个下划线 `__`，比如，`__life_span` 和 `self.__active`。这是 Python 的定义，变量名前面加上一个以上下划线（Underscore）`_` 的话，那么该变量是 “私有变量”（Private Variables），不能被外部引用。而按照 Python 的惯例，我们会使用两个下划线起始，去命名私有变量，如：`__life_span`。你可以回去试试，把所有的 `__life_span` 改成 `_life_span`（即，变量名开头只有一个 `_`，那么，`hasattr(Golem, '_life_span')` 和 `hasattr(g, '_life_span')` 的返回值就都变成了 `True`。\n", "\n", "看看下面的图示，理解起来更为直观一些，其中每个方框代表一个 Scope：\n", "\n", @@ -767,6 +750,83 @@ "> * `self.population` 总是去读取 `Golem` 类中 `population` 的初始值，即使后面通过 `setattr(Golem, 'population', 10)` 更改 `population` 的值后，`self.population` 的值仍为 `0`，但 `Golem.population` 值则为 `10`，你可以自己动手尝试一下。" ] }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"\n", + "import datetime\n", + "\n", + "class Golem:\n", + " population = 0\n", + " __life_span = 10\n", + " \n", + " def __init__(self, name=None):\n", + " self.name = name\n", + " self.built_year = datetime.date.today().year\n", + " self.__active = True\n", + " self.population += 1 # 执行一遍之后，试试把这句改成 population += 1\n", + " \n", + " def say_hi(self):\n", + " print('Hi!')\n", + " \n", + " def cease(self):\n", + " self.__active = False\n", + " Golem.population -= 1\n", + " \n", + " def is_active(self):\n", + " if datetime.date.today().year - self.built_year >= Golem.__life_span:\n", + " self.cease()\n", + " return self.__active\n", + "\n", + "g = Golem()\n", + "Golem.population # 1\n", + "setattr(Golem, 'population', 10) \n", + "Golem.population # 10\n", + "g.population # 10\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "其实一个是看的Golem类的属性，另一个是看的g这个实例的属性，所以我们可以看出同样都是population，但是输出出来就是不一样" + ] + }, { "cell_type": "markdown", "metadata": { @@ -959,36 +1019,36 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1" + "0" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "101" + "100" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "101" + "100" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -1029,29 +1089,32 @@ "data": { "text/plain": [ "mappingproxy({'__module__': '__main__',\n", - " '_Golem__population': 101,\n", + " '_Golem__population': 100,\n", " '_Golem__life_span': 10,\n", " '__init__': ,\n", " 'say_hi': ,\n", " 'cease': ,\n", " 'is_active': ,\n", - " 'population': ,\n", + " 'population': ,\n", " '__dict__': ,\n", " '__weakref__': ,\n", " '__doc__': None})" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "{'name': 'Clay', 'built_year': 2019, '_Golem__active': True}" + "{'name': 'Clay',\n", + " 'built_year': 2022,\n", + " '_Golem__active': True,\n", + " '_Golem__population': 1}" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -1061,17 +1124,17 @@ "True" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -1081,7 +1144,7 @@ "10000" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1136,6 +1199,309 @@ "g.population # 所以，在很多的情况下，不把数据封装在 Class 内部的话，后面会有很多麻烦。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "### 类的实现：\n", + "#### 如何去定义类\n", + " 举个例子：\n", + "```python\n", + "class Golem:\n", + "```\n", + "当然里面要有属性或者方法" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Golem:\n", + " population=0#共有变量，外界可以用\n", + " __life_span=10#私有变量，外界不可用\n", + " def __init__(self,name=None):#类似java中的构造方法，name默认参数为None\n", + " self.name=name\n", + " Golem.population+=1#每生成一个实例，该类的population就+1\n", + " self.__active = True\n", + " def no_active(self):\n", + " self.__active = False #可以将激活状态变成非激活\n", + " def is_active(self):\n", + " return self.__active\n", + "g=Golem('Tony')\n", + "g.population\n", + "g.is_active()\n", + "g.no_active()\n", + "g.is_active()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以继承该类比如" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "跑\n" + ] + } + ], + "source": [ + "class Running_Golem(Golem):\n", + " def running(self):\n", + " print('跑')\n", + "r = Running_Golem('Dog')\n", + "r.running() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也要考虑到共有变量老是会被篡改，所以我们要做的事要把类中的变量改成私有变量" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Golem' object has no attribute '__population'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_5015/3111501231.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__active\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGolem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tony'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__population\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Golem' object has no attribute '__population'" + ] + } + ], + "source": [ + "class Golem:\n", + " __population=0#共有变量，外界可以用\n", + " __life_span=10#私有变量，外界不可用\n", + " def __init__(self,name=None):#类似java中的构造方法，name默认参数为None\n", + " self.name=name\n", + " Golem.__population+=1#每生成一个实例，该类的population就+1\n", + " self.__active = True\n", + " def no_active(self):\n", + " self.__active = False #可以将激活状态变成非激活\n", + " def is_active(self):\n", + " return self.__active\n", + "g=Golem('Tony')\n", + "g.__population\n", + "g.is_active()\n", + "g.no_active()\n", + "g.is_active()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "---------------------------------------------------------------------------\n", + "AttributeError Traceback (most recent call last)\n", + "/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_5015/3111501231.py in \n", + " 11 return self.__active\n", + " 12 g=Golem('Tony')\n", + "---> 13 g.__population\n", + " 14 g.is_active()\n", + " 15 g.no_active()\n", + "\n", + "AttributeError: 'Golem' object has no attribute '__population'\n", + "\n", + "AttributeError: type object 'Golem' has no attribute 'population'\n", + "\n", + "```\n", + "但是这样会报错\n", + "要怎么改造呢，我们可以在定义下函数，通过函数来查找" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Golem:\n", + " __population=0#共有变量，外界可以用\n", + " __life_span=10#私有变量，外界不可用\n", + " def __init__(self,name=None):#类似java中的构造方法，name默认参数为None\n", + " self.name=name\n", + " Golem.__population+=1#每生成一个实例，该类的population就+1\n", + " self.__active = True\n", + " def no_active(self):\n", + " self.__active = False #可以将激活状态变成非激活\n", + " def is_active(self):\n", + " return self.__active\n", + " @property#添加装饰器，能够让其返回属性\n", + " def population(self):\n", + " return Golem.__population\n", + " \n", + "g=Golem('Tony')\n", + "g.population\n", + "g.is_active()\n", + "g.no_active()\n", + "g.is_active()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以set属性，比如" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "100" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Golem:\n", + " __population=0#共有变量，外界可以用\n", + " __life_span=10#私有变量，外界不可用\n", + " def __init__(self,name=None):#类似java中的构造方法，name默认参数为None\n", + " self.name=name\n", + " Golem.__population+=1#每生成一个实例，该类的population就+1\n", + " self.__active = True\n", + " def no_active(self):\n", + " self.__active = False #可以将激活状态变成非激活\n", + " def is_active(self):\n", + " return self.__active\n", + " @property#添加装饰器，能够让其返回属性\n", + " def population(self):\n", + " return Golem.__population\n", + " @population.setter\n", + " def population(self,value):\n", + " Golem.__population=value\n", + " \n", + "g=Golem('Tony')\n", + "g.population\n", + "g.population=100\n", + "g.population\n", + "g.is_active()\n", + "g.no_active()\n", + "g.is_active()" + ] + }, { "cell_type": "markdown", "metadata": {}, From d0bb036714b81112153d92a32c976b5c8805a64a Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Mon, 22 Aug 2022 20:03:18 +0800 Subject: [PATCH 04/12] 20220822 --- Part.2.D.2-aargs.ipynb | 51 +- Part.3.B.2.classes-2.ipynb | 11 +- Part.3.B.3.decorator-iterator-generator.ipynb | 516 ++++++++++++++++-- 3 files changed, 512 insertions(+), 66 deletions(-) diff --git a/Part.2.D.2-aargs.ipynb b/Part.2.D.2-aargs.ipynb index f6a1ead64..fcc7b7825 100644 --- a/Part.2.D.2-aargs.ipynb +++ b/Part.2.D.2-aargs.ipynb @@ -586,7 +586,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "当然我们也可以换其他的方式表达" + "当然我们也可以换其他的方式表达，用关键字参数，关键字参数主要是用来通过传一个值来返回另一个值的，比如字典中我传个key，返回value" ] }, { @@ -616,7 +616,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "如果关键词参数和普通参数放在一起是怎么样的呢" + "如果位置参数和普通参数放在一起是怎么样的呢" ] }, { @@ -676,6 +676,53 @@ "do_sometings(*names)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如果随意变换的位置参数和关键字参数放在一起又会是怎么样呢？\n", + "还是拿def do_somethings()举例：" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mikehello,mike\n", + "mikehello,mike\n", + "mikehello,mike\n", + "davidhi,david\n", + "davidhi,david\n", + "davidhi,david\n", + "andywelcome,andy\n", + "andywelcome,andy\n", + "andywelcome,andy\n" + ] + } + ], + "source": [ + "def do_somethings(*names,**actions):\n", + " for name in names:\n", + " for action in actions:\n", + " print(f'{name}{actions[name]},{name}')\n", + "a_dictionary={'mike':'hello','david':'hi','andy':'welcome'} \n", + "names=['mike','david','andy']\n", + "do_somethings(*names,**a_dictionary)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*args是位置参数，而**kwargs是关键字参数" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.3.B.2.classes-2.ipynb b/Part.3.B.2.classes-2.ipynb index fdfa0852a..3677ae5c3 100644 --- a/Part.3.B.2.classes-2.ipynb +++ b/Part.3.B.2.classes-2.ipynb @@ -1305,7 +1305,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -1315,7 +1315,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_5015/3111501231.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__active\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGolem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tony'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__population\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_9175/3111501231.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__active\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGolem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tony'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__population\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'Golem' object has no attribute '__population'" ] } @@ -1511,8 +1511,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -1526,7 +1529,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" }, "toc-autonumbering": true }, diff --git a/Part.3.B.3.decorator-iterator-generator.ipynb b/Part.3.B.3.decorator-iterator-generator.ipynb index cff500d95..5735da6b9 100644 --- a/Part.3.B.3.decorator-iterator-generator.ipynb +++ b/Part.3.B.3.decorator-iterator-generator.ipynb @@ -218,59 +218,19 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 31, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "11" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "13" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "101\n", - "102\n", - "103\n", - "104\n", - "105\n" + "ename": "TypeError", + "evalue": "'Counter' object is not an iterator", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_3825/3796267106.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'Counter' object is not an iterator" ] - }, - { - "data": { - "text/plain": [ - "type" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -372,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -420,14 +380,14 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " at 0x107cc0048>\n", + " at 0x7fc61c9ed660>\n", "0\n", "2\n", "4\n", @@ -457,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -516,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -704,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -742,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -758,7 +718,7 @@ ".wrapper()>" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -794,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -969,6 +929,40 @@ "装饰器的执行顺序是 “自下而上” —— 其实是 “由里到外” 更为准确。体会一下。\n" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG.\n" + ] + } + ], + "source": [ + "def uppercase(func):\n", + " def wrapper():\n", + " original_result = func()\n", + " modified_restult = original_result.upper()\n", + " return modified_restult\n", + " return wrapper\n", + "def strong(func):\n", + " def wrapper():\n", + " original_result = func()\n", + " modified_restult = ''+original_result+''\n", + " return modified_restult\n", + " return wrapper\n", + "\n", + "@uppercase\n", + "@strong\n", + "def an_output():\n", + " return 'The quick brown fox jumps over the lazy dog.'\n", + "print(an_output())" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1092,6 +1086,405 @@ "为什么有那么多人就是学不会呢？—— 只不过是因为在此之前，遇到 `*args` `**kwargs` 的时候，“一个星号、两个星号、直接晕倒”…… 而后并未再多挣扎一下。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "### 函数工具\n", + "#### 迭代器（iterator）\n", + " 迭代器叫做iterator，迭代是什么，以下面的代码作为例子。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i)\n", + "for i in tuple(range(10)):\n", + " print(i)\n", + "for i in set(range(10)):\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们在容器中不断循环输出的这个过程就叫做迭代，就是iterator\n", + "当然我们也可以把List、tuple、set这种任意一个容器加个迭代函数iter，比如" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n" + ] + } + ], + "source": [ + "i=iter(range(10))\n", + "print(next(i))\n", + "print(next(i))\n", + "print(next(i))\n", + "print(next(i))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这样写就可以迭代出每一个i的元素了，但是它有局限性，当超出容器范围时就会报错\n", + "```python\n", + "---------------------------------------------------------------------------\n", + "StopIteration Traceback (most recent call last)\n", + "/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_3825/1161027952.py in \n", + " 10 next(i)\n", + " 11 next(i)\n", + "---> 12 next(i)\n", + "\n", + "StopIteration: \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如何在类中添加迭代器呢，比如以下计数的例子" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21,22,23,21,22,23," + ] + } + ], + "source": [ + "class Counter(object):\n", + " def __init__(self, start, stop):\n", + " self.current = start\n", + " self.stop = stop\n", + " def __iter__(self):\n", + " return self\n", + " def __next__(self):\n", + " if self.current > self.stop:\n", + " raise StopIteration\n", + " else:\n", + " c = self.current\n", + " self.current += 1\n", + " return c\n", + "for c in Counter(21,23):\n", + " print(c,end=',')\n", + "c = Counter(21,23)\n", + "while True:\n", + " try:\n", + " print(next(c),end=',')\n", + " except StopIteration:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "其中__iter__(self)和__next__(self)组成了迭代器的元素,而__init__(self,start,stop),像这种带\"__\"的情况其实就是python中的定义的魔术方法，就是python自己定义的方法非我们自己定义的方法，首先变量的话我们知道就是私有变量，而带了\"__\"的函数其实就是python自己定义好的内部函数，也就是我们只能在此基础上进行重写，如果我们改了\"__iter__\"成\"__itera__\",或者把\"__next__\"改成\"__nexta__\"这样就会有问题，就无法形成迭代类了\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'Counter' object is not iterable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_3825/3635832865.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m23\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m23\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'Counter' object is not iterable" + ] + } + ], + "source": [ + "class Counter(object):\n", + " def __init__(self, start, stop):\n", + " self.current = start\n", + " self.stop = stop\n", + " def __itera__(self):\n", + " return self\n", + " def __next__(self):\n", + " if self.current > self.stop:\n", + " raise StopIteration\n", + " else:\n", + " c = self.current\n", + " self.current += 1\n", + " return c\n", + "for c in Counter(21,23):\n", + " print(c,end=',')\n", + "c = Counter(21,23)\n", + "while True:\n", + " try:\n", + " print(next(c),end=',')\n", + " except StopIteration:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 生成器（Generator）\n", + "迭代器是用在类中成为类中的一个内置方法，让其类能达到迭代的效果，而生成器则是通过将函数进行改造，达到迭代的效果，如以下例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "101\n", + "102\n", + "103\n", + "104\n", + "105\n" + ] + } + ], + "source": [ + "def counter(start, stop):\n", + " while start <= stop:\n", + " yield start\n", + " start += 1\n", + "for i in counter(101, 105):\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "yield 是生成器在函数中重要的组成部分，相当于玩游戏的读档一样，当我们每次玩好一个关卡，保存好这个关卡，下次玩的时候就从这个关卡开始继续玩，直到这个游戏结束，return 相当于没有读档，返回即代表游戏结束\n", + "```\n", + "当然我们也可以转化成匿名表达式，也就是生成器表达式来替代，这样可以精简很多" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "101\n", + "102\n", + "103\n", + "104\n", + "105\n", + "101\n", + "102\n", + "103\n", + "104\n", + "105\n", + "\n", + "\n", + "140488877858880\n", + "140488878015088\n" + ] + } + ], + "source": [ + "l = [i for i in(range(101,106))]\n", + "for i in l:\n", + " print(i)\n", + "s = (i for i in(range(101,106)))\n", + "for i in s:\n", + " print(i)\n", + "print(type(l))\n", + "print(type(s))\n", + "print(id(l))\n", + "print(id(s))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可以看出带“()”的才是真的生成器generator，而如果我们带“[]”这样就会变成列表List，从以上代码示例可以看出，生成器的作用就是将上一步的结果带到下一步中去，或者把函数中的内部函数所返回的结果带到函数中去比如以下例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm fun_a\n", + "I'm fun_b\n" + ] + } + ], + "source": [ + "def fun_a():\n", + " def fun_b():\n", + " print(\"I'm fun_b\")\n", + " print(\"I'm fun_a\")\n", + " return fun_b()\n", + " \n", + "fun_a()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 装饰器（decorator）\n", + "装饰器是什么，装饰器其实就是将一个函数所返回的结果进行改变，如以下代码中的意思：" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'M FUN_B\n" + ] + } + ], + "source": [ + "def fun_a(fun_b):\n", + " def fun_c():\n", + " origin_result=fun_b()\n", + " now_result=origin_result.upper()\n", + " return now_result\n", + " return fun_c\n", + "@fun_a\n", + "def fun_t():\n", + " return \"I'm fun_b\"\n", + "print(fun_t())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以多用几个装饰器，比如" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'M FUN_B\n" + ] + } + ], + "source": [ + "def fun_a(fun_b):\n", + " def fun_c():\n", + " origin_result=fun_b()\n", + " now_result=origin_result.upper()\n", + " return now_result\n", + " return fun_c\n", + "def fun_d(fun_b):\n", + " def fun_h():\n", + " origin_result=fun_b()\n", + " now_result=''+origin_result+''\n", + " return now_result\n", + " return fun_h\n", + "@fun_a\n", + "@fun_d\n", + "def fun_t():\n", + " return \"I'm fun_b\"\n", + "print(fun_t())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "装饰器还是有带参数的，比如" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1101,8 +1494,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -1116,7 +1512,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" }, "toc-autonumbering": true }, From 56a1e743e6714931a980727c3edbde8ad1ed2c70 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Tue, 23 Aug 2022 15:03:03 +0800 Subject: [PATCH 05/12] 20220823 --- Part.3.B.3.decorator-iterator-generator.ipynb | 82 ++++++++++++++++ Part.3.B.4.regex.ipynb | 93 +++++++++++++++---- 2 files changed, 156 insertions(+), 19 deletions(-) diff --git a/Part.3.B.3.decorator-iterator-generator.ipynb b/Part.3.B.3.decorator-iterator-generator.ipynb index 5735da6b9..146fa6d40 100644 --- a/Part.3.B.3.decorator-iterator-generator.ipynb +++ b/Part.3.B.3.decorator-iterator-generator.ipynb @@ -1485,6 +1485,88 @@ "装饰器还是有带参数的，比如" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trace: You've called a function: say_hi(), with args: ('Hello',); kwargs: {'name': 'Jack'}\n", + "Trace: say_hi('Hello',) returned: Hello! Jack.\n", + "Hello! Jack.\n" + ] + } + ], + "source": [ + "def trace(func):\n", + " def wrapper(*args, **kwargs):\n", + " print(f\"Trace: You've called a function: {func.__name__}(),\",\n", + " f\"with args: {args}; kwargs: {kwargs}\")\n", + " \n", + " original_result = func(*args, **kwargs)\n", + " print(f\"Trace: {func.__name__}{args} returned: {original_result}\")\n", + " return original_result\n", + " return wrapper\n", + "\n", + "@trace\n", + "def say_hi(greeting, name=None):\n", + " return greeting + '! ' + name + '.'\n", + "\n", + "print(say_hi('Hello', name='Jack'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如果在装饰器上带上参数会怎么样" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trace: You've called a function: wrapper3(), with args: ('Hello',); kwargs: {'name': 'Jack'}\n", + "Trace: wrapper3('Hello',) returned: Hello! Jack.Hello! Jack.\n", + "Hello! Jack.Hello! Jack.\n" + ] + } + ], + "source": [ + "def trace(func):\n", + " def wrapper(*args, **kwargs):\n", + " print(f\"Trace: You've called a function: {func.__name__}(),\",\n", + " f\"with args: {args}; kwargs: {kwargs}\")\n", + " \n", + " original_result = func(*args, **kwargs)\n", + " print(f\"Trace: {func.__name__}{args} returned: {original_result}\")\n", + " return original_result\n", + " return wrapper\n", + "def trace2(try_times):\n", + " def wrapper2(func):\n", + " def wrapper3(*args, **kwargs):\n", + " original_result = try_times*func(*args, **kwargs)\n", + " return original_result\n", + " return wrapper3\n", + " return wrapper2\n", + " \n", + "\n", + "@trace\n", + "@trace2(try_times=2)\n", + "def say_hi(greeting, name=None):\n", + " return greeting + '! ' + name + '.'\n", + "\n", + "print(say_hi('Hello', name='Jack'))" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.3.B.4.regex.ipynb b/Part.3.B.4.regex.ipynb index 37bcfa8b0..79d66472b 100644 --- a/Part.3.B.4.regex.ipynb +++ b/Part.3.B.4.regex.ipynb @@ -148,16 +148,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['begin', 'began', 'begun', 'begin']" + "['begin', 'began', 'begun', 'begins', 'begin']" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -936,26 +936,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "['black']" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "text/plain": [ "'The black dog wears a black hat.'" ] }, - "execution_count": 24, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1249,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1261,7 +1251,43 @@ "Talk to the program by typing in plain English, using normal upper-\n", "and lower-case letters and punctuation. Enter \"quit\" when done.\n", "========================================================================\n", - "Hello. How are you feeling today?\n" + "Hello. How are you feeling today?\n", + "hello\n", + "Hi there... how are you today?\n", + "I need sb\n", + "Why do you need sb?\n", + "what\n", + "Can you elaborate on that?\n", + "why\n", + "How do you feel when you say that?\n", + "now\n", + "Can you elaborate on that?\n", + "tommorw\n", + "Very interesting.\n", + "dd\n", + "How do you feel when you say that?\n", + "dd\n", + "Let's change focus a bit... Tell me about your family.\n", + "dd\n", + "Let's change focus a bit... Tell me about your family.\n", + "dd\n", + "Please tell me more.\n", + "dd\n", + "How does that make you feel?\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "Interrupted by user", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_6563/1984203097.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0mcommand_interface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_6563/1984203097.py\u001b[0m in \u001b[0;36mcommand_interface\u001b[0;34m()\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'quit'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'> '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 305\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mEOFError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'quit'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1005\u001b[0m )\n\u001b[0;32m-> 1006\u001b[0;31m return self._input_request(\n\u001b[0m\u001b[1;32m 1007\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1008\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"shell\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m 1049\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1050\u001b[0m \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1052\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user" ] } ], @@ -1615,6 +1641,32 @@ " " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 总结\n", + "|正则元素|作用|举例|\n", + "|--|--|--|\n", + "|\\w|匹配字符||\n", + "|\\W|匹配非字符||\n", + "|\\d|匹配数字||\n", + "|\\D|匹配非数字||\n", + "|\\s|匹配空格||\n", + "|\\S|匹配非空格||\n", + "|+|匹配数量>=1||\n", + "|.*|匹配数量>=0||\n", + "|?|匹配数量<=1||\n", + "|{m,n}|匹配数量在m～n之间||\n", + "|{m,}|匹配数量在m以上||\n", + "|{n}|匹配数量为n|\n", + "|[]|匹配其中的元素|[er]可以匹配e也可以匹配r也可以匹配er|\n", + "|()|匹配一整个元素集合|(er)只匹配er,捕获匹配|\n", + "|^|开头||\n", + "|$|结尾||\n", + "|\\||或||" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1624,8 +1676,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -1639,7 +1694,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, From 11887c4845d0c160cfcda610c65caf57ea7f8a6a Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Fri, 2 Sep 2022 23:52:04 +0800 Subject: [PATCH 06/12] 20220902 --- Part.2.D.8-main.ipynb | 7 +- Part.3.B.4.regex.ipynb | 4 +- Part.3.C.breaking-good-and-bad.ipynb | 14 +- Part.3.D.indispensable-illusion.ipynb | 11 +- Part4.A.1.data-analyzer.ipynb | 468 ++++++++++++++++++++++++++ 5 files changed, 491 insertions(+), 13 deletions(-) create mode 100644 Part4.A.1.data-analyzer.ipynb diff --git a/Part.2.D.8-main.ipynb b/Part.2.D.8-main.ipynb index cf819dcbd..fdf244184 100644 --- a/Part.2.D.8-main.ipynb +++ b/Part.2.D.8-main.ipynb @@ -903,8 +903,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -918,7 +921,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.3.B.4.regex.ipynb b/Part.3.B.4.regex.ipynb index 79d66472b..bed2243ec 100644 --- a/Part.3.B.4.regex.ipynb +++ b/Part.3.B.4.regex.ipynb @@ -421,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -430,7 +430,7 @@ "['542-', '270-']" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } diff --git a/Part.3.C.breaking-good-and-bad.ipynb b/Part.3.C.breaking-good-and-bad.ipynb index f8723692a..df2dd11b2 100644 --- a/Part.3.C.breaking-good-and-bad.ipynb +++ b/Part.3.C.breaking-good-and-bad.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -68,14 +68,15 @@ " src=\"https://www.youtube.com/embed/OOsRMECWKAE?\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", + " \n", " >\n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -267,8 +268,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -282,7 +286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part.3.D.indispensable-illusion.ipynb b/Part.3.D.indispensable-illusion.ipynb index 6e5ad7176..6671bb2a8 100644 --- a/Part.3.D.indispensable-illusion.ipynb +++ b/Part.3.D.indispensable-illusion.ipynb @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -276,7 +276,7 @@ "> - [Part.3.F.social-selfteaching（**自学者的社交**）](Part.3.F.social-selfteaching.ipynb)\n", "> - [Part.3.G.the-golden-age-and-google（**这是自学者的黄金时代**）](Part.3.G.the-golden-age-and-google.ipynb)\n", "> - [Part.3.H.prevent-focus-drifting（**避免注意力漂移**）](Part.3.H.prevent-focus-drifting.ipynb)\n", - "> - [Q.good-communiation（**如何成为优秀沟通者**）](Q.good-communication.ipynb)\n", + "> - [Q.good-communication（**如何成为优秀沟通者**）](Q.good-communication.ipynb)\n", "> - [R.finale（**自学者的终点**）](R.finale.ipynb)\n", "> - [S.whats-next（**下一步干什么？**）](S.whats-next.ipynb)\n", "> - [T-appendix.editor.vscode（**Visual Studio Code 的安装与配置**）](T-appendix.editor.vscode.ipynb)\n", @@ -318,8 +318,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -333,7 +336,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/Part4.A.1.data-analyzer.ipynb b/Part4.A.1.data-analyzer.ipynb new file mode 100644 index 000000000..8108a1519 --- /dev/null +++ b/Part4.A.1.data-analyzer.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 数据分析" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "经常有人说不会数据分析就没有未来，所以数据分析的作用性非常大，在拥有数据分析的工具使用的基础后如果还有数据分析的思维以及能力，那么我们的稀缺性会更高，毕竟在目前公司里编程语言开发使用者占大多数，但能在使用的基础之上还能做到对于数据的分析研究并挖掘，这是比较稀缺的。\n", + "数据分析中用到的模块最多的就是numpy,pandas和matplotlib还有seaborn最后是re，接下来就一一讲解下其中的用法，以及对应的使用场景，毕竟要结合刻意思考，刻意练习还有以教代练。\n", + "pandas经常和其它工具一同使用，如数值计算工具NumPy和SciPy，分析库statsmodels和scikit-learn，和数据可视化库matplotlib。pandas是基于NumPy数组构建的，特别是基于数组的函数和不使用for循环的数据处理。\n", + "虽然pandas采用了大量的NumPy编码风格，但二者最大的不同是pandas是专门为处理表格和混杂数据设计的。而NumPy更适合处理统一的数值数组数据。\n", + "## pandas\n", + "数据分析中用到的模块最多的就是numpy,pandas和matplotlib还有seaborn最后是re，接下来就一一讲解下其中的用法，以及对应的使用场景，毕竟要结合刻意思考，刻意练习还有以教代练。\n", + "pandas经常和其它工具一同使用，如数值计算工具NumPy和SciPy，分析库statsmodels和scikit-learn，和数据可视化库matplotlib。pandas是基于NumPy数组构建的，特别是基于数组的函数和不使用for循环的数据处理。\n", + "虽然pandas采用了大量的NumPy编码风格，但二者最大的不同是pandas是专门为处理表格和混杂数据设计的。而NumPy更适合处理统一的数值数组数据。\n", + "那么如何使用pandas呢？\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### pandas的数据结构应用\n", + "#### Series\n", + "Series是一种类似于数组的对象，类似于Numpy，它由索引和数据所构成\n", + "比如以下例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "1 2\n", + "2 3\n", + "3 4\n", + "4 5\n", + "5 6\n", + "6 7\n", + "7 8\n", + "8 9\n", + "dtype: int64\n", + "[1 2 3 4 5 6 7 8 9]\n", + "RangeIndex(start=0, stop=9, step=1)\n" + ] + } + ], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块\n", + "obj = pd.Series(list(range(1,10)))\n", + "print(obj)\n", + "print(obj.values)\n", + "print(obj.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们也可以修改他们的索引，比如" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1\n", + "3 2\n", + "5 3\n", + "7 4\n", + "9 5\n", + "11 6\n", + "13 7\n", + "15 8\n", + "17 9\n", + "dtype: int64\n", + "[1 2 3 4 5 6 7 8 9]\n", + "Int64Index([1, 3, 5, 7, 9, 11, 13, 15, 17], dtype='int64')\n", + "2\n", + "1 1\n", + "3 2\n", + "5 3\n", + "dtype: int64\n", + "1 1\n", + "3 2\n", + "5 3\n", + "7 4\n", + "9 5\n", + "11 6\n", + "13 7\n", + "15 8\n", + "17 9\n", + "dtype: int64\n", + "11 6\n", + "13 7\n", + "15 8\n", + "17 9\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块\n", + "obj2 = pd.Series(list(range(1,10)),index=list(range(1,19,2)))\n", + "print(obj2)\n", + "print(obj2.values)\n", + "print(obj2.index)\n", + "print(obj2[3])#也可以拿到单个或者多个的值\n", + "print(obj2[[1,3,5]])#注意当中一定要有两次的[]\n", + "print(obj2[:10])#切片的话只会对value进行切片，因此要注意\n", + "print(obj2[obj2>5])#跟上面切片一样的，也是可以通过判断筛选出值\n", + "4 in obj2 #这里又是指的index索引" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们其实看上面例子可以看出来Series是带有索引的数组，那不是和字典类似吗？那我们是不是可以用来应用字典呢？答案是当然可以" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mike hello\n", + "david hi\n", + "andy welcome\n", + "dtype: object\n", + "david hi\n", + "mike hello\n", + "andy welcome\n", + "jack NaN\n", + "dtype: object\n", + "david False\n", + "mike False\n", + "andy False\n", + "jack True\n", + "dtype: bool\n", + "david False\n", + "mike False\n", + "andy False\n", + "jack True\n", + "dtype: bool\n" + ] + } + ], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块\n", + "a_dictionary={'mike':'hello','david':'hi','andy':'welcome'} \n", + "obj3 = pd.Series(a_dictionary)\n", + "print(obj3)\n", + "#当然也可以改变字典的顺序比如\n", + "obj4 = pd.Series(a_dictionary,index=['david','mike','andy','jack'])\n", + "print(obj4)\n", + "#我们发现当中有null值,可以通过isnull来判断\n", + "print(pd.isnull(obj4))\n", + "#也可以这样\n", + "print(obj4.isnull())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataFrame\n", + "DataFrame为表格型的数据结构。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " state year pop\n", + "0 Ohio 2000 1.5\n", + "1 Ohio 2001 1.7\n", + "2 Ohio 2002 3.6\n", + "3 Nevada 2001 2.4\n", + "4 Nevada 2002 2.9\n", + "5 Nevada 2003 3.2\n", + " state year pop\n", + "0 Ohio 2000 1.5\n", + "1 Ohio 2001 1.7\n", + "2 Ohio 2002 3.6\n", + "3 Nevada 2001 2.4\n", + "4 Nevada 2002 2.9\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],\n", + " 'year': [2000, 2001, 2002, 2001, 2002, 2003],\n", + " 'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}\n", + "frame = pd.DataFrame(data)\n", + "print(frame)\n", + "print(frame.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然也可以指定列的顺序" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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popyearstate
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32.42001Nevada
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" + ], + "text/plain": [ + " pop year state\n", + "0 1.5 2000 Ohio\n", + "1 1.7 2001 Ohio\n", + "2 3.6 2002 Ohio\n", + "3 2.4 2001 Nevada\n", + "4 2.9 2002 Nevada\n", + "5 3.2 2003 Nevada" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(data,columns=['pop','year','state'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然也可以在原来的数据中再加一列" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pop year state debt\n", + "0 1.5 2000 Ohio NaN\n", + "1 1.7 2001 Ohio NaN\n", + "2 3.6 2002 Ohio NaN\n", + "3 2.4 2001 Nevada NaN\n", + "4 2.9 2002 Nevada NaN\n", + "5 3.2 2003 Nevada NaN\n", + " pop year state debt\n", + "1 1.5 2000 Ohio NaN\n", + "2 1.7 2001 Ohio NaN\n", + "3 3.6 2002 Ohio NaN\n", + "4 2.4 2001 Nevada NaN\n", + "5 2.9 2002 Nevada NaN\n", + "6 3.2 2003 Nevada NaN\n" + ] + } + ], + "source": [ + "data2=pd.DataFrame(data,columns=['pop','year','state','debt'])\n", + "print(data2)\n", + "data2=pd.DataFrame(data,columns=['pop','year','state','debt'],index=list(range(1,7)))\n", + "print(data2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然可以给null的值赋值" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pop year state debt\n", + "1 1.5 2000 Ohio 1\n", + "2 1.7 2001 Ohio 2\n", + "3 3.6 2002 Ohio 3\n", + "4 2.4 2001 Nevada 4\n", + "5 2.9 2002 Nevada 5\n", + "6 3.2 2003 Nevada 6\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "Name: debt, dtype: int64\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "Name: debt, dtype: int64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_36999/681092870.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data2['debt'][i]=i\n" + ] + } + ], + "source": [ + "for i in range(1,7):\n", + " data2['debt'][i]=i\n", + "print(data2)\n", + "print(data2['debt'])\n", + "print(data2.debt)#这两种方式都是一样的" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 286639ec76b49cbb5fb62b1df8a09ae347a93c54 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Fri, 9 Sep 2022 20:44:00 +0800 Subject: [PATCH 07/12] 20220909 --- Part4.A.1.data-analyzer.ipynb | 593 ++++++++++++++++++++++++++++++++-- 1 file changed, 573 insertions(+), 20 deletions(-) diff --git a/Part4.A.1.data-analyzer.ipynb b/Part4.A.1.data-analyzer.ipynb index 8108a1519..831b7a03f 100644 --- a/Part4.A.1.data-analyzer.ipynb +++ b/Part4.A.1.data-analyzer.ipynb @@ -77,12 +77,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "当然我们也可以修改他们的索引，比如" + "### 索引选取和过滤\n", + "#### 索引和过滤" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -120,18 +121,33 @@ "13 7\n", "15 8\n", "17 9\n", - "dtype: int64\n" + "dtype: int64\n", + "1 5\n", + "3 5\n", + "5 5\n", + "7 4\n", + "9 5\n", + "11 6\n", + "13 7\n", + "15 8\n", + "17 9\n", + "dtype: int64\n", + " one two three four\n", + "Ohio 0 1 2 3\n", + "Colorado 4 5 6 7\n", + "Utah 8 9 10 11\n", + "New York 12 13 14 15\n", + " one two three four\n", + "Ohio True True True True\n", + "Colorado True False False False\n", + "Utah False False False False\n", + "New York False False False False\n", + " one two three four\n", + "Ohio 5 5 5 5\n", + "Colorado 5 5 6 7\n", + "Utah 8 9 10 11\n", + "New York 12 13 14 15\n" ] - }, - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -141,10 +157,19 @@ "print(obj2.values)\n", "print(obj2.index)\n", "print(obj2[3])#也可以拿到单个或者多个的值\n", - "print(obj2[[1,3,5]])#注意当中一定要有两次的[]\n", - "print(obj2[:10])#切片的话只会对value进行切片，因此要注意\n", - "print(obj2[obj2>5])#跟上面切片一样的，也是可以通过判断筛选出值\n", - "4 in obj2 #这里又是指的index索引" + "print(obj2[[1,3,5]])#注意当中一定要有两次的[]，这样就能拿到对应的数据\n", + "print(obj2[:10])#切片的话优先会对value进行切片，因此要注意，除非其中没有value值才会对索引进行过滤\n", + "print(obj2[obj2>5])#跟上面切片一样的，也是可以通过判断筛选出对应的索引然后再选到对应的值\n", + "obj2[1,3,5]=5 #这样对应的是索引\n", + "print(obj2)\n", + "4 in obj2 #这里又是指的index索引\n", + "#再举个例子\n", + "data = pd.DataFrame(np.arange(16).reshape((4, 4)),index=['Ohio', 'Colorado', 'Utah', 'New York'],columns=['one', 'two', 'three', 'four'])\n", + "print(data)\n", + "print(data<5)#可以判断是否<5\n", + "#也可以通过选取<5的单元格进行值替换\n", + "data[(data<5)]=5\n", + "print(data)" ] }, { @@ -199,6 +224,42 @@ "print(obj4.isnull())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们还可以对数据进行重新索引也可以进行填充，用method选项填充" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 blue\n", + "1 blue\n", + "2 purple\n", + "3 purple\n", + "4 yellow\n", + "5 yellow\n", + "dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])\n", + "obj3\n", + "obj3.reindex(range(6))#这样数据就会有部分为NaN\n", + "obj3.reindex(range(6),method='ffill')#可以进行填充" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -438,14 +499,506 @@ "print(data2['debt'])\n", "print(data2.debt)#这两种方式都是一样的" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然也可以添加一个计算列，比如根据判断state是否是'Ohio',如果是就返回True，如果不是就返回False" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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popyearstatedebtexp
01.52000OhioNaNTrue
11.72001OhioNaNTrue
23.62002OhioNaNTrue
32.42001NevadaNaNFalse
42.92002NevadaNaNFalse
53.22003NevadaNaNFalse
\n", + "
" + ], + "text/plain": [ + " pop year state debt exp\n", + "0 1.5 2000 Ohio NaN True\n", + "1 1.7 2001 Ohio NaN True\n", + "2 3.6 2002 Ohio NaN True\n", + "3 2.4 2001 Nevada NaN False\n", + "4 2.9 2002 Nevada NaN False\n", + "5 3.2 2003 Nevada NaN False" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],\n", + " 'year': [2000, 2001, 2002, 2001, 2002, 2003],\n", + " 'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}\n", + "data2=pd.DataFrame(data,columns=['pop','year','state','debt'])\n", + "\n", + "data2['exp']=data2.state == 'Ohio'\n", + "data2\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "也可以用del方法来删除某一列" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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popyearstatedebt
01.52000OhioNaN
11.72001OhioNaN
23.62002OhioNaN
32.42001NevadaNaN
42.92002NevadaNaN
53.22003NevadaNaN
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" + ], + "text/plain": [ + " pop year state debt\n", + "0 1.5 2000 Ohio NaN\n", + "1 1.7 2001 Ohio NaN\n", + "2 3.6 2002 Ohio NaN\n", + "3 2.4 2001 Nevada NaN\n", + "4 2.9 2002 Nevada NaN\n", + "5 3.2 2003 Nevada NaN" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del data2['exp']\n", + "data2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然我们还可以对数据进行转置，类似excel的转置" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data3=data2.T\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们也可以对DataFrame的数据进行重新索引" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pop year state debt\n", + "0 1.5 2000.0 Ohio NaN\n", + "1 1.7 2001.0 Ohio NaN\n", + "2 3.6 2002.0 Ohio NaN\n", + "3 2.4 2001.0 Nevada NaN\n", + "4 2.9 2002.0 Nevada NaN\n", + "5 3.2 2003.0 Nevada NaN\n", + "6 NaN NaN NaN NaN\n", + "7 NaN NaN NaN NaN\n", + " year state debt pop exp\n", + "0 2000 Ohio NaN 1.5 NaN\n", + "1 2001 Ohio NaN 1.7 NaN\n", + "2 2002 Ohio NaN 3.6 NaN\n", + "3 2001 Nevada NaN 2.4 NaN\n", + "4 2002 Nevada NaN 2.9 NaN\n", + "5 2003 Nevada NaN 3.2 NaN\n", + "[[2000 'Ohio' nan 1.5 nan]\n", + " [2001 'Ohio' nan 1.7 nan]\n", + " [2002 'Ohio' nan 3.6 nan]\n", + " [2001 'Nevada' nan 2.4 nan]\n", + " [2002 'Nevada' nan 2.9 nan]\n", + " [2003 'Nevada' nan 3.2 nan]]\n" + ] + } + ], + "source": [ + "data4=data2.reindex(range(8))#这样数据就会有部分为NaN\n", + "print(data4)\n", + "data5=data2.reindex(columns=['year','state','debt','pop','exp'])#也可以对列进行索引\n", + "print(data5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 丢弃制定轴上的项\n", + "我们可以对数据对象的某一列的数据进行丢弃，比如以下例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a 0.0\n", + "b 1.0\n", + "c 2.0\n", + "d 3.0\n", + "e 4.0\n", + "dtype: float64\n", + "a 0.0\n", + "b 1.0\n", + "c 2.0\n", + "d 3.0\n", + "e 4.0\n", + "dtype: float64\n", + "a 0.0\n", + "b 1.0\n", + "d 3.0\n", + "e 4.0\n", + "dtype: float64\n", + "a 0.0\n", + "b 1.0\n", + "dtype: float64\n", + " one two three four\n", + "Ohio 0 1 2 3\n", + "Colorado 4 5 6 7\n", + "Utah 8 9 10 11\n", + "New York 12 13 14 15\n", + " one two three four\n", + "Utah 8 9 10 11\n", + "New York 12 13 14 15\n", + " one three four\n", + "Ohio 0 2 3\n", + "Colorado 4 6 7\n", + "Utah 8 10 11\n", + "New York 12 14 15\n", + " one three four\n", + "Ohio 0 2 3\n", + "Colorado 4 6 7\n", + "Utah 8 10 11\n", + "New York 12 14 15\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "obj = pd.Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])\n", + "print(obj)\n", + "obj.drop('c')#可以对某一列进行丢弃对其本身不会做任何修改\n", + "print(obj)\n", + "obj.drop('c',inplace=True)#可以对某一列进行丢弃对其本身会做任何修改，小心使用inplace，它会销毁所有被删除的数据。\n", + "print(obj)\n", + "#当然也可以对对象多个列进行操作\n", + "obj2=obj.drop(['e','d'])\n", + "print(obj2)\n", + "#再举个例子,DataFrame的例子\n", + "data=pd.DataFrame(np.arange(16).reshape(4,4),index=['Ohio', 'Colorado', 'Utah', 'New York'],columns=['one', 'two', 'three', 'four'])\n", + "print(data)\n", + "#我们可以删除索引\n", + "data2=data.drop(['Ohio','Colorado'])\n", + "print(data2)\n", + "#当然也可以删除对应的列,通过传递axis=1或axis='columns'可以删除列的值,下面两张方式效果是一样的\n", + "data3=data.drop('two',axis=1)\n", + "print(data3)\n", + "data4=data.drop('two',axis='columns')\n", + "print(data4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 用loc和iloc进行选取\n", + "轴标签（loc）或整数索引（iloc），可以通过他们两个从DataFrame选择行和列的子集\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a b c d\n", + "1 0 1 2 3\n", + "2 4 5 6 7\n", + "3 8 9 10 11\n", + "4 12 13 14 15\n", + "b 1\n", + "c 2\n", + "Name: 1, dtype: int64\n", + "b 1\n", + "c 2\n", + "Name: 1, dtype: int64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "data = pd.DataFrame(np.arange(16).reshape(4,4),index=['1','2','3','4'],columns=['a','b','c','d'])\n", + "print(data)\n", + "#我们要选取第1行第2列，第3列\n", + "print(data.loc['1',['b','c']])\n", + "#当然可以用iloc整数索引来选取,数据要从第0行或者第0列开始算\n", + "print(data.iloc[0,[1,2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numpy\n", + "Numpy是带一组索引的数组，我们可以对其进行加工运算。类似于Series。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "1 2\n", + "2 3\n", + "3 4\n", + "4 5\n", + "5 6\n", + "6 7\n", + "7 8\n", + "8 9\n", + "dtype: int64\n", + "3 4\n", + "4 5\n", + "5 6\n", + "6 7\n", + "7 8\n", + "8 9\n", + "dtype: int64\n", + "0 2\n", + "1 4\n", + "2 6\n", + "3 8\n", + "4 10\n", + "5 12\n", + "6 14\n", + "7 16\n", + "8 18\n", + "dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "0 2.718282\n", + "1 7.389056\n", + "2 20.085537\n", + "3 54.598150\n", + "4 148.413159\n", + "5 403.428793\n", + "6 1096.633158\n", + "7 2980.957987\n", + "8 8103.083928\n", + "dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块\n", + "import numpy as np\n", + "obj = pd.Series(list(range(1,10)))\n", + "print(obj)\n", + "print(obj[obj>3])#筛选出大于3的数组\n", + "print(obj*2)#将数组中的值全*2\n", + "np.exp(obj)" + ] } ], "metadata": { "interpreter": { - "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + "hash": "034c70736e9d25f3f57bc70ec9a2164e1c27bdcdecfc656ea6098a86262350a2" }, "kernelspec": { - "display_name": "Python 3.9.7 ('base')", + "display_name": "Python 3.9.12 ('myenv')", "language": "python", "name": "python3" }, @@ -459,7 +1012,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.12" }, "orig_nbformat": 4 }, From 032850c7505ebf042c5f94ad70b8410f6b462947 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Mon, 10 Oct 2022 17:43:21 +0800 Subject: [PATCH 08/12] 20221010 --- Part.1.A.better.teachyourself.ipynb | 6 +- Part4.A.1.data-analyzer.ipynb | 584 +- Part4.A.2data_summury_and_group.ipynb | 212 + Part5.A.fund-choice.ipynb | 93 + Part5.B.downloadcsv.ipynb | 1012 +++ fundchoiceresult.csv | 205 + fundrank.csv | 2250 +++++++ images/groupby.png | Bin 0 -> 24429 bytes maoyan.csv | 0 names.csv | 4 + ua_info.py | 14 + "\346\210\221\347\232\204\345\244\251.html" | 6659 +++++++++++++++++++ 12 files changed, 10985 insertions(+), 54 deletions(-) create mode 100644 Part4.A.2data_summury_and_group.ipynb create mode 100644 Part5.A.fund-choice.ipynb create mode 100644 Part5.B.downloadcsv.ipynb create mode 100644 fundchoiceresult.csv create mode 100644 fundrank.csv create mode 100644 images/groupby.png create mode 100644 maoyan.csv create mode 100644 names.csv create mode 100644 ua_info.py create mode 100644 "\346\210\221\347\232\204\345\244\251.html" diff --git a/Part.1.A.better.teachyourself.ipynb b/Part.1.A.better.teachyourself.ipynb index 74be6bdea..16adb9dab 100644 --- a/Part.1.A.better.teachyourself.ipynb +++ b/Part.1.A.better.teachyourself.ipynb @@ -60,7 +60,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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4cGqLVqp9Fn4QX0bwAb45oo7/Agj341kEH9ZlBIPcz43YxPkE7+XvCMYLVQN/jLh/l/uDoBvxnX19DWHYOI3g92IdQffZl9pY/0mCVqy/mdk2ghbX5rnRXiIINp8QdBnWsHN35ueAxWZWQdDqdG44Bm09wRQr1xEEuPXA1Xz6efVlgn1XStD1/tc9eY0WDPyf1fJxu/t/EQauCvv0QJaJBEdwVhAcNbocuDRik+cTBNpC4DiCLwi1Lcq5jaCLuXlc2v+Eta0HnnFNVdHl2c5fBEREpLOxYPLZx9z9pd2uLCIdhkKYiIiISByoO1JEREQkDhTCREREROJAIUxEREQkDhTCREREROIg2tN2xNWAAQN8+PDh8S5DREREZLfmz59f7O5Zu1uvU4Sw4cOHM2+epksRERGRjs/MojqdlrojRUREROJAIUxEREQkDhTCREREROJAIUxEREQkDhTCREREROJAIUxEREQkDhTCREREROJAIUxEREQkDhTCREREROKgU8yYLyIiIrInGpucTeXVrCutYl1JFetKq8gvreLSo0YydVi/eJcHKISJiIhIJ1VR28D60iryS6pYX/pp0FpfWsWGsirqG33Huj0SjJyMPpRW1sax4p0phImIiEiH1NTkbNlew7qST8PVuojQVVJZt9P66X16kpeZzMQhaXxu0iBy+yeT1z+ZYf2TGZyeRI/EjjUKSyFMRERE4qa2oZENZdVB0CqpJD/sPmwOXbUNTTvWTTAY0q8PeZnJnHjAQHL7p5DbP3nHJT25ZxxfyZ5TCBMREZGYqq5rZF1pFWtLKskvqWRtGLjWFlexqbyapk97DenTM5G8zGRGDkjh2HFZ5Gam7GjRGprRh54drDVrXyiEiYiIyD6rbWhkfWkVq4sqWVMcXFYXB6Fry7adx2FlJPckLzOFg4dnkJuZQ17/ZPIyk8nNTCarb2/MLE6von0phImIiEhU3J3N22pYVVjJqqKKHUFrTXEFG8t2btEa0LcXwzNTOGpMFsMzk8nLTCEvM5m8/imdrtswVmIWwsxsHPBIxKKRwI1AP+BSoChcfp27Px+rOkRERGTP1Dc2kV9SxcrCClYVVbCqsIKV4c/KusYd66X0SmREVgpTh2Vw5rQcRg5IYcSAFIYPSCG9j4LW7sQshLn7cmAqgJklAhuBJ4GLgN+4+22xem4RERHZvcYmJ7+kkk+2VPDJlu0s37KdTzZvZ01xJQ0RzVqD0pIYnd2Xs6fnMDq7L6Oy+jIquy/Zqd2n6zAW2qs78jhglbvn680SERFpf4Xba1hSsI2lm7azIgxcKwsrdjr6MLd/MmMHpnL8xIGMiQhbfXtr9FIstNdePRd4OOL2FWZ2ATAP+J67l7VTHSIiIl1aY5OztqSSxQXbWFKwjSWbgp/FFZ8Ojh+cnsSYgakcPiqTsQNTGTcoldHZfUnupbDVnszdd7/WvjyBWS+gADjA3beY2UCgGHDgJ8Bgd/9aK4+bDcwGyM3NnZ6fnx/TOkVERDqbxiZnVVEFC9ZvZeGGchYVlLNs03aq64NxWz0TjTHZqUwcksbEwWkcMCSN8YPSNDA+xsxsvrvP2N167RF5TwI+cPctAM0/Aczsj8BzrT3I3e8G7gaYMWNGbJOiiIhIB+furCutYsGGchZGhK6qcKB83949OGBIGufNzN0RukZn96VXj64zr1ZX0x4h7DwiuiLNbLC7bwpvngksaocaREREOpWK2gYWrN/K/Pwy5ueX8dH6rZRX1wPQu0cCE4ek8cUZw5ick87knH6MHJBCQoLGXXcmMQ1hZpYMnABcFrH4F2Y2laA7cm2L+0RERLodd2dDWTUfrAsC17y1ZSzbvI0mBzMYNzCVkw8cxOScfkzOSWfswNQuNXN8dxXTEObuVUBmi2Xnx/I5RUREOjp3Z3VxJe+sKuGd1SW8v6aUwu3BwPmUXolMy83gilljmJ6XwdRh/TTnVhelwyBERERirHk8V3PoemdVyY7QNTg9icNGZTIjL4Ppef0ZNyiVRHUrdgsKYSIiIjFQUlHLmyuKeGtFCe+uLmHj1moABvTtzWGjMjl8VCaHjcwkLzNZE552UwphIiIi+0FTk7OooJzXlhXx2vJCFmzYintwsupDR2Zy2dEjOXxUJqOy+ip0CaAQJiIistfKq+v514oiXltWxBufFFJcUYcZTMnpx7ePG8ux47OYNCRdRy1KqxTCRERE9sCm8mpeXLSZFxdtZl5+GY1NTnqfnhw9Notjx2fxmTFZZPbtHe8ypRNQCBMREdmN/JJKXli0mRcWbWbB+q0AjB3Yl8uPHsms8dlMyelHD00ZIXtIIUxERKQFd2dFYQUvfLyZFxZtYtnm7QAcODSdqz87js9NGsSorL5xrlI6O4UwERGR0JriSp75qIBnFmxkVVElZjAjL4MbTpnA5yYNIicjOd4lSheiECYiIt3a5vIanltYwDMLCli4oRwzOGREf756xAg+O3Eg2WlJ8S5RuiiFMBER6Xa2VtXxwqLNPPNRAe+uKcE96Gq84ZQJnDp5CIPSFbwk9hTCRESkW6htaOTVpYX8/YONvPFJIfWNzsgBKXzruDGcPmUIIzXGS9qZQpiIiHRZ7s6H67fy9/kbeG7hJsqr68lO7c2Fhw3njGlDOWBImiZOlbhRCBMRkS5nfWkVT324kSc+3Mia4kqSeibwuQMG8YWDcjhi9ACdm1E6BIUwERHpEipqG3j+4038ff4G3ltTCsChI/vz38eM4uQDB9O3tz7ypGPRb6SIiHRaTU3Ou6tLeHz+Bl5YtJnq+kZGDEjhqhPHcsa0oZpSQjo0hTAREel01hZX8vcPNvDEBxvZuLWa1KQenHnQUM46KIeDcvtpnJd0CgphIiLSKWyrqef5hZt4fP4G5uWXkWBw1JgsrjlpPCdMHEhSz8R4lyiyRxTCRESkw2pobOJfK4p54sONvLJkMzX1TYzO7ssPPjeeM6cN1Xxe0qkphImISIfi7iwu2MbfP9jAswsKKK6oo19yT86ZPoyzpucwJSdd3Y3SJSiEiYhIh1CwtZqnPtrIkx9sZEVhBb0SEzhuQjZnThvKMeOy6dUjId4liuxXCmEiIhI3ZZV1vLh459MHzcjL4KdnTuLUA4eQntwz3iWKxIxCmIiItKvy6npeXryZ5xZu4u2VxTQ0OSPC0wedOW0oeZkp8S5RpF0ohImISMxV1DYwZ+kWnl1QwJufFFPX2ERORh8uOWokp04erNMHSbekECYiIjFRXlXPa8sLeWnxZl5dVkhtQxOD0pI4/7A8Tp08mKnDNJ+XdG8KYSIist+sL63in0u38MqSLby3ppTGJicrtTdfOngYp04ewoy8DBJ03kYRQCFMRET2QfN0Ei8vCYLX0k3bABid3ZfZnxnJCRMHMjWnn4KXSCsUwkREZI+UVNQyd1UJb60o5s0VRWwqryHBYHpeBtedPJ4TJg5ixAANrhfZHYUwERFpU019I++vLeWtFcW8tbKYxQVBa1daUg8OHzWA75yQzXHjs8ns2zvOlYp0LgphIiKyk9qGRhZtLOffa8p4a2UR768to66hiZ6JxkG5GVx14liOGD2AyTn9SFQ3o8hei1kIM7NxwCMRi0YCNwJ/DZcPB9YCX3T3sljVISIibSvaXsv8/DI+WFfG/PwyPt5QTl1jEwDjBqZy/qF5HDlmADOH9yelt767i+wvMftrcvflwFQAM0sENgJPAtcAc9z9VjO7Jrz9g1jVISIin6qpb2T55u18vLGcD/LLmL+ujPySKgB6JSZwYE46Xz1iOAflZjA9L4OsVHUxisRKe32lOQ5Y5e75ZvZ54Jhw+X3A6yiEiYjsV+5O0fZalmzaxpJN21i6aTtLCspZU1xJkwfrDOjbm+l5/fivQ3KZntefSUPT6N0jMb6Fi3Qj7RXCzgUeDq8PdPdNAO6+ycyy26kGEZEupzlsrSyqYHVRJauKKlhZWMGSgm2UVNbtWG9ovz5MGJzGKZOHMHFwKhMHpzOsfx9NlioSRzEPYWbWCzgduHYPHzcbmA2Qm5sbg8pERDqPqroG8kuqyC+pZFVRJasKK1hVXMnqwgq21zbsWK9Pz0RGZacwa3w2E4ekMWFwGhMGpelE2CIdUHu0hJ0EfODuW8LbW8xscNgKNhgobO1B7n43cDfAjBkzvB3qFBGJG3entLKOtSVVrCutJL+kinUlVeSXVpFfUkVxRe1O6w9OT2JkVgpnHjSUUVl9GZmVwqisvgxKS9LEqCKdRHuEsPP4tCsS4BngQuDW8OfT7VCDiEhcuTvl1fVsKKtmQ1kV60uDnxvKqlkf/qyqa9zpMYPTk8jtn8ys8VnkZaaQ2z+Z4ZkpjMhKoa+OUhTp9GL6V2xmycAJwGURi28FHjWzi4F1wDmxrEFEpD00NTlFFbVs3FrNxrLqnX4WhNcjuw0BUnv3IKd/MnmZKRwxegDDMpLJywwuORnJJPXUIHmRriymIczdq4DMFstKCI6WFBHpNGobGtlcXsPGsmo2hKGqYGsYtrZWs2lrzY65tZqlJfVgaEYQqA4dmUlORh9yMpLJyejDsIxkjdMS6ebUni0iQjB/VnNXYfAzCFcbyqrYWFZNUUUt3mJ0anZqb4Zm9OHAoel8btIghvbrE1wygp+pSQpZIrJrCmEi0i00NDZRsLWGdaVVOy7NY7E2llVRXFG30/o9E40hYag6emzWjmDVHLIGpSdpTi0R2ScKYSLSZdTUN5JfUsXqooodRxWuDwPXxq3VNDZ92pTVKzGBoRl9yMnow8SJA3d0Ew7tF3QZZqf21lGGIhJTCmEi0qm4O5u31bC6qJLVRRWsLq4MrhdXsKGseqcuw/4pvRjWP5mpw/px+pQh5GYmk9s/uGgqBxGJN4UwEemwtlbVsWzzdpZv3h7+3MYnWyqoiDjKMLlXIiMGpDB1WAZfmJazY76svMxkjckSkQ5NIUxE4q6xyVlTXMHHG8tZuunTwLVl26cTlPZL7sm4gamcddBQRmf3ZWQ4QemgtCSdekdEOqXdhjAzOwL4EZAXrm+Au/vI2JYmIl1RU5OztqSSjzeWs3BDOR9vLGfxxnIqw4lKe/VIYHRWX44YPYDxg1IZNyiN8YNSyU7trbAlIl1KNC1h9wDfAeYDjbtZV0RkJyUVtczPL2P+ujIWri9n0cbyHZOW9u6RwAFD0jh7eg4H5vTjwKHpjMpKoUdiQpyrFhGJvWhCWLm7vxDzSkSk03N31hRXMi+/jHlrS5mXX8bqokogOBpxwpA0zpg2lAOHpnNgTjpjsvsqcIlIt7XLEGZmB4VXXzOzXwJPADsGaLj7BzGuTUQ6uKYmZ+nmbbyzqoR/ryllfn4ZJZXBfFv9knsyIy+Dc6YP4+DhGUwamq7T8IiIRGirJexXLW7PiLjuwKz9X46IdHTrS6t4a2Uxb60s5p1VJZSGoWt4ZjLHjMtmxvAMDh6ewcgBfTUFhIhIG3YZwtz9WAAzG+nuqyPvMzMNyhfpJkor65i7qpi3w+C1vrQagIFpvTlmXBZHjh7A4aMGMCg9Kc6Vioh0LtGMCXscOKjFsseA6fu/HBGJN3dnRWEFryzZwpylW/hw/VbcITWpB4eNzOSSI0dyxOgBjMpK0dGKIiL7oK0xYeOBA4B0M/tCxF1pgL7yinQh9Y1NvL+mlFeWbmHO0kLWlVYBMDknnW8fN5bPjB3AgUPTNYheRGQ/aqslbBxwKtAPOC1i+Xbg0lgWJSKxV1nbwJxlhfxzyRZeW17I9poGevVI4MjRA7j86FEcNyGbgWn6viUiEittjQl72syeA37g7j9rx5pEJEZq6ht5fXkRzy4oYM6yLdTUN5GZ0ouTJg3i+AkDOXLMAJJ76UQaIiLtoc3/tu7eaGYnAAphIp1UfWMTc1eV8MxHBby8eDPbaxvITOnFOdOHcdqUIUzPyyBRRzGKiLS7aL7yzjWzu4BHgMrmhZonTKTjcnfeX1vGMws28vzHmymtrCO1dw8+O2kQp08ZwuGjMjW+S0QkzqIJYYeHP2+OWKZ5wkQ6oMJtNTw2fwOPzltPfkkVST0TOG7CQE6fMoSjx2ZpslQRkQ5ktyGseb4wEemYGhqbeOOTIv72/npeXVZIY5Mzc0R/vjlrDJ+bNIiU3hr8Z60SAAAgAElEQVTjJSLSEbU1RcVX3P0BM/tua/e7+69jV5aI7M66kioenbeex+avZ8u2Wgb07c0lR43gSzOGMTKrb7zLExGR3WjrK3JK+DO1PQoRkd1rbHJeWbKF+99dy9srS0gwOGZcNjd/fhizxmfTU+O8REQ6jbamqPhD+PPH7VeOiLRmW009j76/nnvnrmVDWTVD+/XhuyeM5ezpOQzp1yfe5YmIyF5oqzsyCfgSUAY8C1wNfAZYBfzE3YvbpUKRbmxNcSX3zV3LY/PWU1nXyMzh/bn+5AmcMHGgjm4UEenk2uqO/CtQT9At+T1gEXAXcCRwL8Fs+iKyn7k7c1eV8Oe31vDq8kJ6JBinTR7CRUeM4MCc9HiXJyIi+0lbIWyiu08ysx7ABnc/Olz+opktaIfaRLqVhsYmnl1YwB/eWM2yzdvJTOnFlbPG8JVDcsnW6YNERLqctkJYHYC7N5hZQYv7GmNXkkj3UtfQxBMfbOD/Xl/FutIqxg1M5RdnT+b0KUM0r5eISBfWVgjLMbM7AIu4Tnh7aMwrE+niauobeeT99fzhjVUUlNcwOSedG06ZzvETBpKg0wiJiHR5bYWwqyOuz2txX8vbIhKlqroGHnx3HXf/azVF22uZkZfB/5w1mc+MGYCZwpeISHfR1hQV9+3rxs2sH/AnYBLBqY6+BnwWuBQoCle7zt2f39fnEunoKmobuG/uWv70r9WUVdVzxOhM7jh3GoeO7K/wJSLSDcX6fCa3Ay+6+9lm1gtIJghhv3H322L83CIdQl1DEw+9l8+dr66kpLKOWeOz+caxo5melxHv0kREJI5iFsLMLI1gXrGvArh7HVCnb/zSXbg7zy3cxG0vLye/pIpDR/bnnpMmMHVYv3iXJiIiHUAsW8JGEnQ5/sXMpgDzgW+F911hZhcQjC37nruXxbAOkXY3d1Uxt76wjIUbyhk/KJW/XHQwx4zNUrejiIjsYO7e9gpmI4ArgeFEhDZ3P303j5sBvAsc4e7vmdntwDaCCV+LCcaI/QQY7O5fa+Xxs4HZALm5udPz8/Ojf1UicbJs8zZufWEZry8vYkh6Et89cRxnThtKoo52FBHpNsxsvrvP2N160bSEPQXcQ3DqoqY9qGEDwSSv74W3HweucfctEUX+EXiutQe7+93A3QAzZsxoOymKxNnm8hp++dJynvhwA6m9e3DtSeO58PDhmudLRER2KZoQVuPud+x+tZ25+2YzW29m49x9OXAcsMTMBrv7pnC1MwlOhyTSKdU1NPHnt9dwx5wVNDQ5lx41kq8fM4p+yb3iXZqIiHRw0YSw283sJuBloLZ5obt/EMVjrwQeDI+MXA1cBNxhZlMJuiPXApftadEiHcHbK4u58elFrCqq5PgJA7nptIkM658c77JERKSTiCaEHQicD8zi0+5ID2+3yd0/Alr2iZ6/JwWKdDSby2u45R9LeG7hJnL7J/Pnr85g1viB8S5LREQ6mWhC2JnAyHCKCZFuq66hib+8vYbb56ygscn5zvFjuezokRr3JSIieyWaELYA6AcUxrgWkQ5r7spibnxmMSsLK9T1KCIi+0U0IWwgsMzM3mfnMWFtTlEh0hWUVdZx83NLePLDjeT2T+aeC2dw3AR1PYqIyL6LJoTdFPMqRDqg5z/exI1PL2JrVT3fPG4MXz9mlLoeRURkv9ltCHP3N8xsIHBwuOjf7q6uSemyCrfXcONTi3lx8WYOHJrO/RcfwoTBafEuS0REupjdhjAz+yLwS+B1wIA7zexqd388xrWJtCt354kPNnLzc0uorm/kmpPGc8mRI+iRmBDv0kREpAuKpjvyeuDg5tYvM8sC/kkwA75Il1CwtZrrnvyY15cXMT0vg1+cPZlRWX3jXZaIiHRh0YSwhBbdjyWAmgakS2hqch5+fx3/8/wyGpucm06byAWHDde5HkVEJOaiCWEvmtlLwMPh7S8Bz8euJJH2sWVbDVc9toB/rSjm8FGZ3PqFyeRmatoJERFpH9EMzL/azL4AHEkwJuxud38y5pWJxNArS7bw/ccXUF3fyC1nTOK/DsnFTK1fIiLSftoMYWaWCLzk7scDT7RPSSKxU13XyC3/WMKD763jgCFp3H7uNEZna+yXiIi0vzZDmLs3mlmVmaW7e3l7FSUSC0sKtvHNv33IysIKZn9mJN87cSy9e2jeLxERiY9oxoTVAB+b2StAZfNCd/9mzKoS2Y+ampw/v72GX7y4nH7JPbn/4pkcNSYr3mWJiEg3F00I+0d4Eel0CrfX8L1Hg8H3x08YyC/Onkz/lF7xLktERGTXIczM5rj7ccBEd/9BO9Yksl+8tryQqx5dQGVdgwbfi4hIh9NWS9hgMzsaON3M/kZwZOQO7v5BTCsT2UtNTc7tc1Zw+5wVTBicxp3nTWV0dmq8yxIREdlJWyHsRuAaIAf4dYv7HJgVq6JE9lZ5VT3ffuRDXltexNnTc7jljEk66baIiHRIuwxh4bkhHzezH7r7T9qxJpG9sqRgG5c/MJ9N5dXqfhQRkQ4vmslaFcCkw3vyww1c+8TH9OvTi0cuO4yDcjPiXZKIiEibojk6UqTDqmto4mfPL+XeuWs5ZER/7vryQWSl9o53WSIiIrulECadVuG2Gr7+4AfMyy/jkiNHcM1J4+mRqHPLi4hI57DbEGZmtwF/cffF7VCPSFTmrS3lvx/8gMraBu48bxqnTRkS75JERET2SDQtYcuAu82sB/AX4GGdwkji6ZkFBVz16AKGZvThwUsOYexATT8hIiKdz277btz9T+5+BHABMBxYaGYPmdmxsS5OJJK787vXV/HNhz9kam4/nvr6EQpgIiLSaUU1gMbMEoHx4aUYWAB8N5zEVSTmGhqbuP6pRfz8xWWcPmUI9188k/TknvEuS0REZK9FMybs18DpwBzgZ+7+7/Cun5vZ8lgWJwJQWdvAFQ99wGvLi/j6MaO46sRxJCRo/i8REencohkTtgi4wd2rWrlv5n6uR2Qnhdtq+Np977OkYBs/PXMS/3VIXrxLEhER2S+iCWFlwI5+HzPrBxzj7k9pgL7E0idbtnPRX96nrKqOey48mGPHZ8e7JBERkf0mmjFhN0WGLXffCtwUu5JEYO6qYs763VzqGpt49LLDFMBERKTLiSaEtbZOVJO8mlk/M3vczJaZ2VIzO8zM+pvZK2a2Ivyp88vITp7+aCMX/vnfDEpL4smvH86koenxLklERGS/iyaEzTOzX5vZKDMbaWa/AeZHuf3bgRfdfTwwBVgKXAPMcfcxBIP9r9mbwqVrevLDDXz7kY84KDeDx//7cHIykuNdkoiISExEE8KuBOqAR4DHgBrgG7t7kJmlAZ8B7gFw97qwK/PzwH3havcBZ+x52dIVPbuggO89uoBDR2Ry70UzSe+jKShERKTr2m23ortXsnetVSOBIuAvZjaFoPXsW8BAd98UbnuTmWmwj/DCx5v49iMfMSOvP/d8dQZ9eiXGuyQREZGYimaesLHAVQSz5e9Y391nRbHtg4Ar3f09M7udPQhzZjYbmA2Qm5sb7cOkE3plyRaufPhDpuSk8+eLDia5l84rLyIiXV80n3aPAb8H/gQ07sG2NwAb3P298PbjBCFsi5kNDlvBBgOFrT3Y3e8G7gaYMWOG78HzSify2rJCvv7gfA4Yms69X5tJ394KYCIi0j1E84nX4O6/29MNu/tmM1tvZuPcfTlwHLAkvFwI3Br+fHpPty1dw5ufFHHZA/MZNyiVv35tJmlJGgMmIiLdRzQh7Fkz+zrwJFDbvNDdS6N47JXAg2bWC1gNXERwMMCjZnYxsA44Z4+rlk5v7spiLv3rPEZl9eWBiw/RIHwREel2oglhF4Y/r45Y5gQD79vk7h8BM1q567gonle6qPdWl3DxffPIy0zmgYtn0i+5V7xLEhERaXfRHB05oj0Kke5h3tpSLrr3fYb0S+LBSw4ls2/veJckIiISF9HOfD8JmAgkNS9z97/GqijpmtYWV/K1e99nYFoSD196KFmpCmAiItJ9RTNFxU3AMQQh7HngJOAtQCFMolZR28Ds++eRkGDcd9FMstOSdv8gERGRLiyaGfPPJhjDtdndLyI4/ZCaMCRqTU3O9x79iJWFFdx13kHkZupURCIiItGEsGp3bwIawlMRFRLFoHyRZr99bSUvLd7CdSdP4MgxA+JdjoiISIcQzZiweWbWD/gjwamHKoB/x7Qq6TL+uWQLv/7nJ5wxdQgXH6ljPERERJpFc3Tk18OrvzezF4E0d18Y27KkK1hZWMF3HvmIA4akcetZkzGzeJckIiLSYey2O9LM5jRfd/e17r4wcplIa7bV1DP7/nn06pHAH86fQVJPnZBbREQk0i5bwswsCUgGBphZBtDcjJEGDGmH2qSTampyvvO3j1hXUsUDlxzC0H594l2SiIhIh9NWd+RlwLcJAtd8Pg1h24Dfxrgu6cT+95+fMGdZITd//gAOHZkZ73JEREQ6pF2GMHe/HbjdzK509zvbsSbpxF5ctIk7Xl3JF2fkcP6hefEuR0REpMOKZoqKpvDoSADMLCM8obfITpZv3s53H13A1GH9uPnzkzQQX0REpA3RhLBL3X1r8w13LwMujV1J0hlV1TVw+QPzSendg99/ZboG4ouIiOxGNCEswSKaNMwsEegVu5KkM/r5C8tYU1zJHedOY1C6TkkkIiKyO9FM1voS8KiZ/R5w4HLgxZhWJZ3K2yuLue+dfC46YjiHjdJAfBERkWhEE8J+QHCk5H8THCH5MvCnWBYlncf2mnq+//hCRgxI4fufHR/vckRERDqNaGbMbzKze4FX3X157EuSzuSW55ayqbyaxy4/nD69NA5MREQkWtHMmH868BFhF6SZTTWzZ2JdmHR8ry0r5JF565n9mVFMz8uIdzkiIiKdSjQD828CZgJbAdz9I2B4DGuSTmBrVR0/+PtCxg7sy3dOGBPvckRERDqdaEJYg7uXx7wS6VR+9MxiSivr+PUXp9K7h7ohRURE9lQ0IWyRmX0ZSDSzMWZ2JzA3xnVJB/biok089VEB3zh2NJOGpse7HBERkU4pmhB2JXAAUAs8THDuyG/HsijpuEoqarn+yUUcMCSNK2aNjnc5IiIinVY0R0dWAdeb2c+Dm7499mVJR+Tu3PDUIrbXNPDQpVPpmRhNhhcREZHWRHN05MFm9jGwEPjYzBaY2fTYlyYdzTMLCnhh0Wa+c8JYxg1KjXc5IiIinVo0k7XeA3zd3f8FYGZHAn8BJseyMOlYtmyr4canFzMttx+zPzMy3uWIiIh0etH0J21vDmAA7v4WoC7JbsTdue6Jj6mpb+S2c6aQmGC7f5CIiIi0KZqWsH+b2R8IBuU78CXgdTM7CMDdP4hhfdIBvLR4M3OWFXLDKRMYldU33uWIiIh0CdGEsKnhz5taLD+cIJTN2q8VSYdSXdfIT55byvhBqXz18OHxLkdERKTLiOboyGPboxDpmH772ko2bq3mkdmH0kNHQ4qIiOw30Rwdeb+ZpUfczjOzOdFs3MzWmtnHZvaRmc0Ll/3IzDaGyz4ys5P3vnyJpTXFldz95mrOmDqEQ0ZmxrscERGRLiWa7si3gPfM7LvAUOBq4Ht78BzHuntxi2W/cffb9mAb0s7cnR8/u5hePRK47uQJ8S5HRESky4mmO/IPZrYYeA0oBqa5++aYVyZx9c+lhby+vIgbTplAdlpSvMsRERHpcqLpjjwf+DNwAXAv8LyZTYly+w68bGbzzWx2xPIrzGyhmf3ZzDL2tGiJrZr6Rn787GLGDuzLhRqMLyIiEhPRjLQ+CzjS3R9292uBy4H7otz+Ee5+EHAS8A0z+wzwO2AUwVGXm4BftfZAM5ttZvPMbF5RUVGUTyf7w+9eX8WGsmp+fPoknZpIREQkRnb7CevuZ7h7YcTtfwMzo9m4uxeEPwuBJ4GZ7r7F3RvdvQn446625e53u/sMd5+RlZUVzdPJfrCupIrfvbGK06YM4bBRGowvIiISK7sMYWb2aMT1n7e4+7ndbdjMUswstfk6cCKwyMwGR6x2JrBojyqWmLr5ucX0TDCu12B8ERGRmGprYP6YiOsnAD+IuB1N09RA4Ekza36eh9z9xXDKi6kE48XWApftUcUSM68u28I/lxZy7UnjGZSuwfgiIiKx1FYI8728L1jBfTXwHwP43f38KOqSdlZT38iPnlnCqKwULjpiRLzLERER6fLaCmHJZjaNoMuyT3jdwkuf9ihO2s/db65mXWkVD15yCL16aDC+iIhIrLUVwjYBvw6vb4643nxbuoj1pVX89rWVnHLgYI4YPSDe5YiIiHQLuwxhOmdk9/GT55aQYMb1p2gwvoiISHtRv1M3N3dlMS8v2cIVs0YzpJ96mUVERNqLQlg31tTk/PT5pQzt14eLj9RgfBERkfakENaNPfXRRhYXbOP7nxtHUs/EeJcjIiLSrURz7kgzs6+Y2Y3h7Vwzi2rGfOm4auobue2l5UzOSee0yUPiXY6IiEi3E01L2P8BhwHnhbe3A7+NWUXSLv789hoKymu47uQJJCRYvMsRERHpdtqaoqLZIe5+kJl9CODuZWbWK8Z1SQyVVNTyf6+t4oSJAzl0pM4PKSIiEg/RtITVm1ki4Sz5ZpYFNMW0Komp2+esoLq+kWtOGh/vUkRERLqtaELYHcCTQLaZ/RR4C/hZTKuSmFlVVMGD763jyzNzGZXVN97liIiIdFu77I40sxHuvsbdHzSz+cBxBKcsOsPdl7ZbhbJf3frCMvr0TORbx4/Z/coiIiISM22NCXscmG5mc9z9OGBZO9UkMfLu6hJeWbKFqz87jgF9e8e7HBERkW6trRCWYGY3AWPN7Lst73T3X7fyGOmgmpqcnz2/lMHpSZqYVUREpANoa0zYuUANQVBLbeUincizCwtYuKGcqz+riVlFREQ6grZO4L0c+LmZLXT3F9qxJtnPauob+cWLyzlgSBpnTB0a73JERESEtgfmf8XdHwAmmtmElverO7LzuHfuWjZureaXZ0/WxKwiIiIdRFtjwlLCn63NY+AxqEVioLSyjt++upLjxmdz+OgB8S5HREREQm11R/4h/PnjlveZ2bdjWZTsP3fMWUFVfSPXnqyJWUVERDqSaCZrbc1/HC0pHc/qogoeeDefLx08jNHZOpZCRESkI9nbEKaBRZ3Az19cRu8eCXzn+LHxLkVERERa2NsQpjFhHdy/15Ty0uIt/Pcxo8hK1cSsIiIiHU1bR0dup/WwZUCfmFUk+6ypyfnpP5YwKC2Ji48cGe9yREREpBVtDczXIKJO6tmFBSzYUM5t50yhTy9NzCoiItIR7W13pHRQzROzThycxhemaWJWERGRjkohrIu5L5yY9YZTJmhiVhERkQ5MIawLKa2s467XVjJLE7OKiIh0eAphXcgdc1ZQWdvAtSdpYlYREZGOTiGsi1hTXMkD7+Zz7sxcxgzUMRUiIiIdXVvnjtxnZrYW2A40Ag3uPsPM+gOPAMOBtcAX3b0slnV0B7e+sJTePRL49vFj4l2KiIiIRKE9WsKOdfep7j4jvH0NMMfdxwBzwtuyD5onZr386FFkpybFuxwRERGJQjy6Iz8P3Bdevw84Iw41dBmRE7NecpQmZhUREeksYh3CHHjZzOab2exw2UB33wQQ/syOcQ1d2nMfb2LBhnKu+uw4TcwqIiLSicR0TBhwhLsXmFk28IqZLYv2gWFomw2Qm5sbq/o6tZr6Rn7+wjImDk7jTE3MKiIi0qnEtCXM3QvCn4XAk8BMYIuZDQYIfxbu4rF3u/sMd5+RlZUVyzI7reaJWa8/ZQKJmphVRESkU4lZCDOzFDNLbb4OnAgsAp4BLgxXuxB4OlY1dGUlFbXc9dpKjh2XxRGamFVERKTTiWV35EDgSTNrfp6H3P1FM3sfeNTMLgbWAefEsIYu69YXllFd18j1p0yIdykiIiKyF2IWwtx9NTClleUlwHGxet7u4P21pTw2fwOXHz2K0dmamFVERKQz0oz5nUx9YxM3PLmIIelJfPO40fEuR0RERPZSrI+OlP3svrlrWb5lO384fzrJvfT2iYiIdFZqCetENpVX85tXPmHW+GxOnDgw3uWIiIjIPlAI60RueW4pDU3Oj047gPCABxEREemkFMI6iTc/KeIfH2/iimNHk5uZHO9yREREZB8phHUCNfWN3Pj0IkYMSGH20To/pIiISFegkd2dwN1vrmZtSRX3XzyT3j10fkgREZGuQC1hHVx+SSV3vbaSUycP5qgxOn2TiIhIV6EQ1oG5Oz96ZjG9EhP44akT412OiIiI7EcKYR3YS4u38NryIr5zwlgGpiXFuxwRERHZjxTCOqjK2gZufnYx4welcuFhefEuR0RERPYzhbAO6o5XV1BQXsNPz5xEj0S9TSIiIl2NPt07oEUby7nnX2v40oxhTM/rH+9yREREJAYUwjqYitoGrnz4Qwb07c01J42PdzkiIiISI5onrIO58alF5JdU8tClh5KR0ive5YiIiEiMqCWsA3l8/gae+HAj3zxuDIeOzIx3OSIiIhJDCmEdxKqiCn741CIOGdGfK2eNiXc5IiIiEmMKYR1ATX0j33jwA/r0SuT2c6eRmGDxLklERERiTGPCOoCfPb+UZZu38+evzmBQuiZlFRER6Q7UEhZnLy7azF/fyeeSI0cwa/zAeJcjIiIi7UQhLI42lFXx/ccXMDknne9/TtNRiIiIdCcKYXFS39jENx/+kCaHO8+bRq8eeitERES6E40Ji5PfvPIJH6zbyp3nTSMvMyXe5YiIiEg7U/NLHPxrRRG/e2MV5x48jNOmDIl3OSIiIhIHCmHtbFN5Nd95ZAGjs/py02kHxLscERERiROFsHa0qbyac+9+l9r6Ru768kH06ZUY75JEREQkThTC2knB1iCAlVbUcd/FMxk3KDXeJYmIiEgcaWB+O2gOYGWVdfz14plMy82Id0kiIiISZ2oJizEFMBEREWlNzEOYmSWa2Ydm9lx4+14zW2NmH4WXqbGuIV42RgSw+y85RAFMREREdmiP7shvAUuBtIhlV7v74+3w3HETBLB32FpVz/2XHMLUYf3iXZKIiIh0IDFtCTOzHOAU4E+xfJ6OJjKAPXCxApiIiIj8p1h3R/4v8H2gqcXyn5rZQjP7jZn1bu2BZjbbzOaZ2byioqIYl7n/bCir2imATVEAExERkVbELISZ2alAobvPb3HXtcB44GCgP/CD1h7v7ne7+wx3n5GVlRWrMverJQXbOO+P7yqAiYiIyG7FckzYEcDpZnYykASkmdkD7v6V8P5aM/sLcFUMa2gXFbUN/OaVT7h37loyknvy4CWHMDlHAUxERER2LWYhzN2vJWj1wsyOAa5y96+Y2WB332RmBpwBLIpVDbHm7rywaDM3P7uELdtrOG9mLj/47HjSk3vGuzQRERHp4OIxWeuDZpYFGPARcHkcathn+SWV3Pj0Yt74pIiJg9P43VcO0hQUIiIiErV2CWHu/jrwenh9Vns8Z6zUNjTyhzdW89vXVtIjwfjhqRO58LA8eiRq3lsRERGJnk5btAfeXlnMD59axOriSk45cDA/PHUig9KT4l2WiIiIdEIKYW2orG3gvTUlvPlJMf9aUcSqokryMpO596KDOWZcdrzLExERkU5MISxCU5OzZNM23lxRxJufFDE/v4z6RiepZwKHjMjk/EPzOHdmLkk9E+NdqoiIiHRyCmHAu6tLeOi9dby1spjSyjoAJg5O42tHjuAzY7KYnpeh4CUiIiL7lUIYsK60irmrSjhmbBZHjR3AkaOzyEptdSJ/ERERkf1CIQw4c9pQzpmeQzB1mYiIiEjsKYQBPTW9hIiIiLQzpQ8RERGROFAIExEREYkDhTARERGROFAIExEREYkDhTARERGROFAIExEREYkDhTARERGROFAIExEREYkDhTARERGROFAIExEREYkDc/d417BbZlYE5Mf4aQYAxTF+DgloX7cP7ef2o33dfrSv24f2877Jc/es3a3UKUJYezCzee4+I951dAfa1+1D+7n9aF+3H+3r9qH93D7UHSkiIiISBwphIiIiInGgEPapu+NdQDeifd0+tJ/bj/Z1+9G+bh/az+1AY8JERERE4kAtYSIiIiJxoBAmIiIiEgcKYSJdnJlZvGsQEZH/pDFhLZiZuXZKu9C+bn/a57Gl/Rs7ZpYJjAFqgDRgpbsXxLeqrsfMRhLs5yQgBZjn7p/Et6quSyGsDfqH2n60r/cfM0sADgcGA/2ArfD/7Z1prJ1VFYaft5VRQJBJ21KqCAWUsYAShmIhglBQMMgUNDIEMQKRKWEQGUVbSmQQS2USqVRBkUlEbEuDBWzLKLQWKBCgTKVAkaGUtq8/1i4eiRCTe+7dt99Zz69zz5fcvFnZZ+/1rb0G7rY9u6qwDiPXdPuQtDwwFugPPAW8CLwFPGJ7XE1tTULSCsCNwGLgH4CJPeQp4DLbcyrKayQd74RJ6gvsAQwA1iF+3LfafqKqsAaStu4ZJH0LOIA4pKYBawMrAPfavqqitMaRa7pnkHQksLvtPSWtCWwIbA4MAV4ATrf9bk2NTUDSEcCetodLWpXYOz4H7EhExk62/VZNjU3jY7UF9AIOBg4C7gNmED/sUZIeAi5Kz7+tpK17hu8BJ9meWK5wPg1sBOwjaXnbo+vKaxS5pnuGl4FXJK1QbDoHuEvSBsB5hJNwR02BDeEx4DVJq9ueS0TRZ0qaBlwM7A1cU1Ng08hImDQJGGH7VkkfJ7z9zxKb67O2R1YV2CDS1t1PScI/EVgRONf2/JZnmwIjgaMyx6M95JruGSR9EhgNvAlMBB4AZtp+T9LNwE22f1lTYxOQtCJwETAQuBOYRETQF0q6CbjFdjZxbSPphEmHAhsAZ7aGWSX1A34DHG97Wi19TSJt3TNIWp/YSOcBk4HJtu8rz14GBtl+u6LExiDpEE+FgVQAAAgMSURBVGAwuaZ7BEmHA+sRtzhrAqsBKwHDc023D0m7EHml6xIvFe8Ci4B9087tJZ0waR3gAmKxjQf+ZPtOSasAM4H1ctG1B0kDgEuI5NrxRO7MpLR19yBpX2BLoB+RO/M4UVF2QlVhDULSQOBCInIwgYgU5P7RRiT1J658ZxDXZWsQzteC8vlvtp+pp7B5lIh6P2B1Ijl/IGHneVWFNZCOdsIk9bG9uHzeGtgV2BbYmIggzLL9o4oSG4OkwbZnls9bA7sBOxBJn5OBJ9PWXaNEX74LbEZckU2WtBaxmc4HliOqyRZVlNlIJA0BdgaGEdHeXNNtoOwVPwPuAbYm1vFDwM2276qprUmUl4m9gIeB6bZfqSypY+hYJ0zSNsCPiTD2/PLd2kRp7jLAsrafrqewOUjaCpgCnE84By+X/JkFhIPgfJPtOpIuJQ6pucDngb8CmxCl5pdlu4T2USJdOxIvbtOAp4GXgFlEpaRz/+g6kkYBr9k+u/z9GWBf4NvAVOAw2wsrSmwEkq4mXt5uARYCTwIzbE8pxT0H2L64psam0slO2AXAPNunSdoC2J8IbT8HjMmeSu1D0k+ICr1XgeczWbl7kDQVGGr7bUmPA78j8sKGAs8Cx+bVWHuQ9AvgHeB5okVFP2AccLvtu2tqaxKSjgW2AX4IPG37vZZnVwOX2p5cS19TkPR74CZinxhC7Nd9gX8C3wDm2t6vnsLm0slO2B+Bs2zfJ+kWouJmNnEduRJwnO3Xa2psCpLuB3YnnNzRxI/7FMLmfYFFGaXpGqV30sXAI8RGeo7t/i3PJwEH2X6uksRGIWkKsNMSp1bSOKKNwk5Ekv71FeU1BknLAKcTeUnjgSeAhbZfkDQTONj2lIoSl3pKc+eBRFDitfLdIGArYBDhAA+1/WAliY2mk/uEXQWMkHQe0Mf2qPL9OEl3EYmf6YR1EUnbAc/YfpFoZLm9pOOA/YAXbU+vKrAh2J5Trm6OIJJp7y2J+X8hqps+kQ5Ye5C0BnH9eLCkW4lr9UG295e0GXCEpFttv1NT59LMkmkDpQXFOcDhwAlENH2upC8Q43TSAesCxc6LifX8PuUq/WlJOwDz0wHrPjoyEiapr+1Fko4mkmg3IZotXkFEwa6wvXFNjU1B0mDCyZ0hacVyVbYa8AOiqei5LQ5w0iZKT7DDifzGjYDf2r6krqrmUA6nQwgHbABwX0lt2BY4z/Z2VQUu5ZRJBF8irtJXBq61/bCkTYjWFLOBObZfrShzqafFzjsRV5B/sD2h5Xl/YHDrd0l76UgnrBVJQ4FdiHDsp4gRGONs/7mqsIbSOk9P0jCiACJt3QVK1/DBRMXpm8DYcmANISp9/07k0yyoKLNxlCub9Ygrsrm235R0DRH5PbmmtqWd0n/tEGJeZD/gQEDElftltt+oKK8xtNj5GuL8O5AYcXYFcGHpmp90Ix3nhJUDawMimfZ14gc9S9KytheUsS7zP/q/JP8PxdYbAl8F3gautP1Iy3NBlJHVUdgMJN1IVEBOBE4mmiw+SMx5m1hTW9NocXh3JUbnXLtk+kCJKmxDVJVlKkMXkHQ7MNr2DS3fDQGOJq4hL6omrkF8iJ23BL4P3J8Vkd1Pn9oCKjCSCL9eD3wRmC7pHmC30jcsHbD2MZI4lK4jyp+nSpoqaZ+WnI90wLpAaYC7vu1TbY8HDgVGEU1xDy7NiJP2MZLYN24grnAeljRN0t62F9m+Jx2wrlFeziYQe8b7OKY+nAh8s7S9SbrAR9j5fuAkYL+0c/fTUYn5LQfW18rfs4DDiM7WexHzyJ6tp7A5/A9bP0nYegYRGZtK2rod9AGmSBpQEu/XBba3fWrpqXSkpFPS2e06H7F/zAB2lzTNdq7pLmLbksYAYyRNAK4l8nQXETNR1wIeramxCaSdewedFgl7/8Aqfy85sH4NPEMcWKqmrll8mK3HEs5X2ro9PAs8BTxa2lB8h8ijgchvHJQOWNvINd0DlL6NuwBjgCuJHo7PSboOOIMY1p2Vp10k7dw76KhIGP99YD1YPrceWF/OA6ttpK17gGLDM0rz4T2AO1saDQ8HLq8mrnnkmu5mSj7ST4lh0e8Aj9neufTB25yIzLxQUWIjSDv3HjouMR9A0qp84MAqyc2X276pqriGkbbuPiQdQwxDH2v7oQ886w8MK1HepI3kmu4+JP2cmNl7vmLu6SVEe5XrJK0M7GP7V3VVLv2knXsPHXMdKekYSSMkbWb7ddtjWzbQ/sD1uYG2h7R1j3Ey0VrlMkmTJB1b7AuwBZBDeNtErukeYwvgbgDbLxORxkPLs6OALSvpahpp515Cx0TCJL1ElPCvR7RLuJHw/GdLGk6MzrmtpsamkLbufkoT3BHAQcBywDDg68CmwHSi0GRodhRvD7mmu5/S4mM74KnWAgfFXMM7iMHdx9t+oJLERpB27l10hBOWB1bPkbbuOSQtD9DaVkXSKsBZwFdsb1RLW5PINd2z6D8TTfrYXixpfeA2YrbhkNr6mkLauXfQEU4Y5IHVk6St67Ck95pihuQ822fW1tQUck3XocVROBt4KZu0dg9p53p0THXkB5uwlgPrDUkLif4oSZtIW9ehpTLvQmLQcdImck3XofSsAjitqpCGk3auR8dEwj4MSesCr9r+V20tTSdtnTSNXNNJknSFjnfCkiRJkiRJatAxLSqSJEmSJEl6E+mEJUmSJEmSVCCdsCRJkiRJkgqkE5YkSZIkSVKBdMKSJEmSJEkq8G+61bPBNHTv3gAAAABJRU5ErkJggg==", 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" ] @@ -137,7 +137,7 @@ "outputs": [ { "data": { - "image/png": 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wQFeXU2MKViIiIuJWrLU8+tF6Gvt68ftLol1dzmlRsBIRERG38vn6fSzflsXvL+lEcICPq8s5LQpWJzj2YOXKXnnlFebOnQs4nukXGxtLr169Kh6uXJ2EhASio6OJjY0lNjaWMWPGOLXmk3nuuefIz88/J8c60fvvv0+XLl2qfWjy2ZozZw5hYWHExsbSrVs3xowZU/GZK//cTrRw4UI2bNhQsZyQkFCjRxeJiEjtKigu5fGPN9K5ZRPGxbd1dTmnTcGqBm6//XZuvvlmwPEHecyYMaxevZoOHTrUeB9vv/02ycnJJCcnVzwjr7a5MljNmjWL119//VeTeZ7uI2pqYuzYsSQnJ7N+/Xp8fHyYP38+cPzP7cQaTgxWIiLiHl79ZgdpOUd55MpueHnWvZhS9yp2gRkzZvD000+zZMkSnnvuOV5++eWKnph58+YRHx9PbGwsU6dOpbS0tMb7HTVqVEWPyquvvsr48eMBR+/JPffcQ2xsLN27d2fFihUA5OXlMWnSJOLj4+nVqxeLFi0CoLS0lPvvv5/u3bvTo0cPXnjhBf7xj3+Qnp7O4MGDK2q944476Nu3L926deORRx6pqCMyMpJHHnmE3r17ExMTw6ZNmwDHY3MmTpxITEwMPXr04IMPPmD27Nnce++9Fdu+/vrr/O53vzvucz322GMsW7aMW2+9lWnTpjFnzhxGjhzJkCFDuPjii7HWMm3aNLp3705MTExFEEpMTGTQoEGMGjWK9u3b8+CDD/L2228THx9PTExMtT2EJSUl5OXlERQUdNzP7dg5vffee+nbty9PPvkkixcvZtq0acTGxlbs9/333yc+Pp5OnTrx3Xff1fjnKCIizrHnYD4vJW7j8h6t6N8hxNXlnBH3ncfq0wdh3zrn7rNlDFz6tzPe/LLLLuP222+ncePG3H///WzcuJH58+ezfPlyvL29ufPOO3n77ber7CUZP348/v7+AAwbNoyZM2fy2muvMWDAAKKionjmmWf48ccfK9rn5+eTnJzMt99+y6RJk/jll1944oknGDJkCLNnzyYnJ4f4+HiGDh3K3LlzSU1NJTk5GS8vL7KzswkODubZZ59l6dKlhIaGAvDEE08QHBxMaWkpF198MWvXrqVHjx4AhIaG8vPPP/PSSy/x9NNP88Ybb/D4448TGBjIunWOn8PBgwfx9vbmiSeeYObMmXh7e/Pmm2/y6quvHvdZH374Yb7++muefvpp+vbty5w5c/j5559Zu3YtwcHBfPDBByQnJ7NmzRoOHDhAXFwcF110EQBr1qxh48aNBAcH0759eyZPnsyKFSt4/vnneeGFF3juued+dW7nz5/PsmXL2Lt3L506deLKK6+s8udXVFRUcblv69atXHHFFcddli0pKWHFihUsWbKERx99lC+//LJmvxgiIuIUf12yCWPg/y6ru0+0qFGPlTFmhDFmszFmmzHmwSrWTzDGZBpjkstfk51fqvv56quvWLVqFXFxccTGxvLVV1+xY8eOKttWvhQ4c+ZMAFq0aMFjjz3G4MGDeeaZZwgODq5of8MNNwBw0UUXcfjwYXJycvjiiy/429/+RmxsLAkJCRQUFLBr1y6+/PJLpk6dipeXIydX3k9l7733Hr1796ZXr16sX7/+uEthV199NQB9+vQhNTUVgC+//JK77rqrok1QUBCNGzdmyJAhfPzxx2zatIni4mJiYmKqPVfDhg2rqGvZsmXccMMNeHp60qJFCwYNGkRSUhIAcXFxtGrVCl9fXzp06MAll1wCQExMTEVdJzp2KXDfvn3ExMRUnN+q2p1KVedARETOje+3H+CTdXu5M+E8Wjfzd3U5Z6zaHitjjCfwIjAM2AMkGWMWW2tPHKAy31p7t9MqO4uepXPFWsstt9zCX//61zPex7p16wgJCSE9Pf2490+cCM0Yg7WWDz74gOjo07/1NCUlhaeffpqkpCSCgoKYMGECBQUFFet9fX0B8PT0rHYc1OTJk/nLX/5C586dmThxYo2OX9MHVh+rA8DDw6Ni2cPDo9q6jDFceeWVvPDCCzz44K/yf7U1nM45EBER5ykpLePRxRuICPJnykXtXV3OWalJj1U8sM1au8NaWwS8C4yq3bLqhosvvpgFCxaQkZEBQHZ2Njt37qzx9itWrODTTz9l9erVPP3006SkpFSsOzbuaNmyZQQGBhIYGMjw4cN54YUXOPbg7NWrVwOO3qBXX321IgxkZ2cD0KRJE3JzcwE4fPgwAQEBBAYGsn//fj799NNq6xs2bBgvvvhixfLBgwcB6NevH7t37+add96p6Fk7HRdeeCHz58+ntLSUzMxMvv32W+Lj4097P1VZtmxZjW4qqHxuRETEtd7+aReb9+cy/fKu+Hl7urqcs1KTYNUa2F1peU/5eye6xhiz1hizwBjTxinVuUB+fj4REREVr2efffakbbt27cqf//xnLrnkEnr06MGwYcPYu3dvlW3Hjx9fMd3C0KFDKSws5LbbbmP27NmEh4fzzDPPMGnSpIrQ5OfnR69evbj99tuZNWsWAA899BDFxcX06NGDbt268dBDDwGOHqS2bdvSo0cPevbsyTvvvAPAlClTGDFiBIMHD6Znz5706tWLzp07M27cOAYMGFDtuZg+fToHDx6ke/fu9OzZ87g7/K677joGDBhQMVD8dIwePbqi1iFDhvDUU0/RsmXL097PMfPnzyc2NpYePXqwevXqivNyKtdffz0zZ848rWkzRETE+bLzinjmi80MPC+U4d1auLqcs2aO/SE/aQNjxgAjrLWTy5dvAvpVvuxnjAkBjlhrC40xU4Gx1tohVexrCjAFoG3btn1O7N3ZuHEjXbrU3QFrzpKQkFAx8NtdXXHFFfzud7/j4osvdnUpLqXfWRGRs/N//1nH/KTdfHbPhXRs0cTV5ZyUMWaVtbbaP8w16bFKAyr3QEWUv1fBWptlrS0sX3wD6FPVjqy1r1lr+1pr+4aFhdXg0OJucnJy6NSpE/7+/g0+VImIyNn5Je0Q/16xi1v6R7p1qDodNZluIQnoaIyJwhGorgfGVW5gjGllrT12DWwksNGpVTYwiYmJri7hpJo1a8aWLVtcXYaIiNRx1lpmLF5PcCMf7hna0dXlOE21wcpaW2KMuRv4HPAEZltr1xtjHgNWWmsXA781xowESoBsYEIt1iwiIiJ13OI16azceZAnr4kh0N/b1eU4TY0mCLXWLgGWnPDew5W+/yPwR2cUZK391VQDIu6ouvGJIiJStbzCEv6yZCM9IgK5tk+dvd+tSm71SBs/Pz+ysrL0B0vcnrWWrKws/Pz8XF2KiEid8+LSbew/XMgjV3bDw6N+daa41SNtIiIi2LNnD5mZma4uRaRafn5+REREuLoMEZE6JfVAHm98l8LVvVrTp93pT9nj7twqWHl7exMVFeXqMkRERKSW/PmTDXh7Gh64tLOrS6kVbnUpUEREROqvpZsz+HJjBr+5uCMtmtbPoRQKViIiIlLr9hzMZ/p/fiEqNICJAyJdXU6tcatLgSIiIlL/7Mg8wvg3fiKvsIR/3doPX6+6/TzAU1GwEhERkVqzce9hbpr1E9bCv6ecT7fwQFeXVKsUrERERKRWrN51kFtmr6CRjxfzJvfjvOaNXV1SrVOwEhEREaf7YXsWk99KIqSxL29P7keb4EauLumcULASERERp1q6KYPb562ibXAj5k3uV2/vAKyKgpWIiIg4zSdr93Lv/NVEt2zC3En9CA7wcXVJ55SClYiIiDjF+yt388AHa+ndNojZE+No6ld/Hq5cUwpWIiIictbm/pDKw4vWM/C8UF67uQ+NfBpmxGiYn1pERESc5qXEbTz12WaGdW3BCzf0ws+7/s5TVR0FKxERETljSzdl8NRnmxkVG87T1/bE27NhP9SlYX96EREROSsLVu0hJMBHoaqczoCIiIickdyCYr7cuJ8rerRSqCqnsyAiIiJn5Iv1+yksKWNkbGtXl+I2FKxERETkjCxak05EkD+92zZzdSluQ8FKRERETltmbiHLtx1gVGw4xhhXl+M2FKxERETktC1Zt5fSMssoXQY8joKViIiInLZFyWl0btmETi2auLoUt6JgJSIiIqdlV1Y+P+/KUW9VFRSsRERE5LR8tDYdgCt7tnJxJe5HwUpERERqzFrLwtVpxEUGERHUyNXluB0FKxEREamxTfty2ZpxRHNXnYSClYiIiNTYouR0vDwMl8foMmBVFKxERESkRsrKLB+tSefCjqEEB/i4uhy3pGAlIiIiNbJq10HSco7qbsBTULASERGRGlmUnIaftwfDurZwdSluS8FKREREqlVcWsYna/cyrGtLAny9XF2O21KwEhERkWot23qAg/nFjOoZ7upS3JqClYiIiFRrUXIagf7eXNQpzNWluDUFKxERETml/KISvtiwn8tiWuHjpehwKjo7IiIiclKHjhZz29yV5BeVck1v3Q1YHY0+ExERkSrtyspn4pwV7MrO5+lre9I3MtjVJbk9BSsRERH5lVU7s5kydxUlZZa5k/rRv0OIq0uqExSsRERE5DiL16Rz//trCA/0Y/aEONqHNXZ1SXWGgpWIiIgAYK3ln19v45n/biE+MphXbuqjR9ecJgUrERERobCklD9+sI4PV6cxuldr/nZNDL5enq4uq85RsBIREWngDuYVMXXeKlakZHPfsE78Zsh5GGNcXVadVKPpFowxI4wxm40x24wxD56i3TXGGGuM6eu8EkVERKS27Mg8wuiXlpO8K4fnr4/ltxd3VKg6C9X2WBljPIEXgWHAHiDJGLPYWrvhhHZNgHuAn2qjUBEREXGuH3dkcfu8VXgYwzu39dN0Ck5Qkx6reGCbtXaHtbYIeBcYVUW7x4EngQIn1iciIiK14INVe7hp1k+EBPjwnzsvUKhykpoEq9bA7krLe8rfq2CM6Q20sdZ+cqodGWOmGGNWGmNWZmZmnnaxIiIicnbKyizPfLGZ37+/hrjIYD68YwDtQgJcXVa9cdaD140xHsCzwITq2lprXwNeA+jbt68922OLiIhIzRUUl3L/+2v4eO1exvZtw59Hd8fbU0+3c6aaBKs0oE2l5Yjy945pAnQHEssHu7UEFhtjRlprVzqrUBERETlzB44UMmXuSn7elcMDIzpz+6D2GqReC2oSrJKAjsaYKByB6npg3LGV1tpDQOixZWNMInC/QpWIiIh72Lo/l0lvJZFxuJCXxvfmsphWri6p3qo2WFlrS4wxdwOfA57AbGvtemPMY8BKa+3i2i5SREREzswP27OY8q+V+Hp5Mn9qf2LbNHN1SfVajcZYWWuXAEtOeO/hk7RNOPuyRERE5GytTz/E5LeSaNXMnzkT44gIauTqkuo9zbwuIiJSD6XlHGXim0k09fdm3q39aBno5+qSGgTdCiAiIlLPHDpazMQ3V3C0qJQ3J8YpVJ1D6rESERGpRwpLSpn6r5WkHMjjrYnxdG7Z1NUl1Q5rIT8LDu2GgOYQ2Lr6bc4BBSsREZF6oqzM8ocFa/lxRzZ/H9uTC84LrX4jd1VwGA6nwaE9/3sdWz6cBofToaT8YS9DH4WB97q23nIKViIiIvXEzC82syg5nWnDoxndK8LV5Zza0YNwMBVydlV67Xb0QB3aDQWHjm9vPKFJK0fPVKtY6Hw5BLaBpq2hVQ+XfISqKFiJiIjUA/N+3MnLidu5Ib4tdyZ0cHU5DvnZcGArZG+HrO2Or9kpcDDl18HJtyk0a+sIS237Q2AENGsDTSMcYapxS/B0/9ji/hWKiIjIKX25YT8PL/qFIZ2b8/iobud+RvWjByFjI+xfDxkbIHMzHNgCeZWeC2w8HcEppANE9IWgSMerWTvH+/71Y34tBSsREZE6bM3uHH7z79V0Cw/khRt64VXbz/7Lz4a0VZC+Gvaugb1r4dCu/633DYTmnaHTCAjt5HiFnAdB7cDTu3ZrcwMKViIiInXUrqx8bn0riZDGPsya0JcAXyf/WbfWcSlv53LY9QPsSYLsHf9bH3Keo/ep70Ro0R1adHWMeWrAzyBUsBIREamDDuYVMeHNFRSXWt6dEk/zJk6aq+pQGuxYCtu/hpTvIC/D8X5Ac2gTD71vhtZ9ITwWfJs455j1iIKViIhIHVN2v6CKAAAgAElEQVRQXMrkuSvZk3OUtyf347zmjc98Z6UlsPsn2PIZbP0CMjc53m/cAtonQOQAaDfQMTaqAfdE1ZSClYiISB1SWmb53fxkft51kH/e0Ju4yODT30lhLmz7EjZ94ghTBYfAw9sRonrdCB2GQPOuClJnQMFKRESkDnnik418+ss+pl/ehct7tKr5hkcyYfMS2PQx7EiE0iJoFAKdr3AMNO8wWJf2nEDBSkREpI6YtSyF2ctTmHBBJLcOjKp+g+wdsGmJo2dq949gyxzTG8RPcUyw2aYfeHjWfuENiIKViIhIHfDpur38+ZMNDO/Wgoeu6Fr1XFVlpbBnJWz93BGoMjc63m/eDS6aBl2udNy9p0t8tUbBSkRExM2tTM3mnvnJxLZpxvPX98LTo1IwOrQHUr6FbV/B9q8ck3UaT2h3AfT+K0RfCsE16N0Sp1CwEhERcWPbM48wee5KwgP9eOOmPvjl7oLdKxzzSqV863hMDEBAGHS6FDoOc4yX8g9ybeENlIKViIiIOyorIyt9G7PmfsCddhs3huXQ6JUp/5tXyqeJo1cq7laIGuS4i8+jlmddl2opWImIiLhKaTHk7oWcXXAw1fHK3gEHtmAPbCOk5Ch/AazxwOR1ckyD0CbeMei8eRcNPHdDClYiIiK1oSjfEZpy9zm+Hk7/39fD6Y6xUUf2Oe7UO8Z4QmAEZaGd+G9+NIlZzbhq+CX0O/9C8Gnkus8iNaZgJSIicjqshbxMRzA6nAaH95YHqPLX4fIwVXjo19t6B0DTVtA03DEOqmlrCGztmAIhKBICI7AeXjy08BfeztjFY6O60a9/5Ln+hHIWFKxEREROVHwUslMga5tjcPjBVDi4E3J2OgJVadHx7T28oHFLR2gK6wTtB0GTltAkvPxrK8c636bVTnXwcuI23v5pF1Mvas/NClV1joKViIg0XGWljvC0dy1kbICMjY6vObsA+792jUIcvUqtejpmKg9s4+hpahru6HVqFOqUgeOLktN46rPNXNkznAdGdD7r/cm5p2AlIiINx6E02LMCdidB+s+OQFWc51jn4QUhHaF1H4gdByHnlb86nJNHvXy//QD3v7+GflHBPH1tDzw8NIlnXaRgJSIi9dexyTNTvoWU7+DwHsf7Xn6O3qdeN0J4LLTsAaGdwMvHJWVu3pfL1H+tIjIkgNdu6ouvl+72q6sUrEREpP4oLXZMnLnlc9j6BRzY4ni/UQhEXghtfwNt4qBFjMtC1In2Hy5g4psr8PP25M2JcQQ28nZ1SXIWFKxERKRuKy6A7V/DhkWw+VPH3XiePhA5EPpMcOvJM48UljDhzSQOHS1m/tT+RARpSoW6TsFKRETqnrIy2LkMkv8NGz+ColzwawZdroDOlzvClG9jV1d5SsWlZdwxbxVb9ucye0Ic3VsHurokcQIFKxERqTtydsPPc2HNv+HQbsf0BV1HQbfRjikOPOvGZTRrLf/34Tq+23qAp67pwaBOYa4uSZxEwUpERNxbWZnjUl/SG7D1c8d7HYbA0BmO3ilvf1dWd0ae/2or76/aw28v7sh1cW1cXY44kYKViIi4p+ICWDsfvn8BsrZCQBgMvM8xbqpZ3Q0j763czXNfbmVMnwh+N7Sjq8sRJ1OwEhER91KYCytehx9fhrwMx7QI18yCLiPd5k6+M/Xtlkz+78N1XNgxlL9eHYOpZhZ2qXsUrERExD0U5UPS67D8ecjPclzuG3CPYyB6PQgg69MPcce8VXRs0YSXxvfG29P97lKUs6dgJSIirlVaDKvmwDdPOXqoOlwMg/8EEX1cXZnTpOUcZeKbSTT19+bNCXE08asbg+zl9ClYiYiIa1gLm5fAfx92PK+v3UAY+y9oe76rK3OqQ0eLmfjmCo4Wl7Lg9gtoGejn6pKkFilYiYjIubd/AyyZ5piLKrQT3DAfOg2vF5f8KissKWXqv1aSciCPtybFE92y9p85KK6lYCUiIudOYS4k/s0xMN2vKVz+DPSeAJ71789RWZnlDwvW8uOObJ4bG8sFHUJdXZKcA/XvN1lERNzTpiXwye8hNx163wwXz4CAEFdXVWtmfrGZRcnpTBsezVW9Wru6HDlHFKxERKR25WfDpw/AuvegRXe4bq7jQcj1TGmZZe2eHBI3Z5K4JZM1u3MY168tdyZ0cHVpcg4pWImISO3ZtAQ+ugeOZkPCHx0TfNbxuagqO3CkkO+2ZpK4OZNvt2RyML8YY6BnRDMeGNGZ2y6M0lxVDUyNgpUxZgTwPOAJvGGt/dsJ628H7gJKgSPAFGvtBifXKiIidUVxAXwx3TEvVcsYuOlDx9c6rrTMkrw7h282Z5C4JZN1aYewFkICfBgc3ZxB0WFc1DGMoID6Ex7l9FQbrIwxnsCLwDBgD5BkjFl8QnB6x1r7Snn7kcCzwIhaqFdERNzdgW2wYALsWwf974aLH6nTvVSZuYV8syWTxM0ZfLf1AIeOFuNhoFfbIO4b2olB0WF0Dw/Ew0M9U1KzHqt4YJu1dgeAMeZdYBRQEaystYcrtQ8ArDOLFBGROmLjx/CfqeDp7ZhCIbru/T92SWkZybuPjZXK4Jc0x5+40Ma+DO3SgoToMC7sGEqzRnU3LErtqUmwag3srrS8B+h3YiNjzF3AfYAPMMQp1YmISN1gLfz4Enz+J2jdxzFAPbDu3AmXcbiAxC2ZfLM5k++2ZnK4oARPD0Pvts2YNjyaQZ3C6NqqqXqlpFpOG7xurX0ReNEYMw6YDtxyYhtjzBRgCkDbtm2ddWgREXGlslL47EFY8Rp0uRKufh28/V1d1SkVl5axelcOiZszSNycyYa9jl6p5k18GdG9JYM6NWdgx1AC/fXoGTk9NQlWaUCbSssR5e+dzLvAy1WtsNa+BrwG0LdvX10uFBGp60qKYMFE2PSxYzzVsMfBwz0fLrzvUAHfbHEEqWXbDpBbUIKXh6FPuyD+MCKahE7N6dKqie7ik7NSk2CVBHQ0xkThCFTXA+MqNzDGdLTWbi1fvBzYioiI1G9lpfCfKY5QNeJJOP92V1d0nOLSMlamHiRxSwbfbM5k075cAFo29ePymFYkRIdxwXmhNNUDkcWJqg1W1toSY8zdwOc4pluYba1db4x5DFhprV0M3G2MGQoUAwep4jKgiIjUI2VlsPi3sP4/jl4qNwlV6TlHK+7gW74tiyOFJXh7Gvq2C+aPl3ZmUHQY0S3UKyW1p0ZjrKy1S4AlJ7z3cKXv73FyXSIi4q6sdYypSp4Hgx6AAb91WSlFJWWsTM0msTxMbdl/BIDwQD+u7BlOQnQYA84LpbGv5sOWc0O/aSIicnqWPw8rXoXz73LMpn6O7TmYT+LmTL7Zksn32w6QV1SKt6chPiqYMX0iGBzdnPOaN1avlLiEgpWIiNTctq/gq0eh61Uw/Ak4B+GlsKSUpJSDjjv4tmSyLcPRK9W6mT9X9WpNQnRzLugQQoB6pcQN6LdQRERqJjsFFkyCsC5w1Uu1Gqp2Z+dXTIXw/fYsjhaX4uPpQb/2wVwf14aE6OZ0CAtQr5S4HQUrERGpXlEezL/R8f3188AnwKm7Lykt4/vtWRWzne/IzAOgTbA/Y/pEkBAdRv8OITTy0Z8tcW/6DRURkVOzFj66BzI2wPj3Ibi9U3efk1/E7fNW8eOObHy8PDi/fQg39mtHQnQYUaHqlZK6RcFKRERObe17sO59GDwdzhvq1F2nHshj0pwk9hw8yl9GxzC6V2v8fTydegyRc0nBSkRETu7gTlhyP7TtDxfe59RdJ6VmM2XuSgDmTe5HfFSwU/cv4goKViIiUrWyUvjPVMf3o18FD+f1JC1cncYfFqwlIsif2RPiiAx17pgtEVdRsBIRkaot+zvs+gFGvwZB7ZyyS2stz325lee/2sr57YN55cY+NGvk45R9i7gDBSsREfm19GRI/Ct0uxp6XOeUXRYUl/LAB2tZlJzONb0j+OvVMfh4uecDm0XOlIKViIgcr7QYFt0NjULhimedMl9V1pFCpv5rFSt3HmTa8GjuTOigu/2kXlKwEhGR4y17Dvavg+vfAf+gs97dtowjTJqTxL7DBfxzXC+u6BHuhCJF3JOClYiI/E/GRvjmScclwM6Xn/Xuvt92gNvnrcLHy4N3p5xP77ZnH9RE3JmClYiIOJSVOi4B+jaBy2ae9e7eW7mb//twHVGhAcyeEEeb4EZOKFLEvSlYiYiIw48vQ9pKuGYWBISe8W7Kyiwzv9jMy4nbubBjKC+O701TP28nFirivhSsREQEsrbD149Dp0uh+zVnvJuC4lLuey+ZJev2Ma5fWx4d2Q1vT935Jw2HgpWISENXVgaLfwuevmd1F2BmbiGT565k7Z4cpl/ehVsHRunOP2lwFKxERBq6VbNh5zIY+QI0PbM79jbvy2XSnCSy84p45cY+DO/W0slFitQNClYiIg1Zzi747yPQPgF63XRGu1i6KYPf/ns1/j6evDe1PzERgU4tUaQuUbASEWmorIWP7nV8vfIfp30JcGdWHn9dsonP1u+jc8smzJ4QR3gz/1oqVqRuULASEWmoVs2B7V/BpTNP61mAhwuKefHrbby5PBUvT8Pvh3Xitova4+ftvIc0i9RVClYiIg1R5mb47I/QfjDETa7RJqVllneTdvHsF1vIyitiTJ8Ipg2PpkVTv1ouVqTuULASEWloSgphwa3g0whGvwIe1U+HsHzbAR7/eAOb9uUSHxnMnIldNZZKpAoKViIiDc2XjzqeBXjDfGhy6rv3dmQe4S9LNvLlxgwigvx5aXxvLu3eUtMoiJyEgpWISEOy9Uv48UWInwLRI07a7FB+Mc9/tZW5P6Ti6+XBAyM6M3FApMZRiVRDwUpEpKHI3AwfTILmXWHYY1U2KS4t452fdvH3L7dw6GgxY/u24b5LOtG8icZRidSEgpWISENwJAPeHgOePnDDv8H719MiJG7O4M+fbGRbxhHObx/MQ1d0pVu4xlGJnA4FKxGR+q4oH/59PRzJhImfQFDkcau37s/lz59s5JstmbQLacSrN/Xhkq4tNI5K5AwoWImI1GelJfDhbZD2M1z/NrTuU7GquLSMJz7ZyL9+3Ekjb0/+dFkXbr6gHb5eGkclcqYUrERE6quCQ/D+RMckoCOehM6XH7f6vZW7mfN9KjfEt+X+SzoR0tjXRYWK1B8KViIi9dHBVHhnLGRtczyups8tx60uK7PMXpZC99ZN+cvo7rrsJ+IkClYiIvXN9qXwwWQoK4YbP4T2g37V5NutmWzPzOPvY3sqVIk4kYKViEh9kbYKvnocdiyF4PYw7j0I7Vhl09nLU2nexJfLY8LPcZEi9ZuClYhIXVVWChkbYfePsOUL2Po5NAqB4X+BvpOqnFIBHHcBfrslk/sv6YSPV/WPsxGRmlOwEhGpi756HFa8BoWHHctNWsHg6XD+7eDb5JSbzl7umE19XL9256BQkYZFwUpEpC4KjIDu10Db/tC2HzRrBzUYK5WdV8SHP+/h6t6tCQ7wOQeFijQsClYiInVR34lntNm/V+yisKSMiQOinFyQiADo4rqISANRVFLGW9+ncmHHUDq1OPXlQhE5MwpWIiINxJJ1e8nILWTSQPVWidQWBSsRkQbAWsusZSl0CAtgUMcwV5cjUm8pWImINAArdx5kXdohJg6IwsNDE4KK1JYaBStjzAhjzGZjzDZjzINVrL/PGLPBGLPWGPOVMUb38IqIuJFZ36UQ6O/NNb0jXF2KSL1WbbAyxngCLwKXAl2BG4wxXU9othroa63tASwAnnJ2oSIicmZ2Z+fzxYZ9jOvXFn8fT1eXI1Kv1aTHKh7YZq3dYa0tAt4FRlVuYK1daq3NL1/8EdD/EomIuIk536fiYQw399fFBJHaVpNg1RrYXWl5T/l7J3Mr8GlVK4wxU4wxK40xKzMzM2tepYiInJHcgmLmJ+3msphWtAqs+hE3IuI8Th28boy5EegLzKxqvbX2NWttX2tt37Aw3ZUiIlLb3l+5hyOFJZpiQeQcqcnM62lAm0rLEeXvHccYMxT4EzDIWlvonPJERORMlZZZ5nyfSp92QcS2aebqckQahJr0WCUBHY0xUcYYH+B6YHHlBsaYXsCrwEhrbYbzyxQRkdP15cb97MrO51b1VomcM9UGK2ttCXA38DmwEXjPWrveGPOYMWZkebOZQGPgfWNMsjFm8Ul2JyIi58jsZSm0bubPJV1buLoUkQajRg9httYuAZac8N7Dlb4f6uS6RETkLPySdoifUrL502Vd8PLUXNAi54r+axMRqYdmL0+hkY8n18W1qb6xiDiNgpWISD2TkVvAR2vSua5vGwL9vV1djkiDomAlIlLPzPthJyVllgkXRLq6FJEGR8FKRKQeKSguZd5Pu7i4cwsiQwNcXY5Ig6NgJSJSjyxKTiM7r4hJAyNdXYpIg6RgJSJST1hrmb0slS6tmtK/fYiryxFpkBSsRETqieXbsti8P5dJAyIxxri6HJEGScFKRKSemL08hdDGPoyMDXd1KSINloKViEg9sD3zCF9vyuDG89vh6+Xp6nJEGiwFKxGRemDO8lR8PD248fx2ri5FpEFTsBIRqeNy8otYsGoPo2LDCW3s6+pyRBo0BSsRkTru3aTdHC0uZdLAKFeXItLgKViJiNRhxaVlvPV9Khd0CKFLq6auLkekwfNydQEiIg3Jofxi7nxnFX5eniREh5EQ3Zw2wY3OeH+f/bKPvYcK+PNV3Z1YpYicKQUrEZFzpLCklCn/WsnqXTk0b+rLV5sygPW0DwsgoVNzEqLDiI8Kxs+75nf1zVqWQlRoAIOjm9de4SJSYwpWIiLnQFmZ5f731/JTSjbPXx/LyJ7h7DiQR+LmTBI3ZzDvp53MXp6Cv7cn/TuEMKhTGAnRYbQLOfnz/n7edZDk3Tk8NqobHh6aEFTEHShYiYicA09+vomP1qTzwIjOjIptDUCHsMZ0CGvMrQOjOFpUyo87skjcnEHilky+3pQBQFRoQEXIOr99yHG9WbOWpdDUz4treke45DOJyK8pWImI1LK5P6Ty6jc7uPH8ttw+qH2Vbfx9PBncuTmDOzsu6aUcyOOb8pD17xW7mPN9Kr5eHhW9WV1aNeWzX/YxeWAUAb76p1zEXei/RhGRWvTfDfuZsXg9Q7s0Z8aV3Wr8DL+o0ACiQqOYMCCKguJjvVmZfLMlk0c/2gCAp4fh5gsia7F6ETldClYiIrVk9a6D/ObfPxPTOpB/3NALL88zm+HGz9uThOjmJJQPUN+Zlcc3WzJp4udF62b+zixZRM6SgpWISC3YmZXH5LdW0ryJH7MmxNHIx3n/3LYLCeDm/icf1C4irqMJQkVEnCw7r4gJbyZRZi1zJsbpMTMiDYh6rEREnKiguJTJbyWRnnOUd27rR/uwxq4uSUTOIQUrEREnKS2z3PPualbvzuHl8b3p0y7Y1SWJyDmmS4EiIk5greXxjzfw+fr9PHR5V0Z0b+XqkkTEBRSsRESc4I3vUpjzfSq3Doxi0sAoV5cjIi6iYCUicpY+XpvOE0s2cllMS/50WRdXlyMiLqRgJSJyFlakZHPf/DX0bRfEs9fF6pl9Ig2cgpWIyBnalpHLbXNXEhHsz+s39z3uOX4i0jApWImInIGM3AJumZ2Et6fhrYnxBAX4uLokEXEDmm5BROQ05RWWMGlOEtl5Rcyfej5tghu5uiQRcRPqsRIROQ0lpWXc/c7PbEg/zD/H9aJHRDNXlyQibkQ9ViIiNWSt5aFFv7B0cyZPjO7OxV1auLokEXEzClYiItXYnZ3PouQ0Fiansy3jCHcmdGB8v3auLktE3JCClYhIFbLzivhkbToLk9NZtfMgAHGRQTx5TQzX9W3j4upExF0pWImIlDtaVMoXG/axKDmdb7dkUlJm6dSiMdOGRzMqNpyIIA1SF5FTU7ASkQatpLSMZdsOsCg5nc/X7yO/qJRWgX7cOjCKUbGt6dKqCcZo0k8RqRkFKxFpcKy1JO/OYVFyOh+vTefAkSKa+nkxsmc4o2Jb0y8qWDOoi8gZUbASkQZjR+YRFianszg5jdSsfHy8PLi4c3NGxbZmcOcwfL00c7qInB0FKxGp1zJyC/hozV4WJaexds8hjIH+7UO4M+E8RsS0pKmft6tLFJF6pEbByhgzAnge8ATesNb+7YT1FwHPAT2A6621C5xdqIhITR0pLOHzX/axMDmN5dsOUGahW3hT/nRZF67sGU7LQD9Xlygi9VS1wcoY4wm8CAwD9gBJxpjF1toNlZrtAiYA99dGkSIi1SkqKePbLZksTE7jy437KSguIyLInzsTzuOqXuGc17yJq0sUkQagJj1W8cA2a+0OAGPMu8AooCJYWWtTy9eV1UKNIiJVKiuzrNp1kIWr0/hk3V5y8osJauTNtX3acFWvcHq3DdIdfSJyTtUkWLUGdlda3gP0q51yRESqt2V/LgtXp7EoOZ20nKP4eXtwSdeWXNUrnAs7huHtqcegiohrnNPB68aYKcAUgLZt257LQ4tIHbf30FEWJztmQt+49zCeHoaB54Vy//BOXNK1JQG+uhdHRFyvJv8SpQGVn98QUf7eabPWvga8BtC3b197JvsQkYbj0NFiPl23l4XJafyUko210LNNMx65sitX9AgnrImvq0sUETlOTYJVEtDRGBOFI1BdD4yr1apEpMEqKC5l6aYMFiansXRTJkWlZUSFBnDPxR0ZFduaqNAAV5coInJS1QYra22JMeZu4HMc0y3MttauN8Y8Bqy01i42xsQB/wGCgCuNMY9aa7vVauUiUm8UlZSRlJrNouQ0Pv1lH7kFJYQ29mX8+W25KrY1PSICNQhdROqEGg1KsNYuAZac8N7Dlb5PwnGJUESkRtJyjvLN5kwSN2ewfNsB8opKCfDxZHj3llwV25oLOoTgpUHoIlLHaLSniJwThSWlrEw9SOLmDBI3Z7I14wgArZv5M6pXaxI6hXFhxzD8ffRYGRGpuxSsRKTW7M7OJ3FLJt9szuD77VnkF5Xi4+lBfFQwY+PakBAdRoewxrrMJyL1hoKViDhNQXEpSanZJJZf4tuemQdARJA/V/duTUKn5vTvEKKpEUSk3tK/biJyVvYfLuDz9ftI3JzJD9uzOFpcio+XB/2ighnXrx0J0WG0Dw1Qr5SINAgKViJy2g4XFPPZL/tYlJzG99uzsBbahTTiur4RDIoOo3/7UI2VEpEGScFKRGqksKSUxM2ZLEpO48uNGRSVlNEupBG/GdKRkT3DOa95Y1eXKCLicgpWInJSZWWWFeXzS32ydi+HC0oICfBhXHxbRsWGE9ummS7xiYhUomAlIr+yce9hFian8VFyOumHCmjk48nwbi0ZFRvOwPNCNb+UiMhJKFiJCABHCkv41w87Wbg6jc37c/HyMFzUKYwHLu3MsK4taOSjfy5ERKqjfylFhNW7DnLv/GR2ZuXTp10Qj4/qxmUxrQhprIcci4icDgUrkQastMzy0tJtPPfVVlo29eO9qf2Jjwp2dVkiInWWgpVIA7U7O5/73ksmKfUgI3uG8/hV3Qn093Z1WSIidZqClUgDtCg5jen/+QUL/H1sT0b30jPURUScQcFKpAE5XFDMwwt/YWFyOn3aBfHc2FjaBDdydVkiIvWGgpVIA7EyNZt73k1m3+ECfje0E3cN7qBpE0REnEzBSqSeKykt4x9fb+OfX2+ldZA/703tT592Qa4uS0SkXlKwEqnHdmblce/8ZFbvyuGa3hHMGNmVJn4aoC4iUlsUrETqIWstH/ycxiOLfsHDw/DCDb24sme4q8sSEan3FKwagKNFpezMziP1QB6pWfl4eRiiQgNoFxJAm2B/fL08XV2inKX8ohLSc46SllNA2sGjfLMlg8/X7yc+Kpi/j42ldTN/V5coItIgKFjVE/lFJezMyq8IT46veezMymff4YKTbudhILyZP5EhAUSGNiIyxBG4okIbERHUCD9vhS5XKyuzHMgrJL08NDkClOOVXv46mF983DY+Xh5MGx7N7YM64OmhhySLiJwrClZ1SF5heXjKcoSmyiEqI7fwuLahjX2IDAlgwHmhRIU2ol1IgCM0hTaitNSSkpXHzqw8Ug7ks7N8X4uT0zlcUFKxD2MgPNC/InA5QlcjokIDaBOs0OUsBcWl7D108tCUfqiAopKy47YJ8PGkdZA/rZv5E9umGeHN/IkI8ie8mePVoomv7vgTEXEBBSs3c6SwhNQDef8LUOXfp2Tlkfmr8ORLVGgjLuoUVn5pr1FF+KlugHJQgA+92/76zrCc/CJSjh3zgCN8pWbl88m6veRU6hU5FrrahTSq6OFyfA2grUJXBWstB/P/v707j7errM44/ntCEpILZGBOLpAIIgYBmVRUNIJUKCC1tLQFKvXDUOuAKFiLYCuD1gqCTEVAEEWjILZ8cAIUiYxWCJNMhjFhECEEQkhIyODTP9Z7k5OcmxDJufdN2Ov7T+69m5yzeLPu2Wu/77vXns8fZszhyZbCqadoemrGHJ6bNW+JvyPBhuusSfeIoWzTPZw937Ix3SOHMnp4FE3dI4cybMhApJyJSimlVU0WVhW8NHf+EoXTlJYlvOdmLVk8bbjOmoxdby1222qDRbNOY0sRs/aanf/nG9E1mB02G8wOyyi6pkzvmeFaPHN29b1Pty1FjRo+pG15cez6XYxZdy2GDn79FF3zFvyJZ2bOXVQ0tc84zWXO/IVL/J0hgwbQXWaWxo0atujrnlmnjYYNYfDAnG1KKaXVkWxXeeOdd97ZkyZN6rPXf+DpmVx22xN99vp/rpfmLih7nma3zVBsNGzNmO0pS3VvKIXImPW6WKsPiqe+8OLL85n6/OxFs109e7ymTH+Z52cv+f+78bAhq22RZZvps+ctKqCefekVlv4VWn/twUsUS90tf3aPHMrIrkE525RSSqsZSbfb3vnV/rvV46z9Gjz94hyuuPOp2mEsMnTQGq7kUDUAAA4fSURBVIxZr4s9xm20xNLZmPW66Bq8+v8zDO8axHZdI9hukxFtx16cM5/Hy3Lm1J4ZuumzuW7ys217h1YHI7sGMXrEUN6z5QZRLI1YvEQ3aviQXAZNKaUGe93OWKWUUkopdcqKzljlRo6UUkoppQ7JwiqllFJKqUOysEoppZRS6pAsrFJKKaWUOiQLq5RSSimlDsnCKqWUUkqpQ7KwSimllFLqkCysUkoppZQ6JAurlFJKKaUOycIqpZRSSqlDsrBKKaWUUuqQas8KlDQNmFrlzetYH3iudhCroByXdjkmvctxaZdj0rscl3Y5Jr37c8ZljO0NXu0/qlZYNY2kSSvy8MamyXFpl2PSuxyXdjkmvctxaZdj0ru+GJdcCkwppZRS6pAsrFJKKaWUOiQLq/5zQe0AVlE5Lu1yTHqX49Iux6R3OS7tckx61/FxyT1WKaWUUkodkjNWKaWUUkodkoVVSimllFKHZGHVTySpdgyrqhybtKIyV9KKylxJteQeq7RKkSRnUgI5Fq8mx2exHIvla/L4SFoP2BKYCwwDHrb9h7pR1Sdpc2JchgBrAZNsP9iR125orvUrSVsDGxMzhHOB+20/XzeqeiQNAN4FjAJGADOAW2w/VTWwVVhTTwyZK3++zJXMlR6ShgATgG7gMeCPwMvAPbYvrRlbTZKGAlcCfwLuAQyMJMbom7anrdTrN/D3r19JOgj4B6Jt/iRgIfELf73tX1cMrRpJhwAHArOJMdkI6AJ+Y/vbFUOrStIawD7AJsCmxIfgz2w/XDWwijJXepe50i5zpZ2kjwF72/6gpA2ANwNvBXYGngZOsP1KzRhrkPRR4IO295U0gsiVNwLvJWawjrM9+7W+/sDOhJmW49PAZ23fIKkb2BrYEThWUrftCXXDq+LjwOdtTyzT1KOAccD+kobYPq9ueNV8GDgYuB14ANgeOF3SXcDZK3sVtZrKXOld5kq7zJV2zwLPSRpacmIacKOkNwFfIwqJX9YMsJIHgRckrWd7OjHZMVnSJOAc4K+B773WF88Zqz4kaSDwFeLK4Bzb81qO7Q4cCXzc9tOVQux3ZUPp54gryf+yPafl2HbAqcCRnVrrXp1Iuh44xfbPJK1FXDltTpxEn7B9atUA+1lLrgwlcmVuy7HMlcyVRZb6XPlK5kqQtC5wHjALmAjcBfze9nxJPwF+bPubNWOsofzO/Dcx4zsRuB74P9sLJP0Y+Knt19w4NAurPiZpR+CrwG3E9PQ9th8qx6YBm7UWF00gaSvgLOIq4TfAjbZvL8eeBcbafrliiFVIOhR4E3By6zS0pNHA94mZz0m14quhXFmfTeTKzcBNtu8ox5qcK4cRG28zVwpJWxK58iKRKzfn50qQdASwBbFKtQGxn2htYN+mjgmApD2AXYnl9M2BV4jtOgeszLhkYdUPylXDocQGQhPT9rOAqbaPrBlbLZIGAfsRS6NbADsBDwGP2P7XmrHVImkzYhp6E+BaYs/M9ZKGAZOBLZr4IVhmI/6WyJFR5c8HaXaubAqcCYwBfgX83Pavm54rAJL+jviM7Wbx58rDTcuVsvXkYGKp+EFin+8IYH75+ibbj9eLsA5JGwP/CPzO9i8krQ+MJs7NmxEF+YyVeo8srPpG+cc6kFjD/nfgUeKk8BbiimogcFuTPvwkDQf+AvhLYvr1eWKj6XOAiDG5x/bCakGuAiS9HdiLuJLaArgFeNT2F6sG1o8kjSU2Z98H3G37BUnrsPi28cE0NFckDbD9p/L124A9gXcS+4lupnm5Mhr4F2JT9im2by4btbuJXFkTuLdJuVLy4gxiReBtxDjcDfzE9o01Y6tJ0vbA6cD9wHuAX9k+uuPvk4VV35D0DWJKEaJHxhXAO4AbbF9TLbCKJF1EjMkzwIZE+4lpwHW2r60ZW02lYNgd2B+40PaNkgYTV1CjAWxPrRhiv5N0MnA4ix+Qelf5cwvbX6sTVX2l6P5PYglnbvnZhkSuDAIG255SL8L+J+l8onCYTly4XgtsS9xGf2FDW0+cBrxg+0vl+zcABwD/RGxLOdz2goohViHpLGCa7ZMljQQuBq6w/Z1SoI+3/YOVfp8G5ly/kHQb8H7bMyU9TPTMGEDM2FwNHNu0xJZ0O7Cb7Znl+zcCewOfAC6x/eWa8dUi6Qxi0+18Ij++YfvrdaOqq7QTuAJ4kpjtHcniZY3zgFubdNNHD0lnAi/a/g9JO7C4lcuTwAVN7NlUPmvH235Z0kPAD4lVgfHAE8DRTVoZAJB0NPB2YrVkiu35LccuAc63fXOt+GqRdANwaE9bEkl7AUfY/htJJwKjbR+xsu+Tj7TpA5I2IfrK7C3pr4iryGNsf4ZY+98WWLdmjP2tNGS7BThH0rjSxPBh22cRPVXeVfaiNdF4op/MJ4APAAeUqXwkHSZpfNXo+lnJjYXACUTBcCbwDWL57y5imbSpn11jiIs0gJOJ2+l/SeydOan05GmMsuT3KHCMpI8AXbaPt32K7X2I5dEmfq6cDTxCzFDtKmlTSaPKsXcQF3GNImltYg/rQEUzWYj9iUMkvQ/YpRxf+ffKGau+IWk/4tbnacTdBicAvyX2AXzL9o71oqujfOh/HphHTNM/TpwYNgAutf2GiuFVIWkn4MTSqG5QuQ36k8Cmtv9N0p3AP9u+rXKo/a5sWj+WeGrB/cB+tveRNM72A3Wjq0PSh4g2LV8j2gfs3XLsRuAQ24/Viq+Gsjz6UeAl4u6uS4FriM/db9vevmJ4/apclLh83QUcQezBm07sad0G+KPtg+tF2f9ax2Xpn0nahWi3cJftd3Tk/bKw6ryWE+SGRGE1HjiI6Ge1K9Ejo5FLPaV/yIHENPU8ohPwAqLY/GHN2GooObINcAexxONyN895wOXAQbb3qhljbZI+TRRYn+nE/ofVlaQ1bC+U9CmiLce2RIPQbxG3zn/L9tY1Y6yt9Kw6gthvNg64zPa5daPqP2UJfRfinLMO8APbv5O0LXEB+xSxx6hRj1Qr4/JO4may0cD/2r6u5fjXgYc6lStZWHVYuXraB9gNOL7nDgxJ7yeKieuIu5zmLvtVXl9KL6I3EfuppgEX2X683DI+i3he08wmbjJdWs8dX+XkeQaxF++U2nH1l5Zc2YfoKXOh7Xsljbd9fd3oVh1leXgP4vbwjYmLtkttX101sH5UcmUrYml4FjChFBE7EW1cfkvsL5q3nJd5XSm98A5l8fMBDyqHziF+l2bWiq2mlnH5HnF3/kFEU92LiZ6KC4BX3KGekllYdZika4GfEne+bUdcIRwCfJfYXNrE5zJdSSz9TQS+QDwodRLwJeDqphZU5cTwZuLEMJeYcbi35fi5xN6rZyuF2O9acuU64Dhihvc24CRiPwQ9rQaaZKmCcwZxknxE0mDb8xSPbGnMxRq0fa4cR3yu3EU8521izdhqkXQNcJ7tK1p+thPwKWCS7bOrBVfRMsZlR+CTwJ22z25tY7LS79fQc1qfKJvWr7K9bfl+BrGE8ThwFHBlk6alYdGY/KJniaL0JzqcaGL4PqJweKJWfDX1UkS8G7gX+KrtH0kaafuFmjH2p+XkyoPEFP6JmStMBI4ncuUO4pFZP21asfkqnyu7AV9sWq6UPYmfA4baPmGpYxsRWws+49KNvilWcFyOdgefVNDUO2v6ykDgVkm7SDoGeNz2ebZ/Tmw4/bCi43iTDCDGpLt8PxbY1fZ3ganAx0riN0o5MWxp+wtlrf9w4DRi+W9PSZs2qagqlpUrlxAXJ5kr9q+IJY1TgXOJpxd0L+/vv04t73NlCg3MlTLzfwHwFknXSTqi7C2CaOeyEXETSKOs4Ljc18n3zMKqs6YCvwfOJ/7BbiqbtSEaQD7U2k+kIZ4AHgPuUzw49iPE+j/EvpCxDV0KXNaJYQIxZo07MZC5sizLKyKaWnBmrixF0ddsD6KIuJjocfakpMuBE4kHLjfqubRQZ1wGdvLFmq78Ip9a9sbMJRpfTi63zA8g+vE0ShmTExWNDfcBfu3FTQz3BS6qFlxdrSeGu8vXrSeG3Zp2YshcWabMlaVkriyp7Bc6hdiEPQd40Pb7FX2+tidmZJrYULfKuOQeqw6RdBRxG+f3bd/d8vM3EtP1P7M9uVZ8NZQx6Sbu1rl7qWPdwO7lqruxFL29ljgxlP00F9n+cdXg+lHmyqvLXAmZK+0knUM8I/L00sLlXKLVxOWKR2btb/s7daPsf7XGJZcCO+c4oivyhZImSjq67JN5mNh0umXd8Ko4jrgd/EJJ15cx6VnO2IF4+HLjSDpK0imS3mp7hu0JLSfKbuBHTTpRFpkrvchc6VXmSrsdiSdbUO4ingAcVo4dWY43UZVxyRmrDpC0FTHdeDDxJPXdgQ8R7RYmE1eZ73WDume/ypjcT8zijbd9a7UgK5H0DHF31xbAy8QjSi6z/ZSkfYGFtq+qGWN/ylxZtsyVJWWutCsbsd8NPNZ6J6Sk/yEed3QA8Fnbd1YKsYqa45KFVYdIGgLQ2ktG0jDieV4fsD2uVmy15Ji0yxND7zJX2mWu9C5zpXda3Jm/p8nwlsBVxBMddqodXy01xiU3r3eIl2rOJ0m2Z0paADTyMRw5Ju1sT5b09+XrWUQPlctbTgxTmnaihMyV3mSu9C5zpXeOB5dTioc1bD8k6VKiWXVj1RiXnLHqY5LGAM/bfql2LKuKHJMllRODJZ1GXEWdVDumVUXmypIyV5Ytc6WdpAHQzKcVLE9fj0sWVimtIvLEkFZU5kpKq64srFJKKaWUOiTbLaSUUkopdUgWVimllFJKHZKFVUoppZRSh2RhlVJKKaXUIVlYpZRSSil1yP8DozEKon7n66cAAAAASUVORK5CYII=\n", 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", 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\n", + "image/png": 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", "text/plain": [ "
" ] diff --git a/Part4.A.1.data-analyzer.ipynb b/Part4.A.1.data-analyzer.ipynb index 831b7a03f..51d656355 100644 --- a/Part4.A.1.data-analyzer.ipynb +++ b/Part4.A.1.data-analyzer.ipynb @@ -884,7 +884,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -899,9 +899,41 @@ "b 1\n", "c 2\n", "Name: 1, dtype: int64\n", + "1 0\n", + "2 4\n", + "3 8\n", + "4 12\n", + "Name: a, dtype: int64\n", + "a 0\n", + "b 1\n", + "c 2\n", + "d 3\n", + "Name: 1, dtype: int64\n", + "1 0\n", + "2 4\n", + "3 8\n", + "4 12\n", + "Name: a, dtype: int64\n", + " a b c d\n", + "1 0 1 2 3\n", + "2 4 5 6 7\n", + "a 4\n", + "b 5\n", + "c 6\n", + "d 7\n", + "Name: 2, dtype: int64\n", "b 1\n", "c 2\n", - "Name: 1, dtype: int64\n" + "Name: 1, dtype: int64\n", + " a b c d\n", + "2 4 5 6 7\n", + "3 8 9 10 11\n", + " b c\n", + "1 1 2\n", + "2 5 6\n", + "3 9 10\n", + "4 13 14\n", + "4\n" ] } ], @@ -910,86 +942,536 @@ "import numpy as np\n", "data = pd.DataFrame(np.arange(16).reshape(4,4),index=['1','2','3','4'],columns=['a','b','c','d'])\n", "print(data)\n", - "#我们要选取第1行第2列，第3列\n", + "# 我们要选取第1行第2列，第3列\n", "print(data.loc['1',['b','c']])\n", - "#当然可以用iloc整数索引来选取,数据要从第0行或者第0列开始算\n", - "print(data.iloc[0,[1,2]])" + "print(data['a'])#只取第1列的数据\n", + "print(data.loc['1'])#只取第一行\n", + "print(data.loc[:,'a'])#只取第1列和data['a']作用一样\n", + "print(data.loc[['1','2']])#要注意是双[],取的是两行第一行和第二行\n", + "print(data.iloc[1])#只能取整数索引，从第0列开始算\n", + "print(data.iloc[0,[1,2]])#通过整数位置，同时选取行和列\n", + "print(data.iloc[[1,2]])#通过整数位置，从DataFrame选取单个行或行子集\n", + "print(data.iloc[:,[1,2]])#通过整数位置，从DataFrame选取单个列或列子集\n", + "print(data.at['2','a'])#通过行和列标签，选取单一的标量\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|类型|说明|\n", + "|--|--|\n", + "|df[val]|从DataFrame选取单列或一组列:在特殊情况下比较便利:布尔型数组(过滤行）、切片（行切片）、或布尔类型DataFrame(根据条件设置值)|\n", + "|df.loc[val]|通过标签，选取DataFrame的单个行或一组行|\n", + "|df.loc[:,val]|通过标签，选取单列或列子集|\n", + "|df.loc[val1,val2]|通过标签，同时选取行和列|\n", + "|df.iloc[where]|通过整数位置，从DataFrame选取单个行或行子集|\n", + "|df.iloc[;,where]|通过整数位置，从DataFrame选取单个列或列子集|\n", + "|df.iloc[where_i,where_j]|通过整数位置，同时选取行和列|\n", + "|df.at[label_i,label_j]|通过行和列标签，选取单一的标量|\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Numpy\n", - "Numpy是带一组索引的数组，我们可以对其进行加工运算。类似于Series。" + "#### 算术运算和数据对齐\n", + "pandas最重要的一个功能是，它可以对不同索引的对象进行算术运算。在将对象相加时，如果存在不同的索引对，则结果的索引就是该索引对的并集。对于有数据库经验的用户，这就像在索引标签上进行自动外连接。看一个简单的例子：" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0 1\n", - "1 2\n", - "2 3\n", - "3 4\n", - "4 5\n", - "5 6\n", - "6 7\n", - "7 8\n", - "8 9\n", - "dtype: int64\n", - "3 4\n", - "4 5\n", - "5 6\n", - "6 7\n", - "7 8\n", - "8 9\n", - "dtype: int64\n", - "0 2\n", - "1 4\n", - "2 6\n", - "3 8\n", - "4 10\n", - "5 12\n", - "6 14\n", - "7 16\n", - "8 18\n", - "dtype: int64\n" + "a 7.3\n", + "b -2.5\n", + "c 3.4\n", + "d 1.5\n", + "dtype: float64\n", + "a -2.1\n", + "b -2.0\n", + "c 3.2\n", + "d 1.7\n", + "dtype: float64\n", + "a 5.2\n", + "b -4.5\n", + "c 6.6\n", + "d 3.2\n", + "dtype: float64\n", + "a 5.2\n", + "b NaN\n", + "c 1.4\n", + "d 4.7\n", + "e NaN\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "s1 = pd.Series([7.3,-2.5,3.4,1.5],index=['a','b','c','d'])\n", + "print(s1)\n", + "s2 = pd.Series([-2.1,-2.0,3.2,1.7],index=['a','b','c','d'])\n", + "print(s2)\n", + "print(s1+s2)#会依照索引进行相加\n", + "s3= pd.Series([-2.1,-2.0,3.2,1.7],index=['a','c','d','e'])\n", + "print(s1+s3)#如果索引中的值为NaN，那么相加也会返回NaN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "对于DataFrame，对齐操作会同时发生在行和列上：" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b c d\n", + "Ohio 0.0 1.0 2.0\n", + "Texas 3.0 4.0 5.0\n", + "Colorado 6.0 7.0 8.0\n", + " b d e\n", + "Utah 0.0 1.0 2.0\n", + "Ohio 3.0 4.0 5.0\n", + "Texas 6.0 7.0 8.0\n", + "Colorado 9.0 10.0 11.0\n", + " b c d e\n", + "Colorado 15.0 NaN 18.0 NaN\n", + "Ohio 3.0 NaN 6.0 NaN\n", + "Texas 9.0 NaN 12.0 NaN\n", + "Utah NaN NaN NaN NaN\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df1 = pd.DataFrame(np.arange(9.).reshape(3,3),columns=list('bcd'),index=['Ohio','Texas','Colorado'])\n", + "print(df1)\n", + "df2 = pd.DataFrame(np.arange(12.).reshape(4,3),columns=list('bde'),index=['Utah', 'Ohio','Texas','Colorado'])\n", + "print(df2)\n", + "print(df1+df2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然也可以拿series和dataframe做算术运算，毕竟两者很类似。默认情况下，DataFrame和Series之间的算术运算会将Series的索引匹配到DataFrame的列，然后沿着行一直向下广播：" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a 7.3\n", + "b -2.5\n", + "c 3.4\n", + "d 1.5\n", + "dtype: float64\n", + " b d e\n", + "Utah 0.0 1.0 2.0\n", + "Ohio 3.0 4.0 5.0\n", + "Texas 6.0 7.0 8.0\n", + "Oregon 9.0 10.0 11.0\n", + " a b c d e\n", + "Utah NaN -2.5 NaN 2.5 NaN\n", + "Ohio NaN 0.5 NaN 5.5 NaN\n", + "Texas NaN 3.5 NaN 8.5 NaN\n", + "Oregon NaN 6.5 NaN 11.5 NaN\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "s1 = pd.Series([7.3,-2.5,3.4,1.5],index=['a','b','c','d'])\n", + "print(s1)\n", + "s2 = pd.DataFrame(np.arange(12.).reshape((4, 3)),columns=list('bde'),index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n", + "print(s2)\n", + "print(s1+s2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 在算术方法中填充值\n", + "在对不同索引的对象进行算术运算时，你可能希望当一个对象中某个轴标签在另一个对象中找不到时填充一个特殊值（比如0）：" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a b c d e\n", + "0 0.0 1.0 2.0 3.0 4.0\n", + "1 5.0 6.0 7.0 8.0 9.0\n", + "2 10.0 11.0 12.0 13.0 14.0\n", + "3 15.0 16.0 17.0 18.0 19.0\n", + " a b c d e\n", + "0 0.0 1.0 2.0 3.0 4.0\n", + "1 5.0 NaN 7.0 8.0 9.0\n", + "2 10.0 11.0 12.0 13.0 14.0\n", + "3 15.0 16.0 17.0 18.0 19.0\n", + " a b c d\n", + "0 0.0 1.0 2.0 3.0\n", + "1 4.0 5.0 6.0 7.0\n", + "2 8.0 9.0 10.0 11.0\n", + " a b c d e\n", + "0 0.0 2.0 4.0 6.0 NaN\n", + "1 9.0 NaN 13.0 15.0 NaN\n", + "2 18.0 20.0 22.0 24.0 NaN\n", + "3 NaN NaN NaN NaN NaN\n", + " a b c d e\n", + "0 0.0 2.0 4.0 6.0 4.0\n", + "1 9.0 5.0 13.0 15.0 9.0\n", + "2 18.0 20.0 22.0 24.0 14.0\n", + "3 15.0 16.0 17.0 18.0 19.0\n", + " a b c d e\n", + "0 0.0 0.0 0.0 0.0 -4.0\n", + "1 -1.0 5.0 -1.0 -1.0 -9.0\n", + "2 -2.0 -2.0 -2.0 -2.0 -14.0\n", + "3 -15.0 -16.0 -17.0 -18.0 -19.0\n", + " a b c d e\n", + "0 NaN 1.000000 1.000000 1.000000 0.0\n", + "1 0.8 inf 0.857143 0.875000 0.0\n", + "2 0.8 0.818182 0.833333 0.846154 0.0\n", + "3 0.0 0.000000 0.000000 0.000000 0.0\n", + " a b c d e\n", + "0 NaN 1.0 1.0 1.0 0.0\n", + "1 0.0 inf 0.0 0.0 0.0\n", + "2 0.0 0.0 0.0 0.0 0.0\n", + "3 0.0 0.0 0.0 0.0 0.0\n", + " a b c d e\n", + "0 0.0 1.0 4.0 9.0 0.0\n", + "1 20.0 0.0 42.0 56.0 0.0\n", + "2 80.0 99.0 120.0 143.0 0.0\n", + "3 0.0 0.0 0.0 0.0 0.0\n", + " a b c d e\n", + "0 1.000000e+00 1.000000e+00 4.000000e+00 2.700000e+01 0.0\n", + "1 1.024000e+03 1.000000e+00 2.799360e+05 5.764801e+06 0.0\n", + "2 1.073742e+09 3.138106e+10 1.000000e+12 3.452271e+13 0.0\n", + "3 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "df1 = pd.DataFrame(np.arange(12.).reshape((3, 4)),columns=list('abcd'))\n", + "df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)),columns=list('abcde'))\n", + "print(df2)\n", + "df2.loc[1,'b']=np.nan\n", + "print(df2)\n", + "#将它们相加时，没有重叠的位置就会产生NA值：\n", + "print(df1)\n", + "print(df1+df2)\n", + "#使用df1的add方法，传入df2以及一个fill_value参数：\n", + "print(df1.add(df2,fill_value=0))\n", + "print(df1.sub(df2,fill_value=0))\n", + "print(df1.div(df2,fill_value=0))\n", + "print(df1.floordiv(df2,fill_value=0))\n", + "print(df1.mul(df2,fill_value=0))\n", + "print(df1.pow(df2,fill_value=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "以下是Series和DataFrame的算术方法\n", + "|方法|说明|用法|\n", + "|--|--|--|\n", + "|add,radd|用于加法(+)的方法|df1.add(df2)或者df1.add(df2,fill_value=0)将NaN的填充为0|\n", + "|sub,rsub|用于减法(-)的方法|df1.sub(df2)或者df1.sub(df2,fill_value=0)将NaN的填充为0|\n", + "|div,rdiv|用于除法(/)的方法|df1.rdiv(df2)或者df1.rdiv(df2,fill_value=0)将NaN的填充为0|\n", + "|floordiv,rfloordiv|用于底除(//)的方法|df1.floordiv(df2)或者df1.floordiv(df2,fill_value=0)将NaN的填充为0|\n", + "|mul,rmul|用于乘法(*)的方法|df1.mul(df2)或者df1.mul(df2,fill_value=0)将NaN的填充为0|\n", + "|pow,rpow|用于指数(**)的方法|df1.pow(df2)或者df1.pow(df2,fill_value=0)将NaN的填充为0|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 函数应用和映射\n", + "NumPy的ufuncs（元素级数组方法）也可用于操作pandas对象，后面我们算数运算以及数据再加工都可以用到：" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b d e\n", + "Utah 0.177277 0.478391 0.300703\n", + "Ohio 0.198568 0.248663 -0.646564\n", + "Texas -0.050653 1.121828 -0.571210\n", + "Oregon -0.157193 -1.688991 0.630852\n", + " b d e\n", + "Utah 0.177277 0.478391 0.300703\n", + "Ohio 0.198568 0.248663 0.646564\n", + "Texas 0.050653 1.121828 0.571210\n", + "Oregon 0.157193 1.688991 0.630852\n", + "b 0.355761\n", + "d 2.810819\n", + "e 1.277416\n", + "dtype: float64\n", + "Utah 0.301114\n", + "Ohio 0.895227\n", + "Texas 1.693038\n", + "Oregon 2.319843\n", + "dtype: float64\n", + " b d e\n", + "min -0.157193 -1.688991 -0.646564\n", + "max 0.198568 1.121828 0.630852\n", + " b d e\n", + "Utah 0.18 0.48 0.30\n", + "Ohio 0.20 0.25 -0.65\n", + "Texas -0.05 1.12 -0.57\n", + "Oregon -0.16 -1.69 0.63\n", + "Utah 0.30\n", + "Ohio -0.65\n", + "Texas -0.57\n", + "Oregon 0.63\n", + "Name: e, dtype: object\n" ] }, { "data": { "text/plain": [ - "0 2.718282\n", - "1 7.389056\n", - "2 20.085537\n", - "3 54.598150\n", - "4 148.413159\n", - "5 403.428793\n", - "6 1096.633158\n", - "7 2980.957987\n", - "8 8103.083928\n", + "Utah 0.318790\n", + "Ohio -0.066444\n", + "Texas 0.166655\n", + "Oregon -0.405110\n", "dtype: float64" ] }, - "execution_count": 4, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd #给pandas别名，更好的使用这个模块\n", - "import numpy as np\n", - "obj = pd.Series(list(range(1,10)))\n", - "print(obj)\n", - "print(obj[obj>3])#筛选出大于3的数组\n", - "print(obj*2)#将数组中的值全*2\n", - "np.exp(obj)" + "frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'),index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n", + "print(frame)#打印下frame\n", + "print(np.abs(frame))#打印frame的绝对值\n", + "f = lambda x: x.max() - x.min() #做一个算术运算逻辑,匿名方法lambda\n", + "print(frame.apply(f))\n", + "#当然也可以指定行\n", + "print(frame.apply(f, axis='columns'))\n", + "#许多最为常见的数组统计功能都被实现成DataFrame的方法（如sum和mean），因此无需使用apply方法。\n", + "#传递到apply的函数不是必须返回一个标量，还可以返回由多个值组成的Series：\n", + "def f(x):\n", + " return pd.Series([x.min(), x.max()], index=['min', 'max'])\n", + "print(frame.apply(f))\n", + "#元素级的Python函数也是可以用的。假如你想得到frame中各个浮点值的格式化字符串，使用applymap即可：\n", + "format = lambda x: '%.2f' % x\n", + "print(frame.applymap(format))\n", + "#之所以叫做applymap，是因为Series有一个用于应用元素级函数的map方法：\n", + "print(frame['e'].map(format))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 汇总和排序方法\n", + "当然除了这些运算外，像求最大值，求累计值，求排名和排序这些都会用到，我们就统一理一个表格来梳理下。\n", + "### 排序方法" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b e d\n", + "2 -0.500263 0.702904 2.335214\n", + "3 3.252250 0.244196 0.171796\n", + "1 0.469565 -0.963382 -1.516873\n", + "4 -0.935815 0.306789 1.418025\n", + " b e d\n", + "1 0.469565 -0.963382 -1.516873\n", + "2 -0.500263 0.702904 2.335214\n", + "3 3.252250 0.244196 0.171796\n", + "4 -0.935815 0.306789 1.418025\n", + " b d e\n", + "2 -0.500263 2.335214 0.702904\n", + "3 3.252250 0.171796 0.244196\n", + "1 0.469565 -1.516873 -0.963382\n", + "4 -0.935815 1.418025 0.306789\n", + " e d b\n", + "2 0.702904 2.335214 -0.500263\n", + "3 0.244196 0.171796 3.252250\n", + "1 -0.963382 -1.516873 0.469565\n", + "4 0.306789 1.418025 -0.935815\n", + " b e d\n", + "3 3.252250 0.244196 0.171796\n", + "1 0.469565 -0.963382 -1.516873\n", + "2 -0.500263 0.702904 2.335214\n", + "4 -0.935815 0.306789 1.418025\n", + " b e d\n", + "2 -0.500263 0.702904 2.335214\n", + "4 -0.935815 0.306789 1.418025\n", + "3 3.252250 0.244196 0.171796\n", + "1 0.469565 -0.963382 -1.516873\n", + " b e d\n", + "2 2.0 4.0 4.0\n", + "3 4.0 2.0 2.0\n", + "1 3.0 1.0 1.0\n", + "4 1.0 3.0 3.0\n", + " b e d\n", + "2 1.0 2.0 3.0\n", + "3 3.0 2.0 1.0\n", + "1 3.0 2.0 1.0\n", + "4 1.0 2.0 3.0\n", + "2 -0.500263\n", + "3 0.171796\n", + "1 -1.516873\n", + "4 -0.935815\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块\n", + "frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bed'),index=['2', '3', '1', '4'])\n", + "print(frame)#打印下frame\n", + "print(frame.sort_index())#我们可以通过sort_index()来返回按照索引的排序\n", + "print(frame.sort_index(axis=1))#之前有说到过axis=0为索引为行，axis=1为列\n", + "print(frame.sort_index(axis=1,ascending=False))#当然我们也可以降序排序\n", + "print(frame.sort_values(by='b',ascending=False))#也可以按照值来排序，比如我们有时希望按照表中哪个字段排序\n", + "print(frame.sort_values(by=['d', 'b'],ascending=False))#也可以多个字段进行排序\n", + "print(frame.rank(axis=0))#按行排序\n", + "print(frame.rank(axis='columns'))#按列排序\n", + "print(frame.min(axis='columns'))#按列排序，求出最小值\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " b e d\n", + "2 -0.194546 2.271235 -1.216932\n", + "3 -1.058202 0.138722 -1.688732\n", + "1 -1.035652 -1.011013 1.142144\n", + "4 0.554035 -0.270207 -0.665943\n", + "b -1.734366\n", + "e 1.128737\n", + "d -2.429464\n", + "dtype: float64\n", + "b 4\n", + "e 4\n", + "d 4\n", + "dtype: int64\n", + "b 4\n", + "e 2\n", + "d 1\n", + "dtype: object\n", + " b e d\n", + "2 -0.194546 2.271235 -1.216932\n", + "3 -1.252749 2.409957 -2.905664\n", + "1 -2.288401 1.398944 -1.763520\n", + "4 -1.734366 1.128737 -2.429464\n", + "2 0.286585\n", + "3 -0.869404\n", + "1 -0.301507\n", + "4 -0.127372\n", + "dtype: float64\n", + " b e d\n", + "count 4.000000 4.000000 4.000000\n", + "mean -0.433591 0.282184 -0.607366\n", + "std 0.771398 1.408829 1.238970\n", + "min -1.058202 -1.011013 -1.688732\n", + "25% -1.041290 -0.455408 -1.334882\n", + "50% -0.615099 -0.065742 -0.941438\n", + "75% -0.007401 0.671850 -0.213922\n", + "max 0.554035 2.271235 1.142144\n" + ] + } + ], + "source": [ + "import pandas as pd #给pandas别名，更好的使用这个模块\n", + "frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bed'),index=['2', '3', '1', '4'])\n", + "print(frame)#打印数据\n", + "print(frame.sum())#打印汇总数据\n", + "print(frame.count())#打印行数\n", + "# #这样看行数都是4\n", + "# #我们试着排除NA值看看\n", + "# frame2 =frame[frame<1.2]\n", + "# print(frame2)\n", + "\n", + "# print(frame2.count(skipna=True))#打印行数\n", + "print(frame.idxmax())#求出每一列中最大的索引值\n", + "print(frame.cumsum())#求出每一列的累计值\n", + "print(frame.mean(axis='columns', skipna=False))#求平均数\n", + "print(frame.describe())#描述一整个模型，如行数是多少，平均值是多少，分位数，最小值，最大值，标准差是多少" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|方法|说明|\n", + "|--|--|\n", + "|count|非NA值的数量|\n", + "|describe|针对Series或各DataFrame列计算汇总统计|\n", + "|min、max|计算最小值和最大值|\n", + "|argmin、argmax|计算能够获取最小值和最大值的索引位置（整数）|\n", + "|idxmin、idxmax|计算能够获取到最小值和最大值的索引值|\n", + "|quantile|计算样本的分位数（0到1）|\n", + "|sum|值的总和|\n", + "|mean|值的平均数|\n", + "|median|值的算数中位数（50%分位数）|\n", + "|mad|根据平均值平均绝对离差|\n", + "|var|样本值的方差|\n", + "|std|样本值的标准差|\n", + "|skew|样本值的偏度（三阶矩）|\n", + "|kurt|样本值的偏度（四阶矩）|\n", + "|cumsum|样本值的累计和|\n", + "|cummin、cummax|样本值的累计最大值和累计最小值|\n", + "|cumprod|样本值的累计积|\n", + "|diff|计算一阶差分（对时间序列很有用）|\n", + "|pct_change|计算本分数变化|" ] } ], diff --git a/Part4.A.2data_summury_and_group.ipynb b/Part4.A.2data_summury_and_group.ipynb new file mode 100644 index 000000000..53abb4b25 --- /dev/null +++ b/Part4.A.2data_summury_and_group.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 数据聚合与分组运算\n", + "对数据集进行分组并对各组应用一个函数（无论是聚合还是转换），通常是数据分析工作中的重要环节。在将数据集加载、融合、准备好之后，通常就是计算分组统计或生成透视表。pandas提供了一个灵活高效的gruopby功能，它使你能以一种自然的方式对数据集进行切片、切块、摘要等操作。\n", + "\n", + "关系型数据库和SQL（Structured Query Language，结构化查询语言）能够如此流行的原因之一就是其能够方便地对数据进行连接、过滤、转换和聚合。但是，像SQL这样的查询语言所能执行的分组运算的种类很有限。在本章中你将会看到，由于Python和pandas强大的表达能力，我们可以执行复杂得多的分组运算（利用任何可以接受pandas对象或NumPy数组的函数）。在本章中，你将会学到：\n", + "\n", + "* 使用一个或多个键（形式可以是函数、数组或DataFrame列名）分割pandas对象。\n", + "* 计算分组的概述统计，比如数量、平均值或标准差，或是用户定义的函数。\n", + "* 应用组内转换或其他运算，如规格化、线性回归、排名或选取子集等。\n", + "* 计算透视表或交叉表。\n", + "* 执行分位数分析以及其它统计分组分析。\n", + "## Group by机制\n", + "![how-to-groupby](images/groupby.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " key1 key2 data1 data2\n", + "0 a one -0.001708 -0.578242\n", + "1 a two 0.797194 -0.040421\n", + "2 b one 1.269027 0.322051\n", + "3 b two -0.519620 -0.760153\n", + "4 a one -0.478164 -1.497868\n", + "0 -0.001708\n", + "1 0.797194\n", + "2 1.269027\n", + "3 -0.519620\n", + "4 -0.478164\n", + "Name: data1, dtype: float64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],'key2' : ['one', 'two', 'one', 'two', 'one'],'data1' : np.random.randn(5),'data2' : np.random.randn(5)})\n", + "print(df)\n", + "# print(df.index)\n", + "# summmury = df['data1'].groupby(df['key1']).sum()#group by的写法\n", + "# print(summmury)\n", + "# summmury2 = df['data1'].groupby([df['key1'],df['key2']]).sum()#group by可以多个字段，注意group by([])\n", + "# print(summmury2)\n", + "# print(summmury2.unstack())#层次化索引,看的更清晰一些\n", + "# states = np.array(['Ohio', 'Ohio', 'California', 'California', 'Ohio'])\n", + "# years = np.array([2005, 2006, 2005, 2006, 2005])\n", + "#打印数据\n", + "print(df['data1'])\n", + "print(df['data1'].groupby(df['key1']).sum())\n", + "# print(states)\n", + "# print(years)\n", + "# print(df['data1'].groupby([states, years]).sum())#其中states就是key1，years就是key2，只是其中的内容变成了states和years，类似sql中的groupby sum\n", + "# print(df.groupby(['key1', 'key2']).size())#类似sql中的groupby count\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 对分组进行迭代\n", + "GroupBy对象支持迭代，可以产生一组二元元组（由分组名和数据块组成）。看下面的例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " key1 key2 data1 data2\n", + "0 a one -0.595198 -0.581745\n", + "1 a two -1.375262 0.054339\n", + "2 b one 1.261285 0.603038\n", + "3 b two -0.711359 1.013885\n", + "4 a one 0.021047 1.038116\n", + "a\n", + " key1 key2 data1 data2\n", + "0 a one -0.595198 -0.581745\n", + "1 a two -1.375262 0.054339\n", + "4 a one 0.021047 1.038116\n", + "b\n", + " key1 key2 data1 data2\n", + "2 b one 1.261285 0.603038\n", + "3 b two -0.711359 1.013885\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],'key2' : ['one', 'two', 'one', 'two', 'one'],'data1' : np.random.randn(5),'data2' : np.random.randn(5)})\n", + "print(df)\n", + "for name, group in df.groupby('key1'):#迭代打印\n", + " print(name)#打印key1分组中分组名称\n", + " print(group)#打印分组信息" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "对于多重键的情况，元组的第一个元素将会是由键值组成的元组：" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('a', 'one')\n", + " key1 key2 data1 data2\n", + "0 a one -0.595198 -0.581745\n", + "4 a one 0.021047 1.038116\n", + "('a', 'two')\n", + " key1 key2 data1 data2\n", + "1 a two -1.375262 0.054339\n", + "('b', 'one')\n", + " key1 key2 data1 data2\n", + "2 b one 1.261285 0.603038\n", + "('b', 'two')\n", + " key1 key2 data1 data2\n", + "3 b two -0.711359 1.013885\n" + ] + } + ], + "source": [ + "for (k1, k2), group in df.groupby(['key1', 'key2']):\n", + " print((k1, k2))\n", + " print(group)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当然，你可以对这些数据片段做任何操作。有一个你可能会觉得有用的运算：将这些数据片段做成一个字典：" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': key1 key2 data1 data2\n", + " 0 a one -0.595198 -0.581745\n", + " 1 a two -1.375262 0.054339\n", + " 4 a one 0.021047 1.038116,\n", + " 'b': key1 key2 data1 data2\n", + " 2 b one 1.261285 0.603038\n", + " 3 b two -0.711359 1.013885}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pieces = dict(list(df.groupby('key1')))\n", + "pieces#还可以以key1为key" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part5.A.fund-choice.ipynb b/Part5.A.fund-choice.ipynb new file mode 100644 index 000000000..6be0726c4 --- /dev/null +++ b/Part5.A.fund-choice.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 基金排行筛选\n", + "之前通过excel来筛选基金，但是太多的人工操作，希望通过导出excel，然后通过pandas来达到基金筛选的效果\n", + "4433法则筛选股票型基金\n", + "每项取top1/x:\n", + "\t\t1.选择1年期间排名 前top1/4\n", + "\t\t2.选择2/3/5年 前top1/4\n", + "\t\t3.近6个月 前top1/3\n", + "\t\t4.近3个月 前top1/3\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re\n", + "import numpy as np\n", + "data = pd.read_csv(\"fundrank.csv\")\n", + "data\n", + "data2=data.drop([0])\n", + "# data2\n", + "# data2.loc[data2['近2年'] =='---', '近2年'] = '0%'#loc相当于sql 中的where和replace\n", + "def get_num(data2):\n", + " if data2=='---':\n", + " f=list('0')\n", + " else:\n", + " pat_K = r\"(.*)%\"\n", + " f=re.findall(pattern=pat_K, string=data2)\n", + " return f[0]\n", + "\n", + "data2['1y']= data2['近1年'].astype(str).apply(get_num).astype(float) # 先将数字转换成字符串用正则转换，转换完\n", + "data2['2y']= data2['近2年'].astype(str).apply(get_num).astype(float) # 先将数字转换成字符串用正则转换，转换完\n", + "data2['3y']= data2['近3年'].astype(str).apply(get_num).astype(float) # 先将数字转换成字符串用正则转换，转换完\n", + "data2['5y']= data2['近5年'].astype(str).apply(get_num).astype(float) # 先将数字转换成字符串用正则转换，转换完\n", + "data2['3m']= data2['近3月'].astype(str).apply(get_num).astype(float) # 先将数字转换成字符串用正则转换，转换完\n", + "data2['6m']= data2['近6月'].astype(str).apply(get_num).astype(float) # 先将数字转换成字符串用正则转换，转换完\n", + "data2['1y_rank']=data2['1y'].rank(method='dense',ascending=False)\n", + "data2['2y_rank']=data2['2y'].rank(method='dense',ascending=False)\n", + "data2['3y_rank']=data2['3y'].rank(method='dense',ascending=False)\n", + "data2['5y_rank']=data2['5y'].rank(method='dense',ascending=False)\n", + "data2['3m_rank']=data2['3m'].rank(method='dense',ascending=False)\n", + "data2['6m_rank']=data2['6m'].rank(method='dense',ascending=False)\n", + "data3=data2[['基金','基金简称','1y','2y','3y','5y','3m','6m','1y_rank','2y_rank','3y_rank','5y_rank','3m_rank','6m_rank']]\n", + "data4=data3[data3['1y_rank']<2248/4]\n", + "data4\n", + "data5=data4[data4['2y_rank']<2248/4]\n", + "data5\n", + "data6=data5[data5['3y_rank']<2248/4]\n", + "data6\n", + "data7=data6[data6['5y_rank']<2248/4]\n", + "data7\n", + "data8=data7[data7['3m_rank']<2248/3]\n", + "data8\n", + "data9=data8[data8['6m_rank']<2248/3]\n", + "data9\n", + "data9.to_csv('fundchoiceresult.csv')\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part5.B.downloadcsv.ipynb b/Part5.B.downloadcsv.ipynb new file mode 100644 index 000000000..a06fc59d1 --- /dev/null +++ b/Part5.B.downloadcsv.ipynb @@ -0,0 +1,1012 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 下载基金排行\n", + "需要下载http://fund.eastmoney.com/data/fundranking.html#tgp;c0;r;s1nzf;pn50;ddesc;qsd20210926;qed20220926;qdii;zq;gg;gzbd;gzfs;bbzt;sfbb\n", + "上的基金排行到fundrank.csv中" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如何将网站上的信息down下来存放在csv中，首先需要了解爬虫，爬虫是如何从网站上爬下来数据，并将数据存放在csv中的。\n", + "需要了解下爬虫的知识点\n", + "## 编写爬虫的流程\n", + "爬虫程序与其他程序不同，它的的思维逻辑一般都是相似的， 所以无需我们在逻辑方面花费大量的时间。下面对 Python 编写爬虫程序的流程做简单地说明：\n", + "先由 urllib 模块的 request 方法打开 URL 得到网页 HTML 对象。\n", + "使用浏览器打开网页源代码分析网页结构以及元素节点。\n", + "通过 Beautiful Soup 或则正则表达式提取数据。\n", + "存储数据到本地磁盘或数据库。\n", + "## 前端知识\n", + "爬虫程序之所以可以抓取数据，是因为爬虫能够对网页进行分析，并在网页中提取出想要的数据。在学习 Python 爬虫模块前，我们有必要先熟悉网页的基本结构，这是编写爬虫程序的必备知识。\n", + "如果您熟悉前端语言，那么您可以轻松地掌握本节知识。\n", + "网页一般由三部分组成，分别是 HTML（超文本标记语言）、CSS（层叠样式表）和 JavaScript（简称“JS”动态脚本语言），它们三者在网页中分别承担着不同的任务。\n", + "* HTML 负责定义网页的内容\n", + "* CSS 负责描述网页的布局\n", + "* JavaScript 负责网页的行为\n", + "由于前端内容太多，爬数据其实爬的是网页内容，所以学习下HTML先" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "html" + } + }, + "outputs": [], + "source": [ + " \n", + "\n", + "\n", + "\n", + "编程帮\n", + "\n", + "\n", + "点击访问\n", + "

编程帮www.biancheng.net

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Python爬虫

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认识网页结构

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基 金

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定投排行
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  • 近1周涨幅排名：不限前10名前20名前50名前100名
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\n", + " QDII基金分类筛选：\n", + "

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\n", + "郑重声明：天天基金网发布此信息目的在于传播更多信息，与本网站立场无关。天天基金网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资决策建议，据此操作，风险自担。数据来源：东方财富Choice数据。\n", + "
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将天天基金网设为上网首页吗？      将天天基金网添加到收藏夹吗？

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中国证监会上海监管局网址：www.csrc.gov.cn/pub/shanghai
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(.*?)

(.*?)

(.*?)

'\n", + " # '我不是药神'\n", + " re_bds = '
(.*?)

(.*?)

(.*?)

'\n", + " # 生成正则表达式对象\n", + " pattern = re.compile(re_bds,re.S)\n", + " # r_list: [('我不是药神','徐峥,周一围,王传君','2018-07-05'),...] 列表元组\n", + " r_list = pattern.findall(html)\n", + " self.save_html(r_list)\n", + " # 保存数据函数，使用python内置csv模块\n", + " def save_html(self,r_list):\n", + " #生成文件对象 \n", + " with open('maoyan.csv','a',newline='',encoding=\"utf-8\") as f:\n", + " #生成csv操作对象\n", + " writer = csv.writer(f)\n", + " #整理数据\n", + " for r in r_list:\n", + " name = r[0].strip()\n", + " star = r[1].strip()[3:]\n", + " # 上映时间：2018-07-05\n", + " # 切片截取时间\n", + " time = r[2].strip()[5:15]\n", + " L = [name,star,time]\n", + " # 写入csv文件\n", + " writer.writerow(L)\n", + " print(name,time,star)\n", + " # 主函数\n", + " def run(self):\n", + " #抓取第一页数据\n", + " for offset in range(0,11,10):\n", + " url = self.url.format(offset)\n", + " self.get_html(url)\n", + " #生成1-2之间的浮点数\n", + " time.sleep(random.uniform(1,2))\n", + "# 以脚本方式启动\n", + "if __name__ == '__main__':\n", + " #捕捉异常错误\n", + " try:\n", + " spider = MaoyanSpider()\n", + " spider.run()\n", + " except Exception as e:\n", + " print(\"错误:\",e)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://www.baidu.com/s?wd=%E5%A4%A9%E5%A4%A9\n" + ] + } + ], + "source": [ + "#导入parse模块\n", + "from urllib import parse\n", + "#构建查询字符串字典\n", + "query_string = {\n", + "'wd' : '天天'\n", + "}\n", + "#调用parse模块的urlencode()进行编码\n", + "result = parse.urlencode(query_string)\n", + "#使用format函数格式化字符串，拼接url地址\n", + "url = 'http://www.baidu.com/s?{}'.format(result)\n", + "print(url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from urllib import request,parse\n", + "# 1.拼url地址\n", + "url = 'http://www.baidu.com/s?wd={}'\n", + "word = input('请输入搜索内容:')\n", + "params = parse.quote(word)\n", + "full_url = url.format(params)\n", + "# 2.发请求保存到本地\n", + "headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:6.0) Gecko/20100101 Firefox/6.0'}\n", + "req = request.Request(url=full_url,headers=headers)\n", + "res = request.urlopen(req)\n", + "html = res.read().decode('utf-8')\n", + "# 3.保存文件至当前目录\n", + "filename = word + '.html'\n", + "with open(filename,'w',encoding='utf-8') as f:\n", + " f.write(html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "re模块常用方法\n", + "--------\n", + "\n", + "#### 1) re.compile()\n", + "\n", + "该方法用来生成正则表达式对象，其语法格式如下：\n", + "```python\n", + "regex=re.compile(pattern,flags=0)\n", + "```\n", + "参数说明：\n", + "\n", + "- pattern：正则表达式对象。\n", + "- flags：代表功能标志位，扩展正则表达式的匹配。\n", + "\n", + "#### 2) re.findall()\n", + "\n", + "根据正则表达式匹配目标字符串内容。\n", + "```python\n", + "re.findall(pattern,string,flags=0)\n", + "```\n", + "该函数的返回值是匹配到的内容列表，如果正则表达式有子组，则只能获取到子组对应的内容。参数说明如下：\n", + "\n", + "- pattern：正则表达式对象。\n", + "- string：目标字符串\n", + "- flags：代表功能标志位，扩展正则表达式的匹配。\n", + "\n", + "#### 3) regex.findall()\n", + "\n", + "该函数根据正则表达式对象匹配目标字符串内容。其语法格式如下：\n", + "```python\n", + "regex.findall(string,pos,endpos)\n", + "```\n", + "参数说明：\n", + "\n", + "- string 目标字符串。\n", + "- pos 截取目标字符串的开始匹配位置。\n", + "- endpos 截取目标字符串的结束匹配位置。\n", + "\n", + "#### 4) re.split()\n", + "\n", + "该函数使用正则表达式匹配内容，切割目标字符串。返回值是切割后的内容列表。参数说明：\n", + "```python\n", + "re.split(pattern,string,flags = 0)\n", + "```\n", + "参数说明：\n", + "\n", + "- pattern：正则表达式。\n", + "- string：目标字符串。\n", + "- flags：功能标志位,扩展正则表达式的匹配。\n", + "\n", + "5) re.sub\\\n", + "该函数使用一个字符串替换正则表达式匹配到的内容。返回值是替换后的字符串。其语法格式如下：\n", + "```python\n", + "re.sub(pattern,replace,string,max,flags = 0)\n", + "```\n", + "其参数说明：\n", + "\n", + "- pattern：正则表达式。\n", + "- replace：替换的字符串。\n", + "- string：目标字符串。\n", + "- max：最多替换几处，默认替换全部，\n", + "- flags：功能标志位,扩展正则表达式的匹配。\n", + "\n", + "#### 5) re.search()\n", + "\n", + "匹配目标字符串第一个符合的内容，返回值为匹配的对象。语法格式如下：\n", + "\n", + "```python\n", + "re.search(pattern,string,flags=0)\n", + "```\n", + "\n", + "参数说明：\n", + "\n", + "- pattern：正则表达式\n", + "- string：目标字符串\n", + "\n", + "flags功能标志位\n", + "----------\n", + "\n", + "功能标志位的作用是扩展正则表达的匹配功能。常用的 flag 如下所示：\n", + "\n", + "flag功能标志位\n", + "| 缩写元字符 | 说明 |\n", + "| A | 元字符只能匹配 ASCII码。 |\n", + "| I | 匹配忽略字母大小写。 |\n", + "| S | 使得`.`元字符可以匹配换行符。 |\n", + "| M | 使 ^ $ 可以匹配每一行的开头和结尾位置。 |\\\n", + "注意：可以同时使用福多个功能标志位，比如 flags=re.I|re.S。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "violation_ad_spend_amt_28d_total DECIMAL(18,2) COMMENT '违规广告近28日总消耗 - 大盘分子'\n", + "['28', '18', '2', '28']\n", + "['violation_ad_spend_amt_', 'd_total DECIMAL(', ',', \") COMMENT '违规广告近\", \"日总消耗 - 大盘分子'\"]\n", + "violation_ad_spend_amt_fuckd_total DECIMAL(fuck,2) COMMENT '违规广告近28日总消耗 - 大盘分子'\n", + "\n" + ] + } + ], + "source": [ + "import re\n", + "import regex\n", + "str ='violation_ad_spend_amt_28d_total DECIMAL(18,2) COMMENT \\'违规广告近28日总消耗 - 大盘分子\\''\n", + "pttn = re.compile(r'\\d+')\n", + "str2 = re.findall(pttn,str)#找到所有匹配元素\n", + "print(str)\n", + "print(str2)\n", + "str3= re.split(pttn,str,flags = 0)#按照正则进行分割\n", + "print(str3)\n", + "str4=re.sub(pttn,'fuck',str,2)\n", + "print(str4)\n", + "str5 = re.search(pttn,str,flags=0)#返回第一个匹配所在的位置\n", + "print(str5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## csv写入" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "with open('names.csv', 'w', newline='') as csvfile:\n", + " #构建字段名称，也就是key\n", + " fieldnames = ['first_name', 'last_name']\n", + " writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n", + " # 写入字段名，当做表头\n", + " writer.writeheader()\n", + " # 多行写入\n", + " writer.writerows([{'first_name': 'Baked', 'last_name': 'Beans'},{'first_name': 'Lovely', 'last_name': 'Spam'}])\n", + " # 单行写入\n", + " writer.writerow({'first_name': 'Wonderful', 'last_name': 'Spam'})" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/fundchoiceresult.csv b/fundchoiceresult.csv new file mode 100644 index 000000000..5b19fad85 --- /dev/null +++ b/fundchoiceresult.csv @@ -0,0 +1,205 @@ +,基金,基金简称,1y,2y,3y,5y,3m,6m,1y_rank,2y_rank,3y_rank,5y_rank,3m_rank,6m_rank +1,1678,英大国企改革,19.96,53.24,105.13,14.71,9.33,34.28,1.0,36.0,64.0,9.0,12.0,1.0 +2,756,建信潜力新蓝,14.93,89.91,181.03,0.0,4.5,19.21,2.0,11.0,13.0,80.0,50.0,10.0 +3,161032,富国中证煤炭,13.52,128.26,133.16,7.83,9.98,24.65,3.0,3.0,39.0,23.0,8.0,2.0 +6,168204,中融中证煤炭,12.33,128.48,128.55,0.0,3.58,22.01,6.0,2.0,41.0,80.0,61.0,6.0 +8,729,建信中小盘先,11.44,98.44,216.35,0.0,4.51,16.22,8.0,8.0,3.0,80.0,49.0,20.0 +10,3298,嘉实物流产业,9.68,33.69,85.55,7.95,1.68,4.61,10.0,89.0,99.0,21.0,106.0,107.0 +12,3299,嘉实物流产业,9.17,32.44,83.25,7.43,1.59,4.36,12.0,90.0,104.0,30.0,110.0,113.0 +13,6195,国金量化多因,8.12,29.76,48.81,8.77,3.23,9.76,13.0,96.0,271.0,18.0,67.0,50.0 +14,991,工银战略转型,6.66,40.88,174.01,6.08,1.73,3.51,14.0,66.0,16.0,33.0,105.0,134.0 +16,161724,招商中证煤炭,5.89,122.11,134.35,1.28,4.35,18.89,16.0,6.0,36.0,59.0,53.0,12.0 +19,7671,建信中证红利,4.97,0.05,33.28,0.0,0.79,5.42,19.0,414.0,414.0,80.0,125.0,95.0 +21,7672,建信中证红利,4.55,-0.77,31.63,0.0,0.68,5.2,21.0,438.0,436.0,80.0,129.0,101.0 +25,501059,西部利得国企,3.57,36.68,77.34,0.59,3.48,13.14,25.0,76.0,119.0,70.0,65.0,28.0 +27,979,景顺长城沪港,3.24,12.69,38.89,0.0,4.84,6.35,27.0,209.0,353.0,80.0,44.0,75.0 +30,5561,创金合信中证,2.93,34.75,65.49,0.0,3.13,8.5,29.0,83.0,163.0,80.0,70.0,56.0 +31,5562,创金合信中证,2.72,34.22,64.54,0.0,3.08,8.39,30.0,86.0,168.0,80.0,72.0,59.0 +32,320020,诺安策略精选,2.52,22.86,55.48,1.54,-0.63,4.34,31.0,125.0,229.0,53.0,165.0,114.0 +36,1208,诺安低碳经济,2.2,25.99,76.57,1.43,-0.84,3.44,35.0,110.0,120.0,56.0,171.0,138.0 +37,7466,华泰柏瑞中证,2.2,26.24,48.69,-0.89,3.15,8.19,35.0,107.0,272.0,92.0,68.0,60.0 +39,7467,华泰柏瑞中证,1.95,25.61,47.43,-1.14,3.09,8.06,37.0,111.0,284.0,98.0,71.0,61.0 +42,1476,中银智能制造,1.45,69.48,211.48,1.5,4.83,11.85,40.0,19.0,4.0,54.0,45.0,35.0 +44,1473,建信大安全战,0.94,17.1,86.4,0.0,-0.62,0.37,42.0,168.0,96.0,80.0,164.0,265.0 +45,501029,华宝标普中国,0.89,16.94,39.53,-0.96,0.29,2.69,43.0,170.0,348.0,95.0,135.0,166.0 +46,3634,嘉实农业产业,0.82,-2.15,54.87,2.93,-4.89,0.75,44.0,483.0,232.0,45.0,419.0,249.0 +47,2236,大成360互,0.56,9.31,53.0,0.82,1.58,-1.95,45.0,244.0,243.0,67.0,111.0,386.0 +48,5125,华宝标普中国,0.49,16.01,37.86,-1.35,0.18,2.48,46.0,175.0,368.0,101.0,138.0,176.0 +51,2229,华夏经济转型,0.35,5.14,73.9,0.22,7.1,11.8,49.0,309.0,130.0,76.0,24.0,36.0 +54,4698,博时军工主题,0.05,24.17,103.94,-1.51,2.91,6.04,51.0,118.0,66.0,104.0,76.0,83.0 +56,160643,鹏华空天军工,0.0,23.81,73.0,0.0,7.41,8.47,52.0,120.0,133.0,80.0,20.0,57.0 +59,3359,大成360互,-0.06,7.98,50.19,0.19,1.37,-2.26,55.0,263.0,266.0,77.0,117.0,404.0 +60,164402,前海开源中航,-0.09,27.08,60.41,0.0,9.1,10.69,56.0,106.0,193.0,80.0,14.0,44.0 +63,1404,招商移动互联,-0.16,25.51,84.45,-1.93,13.1,9.36,59.0,112.0,102.0,110.0,5.0,55.0 +64,5161,华商上游产业,-0.17,56.5,133.27,0.0,-1.27,5.69,60.0,33.0,38.0,80.0,190.0,92.0 +69,1583,安信新常态股,-0.67,6.77,42.04,0.0,-2.43,-2.52,64.0,280.0,322.0,80.0,237.0,424.0 +74,596,前海开源中证,-1.21,19.18,63.8,0.0,6.39,5.97,69.0,149.0,172.0,80.0,35.0,85.0 +75,1521,国寿安保成长,-1.29,18.06,75.62,0.0,5.7,9.52,70.0,159.0,123.0,80.0,39.0,53.0 +76,2199,前海开源中证,-1.46,18.66,62.8,0.0,6.4,5.88,71.0,154.0,176.0,80.0,34.0,88.0 +77,1579,国泰大农业股,-1.55,-4.79,50.97,-0.5,-5.29,-0.46,72.0,553.0,264.0,88.0,454.0,306.0 +80,6712,前海开源MS,-1.83,8.3,57.03,0.0,-5.36,6.22,75.0,257.0,220.0,80.0,460.0,78.0 +83,6713,前海开源MS,-2.03,7.86,56.08,0.0,-5.41,6.11,78.0,266.0,225.0,80.0,465.0,81.0 +85,1042,华夏领先股票,-2.09,5.56,37.65,-1.73,1.79,4.46,80.0,301.0,372.0,107.0,102.0,111.0 +90,5235,银华食品饮料,-2.38,12.42,80.33,-0.1,-6.08,2.82,85.0,214.0,110.0,81.0,523.0,161.0 +91,1048,富国新兴产业,-2.57,28.01,70.96,-2.16,14.76,19.56,86.0,103.0,142.0,115.0,3.0,9.0 +92,1645,国泰大健康股,-2.67,2.51,90.54,-0.46,-0.96,-1.81,87.0,353.0,87.0,85.0,176.0,375.0 +95,5236,银华食品饮料,-2.78,11.53,78.2,-0.49,-6.17,2.61,89.0,224.0,115.0,87.0,530.0,171.0 +102,893,工银创新动力,-3.21,15.43,51.09,-3.83,-4.23,-1.63,95.0,180.0,261.0,145.0,362.0,363.0 +104,6868,华夏科技成长,-3.35,3.26,76.52,-3.84,5.66,9.98,97.0,338.0,121.0,146.0,40.0,48.0 +108,160630,鹏华国防A,-3.85,15.18,64.82,0.0,7.87,7.69,101.0,184.0,165.0,80.0,17.0,65.0 +112,1097,华泰柏瑞积极,-4.01,28.85,65.98,-1.91,1.74,3.84,104.0,98.0,160.0,109.0,104.0,125.0 +113,2871,华夏智胜价值,-4.12,21.02,56.4,-4.81,0.01,6.16,105.0,137.0,222.0,173.0,143.0,80.0 +115,6138,国联安价值优,-4.18,-4.69,38.75,-4.46,-5.75,-0.54,107.0,548.0,356.0,162.0,494.0,312.0 +118,2872,华夏智胜价值,-4.36,20.43,89.25,-5.06,-0.06,6.02,109.0,142.0,92.0,181.0,146.0,84.0 +119,161907,万家中证红利,-4.44,22.13,37.11,-5.56,1.43,4.08,110.0,130.0,377.0,194.0,116.0,119.0 +131,985,嘉实逆向策略,-4.87,36.07,95.31,-5.26,2.34,10.1,119.0,80.0,81.0,188.0,93.0,47.0 +133,326,南方中小盘成,-4.92,12.9,72.93,-4.69,-6.75,3.38,121.0,205.0,135.0,170.0,583.0,140.0 +134,4640,华夏节能环保,-5.02,39.76,168.15,-4.48,1.64,2.43,122.0,69.0,19.0,163.0,107.0,179.0 +137,90010,大成中证红利,-5.27,17.42,29.39,-6.5,1.55,3.55,124.0,165.0,472.0,228.0,113.0,132.0 +138,160919,大成产业升级,-5.3,24.58,102.22,-4.87,5.36,5.28,125.0,116.0,71.0,175.0,41.0,100.0 +140,1917,招商量化精选,-5.3,30.33,102.22,-5.62,0.0,5.39,125.0,94.0,71.0,195.0,144.0,96.0 +141,1725,汇添富高端制,-5.31,15.42,77.99,-6.02,-1.49,5.67,126.0,181.0,117.0,210.0,201.0,93.0 +142,7801,大成中证红利,-5.34,17.19,28.87,-6.57,1.56,3.47,127.0,166.0,479.0,230.0,112.0,137.0 +145,161725,招商中证白酒,-5.47,20.59,78.13,-2.21,-6.94,3.59,130.0,140.0,116.0,116.0,602.0,131.0 +146,628,大成高新技术,-5.47,17.84,79.15,-6.01,-5.72,-2.36,130.0,161.0,112.0,209.0,493.0,409.0 +154,7950,招商量化精选,-5.87,28.75,99.23,-6.18,-0.15,5.07,138.0,99.0,75.0,215.0,151.0,102.0 +156,418,景顺长城成长,-5.96,18.2,99.3,0.0,-0.07,5.73,140.0,158.0,74.0,80.0,147.0,90.0 +158,502003,易方达中证军,-6.09,13.19,63.37,-7.62,3.14,2.25,142.0,202.0,175.0,264.0,69.0,186.0 +161,5313,万家中证10,-6.23,22.21,69.2,-6.36,-0.79,0.59,144.0,129.0,148.0,222.0,170.0,253.0 +163,160632,鹏华酒A,-6.25,17.56,85.91,0.0,-6.62,1.86,146.0,163.0,98.0,80.0,570.0,206.0 +166,100032,富国中证红利,-6.3,17.16,30.15,-7.39,0.29,2.99,148.0,167.0,463.0,256.0,135.0,154.0 +167,450009,国富中小盘股,-6.32,9.43,62.41,-5.93,-2.65,1.57,149.0,242.0,179.0,205.0,253.0,218.0 +169,163115,申万菱信中证,-6.32,10.26,47.98,-7.83,3.05,1.89,149.0,234.0,279.0,273.0,73.0,203.0 +174,6748,富国中证价值,-6.53,13.47,45.87,-7.54,-4.94,-3.88,153.0,200.0,290.0,261.0,424.0,525.0 +175,1631,天弘中证食品,-6.62,-4.15,52.35,-4.41,-7.0,1.05,154.0,533.0,249.0,159.0,608.0,233.0 +176,5314,万家中证10,-6.62,21.21,67.06,-6.74,-0.89,0.38,154.0,133.0,156.0,236.0,173.0,264.0 +183,161024,富国中证军工,-6.73,8.28,38.94,-8.29,2.86,1.95,159.0,258.0,352.0,286.0,78.0,200.0 +187,1632,天弘中证食品,-6.81,-4.53,51.44,-4.6,-7.05,0.95,162.0,544.0,258.0,168.0,613.0,238.0 +189,1692,南方国策动力,-6.88,21.74,93.33,-7.77,-1.11,3.49,164.0,131.0,85.0,270.0,184.0,135.0 +191,7191,富国中证价值,-6.9,12.54,44.11,-7.9,-5.03,-4.07,166.0,212.0,299.0,276.0,431.0,538.0 +195,6165,建信中证10,-7.1,12.75,64.39,0.0,-1.45,0.57,170.0,207.0,169.0,80.0,199.0,255.0 +196,6751,富国互联科技,-7.12,20.4,113.93,-6.58,4.12,10.73,171.0,143.0,52.0,231.0,57.0,43.0 +206,110022,易方达消费行,-7.33,-3.82,35.63,-4.53,-4.3,7.05,180.0,524.0,391.0,165.0,369.0,70.0 +207,3017,广发中证军工,-7.33,7.39,39.64,-8.86,2.45,1.64,180.0,273.0,346.0,307.0,89.0,214.0 +215,6166,建信中证10,-7.48,11.85,62.43,0.0,-1.56,0.36,186.0,222.0,178.0,80.0,205.0,266.0 +216,6692,金信消费升级,-7.49,38.83,103.5,-8.93,4.55,5.31,187.0,70.0,67.0,310.0,47.0,99.0 +217,5693,广发中证军工,-7.51,6.96,38.78,-9.04,2.41,1.53,188.0,278.0,355.0,314.0,91.0,221.0 +226,519677,银河定投宝腾,-7.79,18.85,70.54,-9.28,-4.6,-4.16,196.0,151.0,147.0,322.0,395.0,545.0 +227,519714,交银消费新驱,-7.81,12.36,82.66,-5.81,-7.48,2.89,197.0,215.0,106.0,203.0,652.0,159.0 +229,4194,招商中证10,-7.83,10.55,63.56,-8.12,-3.95,-1.09,198.0,232.0,174.0,281.0,338.0,334.0 +230,501019,国泰国证航天,-7.83,12.34,52.8,-9.71,3.55,1.78,198.0,216.0,245.0,335.0,63.0,209.0 +231,3190,创金合信消费,-7.87,3.17,51.4,0.0,-6.6,-1.81,199.0,342.0,259.0,80.0,568.0,375.0 +236,7548,易方达ESG,-8.04,2.22,53.56,-6.02,-5.37,2.3,204.0,361.0,241.0,210.0,461.0,184.0 +239,577,安信价值精选,-8.09,3.19,53.64,0.0,-7.42,-5.04,206.0,341.0,239.0,80.0,646.0,622.0 +242,4195,招商中证10,-8.2,9.67,61.6,-8.48,-4.05,-1.29,209.0,241.0,186.0,294.0,347.0,342.0 +243,6693,金信消费升级,-8.23,36.64,98.73,-9.65,4.34,4.88,210.0,77.0,76.0,332.0,54.0,104.0 +245,835,华润元大富时,-8.25,-0.09,31.12,-6.95,-5.93,-1.77,212.0,421.0,446.0,244.0,512.0,373.0 +252,4997,广发高端制造,-8.44,30.15,182.38,-8.71,0.02,-3.43,218.0,95.0,11.0,302.0,142.0,488.0 +256,3191,创金合信消费,-8.53,1.73,48.07,0.0,-6.77,-2.16,221.0,378.0,277.0,80.0,585.0,397.0 +258,1577,嘉实低价策略,-8.56,2.62,77.82,-7.84,-2.53,-0.28,223.0,350.0,118.0,274.0,244.0,297.0 +262,916,前海开源股息,-8.66,19.5,53.6,0.0,-1.39,-0.73,227.0,148.0,240.0,80.0,195.0,322.0 +263,50024,博时上证自然,-8.67,69.26,79.03,-10.16,0.77,6.99,228.0,20.0,113.0,355.0,127.0,71.0 +264,6560,华夏四川国改,-8.67,34.65,57.68,-9.15,-3.51,1.85,228.0,85.0,212.0,318.0,307.0,207.0 +268,925,汇添富外延增,-8.79,1.81,59.08,-8.74,0.18,2.91,231.0,375.0,200.0,303.0,138.0,158.0 +274,2670,万家沪深30,-8.91,-0.33,38.82,-8.62,-3.24,0.78,235.0,428.0,354.0,300.0,286.0,246.0 +275,6561,华夏四川国改,-8.95,33.83,56.25,-9.42,-3.59,1.69,236.0,88.0,223.0,326.0,312.0,212.0 +277,1719,工银国家战略,-9.01,38.79,88.47,-8.44,0.78,-1.23,237.0,71.0,94.0,292.0,126.0,341.0 +279,1043,工银美丽城镇,-9.03,21.08,89.27,-9.43,-5.51,-3.64,238.0,136.0,91.0,327.0,475.0,505.0 +284,7431,浙商之江凤凰,-9.17,16.29,56.56,0.0,-3.95,-4.86,242.0,173.0,221.0,80.0,338.0,608.0 +285,3646,创金合信中证,-9.18,11.75,53.69,0.0,-3.51,0.63,243.0,223.0,238.0,80.0,307.0,251.0 +290,2671,万家沪深30,-9.27,-1.12,37.17,-8.98,-3.34,0.57,247.0,450.0,376.0,312.0,293.0,255.0 +292,3647,创金合信中证,-9.37,11.3,52.72,0.0,-3.56,0.52,249.0,226.0,246.0,80.0,310.0,257.0 +294,1542,国泰互联网+,-9.41,6.08,66.17,-6.91,-2.23,4.58,251.0,290.0,159.0,242.0,228.0,109.0 +296,161217,国投瑞银中证,-9.43,73.44,106.65,-10.97,-0.61,6.18,252.0,16.0,62.0,374.0,163.0,79.0 +300,1040,新华策略精选,-9.47,-4.54,73.47,0.0,5.0,16.82,256.0,545.0,132.0,80.0,43.0,19.0 +304,1672,国寿安保智慧,-9.56,12.02,79.68,0.0,-2.83,1.26,259.0,220.0,111.0,80.0,266.0,228.0 +308,6081,海富通电子传,-9.67,40.67,118.61,-9.85,9.46,6.39,263.0,67.0,47.0,344.0,11.0,74.0 +312,1036,嘉实企业变革,-9.75,3.82,56.08,-10.06,-2.24,1.35,266.0,329.0,225.0,352.0,229.0,226.0 +321,471,富国城镇发展,-9.92,5.85,64.07,-9.71,-4.85,-3.81,274.0,296.0,170.0,335.0,416.0,519.0 +325,971,诺安新经济股,-10.05,7.9,69.05,-9.07,-2.34,5.39,277.0,265.0,150.0,316.0,234.0,96.0 +336,6080,海富通电子传,-10.38,38.44,113.94,-10.56,9.24,5.97,286.0,72.0,51.0,364.0,13.0,85.0 +345,1726,汇添富新兴消,-10.61,0.31,47.15,-9.77,-7.77,-1.22,293.0,409.0,286.0,339.0,676.0,340.0 +347,4788,富荣沪深30,-10.72,6.25,82.77,-9.84,-8.02,-5.56,294.0,287.0,105.0,343.0,697.0,667.0 +348,5669,前海开源公用,-10.73,146.46,167.06,0.0,-8.51,-3.51,295.0,1.0,21.0,80.0,739.0,495.0 +349,368,汇添富沪深3,-10.73,3.84,43.2,-11.85,-6.07,-1.87,295.0,328.0,307.0,410.0,522.0,378.0 +350,1541,汇添富民营新,-10.74,-3.53,60.55,-11.61,-3.04,-1.1,296.0,517.0,192.0,399.0,275.0,335.0 +351,7130,中庚小盘价值,-10.76,50.34,112.09,-12.19,-2.66,0.41,297.0,40.0,55.0,423.0,254.0,262.0 +352,4789,富荣沪深30,-10.81,6.04,82.2,-9.92,-8.05,-5.61,298.0,291.0,107.0,346.0,699.0,672.0 +353,1637,嘉实量化精选,-10.82,14.75,61.32,-10.62,-5.36,-0.65,299.0,186.0,189.0,366.0,460.0,317.0 +360,6502,财通集成电路,-10.96,0.74,39.69,-12.29,7.89,1.6,305.0,399.0,344.0,426.0,16.0,217.0 +373,866,华宝制造股票,-11.45,21.2,102.59,-11.85,0.17,3.44,318.0,134.0,70.0,410.0,139.0,138.0 +375,1490,汇添富国企创,-11.52,12.11,93.84,-11.09,-2.41,-0.8,319.0,218.0,84.0,379.0,236.0,324.0 +378,5267,嘉实价值精选,-11.55,10.7,74.25,-11.27,-2.32,0.49,322.0,231.0,125.0,387.0,232.0,259.0 +382,162107,金鹰先进制造,-11.63,3.47,22.87,0.0,2.77,0.3,326.0,335.0,556.0,80.0,81.0,269.0 +384,6503,财通集成电路,-11.66,-0.86,36.37,-12.98,7.67,1.2,328.0,442.0,385.0,457.0,19.0,231.0 +400,6729,万家中证50,-12.21,10.79,57.95,-12.36,2.35,2.44,344.0,230.0,208.0,428.0,92.0,178.0 +402,1736,圆信永丰优加,-12.27,18.29,94.8,-13.03,-2.54,-2.22,346.0,157.0,83.0,459.0,245.0,401.0 +403,5870,鹏华沪深30,-12.27,1.45,28.72,0.0,-6.22,-1.62,346.0,387.0,482.0,80.0,535.0,362.0 +408,163114,申万菱信中证,-12.39,54.27,117.57,-13.11,-8.38,-4.45,350.0,35.0,49.0,462.0,726.0,571.0 +410,671030,西部利得事件,-12.41,34.68,72.76,-9.99,-3.88,2.88,352.0,84.0,137.0,348.0,332.0,160.0 +411,2210,创金合信量化,-12.42,11.43,58.17,0.0,-3.7,-0.92,353.0,225.0,206.0,80.0,318.0,326.0 +412,2289,华商改革创新,-12.46,19.5,68.55,0.0,-5.95,6.72,354.0,148.0,154.0,80.0,513.0,72.0 +415,501060,中金优选30,-12.55,5.57,35.34,0.0,-3.39,-2.76,356.0,300.0,394.0,80.0,298.0,438.0 +416,6730,万家中证50,-12.55,9.92,56.16,-12.7,2.24,2.24,356.0,238.0,224.0,446.0,94.0,187.0 +419,1397,建信精工制造,-12.57,13.31,61.67,0.0,-3.95,-2.5,358.0,201.0,185.0,80.0,338.0,422.0 +420,1396,建信互联网+,-12.6,13.49,63.9,0.0,-2.17,2.27,359.0,199.0,171.0,80.0,223.0,185.0 +430,867,华宝品质生活,-12.71,9.98,63.62,-11.8,-4.32,2.14,365.0,236.0,173.0,408.0,371.0,190.0 +431,501061,中金优选30,-12.77,5.05,34.33,0.0,-3.45,-2.89,366.0,310.0,400.0,80.0,303.0,449.0 +439,202025,南方上证38,-12.92,6.04,34.3,-13.37,-3.43,-1.91,372.0,291.0,401.0,475.0,301.0,382.0 +441,5829,建信MSCI,-12.95,0.8,26.95,0.0,-6.03,-4.11,373.0,398.0,509.0,80.0,519.0,541.0 +445,690008,民生中证内地,-13.05,60.43,65.77,-14.33,-2.61,4.18,375.0,29.0,161.0,522.0,250.0,116.0 +447,540007,汇丰晋信中小,-13.06,-3.18,48.23,0.0,-3.56,4.79,376.0,506.0,276.0,80.0,310.0,105.0 +449,3865,创金合信量化,-13.08,9.77,54.69,0.0,-3.88,-1.29,377.0,239.0,233.0,80.0,332.0,342.0 +452,502006,易方达中证国,-13.11,-1.88,33.6,-13.21,-4.8,-2.77,379.0,471.0,409.0,468.0,411.0,439.0 +459,4716,信诚量化阿尔,-13.23,-2.03,36.99,-12.75,-6.31,-5.09,385.0,480.0,380.0,448.0,544.0,627.0 +462,7571,南方上证38,-13.27,5.2,32.71,-13.71,-3.53,-2.11,387.0,306.0,421.0,494.0,309.0,393.0 +466,5830,建信MSCI,-13.31,-0.02,25.4,0.0,-6.13,-4.31,389.0,418.0,522.0,80.0,526.0,558.0 +469,6193,鑫元核心资产,-13.39,-0.36,34.74,0.0,-3.41,3.05,392.0,429.0,398.0,80.0,300.0,152.0 +472,161039,富国中证10,-13.42,10.19,62.36,-12.96,-5.11,-2.67,394.0,235.0,180.0,455.0,439.0,434.0 +485,164304,新华中证环保,-13.61,49.78,97.61,0.0,-8.53,-5.91,403.0,42.0,77.0,80.0,741.0,698.0 +488,6704,易方达MSC,-13.61,-0.49,33.81,-13.37,-6.15,-3.79,403.0,434.0,407.0,475.0,528.0,517.0 +490,5960,博时量化价值,-13.63,3.21,30.08,-14.67,-3.09,-2.91,404.0,340.0,464.0,542.0,279.0,451.0 +494,6705,易方达MSC,-13.7,-0.69,33.38,-13.45,-6.17,-3.85,406.0,436.0,412.0,481.0,530.0,522.0 +497,1409,工银互联网加,-13.77,2.41,54.15,-13.39,-3.41,0.85,409.0,355.0,236.0,477.0,300.0,242.0 +500,5009,申万菱信行业,-13.79,41.09,126.13,-14.76,-4.38,6.29,411.0,65.0,43.0,548.0,377.0,77.0 +502,6286,华泰MSCI,-13.81,-2.01,31.8,-13.19,-5.86,-3.6,413.0,479.0,434.0,467.0,505.0,501.0 +505,1714,工银文体产业,-13.83,0.51,70.75,-13.14,-5.34,-2.39,415.0,404.0,144.0,465.0,458.0,412.0 +508,7275,银河沪深30,-13.84,-0.97,32.18,-13.43,-6.62,-5.26,416.0,445.0,428.0,480.0,570.0,641.0 +512,6251,银华兴盛股票,-13.87,33.96,108.51,-14.24,-5.47,4.96,418.0,87.0,59.0,517.0,471.0,103.0 +526,5607,华宝中证50,-13.99,-4.86,25.76,-14.48,-1.78,-1.08,428.0,556.0,519.0,530.0,215.0,333.0 +528,160807,长盛沪深30,-14.0,-3.07,32.53,-13.47,-6.48,-4.05,429.0,505.0,423.0,482.0,559.0,536.0 +529,5802,汇添富智能制,-14.0,8.61,62.0,-13.8,-0.07,3.05,429.0,253.0,184.0,500.0,147.0,152.0 +532,656,前海开源沪深,-14.02,-1.86,33.67,0.0,-6.51,-4.54,431.0,469.0,408.0,80.0,562.0,579.0 +534,6293,华泰MSCI,-14.03,-2.49,30.66,-13.41,-5.92,-3.73,432.0,493.0,453.0,478.0,511.0,513.0 +537,6194,鑫元核心资产,-14.09,-1.94,31.51,0.0,-3.61,2.63,435.0,474.0,440.0,80.0,314.0,170.0 +538,5113,平安沪深30,-14.12,-2.41,30.3,-13.27,-5.5,-4.6,436.0,489.0,459.0,471.0,474.0,585.0 +554,7276,银河沪深30,-14.28,-1.96,30.17,-13.87,-6.74,-5.5,446.0,476.0,462.0,504.0,582.0,662.0 +557,160620,鹏华资源A,-14.3,52.02,73.97,0.0,-4.33,1.55,448.0,37.0,128.0,80.0,372.0,219.0 +559,5457,景顺长城量化,-14.3,12.19,57.06,0.0,-6.31,-5.4,448.0,217.0,219.0,80.0,544.0,654.0 +571,3318,景顺中证50,-14.42,3.87,29.71,0.0,-4.66,-4.44,455.0,326.0,468.0,80.0,400.0,570.0 +576,580008,东吴新产业精,-14.48,6.74,51.4,0.0,-2.46,-1.53,459.0,282.0,259.0,80.0,239.0,358.0 +579,1651,工银新蓝筹股,-14.51,1.79,59.03,-14.82,-4.81,-5.81,462.0,376.0,201.0,550.0,412.0,689.0 +583,460220,华泰柏瑞上证,-14.55,2.1,27.74,-14.95,-4.9,-3.2,465.0,367.0,495.0,558.0,420.0,472.0 +586,5114,平安沪深30,-14.55,-3.39,28.33,-13.7,-5.62,-4.84,465.0,511.0,489.0,493.0,486.0,606.0 +588,778,鹏华先进制造,-14.58,-3.82,57.51,0.0,-6.41,-2.76,466.0,524.0,214.0,80.0,553.0,438.0 +591,320014,诺安沪深30,-14.61,-2.68,26.1,-14.26,-6.79,-4.76,468.0,499.0,516.0,518.0,587.0,599.0 +593,2595,博时工业4.,-14.62,0.34,51.89,-14.84,-6.18,-3.76,469.0,408.0,253.0,551.0,531.0,514.0 +604,6034,富国MSCI,-14.79,8.88,51.85,-14.48,-6.24,-2.35,478.0,250.0,254.0,530.0,537.0,408.0 +610,206012,鹏华价值精选,-14.86,22.62,125.97,0.0,-1.32,-2.51,481.0,128.0,44.0,80.0,191.0,423.0 +617,3016,中金中证50,-14.91,10.82,57.17,0.0,-4.78,-4.22,485.0,229.0,217.0,80.0,410.0,550.0 +621,975,MSCI中国,-14.96,-3.29,23.56,-14.71,-6.55,-4.2,489.0,509.0,543.0,545.0,565.0,548.0 +628,7143,国投瑞银沪深,-15.03,-0.75,32.33,-14.69,-7.6,-5.08,494.0,437.0,427.0,544.0,660.0,626.0 +634,5660,嘉实资源精选,-15.1,47.14,126.17,-14.86,3.84,9.91,499.0,47.0,42.0,552.0,59.0,49.0 +635,163111,申万菱信中小,-15.15,-4.11,35.67,-14.66,-8.44,-5.37,500.0,532.0,390.0,541.0,732.0,651.0 +636,160638,鹏华中证一带,-15.16,25.4,51.94,0.0,-3.26,-0.06,501.0,113.0,252.0,80.0,288.0,286.0 +642,5735,MSCI中国,-15.23,-3.87,22.48,-14.97,-6.62,-4.35,504.0,527.0,560.0,559.0,570.0,562.0 +644,3578,中金中证50,-15.25,9.94,55.04,0.0,-4.88,-4.41,506.0,237.0,231.0,80.0,418.0,567.0 +646,5788,南方MSCI,-15.26,-4.09,29.22,-14.91,-6.78,-4.44,507.0,531.0,474.0,554.0,586.0,570.0 +647,1166,建信环保产业,-15.28,43.01,112.74,0.0,-6.86,-1.69,508.0,60.0,54.0,80.0,594.0,368.0 +654,160806,长盛同庆中证,-15.33,-1.86,30.31,-14.78,-6.78,-5.2,513.0,469.0,458.0,549.0,586.0,636.0 +668,7799,申万菱信中小,-15.41,-4.69,34.46,-14.92,-8.51,-5.51,521.0,548.0,399.0,555.0,739.0,663.0 +698,7593,鹏扬中证50,-15.69,10.79,61.55,0.0,-7.66,-4.44,537.0,230.0,187.0,80.0,665.0,570.0 +714,167503,安信中证一带,-15.82,28.42,43.17,0.0,-3.47,-0.53,548.0,101.0,308.0,80.0,304.0,311.0 +722,3145,中融竞争优势,-15.89,21.17,118.48,0.0,-0.58,0.68,552.0,135.0,48.0,80.0,162.0,250.0 +724,3624,创金合信资源,-15.91,61.16,159.21,0.0,-4.93,0.87,553.0,27.0,23.0,80.0,423.0,241.0 +734,7594,鹏扬中证50,-16.02,9.92,59.4,0.0,-7.76,-4.63,560.0,238.0,198.0,80.0,675.0,588.0 diff --git a/fundrank.csv b/fundrank.csv new file mode 100644 index 000000000..f03626668 --- /dev/null +++ b/fundrank.csv @@ -0,0 +1,2250 @@ +比较,序号,基金,基金简称,日期,单位净值,累计净值,日增长率,近1周,近1月,近3月,近6月,近1年,近2年,近3年,今年来,成立来,近5年,手续费,可购 +,,代码,,,,,,,,,,,,,,,,,全部 +,1,1678,英大国企改革,9月19日,1.5935,2.2435,-0.34%,-3.52%,4.11%,9.33%,34.28%,19.96%,53.24%,105.13%,28.26%,143.15%,14.71%,0.15%, +,2,756,建信潜力新蓝,9月19日,3.971,3.971,0.03%,-7.11%,-6.21%,4.50%,19.21%,14.93%,89.91%,181.03%,5.98%,297.10%,---,0.15%, +,3,161032,富国中证煤炭,9月20日,2.149,1.497,1.08%,-3.63%,3.02%,9.98%,24.65%,13.52%,128.26%,133.16%,38.56%,49.73%,7.83%,0.00%, +,4,13275,富国中证煤炭,9月20日,2.145,2.145,1.08%,-3.64%,3.03%,9.94%,24.49%,13.31%,---,---,38.30%,43.10%,7.63%,0.00%, +,5,8279,国泰中证煤炭,9月19日,2.1915,2.4915,0.80%,-5.82%,1.89%,3.56%,22.96%,12.34%,128.17%,---,37.31%,159.66%,7.82%,0.10%, +,6,168204,中融中证煤炭,9月19日,1.968,1.968,0.82%,-5.93%,1.71%,3.58%,22.01%,12.33%,128.48%,128.55%,35.63%,48.91%,---,0.00%, +,7,8280,国泰中证煤炭,9月19日,2.1722,2.4722,0.80%,-5.83%,1.86%,3.48%,22.77%,12.00%,126.82%,---,37.02%,157.64%,7.51%,0.00%, +,8,729,建信中小盘先,9月19日,4.334,4.334,0.00%,-7.27%,-6.47%,4.51%,16.22%,11.44%,98.44%,216.35%,2.58%,333.40%,---,0.15%, +,9,12724,国泰中证畜牧,9月19日,1.0164,1.0164,0.48%,-1.99%,-2.92%,-0.17%,1.18%,9.86%,---,---,-5.42%,1.64%,11.15%,0.10%, +,10,3298,嘉实物流产业,9月20日,2.607,2.607,-0.46%,-2.69%,0.58%,1.68%,4.61%,9.68%,33.69%,85.55%,1.56%,160.70%,7.95%,0.15%, +,11,12725,国泰中证畜牧,9月19日,1.0127,1.0127,0.48%,-2.00%,-2.96%,-0.26%,1.02%,9.52%,---,---,-5.62%,1.27%,10.83%,0.00%, +,12,3299,嘉实物流产业,9月20日,2.56,2.56,-0.43%,-2.66%,0.55%,1.59%,4.36%,9.17%,32.44%,83.25%,1.23%,156.00%,7.43%,0.00%, +,13,6195,国金量化多因,9月19日,1.7504,1.7504,-1.56%,-5.93%,-8.05%,3.23%,9.76%,8.12%,29.76%,48.81%,6.58%,75.04%,8.77%,0.15%, +,14,991,工银战略转型,9月20日,3.891,3.891,-1.49%,-3.06%,1.75%,1.73%,3.51%,6.66%,40.88%,174.01%,-0.69%,289.10%,6.08%,0.15%, +,15,11473,工银战略转型,9月20日,3.861,3.861,-1.48%,-3.06%,1.71%,1.61%,3.24%,6.13%,---,---,-1.03%,20.09%,5.55%,0.00%, +,16,161724,招商中证煤炭,9月20日,1.9754,1.3965,1.94%,-3.18%,0.38%,4.35%,18.89%,5.89%,122.11%,134.35%,33.01%,49.40%,1.28%,0.10%, +,17,13596,招商中证煤炭,9月20日,1.9734,1.9734,1.94%,-3.18%,0.37%,4.32%,18.83%,5.78%,---,---,32.92%,-0.19%,1.18%,0.00%, +,18,11229,创金合信数字,9月19日,1.5281,1.5281,-0.86%,-3.45%,-5.96%,0.69%,3.98%,5.28%,---,---,-9.10%,52.81%,---,0.15%, +,19,7671,建信中证红利,9月19日,1.3329,1.3329,0.41%,-1.63%,2.89%,0.79%,5.42%,4.97%,0.05%,33.28%,-2.96%,33.29%,---,0.12%, +,20,11230,创金合信数字,9月19日,1.5155,1.5155,-0.87%,-3.46%,-6.00%,0.57%,3.72%,4.76%,---,---,-9.42%,51.55%,---,0.00%, +,21,7672,建信中证红利,9月19日,1.3164,1.3164,0.40%,-1.64%,2.86%,0.68%,5.20%,4.55%,-0.77%,31.63%,-3.24%,31.64%,---,0.00%, +,22,8445,融通产业趋势,9月20日,1.4742,1.4742,2.32%,-5.63%,-8.71%,10.20%,15.88%,4.37%,28.61%,---,-4.07%,47.42%,4.04%,0.15%, +,23,8928,泰达消费红利,9月20日,1.7271,1.7271,1.07%,-3.27%,-2.65%,-4.70%,2.66%,4.22%,19.18%,---,-9.38%,72.71%,5.66%,0.12%, +,24,8929,泰达消费红利,9月20日,1.7165,1.7165,1.07%,-3.27%,-2.67%,-4.76%,2.53%,3.97%,18.58%,---,-9.54%,71.65%,5.40%,0.00%, +,25,501059,西部利得国企,9月19日,2.1002,2.1002,0.53%,-3.75%,2.92%,3.48%,13.14%,3.57%,36.68%,77.34%,13.38%,110.02%,0.59%,0.12%, +,26,9439,西部利得国企,9月19日,2.0533,2.0533,0.52%,-3.76%,2.89%,3.40%,12.97%,3.25%,35.84%,---,13.14%,62.48%,0.29%,0.00%, +,27,979,景顺长城沪港,9月19日,1.625,1.625,-0.49%,-2.34%,3.04%,4.84%,6.35%,3.24%,12.69%,38.89%,1.06%,62.50%,---,0.15%, +,28,160628,鹏华中证80,9月19日,1.019,1.75,0.49%,-2.95%,4.73%,6.70%,0.89%,3.24%,-17.76%,-0.85%,-2.11%,64.05%,---,0.12%, +,29,9891,融通产业趋势,9月20日,1.1154,1.1154,2.32%,-5.72%,-9.02%,9.59%,15.23%,3.04%,12.73%,---,-4.76%,11.54%,2.60%,0.15%, +,30,5561,创金合信中证,9月19日,1.6147,1.6147,0.00%,-3.62%,3.48%,3.13%,8.50%,2.93%,34.75%,65.49%,8.27%,61.47%,---,0.15%, +,31,5562,创金合信中证,9月19日,1.6011,1.6011,0.00%,-3.62%,3.47%,3.08%,8.39%,2.72%,34.22%,64.54%,8.12%,60.11%,---,0.00%, +,32,320020,诺安策略精选,9月20日,1.9676,2.868,-0.40%,-3.32%,-2.25%,-0.63%,4.34%,2.52%,22.86%,55.48%,-6.46%,186.80%,1.54%,0.15%, +,33,8114,天弘中证红利,9月19日,1.2735,1.2735,-0.33%,-3.64%,-0.94%,-1.58%,0.30%,2.51%,20.44%,---,-0.48%,27.35%,0.86%,0.10%, +,34,5505,前海开源中药,9月19日,1.9205,1.9205,-0.75%,-4.55%,-5.96%,-10.37%,-7.15%,2.37%,6.52%,29.61%,-16.55%,92.05%,---,0.15%, +,35,8115,天弘中证红利,9月19日,1.2662,1.2662,-0.34%,-3.65%,-0.96%,-1.63%,0.19%,2.30%,19.96%,---,-0.62%,26.62%,0.65%,0.00%, +,36,1208,诺安低碳经济,9月20日,2.133,2.133,-0.47%,-3.53%,-2.29%,-0.84%,3.44%,2.20%,25.99%,76.57%,-7.06%,113.30%,1.43%,0.15%, +,37,7466,华泰柏瑞中证,9月19日,1.5004,1.5004,0.06%,-3.37%,3.77%,3.15%,8.19%,2.20%,26.24%,48.69%,8.14%,50.04%,-0.89%,0.12%, +,38,5506,前海开源中药,9月19日,1.9044,1.9044,-0.76%,-4.56%,-5.98%,-10.43%,-7.26%,2.11%,5.99%,28.66%,-16.70%,90.44%,---,0.00%, +,39,7467,华泰柏瑞中证,9月19日,1.4864,1.4864,0.07%,-3.38%,3.76%,3.09%,8.06%,1.95%,25.61%,47.43%,7.95%,48.64%,-1.14%,0.00%, +,40,9476,建信食品饮料,9月19日,1.1328,1.1328,0.90%,1.02%,-0.74%,-1.32%,5.70%,1.78%,15.52%,---,-8.60%,13.28%,---,0.15%, +,41,10349,诺安低碳经济,9月20日,2.125,2.125,-0.42%,-3.54%,-2.34%,-0.93%,3.21%,1.72%,---,---,-7.41%,26.19%,0.90%,0.00%, +,42,1476,中银智能制造,9月20日,2.171,2.171,2.31%,-4.28%,-8.05%,4.83%,11.85%,1.45%,69.48%,211.48%,-5.16%,117.10%,1.50%,0.15%, +,43,12181,中银智能制造,9月20日,2.161,2.161,2.27%,-4.30%,-8.12%,4.70%,11.56%,0.98%,---,---,-5.47%,35.83%,1.08%,0.00%, +,44,1473,建信大安全战,9月19日,2.9934,2.9934,0.70%,-1.57%,-1.73%,-0.62%,0.37%,0.94%,17.10%,86.40%,-7.59%,199.34%,---,0.15%, +,45,501029,华宝标普中国,9月20日,1.3304,1.3604,0.19%,-3.45%,-0.53%,0.29%,2.69%,0.89%,16.94%,39.53%,-1.99%,36.75%,-0.96%,0.10%, +,46,3634,嘉实农业产业,9月20日,2.0337,2.0337,-0.16%,-4.12%,-6.29%,-4.89%,0.75%,0.82%,-2.15%,54.87%,-9.53%,103.37%,2.93%,0.15%, +,47,2236,大成360互,9月20日,1.608,1.608,0.94%,-5.24%,-7.64%,1.58%,-1.95%,0.56%,9.31%,53.00%,-8.22%,60.80%,0.82%,0.12%, +,48,5125,华宝标普中国,9月20日,1.3035,1.3335,0.18%,-3.45%,-0.56%,0.18%,2.48%,0.49%,16.01%,37.86%,-2.26%,23.56%,-1.35%,0.00%, +,49,160218,国泰国证房地,9月20日,0.8965,1.7522,-2.02%,-3.61%,1.09%,1.06%,-1.46%,0.46%,-22.47%,-10.90%,-5.16%,43.12%,-2.29%,0.10%, +,50,9179,嘉实中证主要,9月20日,1.2836,1.2836,0.45%,-1.19%,-1.62%,-4.09%,2.73%,0.37%,-0.48%,---,-12.15%,28.36%,2.81%,0.12%, +,51,2229,华夏经济转型,9月20日,2.292,2.456,0.53%,-3.13%,-3.94%,7.10%,11.80%,0.35%,5.14%,73.90%,-10.19%,162.69%,0.22%,0.15%, +,52,6809,泰康香港银行,9月19日,0.8597,0.8597,0.23%,-0.68%,-1.64%,-5.28%,-4.92%,0.35%,14.67%,-8.83%,-0.02%,-14.03%,---,0.10%, +,53,9180,嘉实中证主要,9月20日,1.2774,1.2774,0.45%,-1.20%,-1.64%,-4.14%,2.63%,0.16%,-0.88%,---,-12.28%,27.74%,2.60%,0.00%, +,54,4698,博时军工主题,9月20日,2.019,2.019,-0.30%,-1.37%,-2.09%,2.91%,6.04%,0.05%,24.17%,103.94%,-15.31%,101.90%,-1.51%,0.15%, +,55,501089,方正富邦消费,9月20日,1.3763,1.3763,1.08%,-3.40%,-2.81%,-5.96%,0.34%,0.05%,2.38%,---,-12.10%,37.63%,1.45%,0.12%, +,56,160643,鹏华空天军工,9月19日,1.5513,1.5513,-1.74%,-0.53%,-1.08%,7.41%,8.47%,0.00%,23.81%,73.00%,-17.34%,55.13%,---,0.12%, +,57,501025,鹏华香港银行,9月19日,0.9437,0.9937,0.23%,-0.63%,-1.63%,-5.27%,-4.96%,-0.02%,14.47%,-9.60%,0.17%,-1.83%,---,0.12%, +,58,6810,泰康香港银行,9月19日,0.8481,0.8481,0.22%,-0.69%,-1.67%,-5.38%,-5.11%,-0.05%,13.75%,-9.91%,-0.32%,-15.19%,---,0.00%, +,59,3359,大成360互,9月20日,1.556,1.556,0.91%,-5.24%,-7.71%,1.37%,-2.26%,-0.06%,7.98%,50.19%,-8.63%,38.07%,0.19%,0.00%, +,60,164402,前海开源中航,9月19日,1.145,1.145,-1.40%,-0.57%,-0.16%,9.10%,10.69%,-0.09%,27.08%,60.41%,-15.87%,18.20%,---,0.12%, +,61,10364,鹏华空天军工,9月19日,1.243,1.243,-1.75%,-0.53%,-1.09%,7.38%,8.42%,-0.10%,---,---,-17.40%,24.30%,---,0.00%, +,62,10365,鹏华香港银行,9月19日,1.0991,1.0991,0.24%,-0.63%,-1.63%,-5.29%,-5.01%,-0.12%,---,---,0.10%,9.91%,---,0.00%, +,63,1404,招商移动互联,9月20日,1.5475,1.5475,-0.36%,-3.20%,-4.49%,13.10%,9.36%,-0.16%,25.51%,84.45%,-7.94%,54.75%,-1.93%,0.15%, +,64,5161,华商上游产业,9月19日,2.231,2.231,0.20%,-5.45%,-3.00%,-1.27%,5.69%,-0.17%,56.50%,133.27%,-4.28%,123.10%,---,0.15%, +,65,10769,天弘中证农业,9月20日,0.9035,0.9035,-0.28%,-4.71%,-4.70%,-5.52%,1.45%,-0.29%,---,---,-7.55%,-9.65%,1.18%,0.10%, +,66,10770,天弘中证农业,9月20日,0.9004,0.9004,-0.29%,-4.72%,-4.73%,-5.58%,1.33%,-0.50%,---,---,-7.69%,-9.96%,0.96%,0.00%, +,67,11592,博时军工主题,9月20日,2.002,2.002,-0.30%,-1.33%,-2.10%,2.72%,5.76%,-0.50%,---,---,-15.63%,9.16%,-2.05%,0.00%, +,68,8088,华夏中证全指,9月20日,0.934,0.934,-1.35%,-3.13%,0.90%,0.00%,-2.10%,-0.59%,-12.39%,---,-4.77%,-6.60%,-3.01%,0.12%, +,69,1583,安信新常态股,9月19日,1.4724,1.804,-0.53%,-3.31%,1.40%,-2.43%,-2.52%,-0.67%,6.77%,42.04%,-3.70%,97.47%,---,0.15%, +,70,248,汇添富中证主,9月20日,2.7774,2.7774,0.45%,-1.19%,-1.73%,-4.68%,1.95%,-0.78%,-5.01%,38.14%,-13.03%,177.74%,1.64%,0.10%, +,71,8089,华夏中证全指,9月20日,0.9263,0.9263,-1.35%,-3.14%,0.87%,-0.08%,-2.25%,-0.89%,-12.91%,---,-4.98%,-7.37%,-3.29%,0.00%, +,72,12857,汇添富中证主,9月20日,2.7694,2.7694,0.45%,-1.20%,-1.75%,-4.74%,1.82%,-1.02%,---,---,-13.18%,-11.06%,1.40%,0.00%, +,73,11726,安信新常态股,9月19日,1.4638,1.4638,-0.53%,-3.32%,1.36%,-2.53%,-2.71%,-1.07%,---,---,-3.98%,-0.60%,---,0.00%, +,74,596,前海开源中证,9月19日,2.131,2.131,-1.71%,-1.02%,-1.21%,6.39%,5.97%,-1.21%,19.18%,63.80%,-16.30%,113.10%,---,0.12%, +,75,1521,国寿安保成长,9月19日,1.909,1.979,-0.99%,-5.26%,-10.59%,5.70%,9.52%,-1.29%,18.06%,75.62%,-9.57%,103.10%,---,0.15%, +,76,2199,前海开源中证,9月19日,1.081,2.041,-1.73%,-1.01%,-1.19%,6.40%,5.88%,-1.46%,18.66%,62.80%,-16.40%,6.86%,---,0.12%, +,77,1579,国泰大农业股,9月20日,2.3174,2.3174,0.08%,-4.10%,-5.69%,-5.29%,-0.46%,-1.55%,-4.79%,50.97%,-7.04%,131.74%,-0.50%,0.15%, +,78,9875,天弘甄选食品,9月20日,1.0806,1.0806,0.71%,-0.71%,-2.19%,-5.89%,3.48%,-1.68%,8.59%,---,-12.74%,8.06%,0.26%,0.12%, +,79,8234,光大消费主题,9月20日,1.4174,1.4174,1.32%,-0.88%,-1.56%,-3.03%,7.81%,-1.69%,5.91%,---,-12.57%,41.74%,-0.31%,0.15%, +,80,6712,前海开源MS,9月19日,2.1187,2.1187,0.78%,-1.15%,-5.31%,-5.36%,6.22%,-1.83%,8.30%,57.03%,-10.44%,111.87%,---,0.12%, +,81,4642,南方房地产E,9月20日,0.786,0.786,-1.37%,-3.24%,0.82%,-0.62%,-3.03%,-1.86%,-21.81%,-11.47%,-5.54%,-21.40%,-4.04%,0.12%, +,82,9876,天弘甄选食品,9月20日,1.076,1.076,0.71%,-0.71%,-2.20%,-5.93%,3.36%,-1.89%,8.14%,---,-12.87%,7.60%,0.06%,0.00%, +,83,6713,前海开源MS,9月19日,2.1029,2.1029,0.78%,-1.16%,-5.32%,-5.41%,6.11%,-2.03%,7.86%,56.08%,-10.58%,110.29%,---,0.00%, +,84,166402,浦银安盛沪港,9月19日,0.943,0.943,0.12%,-1.68%,1.27%,-1.51%,-0.65%,-2.06%,-1.87%,-12.69%,-0.56%,-5.70%,---,0.12%, +,85,1042,华夏领先股票,9月20日,0.797,0.797,1.01%,-6.01%,-10.65%,1.79%,4.46%,-2.09%,5.56%,37.65%,-10.95%,-20.30%,-1.73%,0.15%, +,86,7751,景顺中证红利,9月19日,1.0353,1.1136,-0.14%,-3.73%,-1.84%,-2.25%,0.20%,-2.11%,8.88%,11.29%,-1.15%,11.30%,---,0.12%, +,87,4643,南方房地产E,9月20日,0.7703,0.7703,-1.37%,-3.24%,0.79%,-0.72%,-3.23%,-2.25%,-22.43%,-12.53%,-5.81%,-22.97%,-4.42%,0.00%, +,88,10989,南方房地产E,9月20日,0.7703,0.7703,-1.37%,-3.24%,0.79%,-0.72%,-3.24%,-2.26%,---,---,-5.81%,-14.02%,-4.42%,0.00%, +,89,7760,景顺中证红利,9月19日,1.0316,1.1055,-0.15%,-3.73%,-1.86%,-2.31%,0.07%,-2.36%,8.34%,10.47%,-1.33%,10.48%,---,0.00%, +,90,5235,银华食品饮料,9月20日,2.2559,2.2559,1.04%,0.15%,-1.32%,-6.08%,2.82%,-2.38%,12.42%,80.33%,-15.38%,125.59%,-0.10%,0.15%, +,91,1048,富国新兴产业,9月20日,2.084,2.084,1.17%,-3.56%,-6.38%,14.76%,19.56%,-2.57%,28.01%,70.96%,0.29%,108.40%,-2.16%,0.15%, +,92,1645,国泰大健康股,9月19日,2.985,3.577,-0.90%,-6.13%,-6.81%,-0.96%,-1.81%,-2.67%,2.51%,90.54%,-9.13%,267.55%,-0.46%,0.15%, +,93,161721,招商沪深30,9月20日,0.7031,1.3732,-2.46%,-2.81%,1.12%,-1.04%,-5.64%,-2.70%,-27.87%,-12.85%,-8.09%,13.85%,-6.89%,0.10%, +,94,5106,银华农业产业,9月20日,2.0368,2.0368,-0.49%,-5.47%,-5.75%,-3.80%,3.11%,-2.78%,-8.30%,51.59%,-10.04%,103.68%,-0.86%,0.15%, +,95,5236,银华食品饮料,9月20日,2.2254,2.2254,1.04%,0.14%,-1.35%,-6.17%,2.61%,-2.78%,11.53%,78.20%,-15.62%,122.54%,-0.49%,0.00%, +,96,13273,招商沪深30,9月20日,0.7025,0.7025,-2.46%,-2.81%,1.11%,-1.07%,-5.68%,-2.78%,---,---,-8.16%,-3.78%,-6.97%,0.00%, +,97,12712,建信沪深30,9月19日,0.9568,0.9568,0.05%,-2.59%,1.30%,-1.25%,1.91%,-2.82%,---,---,-0.97%,-4.32%,---,0.15%, +,98,11966,招商中证光伏,9月20日,1.0053,1.0053,2.87%,-7.01%,-14.22%,-2.24%,3.42%,-2.94%,---,---,-7.76%,0.53%,-4.14%,0.12%, +,99,11321,国泰大健康股,9月19日,2.966,2.966,-0.90%,-6.14%,-6.82%,-1.03%,-2.02%,-3.04%,---,---,-9.38%,3.31%,-0.82%,0.00%, +,100,12713,建信沪深30,9月19日,0.9539,0.9539,0.05%,-2.59%,1.27%,-1.32%,1.76%,-3.11%,---,---,-1.18%,-4.61%,---,0.00%, +,101,501011,汇添富中证中,9月20日,1.0373,1.0373,1.06%,-4.62%,-4.74%,-12.07%,-12.91%,-3.17%,3.37%,30.53%,-21.32%,3.73%,-3.21%,0.10%, +,102,893,工银创新动力,9月20日,0.905,0.905,0.00%,-4.13%,-3.62%,-4.23%,-1.63%,-3.21%,15.43%,51.09%,-6.41%,-9.50%,-3.83%,0.15%, +,103,11967,招商中证光伏,9月20日,1.0001,1.0001,2.86%,-7.02%,-14.24%,-2.33%,3.21%,-3.33%,---,---,-8.03%,0.01%,-4.52%,0.00%, +,104,6868,华夏科技成长,9月20日,1.8178,1.8178,0.60%,-3.43%,-5.07%,5.66%,9.98%,-3.35%,3.26%,76.52%,-12.98%,81.78%,-3.84%,0.15%, +,105,160716,嘉实基本面5,9月20日,1.5916,1.5916,-1.06%,-1.65%,2.23%,-1.69%,0.29%,-3.41%,-1.67%,0.63%,-3.40%,60.86%,-4.83%,0.12%, +,106,501012,汇添富中证中,9月20日,1.019,1.019,1.05%,-4.62%,-4.78%,-12.16%,-13.09%,-3.57%,2.55%,28.99%,-21.56%,1.90%,-3.60%,0.00%, +,107,160725,嘉实基本面5,9月20日,1.1052,1.1052,-1.06%,-1.66%,2.20%,-1.79%,0.08%,-3.80%,-2.45%,-0.59%,-3.68%,9.83%,-5.21%,0.00%, +,108,160630,鹏华国防A,9月19日,1.275,1.32,-1.77%,-0.86%,-1.32%,7.87%,7.69%,-3.85%,15.18%,64.82%,-18.69%,20.24%,---,0.12%, +,109,7287,合煦智远消费,9月19日,1.0856,1.0856,-0.05%,-2.58%,-5.29%,-1.24%,2.33%,-3.98%,-15.88%,7.73%,-13.55%,8.56%,-1.34%,0.15%, +,110,12041,鹏华国防C,9月19日,1.157,1.157,-1.87%,-0.94%,-1.36%,7.83%,7.53%,-3.98%,---,---,-18.81%,15.70%,---,0.00%, +,111,12126,泰达宏利新能,9月20日,1.4288,1.4288,3.31%,-5.40%,-12.55%,-4.01%,12.11%,-4.00%,---,---,0.65%,42.88%,-5.18%,0.15%, +,112,1097,华泰柏瑞积极,9月20日,1.076,1.076,2.09%,-6.56%,-12.82%,1.74%,3.84%,-4.01%,28.85%,65.98%,-9.84%,5.40%,-1.91%,0.15%, +,113,2871,华夏智胜价值,9月20日,1.414,1.414,1.07%,-5.06%,-6.06%,0.01%,6.16%,-4.12%,21.02%,56.40%,-4.33%,41.40%,-4.81%,0.15%, +,114,7605,嘉实沪深30,9月20日,1.0772,1.0772,-0.57%,-3.04%,-0.30%,-2.80%,-1.42%,-4.13%,2.27%,---,-4.82%,7.72%,-5.64%,0.12%, +,115,6138,国联安价值优,9月20日,1.7688,1.7688,-0.22%,-3.69%,-3.93%,-5.75%,-0.54%,-4.18%,-4.69%,38.75%,-8.49%,77.27%,-4.46%,0.15%, +,116,9069,大成睿鑫股票,9月20日,1.0448,1.0448,0.10%,-3.30%,-4.81%,-4.00%,1.46%,-4.18%,3.45%,---,-16.01%,4.48%,-4.26%,0.15%, +,117,12127,泰达宏利新能,9月20日,1.4232,1.4232,3.31%,-5.40%,-12.57%,-4.08%,11.93%,-4.28%,---,---,0.43%,42.32%,-5.46%,0.00%, +,118,2872,华夏智胜价值,9月20日,1.4044,1.4044,1.07%,-5.07%,-6.08%,-0.06%,6.02%,-4.36%,20.43%,89.25%,-4.50%,40.44%,-5.06%,0.00%, +,119,161907,万家中证红利,9月19日,2.2877,2.2877,0.09%,-4.33%,-0.35%,1.43%,4.08%,-4.44%,22.13%,37.11%,0.22%,128.77%,-5.56%,0.15%, +,120,7288,合煦智远消费,9月19日,1.0673,1.0673,-0.05%,-2.60%,-5.32%,-1.34%,2.09%,-4.44%,-16.68%,6.06%,-13.84%,6.73%,-1.81%,0.00%, +,121,12548,华宝中证细分,9月20日,0.8239,0.8239,1.01%,-0.48%,-2.16%,-6.50%,1.99%,-4.44%,---,---,-15.21%,-17.61%,-2.06%,0.10%, +,122,9240,泰康蓝筹优势,9月19日,0.996,0.996,-0.49%,-3.74%,3.90%,-1.63%,3.07%,-4.46%,-0.52%,---,-10.69%,-0.40%,---,0.15%, +,123,7606,嘉实沪深30,9月20日,1.0652,1.0652,-0.57%,-3.05%,-0.34%,-2.89%,-1.62%,-4.51%,1.46%,---,-5.09%,6.52%,-6.02%,0.00%, +,124,8163,南方大盘红利,9月20日,1.1532,1.2032,-0.32%,-3.13%,-0.13%,-2.25%,-0.52%,-4.53%,11.88%,---,-5.59%,20.40%,-7.19%,0.12%, +,125,9070,大成睿鑫股票,9月20日,1.036,1.036,0.09%,-3.30%,-4.84%,-4.09%,1.25%,-4.57%,2.63%,---,-16.26%,3.60%,-4.64%,0.00%, +,126,1576,国泰智能装备,9月20日,2.667,2.667,2.14%,-7.33%,-14.46%,-11.95%,-7.14%,-4.61%,26.22%,133.54%,-21.14%,166.70%,-4.13%,0.15%, +,127,1047,光大国企改革,9月20日,1.746,1.746,-0.06%,-2.13%,-0.96%,6.46%,11.42%,-4.64%,-11.33%,36.51%,-10.46%,74.60%,-3.75%,0.15%, +,128,12549,华宝中证细分,9月20日,0.8219,0.8219,1.00%,-0.50%,-2.19%,-6.55%,1.88%,-4.64%,---,---,-15.34%,-17.81%,-2.26%,0.00%, +,129,9051,易方达中证红,9月20日,1.1767,1.2237,0.09%,-3.70%,-0.39%,1.82%,3.83%,-4.74%,21.00%,---,0.47%,22.33%,-6.02%,0.06%, +,130,9052,易方达中证红,9月20日,1.174,1.221,0.09%,-3.71%,-0.40%,1.79%,3.77%,-4.84%,20.75%,---,0.39%,22.05%,-6.11%,0.00%, +,131,985,嘉实逆向策略,9月20日,2.539,2.539,1.36%,-5.40%,-12.15%,2.34%,10.10%,-4.87%,36.07%,95.31%,-12.87%,153.90%,-5.26%,0.15%, +,132,8164,南方大盘红利,9月20日,1.1407,1.1907,-0.32%,-3.14%,-0.16%,-2.34%,-0.72%,-4.91%,11.01%,---,-5.87%,19.13%,-7.56%,0.00%, +,133,326,南方中小盘成,9月20日,1.369,2.053,0.53%,-3.90%,-6.52%,-6.75%,3.38%,-4.92%,12.90%,72.93%,-9.17%,109.78%,-4.69%,0.15%, +,134,4640,华夏节能环保,9月20日,2.2482,2.2482,3.51%,-7.13%,-14.55%,1.64%,2.43%,-5.02%,39.76%,168.15%,-15.95%,124.82%,-4.48%,0.15%, +,135,11322,国泰智能装备,9月20日,2.648,2.648,2.12%,-7.35%,-14.50%,-12.06%,-7.35%,-5.02%,---,---,-21.38%,-1.49%,-4.54%,0.00%, +,136,12885,华夏中证光伏,9月20日,0.9103,0.9103,2.86%,-7.06%,-14.37%,-2.56%,2.92%,-5.17%,---,---,-8.46%,-8.97%,-6.36%,0.12%, +,137,90010,大成中证红利,9月20日,2.157,2.313,0.09%,-3.71%,-0.51%,1.55%,3.55%,-5.27%,17.42%,29.39%,-0.05%,136.34%,-6.50%,0.12%, +,138,160919,大成产业升级,9月20日,2.732,4.718,0.48%,-3.80%,-4.94%,5.36%,5.28%,-5.30%,24.58%,102.22%,-14.30%,157.95%,-4.87%,0.15%, +,139,11102,天弘中证光伏,9月20日,1.2612,1.2612,2.86%,-7.05%,-14.41%,-2.81%,2.50%,-5.30%,---,---,-8.77%,26.12%,-6.49%,0.10%, +,140,1917,招商量化精选,9月20日,2.0218,2.0918,1.23%,-5.30%,-7.33%,0.00%,5.39%,-5.30%,30.33%,102.22%,0.39%,115.09%,-5.62%,0.15%, +,141,1725,汇添富高端制,9月20日,2.515,2.515,0.32%,-2.71%,-3.79%,-1.49%,5.67%,-5.31%,15.42%,77.99%,-14.57%,151.50%,-6.02%,0.15%, +,142,7801,大成中证红利,9月20日,2.147,2.147,0.09%,-3.72%,-0.51%,1.56%,3.47%,-5.34%,17.19%,28.87%,-0.14%,32.37%,-6.57%,0.00%, +,143,11319,国泰上证综合,9月19日,0.9767,0.9767,-0.54%,-4.25%,-3.68%,-2.07%,0.40%,-5.35%,---,---,-8.57%,-2.33%,-5.39%,0.10%, +,144,12886,华夏中证光伏,9月20日,0.9074,0.9074,2.87%,-7.07%,-14.39%,-2.62%,2.76%,-5.44%,---,---,-8.64%,-9.26%,-6.63%,0.00%, +,145,161725,招商中证白酒,9月20日,1.122,2.8381,1.44%,-0.11%,-1.35%,-6.94%,3.59%,-5.47%,20.59%,78.13%,-14.24%,353.80%,-2.21%,0.10%, +,146,628,大成高新技术,9月20日,3.316,3.316,-0.12%,-3.86%,-5.20%,-5.72%,-2.36%,-5.47%,17.84%,79.15%,-18.24%,231.60%,-6.01%,0.15%, +,147,11103,天弘中证光伏,9月20日,1.2571,1.2571,2.86%,-7.05%,-14.42%,-2.86%,2.40%,-5.49%,---,---,-8.90%,25.71%,-6.67%,0.00%, +,148,8303,宝盈龙头优选,9月20日,0.9845,0.9845,0.60%,-2.19%,0.42%,-4.56%,3.60%,-5.51%,-15.15%,---,-10.69%,-1.55%,-3.40%,0.15%, +,149,9379,中银成长优选,9月20日,1.0578,1.0578,2.57%,-5.60%,-12.06%,-0.77%,4.16%,-5.55%,---,---,-12.22%,5.78%,-6.26%,0.15%, +,150,12414,招商中证白酒,9月20日,1.1206,1.2056,1.44%,-0.11%,-1.36%,-6.97%,3.54%,-5.56%,---,---,-14.30%,-17.45%,-2.30%,0.00%, +,151,11320,国泰上证综合,9月19日,0.9719,0.9719,-0.55%,-4.26%,-3.71%,-2.14%,0.25%,-5.64%,---,---,-8.77%,-2.81%,-5.67%,0.00%, +,152,11506,建信高端装备,9月19日,1.1369,1.1369,-0.92%,-6.59%,-13.83%,-4.35%,1.87%,-5.71%,---,---,-15.93%,13.69%,---,0.15%, +,153,11066,大成高新技术,9月20日,3.294,3.294,-0.09%,-3.85%,-5.24%,-5.78%,-2.54%,-5.83%,---,---,-18.47%,6.57%,-6.37%,0.00%, +,154,7950,招商量化精选,9月20日,1.9903,1.9903,1.23%,-5.30%,-7.38%,-0.15%,5.07%,-5.87%,28.75%,99.23%,-0.05%,101.41%,-6.18%,0.00%, +,155,6675,宝盈品牌消费,9月20日,1.2327,1.2327,0.77%,-1.91%,0.90%,-3.14%,5.39%,-5.89%,-15.86%,15.91%,-8.09%,23.27%,-4.03%,0.15%, +,156,418,景顺长城成长,9月19日,4.247,4.247,-0.91%,-2.17%,-3.15%,-0.07%,5.73%,-5.96%,18.20%,99.30%,-9.96%,324.70%,---,0.15%, +,157,12763,华泰紫金中证,9月19日,0.9164,0.9164,0.88%,-0.30%,-3.20%,-6.81%,0.84%,-6.01%,---,---,-16.77%,-8.36%,---,0.06%, +,158,502003,易方达中证军,9月20日,1.3713,0.8815,0.07%,-3.71%,-3.88%,3.14%,2.25%,-6.09%,13.19%,63.37%,-18.88%,-9.30%,-7.62%,0.10%, +,159,11507,建信高端装备,9月19日,1.1306,1.1306,-0.93%,-6.60%,-13.86%,-4.45%,1.67%,-6.10%,---,---,-16.17%,13.06%,---,0.00%, +,160,12764,华泰紫金中证,9月19日,0.9156,0.9156,0.88%,-0.30%,-3.21%,-6.84%,0.79%,-6.10%,---,---,-16.82%,-8.44%,---,0.00%, +,161,5313,万家中证10,9月19日,1.2343,1.8702,-0.92%,-6.66%,-8.85%,-0.79%,0.59%,-6.23%,22.21%,69.20%,-8.20%,112.77%,-6.36%,0.15%, +,162,12364,广发中证光伏,9月20日,1.0979,1.0979,2.89%,-6.96%,-14.43%,-2.90%,1.98%,-6.24%,---,---,-9.39%,9.79%,-7.42%,0.12%, +,163,160632,鹏华酒A,9月19日,0.494,2.298,1.02%,-0.20%,-2.95%,-6.62%,1.86%,-6.25%,17.56%,85.91%,-16.13%,257.60%,---,0.12%, +,164,8304,宝盈龙头优选,9月20日,0.9651,0.9651,0.60%,-2.20%,0.36%,-4.74%,3.18%,-6.26%,-16.50%,---,-11.19%,-3.49%,-4.15%,0.00%, +,165,8975,富国中证消费,9月20日,1.3497,1.3497,0.15%,-1.52%,-0.13%,-4.25%,2.51%,-6.30%,-9.17%,---,-14.76%,34.97%,-3.72%,0.12%, +,166,100032,富国中证红利,9月20日,1.047,3.053,0.10%,-3.59%,-0.66%,0.29%,2.99%,-6.30%,17.16%,30.15%,-0.03%,296.74%,-7.39%,0.15%, +,167,450009,国富中小盘股,9月20日,2.459,3.473,0.00%,-3.11%,-1.17%,-2.65%,1.57%,-6.32%,9.43%,62.41%,-11.55%,344.65%,-5.93%,0.15%, +,168,12842,易方达中证军,9月20日,1.3675,1.3675,0.07%,-3.71%,-3.89%,3.08%,2.13%,-6.32%,---,---,-19.02%,0.98%,-7.84%,0.00%, +,169,163115,申万菱信中证,9月20日,1.1436,1.9623,0.07%,-3.69%,-3.92%,3.05%,1.89%,-6.32%,10.26%,47.98%,-19.03%,62.65%,-7.83%,0.10%, +,170,12365,广发中证光伏,9月20日,1.0953,1.0953,2.89%,-6.96%,-14.45%,-2.95%,1.88%,-6.42%,---,---,-9.52%,9.53%,-7.60%,0.00%, +,171,8871,大成睿裕六月,9月20日,1.2149,1.2149,-0.10%,-3.63%,-4.60%,-4.93%,2.46%,-6.49%,7.74%,---,-14.87%,21.49%,-6.80%,0.15%, +,172,8298,华夏中证银行,9月19日,1.0807,1.0807,0.35%,-0.53%,1.48%,-4.02%,-4.24%,-6.52%,4.76%,---,-5.13%,8.07%,-5.65%,0.12%, +,173,8682,富国中证红利,9月20日,1.038,1.298,0.10%,-3.62%,-0.67%,0.29%,2.81%,-6.52%,16.50%,---,-0.22%,29.88%,-7.68%,0.00%, +,174,6748,富国中证价值,9月20日,1.7307,1.7307,0.13%,-4.52%,-3.91%,-4.94%,-3.88%,-6.53%,13.47%,45.87%,-8.17%,73.07%,-7.54%,0.12%, +,175,1631,天弘中证食品,9月20日,2.7741,2.8522,0.98%,-0.72%,-2.19%,-7.00%,1.05%,-6.62%,-4.15%,52.35%,-16.48%,191.97%,-4.41%,0.10%, +,176,5314,万家中证10,9月19日,1.2296,1.8393,-0.92%,-6.67%,-8.88%,-0.89%,0.38%,-6.62%,21.21%,67.06%,-8.46%,107.66%,-6.74%,0.00%, +,177,9116,东兴中证消费,9月20日,1.2785,1.2785,0.10%,-1.65%,-0.23%,-4.54%,2.04%,-6.62%,-4.16%,---,-15.10%,27.85%,-4.72%,0.06%, +,178,8934,大成科技消费,9月20日,0.9323,0.9323,1.05%,-2.38%,-5.59%,2.43%,12.24%,-6.62%,-7.76%,---,-6.23%,-6.64%,-6.37%,0.15%, +,179,6676,宝盈品牌消费,9月20日,1.2005,1.2005,0.76%,-1.93%,0.84%,-3.33%,4.96%,-6.65%,-17.21%,13.15%,-8.62%,20.05%,-4.79%,0.00%, +,180,4098,前海开源港股,9月19日,0.9005,0.9005,-0.74%,-4.19%,-2.37%,-0.14%,-0.44%,-6.66%,-0.21%,-3.63%,-4.08%,-9.95%,---,0.15%, +,181,8976,富国中证消费,9月20日,1.3362,1.3362,0.14%,-1.53%,-0.16%,-4.35%,2.30%,-6.68%,-9.90%,---,-15.01%,33.62%,-4.11%,0.00%, +,182,9117,东兴中证消费,9月20日,1.2754,1.2754,0.11%,-1.65%,-0.24%,-4.56%,2.00%,-6.71%,-4.35%,---,-15.16%,27.54%,-4.81%,0.00%, +,183,161024,富国中证军工,9月20日,1.151,1.774,0.09%,-3.76%,-4.00%,2.86%,1.95%,-6.73%,8.28%,38.94%,-19.28%,74.78%,-8.29%,0.12%, +,184,11179,浙商智选食品,9月19日,0.8715,0.8715,-0.01%,-2.00%,-4.49%,-3.48%,6.32%,-6.74%,---,---,-12.03%,-12.85%,---,0.15%, +,185,8299,华夏中证银行,9月19日,1.0718,1.0718,0.35%,-0.54%,1.46%,-4.10%,-4.38%,-6.80%,4.13%,---,-5.33%,7.18%,-5.93%,0.00%, +,186,160631,鹏华银行A,9月19日,0.917,1.044,0.44%,-0.54%,1.55%,-3.88%,-4.28%,-6.81%,-1.01%,1.03%,-5.27%,5.44%,---,0.12%, +,187,1632,天弘中证食品,9月20日,2.7317,2.8093,0.98%,-0.72%,-2.20%,-7.05%,0.95%,-6.81%,-4.53%,51.44%,-16.60%,187.52%,-4.60%,0.00%, +,188,11971,东财银行A,9月20日,0.869,0.869,-0.90%,-2.17%,0.66%,-3.90%,-4.66%,-6.86%,---,---,-5.57%,-13.10%,-4.92%,0.12%, +,189,1692,南方国策动力,9月20日,2.492,2.522,0.81%,-5.14%,-7.74%,-1.11%,3.49%,-6.88%,21.74%,93.33%,-10.42%,155.26%,-7.77%,0.15%, +,190,13035,富国中证军工,9月20日,1.148,1.148,0.09%,-3.69%,-3.93%,2.78%,1.86%,-6.89%,---,---,-19.38%,-0.35%,-8.45%,0.00%, +,191,7191,富国中证价值,9月20日,1.7057,1.7057,0.14%,-4.52%,-3.94%,-5.03%,-4.07%,-6.90%,12.54%,44.11%,-8.43%,35.67%,-7.90%,0.00%, +,192,11149,创金合信ES,9月19日,1.0317,1.0317,-0.89%,-3.42%,-7.50%,-0.93%,5.95%,-6.95%,---,---,-7.23%,3.17%,---,0.15%, +,193,12042,鹏华银行C,9月19日,0.894,0.894,0.34%,-0.56%,1.48%,-3.97%,-4.39%,-6.97%,---,---,-5.40%,-10.60%,---,0.00%, +,194,8872,大成睿裕六月,9月20日,1.1982,1.1982,-0.10%,-3.65%,-4.66%,-5.07%,2.15%,-7.06%,6.44%,---,-15.24%,19.82%,-7.37%,0.00%, +,195,6165,建信中证10,9月19日,1.9022,2.1066,-1.14%,-7.71%,-10.89%,-1.45%,0.57%,-7.10%,12.75%,64.39%,-9.97%,112.86%,---,0.15%, +,196,6751,富国互联科技,9月20日,2.7988,2.7988,1.70%,-4.84%,-8.44%,4.12%,10.73%,-7.12%,20.40%,113.93%,-5.72%,179.88%,-6.58%,0.15%, +,197,6803,嘉实互通精选,9月20日,1.2461,1.2461,0.05%,-2.32%,-5.83%,-2.92%,4.42%,-7.14%,0.64%,19.24%,-12.40%,24.61%,-6.67%,0.15%, +,198,11220,南方匠心优选,9月20日,0.726,0.726,0.12%,-5.38%,-4.08%,-3.79%,0.68%,-7.15%,---,---,-13.34%,-27.40%,-5.23%,0.15%, +,199,161029,富国中证银行,9月19日,1.176,1.262,0.34%,-0.51%,1.47%,-3.92%,-4.31%,-7.18%,0.26%,10.53%,-5.39%,28.41%,---,0.12%, +,200,4597,南方银行ET,9月19日,1.2089,1.2089,0.36%,-0.53%,1.54%,-4.23%,-4.38%,-7.20%,1.04%,8.61%,-5.50%,20.89%,-6.05%,0.12%, +,201,11180,浙商智选食品,9月19日,0.865,0.865,-0.02%,-2.03%,-4.55%,-3.61%,6.04%,-7.22%,---,---,-12.35%,-13.50%,---,0.00%, +,202,11972,东财银行C,9月20日,0.8642,0.8642,-0.91%,-2.17%,0.63%,-4.00%,-4.84%,-7.22%,---,---,-5.85%,-13.58%,-5.30%,0.00%, +,203,10680,华夏新兴成长,9月20日,0.8426,0.8426,0.33%,-2.84%,-2.88%,2.13%,4.67%,-7.26%,---,---,-15.56%,-15.74%,-7.38%,0.15%, +,204,160222,国泰国证食品,9月19日,1.0389,2.8119,0.77%,-0.66%,-3.56%,-7.60%,-0.24%,-7.28%,-5.60%,47.62%,-17.72%,337.97%,-4.11%,0.00%, +,205,160517,博时中证银行,9月20日,1.1728,1.3199,-0.90%,-2.19%,0.60%,-4.12%,-5.05%,-7.31%,1.42%,15.96%,-5.97%,31.99%,-5.42%,0.10%, +,206,110022,易方达消费行,9月20日,4.008,4.008,0.68%,-0.87%,0.65%,-4.30%,7.05%,-7.33%,-3.82%,35.63%,-14.43%,300.80%,-4.53%,0.15%, +,207,3017,广发中证军工,9月20日,1.1298,1.1298,0.07%,-3.76%,-4.03%,2.45%,1.64%,-7.33%,7.39%,39.64%,-19.58%,12.98%,-8.86%,0.12%, +,208,165312,建信央视财经,9月19日,1.0795,2.286,-0.10%,-1.90%,-2.12%,-3.94%,-3.15%,-7.34%,-5.78%,20.38%,-13.38%,161.72%,---,0.12%, +,209,8935,大成科技消费,9月20日,0.9161,0.9161,1.05%,-2.40%,-5.65%,2.22%,11.79%,-7.38%,-9.23%,---,-6.77%,-8.26%,-7.11%,0.00%, +,210,11150,创金合信ES,9月19日,1.0228,1.0228,-0.90%,-3.44%,-7.55%,-1.06%,5.67%,-7.42%,---,---,-7.56%,2.28%,---,0.00%, +,211,13330,富国中证银行,9月19日,1.173,1.173,0.34%,-0.59%,1.38%,-4.01%,-4.40%,-7.42%,---,---,-5.56%,-7.64%,---,0.00%, +,212,160418,华安中证银行,9月19日,0.8845,1.0385,0.36%,-0.52%,1.49%,-4.25%,-4.81%,-7.43%,-1.79%,4.95%,-5.72%,2.88%,---,0.12%, +,213,13442,建信中证10,9月19日,1.8742,1.9753,-1.14%,-7.72%,-10.92%,-1.55%,0.37%,-7.46%,---,---,-10.22%,-7.40%,---,0.00%, +,214,1027,前海开源中证,9月19日,1.2212,1.2212,0.47%,-3.14%,-4.32%,-6.94%,0.10%,-7.48%,-10.27%,30.19%,-12.58%,22.12%,---,0.12%, +,215,6166,建信中证10,9月19日,1.8738,2.0759,-1.14%,-7.73%,-10.92%,-1.56%,0.36%,-7.48%,11.85%,62.43%,-10.23%,109.65%,---,0.00%, +,216,6692,金信消费升级,9月20日,1.7997,2.4071,0.10%,-1.98%,-8.20%,4.55%,5.31%,-7.49%,38.83%,103.50%,-1.04%,158.03%,-8.93%,0.15%, +,217,5693,广发中证军工,9月20日,1.1238,1.1238,0.07%,-3.76%,-4.04%,2.41%,1.53%,-7.51%,6.96%,38.78%,-19.69%,57.22%,-9.04%,0.00%, +,218,10709,安信医药健康,9月19日,1.1426,1.1426,-1.00%,-6.86%,-10.83%,-9.10%,-11.69%,-7.52%,---,---,-20.01%,14.26%,---,0.15%, +,219,4598,南方银行ET,9月19日,1.184,1.184,0.36%,-0.55%,1.50%,-4.32%,-4.58%,-7.57%,0.24%,7.31%,-5.78%,18.40%,---,0.00%, +,220,12547,南方银行ET,9月19日,1.203,1.203,0.36%,-0.54%,1.50%,-4.32%,-4.57%,-7.57%,---,---,-5.77%,-14.14%,---,0.00%, +,221,8134,鹏华优选价值,9月19日,1.1043,1.1043,-0.72%,-2.35%,-3.36%,-4.73%,-2.92%,-7.58%,-3.79%,---,-13.52%,10.43%,---,0.15%, +,222,11126,富国互联科技,9月20日,2.7709,2.7709,1.70%,-4.85%,-8.49%,3.96%,10.39%,-7.67%,---,---,-6.12%,8.17%,-7.13%,0.00%, +,223,11221,南方匠心优选,9月20日,0.7189,0.7189,0.13%,-5.40%,-4.13%,-3.93%,0.36%,-7.72%,---,---,-13.73%,-28.11%,-5.80%,0.00%, +,224,161121,易方达中证银,9月20日,1.0572,1.1734,-0.91%,-2.19%,0.59%,-4.37%,-5.35%,-7.75%,0.47%,11.58%,-6.16%,20.61%,-5.95%,0.10%, +,225,11384,南方远见回报,9月20日,0.863,0.863,0.13%,-5.37%,-4.14%,-4.02%,0.02%,-7.76%,---,---,-14.15%,-13.70%,-5.87%,0.15%, +,226,519677,银河定投宝腾,9月20日,2.698,2.698,0.86%,-5.80%,-6.32%,-4.60%,-4.16%,-7.79%,18.85%,70.54%,-11.63%,169.80%,-9.28%,0.00%, +,227,519714,交银消费新驱,9月20日,1.782,4.379,0.79%,-1.87%,-5.01%,-7.48%,2.89%,-7.81%,12.36%,82.66%,-14.66%,449.74%,-5.81%,0.15%, +,228,161723,招商中证银行,9月20日,1.0931,1.2208,-0.91%,-2.19%,0.58%,-4.23%,-5.22%,-7.83%,-0.45%,6.33%,-6.13%,24.29%,-5.94%,0.10%, +,229,4194,招商中证10,9月20日,1.4485,1.4485,1.07%,-5.79%,-9.85%,-3.95%,-1.09%,-7.83%,10.55%,63.56%,-11.78%,44.85%,-8.12%,0.12%, +,230,501019,国泰国证航天,9月19日,1.3086,1.3086,-1.75%,-3.02%,-4.10%,3.55%,1.78%,-7.83%,12.34%,52.80%,-19.09%,30.86%,-9.71%,0.12%, +,231,3190,创金合信消费,9月19日,2.3812,2.2662,0.74%,-1.67%,-3.74%,-6.60%,-1.81%,-7.87%,3.17%,51.40%,-15.58%,126.62%,---,0.15%, +,232,5051,上投标普港股,9月19日,0.809,0.809,-0.65%,-2.09%,-1.84%,-3.60%,-3.48%,-7.89%,5.19%,-14.50%,-4.25%,-19.10%,---,0.10%, +,233,10681,华夏新兴成长,9月20日,0.8328,0.8328,0.35%,-2.85%,-2.94%,1.96%,4.30%,-7.91%,---,---,-15.98%,-16.72%,-8.02%,0.00%, +,234,10710,安信医药健康,9月19日,1.1331,1.1331,-1.00%,-6.86%,-10.88%,-9.22%,-11.91%,-7.98%,---,---,-20.29%,13.31%,---,0.00%, +,235,9860,易方达中证银,9月20日,1.0505,1.0505,-0.91%,-2.20%,0.56%,-4.45%,-5.50%,-8.03%,-0.14%,---,-6.36%,2.69%,-6.23%,0.00%, +,236,7548,易方达ESG,9月20日,1.5368,1.5368,0.81%,-1.23%,-0.88%,-5.37%,2.30%,-8.04%,2.22%,53.56%,-12.56%,53.68%,-6.02%,0.15%, +,237,6926,长城量化精选,9月20日,1.332,1.332,1.69%,-0.27%,-2.84%,-8.71%,0.10%,-8.04%,-1.97%,30.96%,-18.26%,33.20%,-5.25%,0.15%, +,238,1594,天弘中证银行,9月20日,1.1661,1.1661,-0.92%,-2.21%,0.56%,-4.33%,-5.36%,-8.08%,-3.49%,-1.27%,-6.16%,16.61%,-6.18%,0.10%, +,239,577,安信价值精选,9月19日,4.331,4.331,0.46%,-2.70%,-5.27%,-7.42%,-5.04%,-8.09%,3.19%,53.64%,-14.73%,333.10%,---,0.15%, +,240,970042,国海量化优选,9月20日,1.6992,1.8335,0.97%,-5.26%,-7.27%,-2.20%,2.94%,-8.14%,---,---,-2.72%,-1.79%,-8.82%,0.10%, +,241,12722,平安中证光伏,9月19日,0.9956,0.9956,-0.96%,-9.87%,-16.52%,-4.35%,-0.52%,-8.15%,---,---,-11.49%,-0.44%,-6.71%,0.12%, +,242,4195,招商中证10,9月20日,1.4326,1.4326,1.07%,-5.80%,-9.89%,-4.05%,-1.29%,-8.20%,9.67%,61.60%,-12.04%,43.26%,-8.48%,0.00%, +,243,6693,金信消费升级,9月20日,1.8481,2.4357,0.10%,-1.99%,-8.27%,4.34%,4.88%,-8.23%,36.64%,98.73%,-1.60%,150.72%,-9.65%,0.00%, +,244,11349,淳厚现代服务,9月19日,0.9169,0.9169,-0.48%,-4.35%,-5.12%,-1.38%,-0.04%,-8.24%,---,---,-14.48%,-8.31%,---,--, +,245,835,华润元大富时,9月20日,2.6066,2.6066,-0.08%,-3.16%,-3.44%,-5.93%,-1.77%,-8.25%,-0.09%,31.12%,-12.41%,160.66%,-6.95%,0.12%, +,246,164205,天弘文化新兴,9月20日,2.1966,2.5542,0.53%,-1.72%,-1.85%,-6.79%,2.13%,-8.27%,-6.62%,42.07%,-15.63%,155.42%,-6.23%,0.15%, +,247,1595,天弘中证银行,9月20日,1.147,1.147,-0.92%,-2.22%,0.53%,-4.38%,-5.47%,-8.27%,-3.89%,-1.87%,-6.31%,14.70%,-6.38%,0.00%, +,248,11385,南方远见回报,9月20日,0.8553,0.8553,0.13%,-5.39%,-4.20%,-4.16%,-0.28%,-8.32%,---,---,-14.52%,-14.47%,-6.43%,0.00%, +,249,5052,上投标普港股,9月19日,0.7917,0.7917,-0.65%,-2.10%,-1.88%,-3.72%,-3.72%,-8.36%,4.14%,-15.72%,-4.60%,-20.83%,---,0.00%, +,250,12723,平安中证光伏,9月19日,0.9926,0.9926,-0.96%,-9.88%,-16.54%,-4.42%,-0.65%,-8.38%,---,---,-11.65%,-0.74%,-6.94%,0.00%, +,251,12043,鹏华酒C,9月19日,0.818,0.898,1.24%,-0.12%,-2.85%,-6.51%,1.87%,-8.39%,---,---,-16.10%,-11.51%,---,0.00%, +,252,4997,广发高端制造,9月20日,2.8839,2.8839,2.77%,-5.60%,-9.40%,0.02%,-3.43%,-8.44%,30.15%,182.38%,-13.52%,188.36%,-8.71%,0.15%, +,253,501090,华宝中证消费,9月20日,1.288,1.288,0.65%,-2.11%,-2.44%,-5.63%,2.46%,-8.45%,-8.26%,---,-15.77%,28.80%,-6.15%,0.10%, +,254,11463,长城量化精选,9月20日,1.3213,1.3213,1.69%,-0.27%,-2.88%,-8.83%,-0.15%,-8.48%,---,---,-18.55%,-3.31%,-5.70%,0.00%, +,255,12461,东财家电A,9月19日,0.8474,0.8474,-0.14%,-3.35%,-2.27%,0.47%,5.90%,-8.48%,---,---,-12.97%,-15.26%,---,0.12%, +,256,3191,创金合信消费,9月19日,2.3123,2.1832,0.74%,-1.69%,-3.80%,-6.77%,-2.16%,-8.53%,1.73%,48.07%,-16.00%,118.32%,---,0.00%, +,257,10573,华润元大富时,9月20日,2.5919,2.5919,-0.08%,-3.16%,-3.46%,-6.01%,-1.93%,-8.54%,---,---,-12.61%,-6.60%,-7.24%,0.00%, +,258,1577,嘉实低价策略,9月20日,2.116,2.116,-0.89%,-3.02%,0.91%,-2.53%,-0.28%,-8.56%,2.62%,77.82%,-12.63%,111.60%,-7.84%,0.15%, +,259,7153,汇添富中证银,9月20日,0.9746,0.9746,-0.90%,-2.19%,0.52%,-4.52%,-5.47%,-8.57%,-3.81%,-3.36%,-6.58%,-2.54%,-6.68%,0.10%, +,260,240019,华宝中证银行,9月20日,1.0815,1.6463,-0.91%,-2.16%,0.55%,-4.75%,-5.85%,-8.62%,-4.16%,-1.46%,-6.65%,69.23%,-6.75%,0.10%, +,261,7154,汇添富中证银,9月20日,0.972,0.972,-0.90%,-2.18%,0.52%,-4.54%,-5.51%,-8.65%,-3.99%,-3.61%,-6.64%,-2.80%,-6.77%,0.00%, +,262,916,前海开源股息,9月19日,1.771,2.072,-0.17%,-4.11%,-0.90%,-1.39%,-0.73%,-8.66%,19.50%,53.60%,-6.25%,128.73%,---,0.15%, +,263,50024,博时上证自然,9月20日,1.1141,1.1141,1.20%,-5.13%,-1.26%,0.77%,6.99%,-8.67%,69.26%,79.03%,5.34%,11.41%,-10.16%,0.12%, +,264,6560,华夏四川国改,9月20日,1.6116,1.6116,1.09%,-3.54%,-3.38%,-3.51%,1.85%,-8.67%,34.65%,57.68%,-13.11%,61.16%,-9.15%,0.12%, +,265,9329,华宝中证消费,9月20日,1.2807,1.2807,0.65%,-2.11%,-2.46%,-5.69%,2.33%,-8.68%,-8.71%,---,-15.92%,38.72%,-6.38%,0.00%, +,266,13105,华安中证光伏,9月19日,0.8477,0.8477,-0.95%,-9.83%,-16.38%,-4.47%,-0.28%,-8.68%,---,---,-11.02%,-15.23%,---,0.05%, +,267,7178,浙商中华预期,9月19日,1.0995,1.0995,-2.35%,-5.02%,-1.94%,-8.01%,-11.44%,-8.73%,18.43%,---,-7.50%,9.95%,---,0.12%, +,268,925,汇添富外延增,9月20日,1.629,1.629,0.43%,-3.61%,-4.51%,0.18%,2.91%,-8.79%,1.81%,59.08%,-11.08%,62.90%,-8.74%,0.15%, +,269,11350,淳厚现代服务,9月19日,0.911,0.911,-0.48%,-4.37%,-5.17%,-1.53%,-0.35%,-8.79%,---,---,-14.84%,-8.90%,---,0.00%, +,270,6697,华宝中证银行,9月20日,1.07,1.07,-0.91%,-2.17%,0.53%,-4.80%,-5.95%,-8.80%,-4.55%,-2.20%,-6.79%,7.00%,-6.94%,0.00%, +,271,10160,广发高端制造,9月20日,2.8618,2.8618,2.77%,-5.61%,-9.43%,-0.08%,-3.62%,-8.80%,29.15%,---,-13.77%,30.73%,-9.07%,0.00%, +,272,6743,中融央视财经,9月19日,1.237,1.237,-0.02%,-1.68%,-1.80%,-4.24%,-4.71%,-8.82%,-8.19%,17.14%,-14.12%,23.70%,---,0.12%, +,273,12462,东财家电C,9月19日,0.8434,0.8434,-0.14%,-3.36%,-2.29%,0.38%,5.69%,-8.83%,---,---,-13.21%,-15.66%,---,0.00%, +,274,2670,万家沪深30,9月19日,1.3869,1.5449,-0.09%,-3.70%,-2.72%,-3.24%,0.78%,-8.91%,-0.33%,38.82%,-9.42%,53.66%,-8.62%,0.10%, +,275,6561,华夏四川国改,9月20日,1.5938,1.5938,1.08%,-3.55%,-3.41%,-3.59%,1.69%,-8.95%,33.83%,56.25%,-13.31%,59.38%,-9.42%,0.00%, +,276,13106,华安中证光伏,9月19日,0.8451,0.8451,-0.95%,-9.83%,-16.39%,-4.54%,-0.42%,-8.95%,---,---,-11.20%,-15.49%,---,0.00%, +,277,1719,工银国家战略,9月20日,2.322,2.322,-1.98%,-4.52%,2.52%,0.78%,-1.23%,-9.01%,38.79%,88.47%,-5.76%,132.20%,-8.44%,0.15%, +,278,6744,中融央视财经,9月19日,1.2215,1.2215,-0.03%,-1.68%,-1.82%,-4.29%,-4.82%,-9.01%,-8.59%,16.19%,-14.25%,22.15%,---,0.00%, +,279,1043,工银美丽城镇,9月20日,2.487,2.487,0.00%,-3.75%,-5.18%,-5.51%,-3.64%,-9.03%,21.08%,89.27%,-13.31%,148.70%,-9.43%,0.15%, +,280,7216,浙商中华预期,9月19日,1.0888,1.0888,-2.35%,-5.02%,-1.97%,-8.09%,-11.60%,-9.04%,17.63%,---,-7.72%,8.88%,---,0.00%, +,281,11457,新华行业龙头,9月19日,1.007,1.007,-1.01%,-6.86%,-10.73%,4.30%,17.56%,-9.04%,---,---,-8.10%,0.70%,---,0.15%, +,282,5825,申万菱信智能,9月20日,3.4141,3.4141,0.90%,-5.60%,-10.49%,-4.24%,-6.49%,-9.08%,45.04%,148.39%,-19.62%,241.41%,-9.22%,0.15%, +,283,11424,汇添富外延增,9月20日,1.616,1.616,0.44%,-3.64%,-4.55%,0.06%,2.67%,-9.16%,---,---,-11.35%,-15.04%,-9.16%,0.00%, +,284,7431,浙商之江凤凰,9月19日,1.5612,1.5612,-0.67%,-5.83%,-5.44%,-3.95%,-4.86%,-9.17%,16.29%,56.56%,-5.43%,56.12%,---,0.08%, +,285,3646,创金合信中证,9月19日,1.5294,1.5294,-0.89%,-7.03%,-11.05%,-3.51%,0.63%,-9.18%,11.75%,53.69%,-13.50%,52.94%,---,0.15%, +,286,5063,广发中证全指,9月20日,1.1628,1.1628,0.41%,-3.93%,-2.81%,-1.40%,7.63%,-9.20%,-9.99%,17.70%,-14.08%,16.28%,-7.28%,0.12%, +,287,8713,国泰中证全指,9月19日,1.1127,1.1127,-0.34%,-3.58%,-3.65%,0.33%,7.81%,-9.20%,-11.91%,---,-13.59%,11.27%,-6.88%,0.10%, +,288,1223,鹏华文化传媒,9月19日,1.142,1.142,-1.89%,-6.16%,-10.36%,-2.48%,-4.75%,-9.22%,-21.35%,4.01%,-20.08%,14.20%,---,0.15%, +,289,501305,汇添富中证港,9月20日,0.8071,0.8071,0.29%,-2.55%,-0.91%,-4.72%,-3.13%,-9.26%,-4.38%,-8.77%,-2.69%,-19.29%,-7.73%,0.10%, +,290,2671,万家沪深30,9月19日,1.7351,1.9351,-0.10%,-3.71%,-2.75%,-3.34%,0.57%,-9.27%,-1.12%,37.17%,-9.68%,92.33%,-8.98%,0.00%, +,291,9726,招商中证50,9月20日,1.1984,1.1984,0.87%,-4.24%,-5.65%,-2.13%,0.83%,-9.34%,---,---,-8.10%,19.84%,-9.56%,0.15%, +,292,3647,创金合信中证,9月19日,1.5095,1.5095,-0.89%,-7.04%,-11.07%,-3.56%,0.52%,-9.37%,11.30%,52.72%,-13.63%,50.95%,---,0.00%, +,293,5064,广发中证全指,9月20日,1.154,1.154,0.42%,-3.94%,-2.83%,-1.45%,7.52%,-9.38%,-10.34%,16.99%,-14.21%,15.40%,-7.47%,0.00%, +,294,1542,国泰互联网+,9月19日,2.898,3.057,-0.21%,-5.20%,-7.74%,-2.23%,4.58%,-9.41%,6.08%,66.17%,-8.52%,219.01%,-6.91%,0.15%, +,295,11923,大成消费精选,9月19日,0.8291,0.8291,-0.11%,-2.04%,-3.94%,-5.29%,0.93%,-9.41%,---,---,-15.54%,-17.09%,-7.14%,0.15%, +,296,161217,国投瑞银中证,9月20日,1.306,1.306,1.71%,-4.39%,-1.14%,-0.61%,6.18%,-9.43%,73.44%,106.65%,3.57%,30.60%,-10.97%,0.12%, +,297,12210,申万菱信智能,9月20日,0.9805,0.9805,0.57%,-5.53%,-9.49%,2.87%,-0.26%,-9.44%,---,---,-15.93%,-1.95%,-9.82%,0.15%, +,298,12679,华泰柏瑞光伏,9月19日,0.8652,0.8652,-0.96%,-9.69%,-16.45%,-4.50%,-1.48%,-9.45%,---,---,-12.52%,-13.48%,---,0.12%, +,299,217027,招商央视财经,9月20日,2.5677,2.5677,-0.46%,-3.20%,-2.52%,-5.47%,-5.41%,-9.46%,-8.34%,18.40%,-15.55%,156.77%,-8.77%,0.12%, +,300,1040,新华策略精选,9月19日,2.0972,2.0972,-1.16%,-6.96%,-11.13%,5.00%,16.82%,-9.47%,-4.54%,73.47%,-8.55%,109.72%,---,0.15%, +,301,8714,国泰中证全指,9月19日,1.1047,1.1047,-0.34%,-3.58%,-3.67%,0.26%,7.65%,-9.47%,-12.42%,---,-13.78%,10.47%,-7.15%,0.00%, +,302,11478,工银美丽城镇,9月20日,2.469,2.469,0.04%,-3.74%,-5.22%,-5.62%,-3.89%,-9.49%,---,---,-13.61%,-13.06%,-9.86%,0.00%, +,303,12680,华泰柏瑞光伏,9月19日,0.8643,0.8643,-0.96%,-9.69%,-16.45%,-4.53%,-1.53%,-9.54%,---,---,-12.57%,-13.57%,---,0.00%, +,304,1672,国寿安保智慧,9月19日,1.855,2.077,-0.70%,-6.31%,-11.96%,-2.83%,1.26%,-9.56%,12.02%,79.68%,-19.10%,119.79%,---,0.15%, +,305,9341,易方达均衡成,9月20日,1.1405,1.1405,1.61%,-2.15%,-4.22%,-3.31%,0.51%,-9.59%,9.02%,---,-14.79%,14.05%,-8.92%,0.15%, +,306,501306,汇添富中证港,9月20日,0.7921,0.7921,0.29%,-2.55%,-0.94%,-4.82%,-3.33%,-9.63%,-5.16%,-9.86%,-2.96%,-20.79%,-8.10%,0.00%, +,307,10673,兴全中证80,9月20日,0.8962,0.8962,0.36%,-3.42%,-2.61%,-3.25%,-2.68%,-9.66%,---,---,-12.69%,-10.38%,-9.05%,0.12%, +,308,6081,海富通电子传,9月19日,2.9173,2.9173,-1.39%,-7.24%,-8.83%,9.46%,6.39%,-9.67%,40.67%,118.61%,-15.71%,191.73%,-9.85%,0.15%, +,309,160639,鹏华中证高铁,9月19日,0.801,0.355,-0.25%,-4.76%,-4.53%,-3.84%,-4.42%,-9.70%,-6.75%,0.57%,-9.59%,-65.33%,---,0.12%, +,310,240016,华宝上证18,9月20日,2.084,2.114,-0.76%,-3.61%,-0.14%,-3.38%,-4.27%,-9.71%,-0.95%,14.32%,-8.15%,114.25%,-9.98%,0.15%, +,311,1133,广发中证全指,9月20日,0.8821,0.8821,1.10%,-4.05%,-5.06%,-5.81%,4.03%,-9.71%,-5.47%,25.03%,-13.53%,-11.79%,-8.33%,0.12%, +,312,1036,嘉实企业变革,9月20日,1.654,1.654,-0.12%,-3.10%,-7.23%,-2.24%,1.35%,-9.75%,3.82%,56.08%,-17.08%,65.60%,-10.06%,0.15%, +,313,530010,建信上证社会,9月19日,2.3645,2.3645,0.03%,-2.80%,-0.25%,-5.13%,-4.31%,-9.77%,-3.73%,18.50%,-11.36%,136.45%,---,0.15%, +,314,12211,申万菱信智能,9月20日,0.9755,0.9755,0.57%,-5.55%,-9.53%,2.76%,-0.47%,-9.81%,---,---,-16.18%,-2.45%,-10.19%,0.00%, +,315,4410,招商央视财经,9月20日,2.5144,2.5144,-0.46%,-3.20%,-2.55%,-5.56%,-5.60%,-9.82%,-9.08%,16.96%,-15.79%,39.15%,-9.13%,0.00%, +,316,7782,大成MSCI,9月20日,1.1783,1.1783,0.20%,-3.28%,-2.73%,-4.35%,-0.05%,-9.83%,-8.71%,---,-10.22%,17.83%,-10.00%,0.12%, +,317,10409,富国消费精选,9月20日,0.8137,0.8137,0.48%,-2.14%,-4.77%,-5.69%,1.62%,-9.83%,---,---,-16.45%,-18.63%,-7.76%,0.15%, +,318,11853,招商中证消费,9月20日,0.8343,0.8343,0.68%,-1.65%,-1.95%,-6.40%,1.76%,-9.85%,---,---,-17.79%,-16.57%,-7.72%,0.12%, +,319,2977,广发中证全指,9月20日,0.874,0.874,1.10%,-4.05%,-5.07%,-5.86%,3.92%,-9.90%,-5.84%,24.29%,-13.65%,11.07%,-8.52%,0.00%, +,320,12445,华富新能源股,9月20日,0.9064,0.9064,3.60%,-5.97%,-14.09%,-7.99%,-0.18%,-9.91%,---,---,-7.88%,-9.36%,-9.79%,0.15%, +,321,471,富国城镇发展,9月20日,2.297,2.797,0.35%,-3.73%,-5.47%,-4.85%,-3.81%,-9.92%,5.85%,64.07%,-18.66%,188.27%,-9.71%,0.15%, +,322,7783,大成MSCI,9月20日,1.1747,1.1747,0.20%,-3.29%,-2.75%,-4.37%,-0.10%,-9.92%,-8.90%,---,-10.29%,17.47%,-10.09%,0.00%, +,323,10196,易方达核心优,9月20日,0.7535,0.7535,0.83%,-1.12%,-1.08%,-5.34%,0.98%,-9.97%,---,---,-14.13%,-24.65%,-7.74%,0.15%, +,324,10674,兴全中证80,9月20日,0.8905,0.8905,0.36%,-3.42%,-2.64%,-3.33%,-2.87%,-10.01%,---,---,-12.94%,-10.95%,-9.41%,0.00%, +,325,971,诺安新经济股,9月20日,1.584,1.584,0.57%,-1.86%,-2.34%,-2.34%,5.39%,-10.05%,7.90%,69.05%,-10.86%,58.40%,-9.07%,0.15%, +,326,920003,中金新锐股票,9月20日,3.7024,4.1824,0.81%,-4.61%,-10.06%,-3.59%,-1.90%,-10.07%,37.58%,---,-14.75%,77.84%,-10.09%,--, +,327,9727,招商中证50,9月20日,1.1819,1.1819,0.86%,-4.25%,-5.71%,-2.33%,0.42%,-10.07%,---,---,-8.62%,18.19%,-10.27%,0.00%, +,328,11926,大成消费精选,9月19日,0.8204,0.8204,-0.11%,-2.07%,-4.00%,-5.48%,0.53%,-10.12%,---,---,-16.01%,-17.96%,-7.87%,0.00%, +,329,2334,汇丰晋信大盘,9月19日,1.4324,1.4324,-0.48%,-3.76%,-3.99%,-2.21%,0.58%,-10.13%,-1.15%,10.47%,-8.86%,43.23%,---,0.15%, +,330,11854,招商中证消费,9月20日,0.8299,0.8299,0.68%,-1.66%,-1.97%,-6.49%,1.54%,-10.22%,---,---,-18.03%,-17.01%,-8.09%,0.00%, +,331,501310,华宝沪港深中,9月20日,0.8448,0.8448,-0.21%,-3.88%,-1.81%,-5.84%,-5.04%,-10.23%,-2.65%,-14.05%,-5.48%,-15.53%,-9.67%,0.12%, +,332,7137,鹏扬元合量化,9月19日,1.5771,1.5771,0.15%,-6.56%,-11.34%,-9.63%,3.30%,-10.25%,25.37%,53.82%,-18.49%,57.71%,---,0.15%, +,333,10955,天弘中证智能,9月19日,0.8171,0.8171,-0.15%,-7.40%,-12.64%,-6.42%,-7.03%,-10.25%,---,---,-27.14%,-18.29%,-8.34%,0.10%, +,334,160135,南方中证高铁,9月19日,0.93,0.93,-0.30%,-4.73%,-4.65%,-4.23%,-4.88%,-10.30%,-8.11%,-11.33%,-10.15%,-58.00%,---,0.06%, +,335,10197,易方达核心优,9月20日,0.7486,0.7486,0.84%,-1.12%,-1.10%,-5.42%,0.77%,-10.33%,---,---,-14.38%,-25.14%,-8.09%,0.00%, +,336,6080,海富通电子传,9月19日,2.8062,2.8062,-1.39%,-7.26%,-8.89%,9.24%,5.97%,-10.38%,38.44%,113.94%,-16.19%,180.62%,-10.56%,0.00%, +,337,8294,朱雀企业优胜,9月20日,1.4306,1.4306,1.15%,-4.72%,-7.82%,-4.43%,-2.01%,-10.41%,11.11%,---,-19.52%,43.06%,-10.97%,0.15%, +,338,10956,天弘中证智能,9月19日,0.8144,0.8144,-0.13%,-7.40%,-12.65%,-6.47%,-7.12%,-10.43%,---,---,-27.23%,-18.56%,-8.53%,0.00%, +,339,161033,富国中证智能,9月20日,1.571,1.571,1.16%,-6.60%,-11.84%,-7.32%,-6.71%,-10.48%,-6.77%,40.52%,-26.76%,57.10%,-9.66%,0.12%, +,340,920923,中金新锐股票,9月20日,3.6578,3.6578,0.81%,-4.62%,-10.10%,-3.71%,-2.15%,-10.52%,36.21%,---,-15.06%,74.89%,-10.53%,0.00%, +,341,11042,国泰价值先锋,9月19日,0.906,0.906,-0.53%,-7.27%,-10.83%,-6.24%,0.02%,-10.52%,---,---,-19.99%,-9.40%,-8.94%,0.15%, +,342,470007,汇添富上证综,9月20日,1.003,1.327,0.00%,-3.56%,-2.05%,-4.12%,-1.82%,-10.59%,-3.84%,12.76%,-11.56%,32.76%,-10.82%,0.10%, +,343,7397,华宝沪港深中,9月20日,0.8338,0.8338,-0.22%,-3.90%,-1.85%,-5.93%,-5.24%,-10.59%,-3.43%,-15.07%,-5.76%,-15.62%,-10.02%,0.00%, +,344,13292,富国中证智能,9月20日,1.568,1.568,1.23%,-6.61%,-11.86%,-7.33%,-6.83%,-10.60%,---,---,-26.87%,-16.95%,-9.83%,0.00%, +,345,1726,汇添富新兴消,9月20日,1.626,1.626,1.75%,-4.97%,-11.39%,-7.77%,-1.22%,-10.61%,0.31%,47.15%,-18.94%,62.60%,-9.77%,0.15%, +,346,2335,汇丰晋信大盘,9月19日,1.3875,1.3875,-0.49%,-3.78%,-4.04%,-2.34%,0.32%,-10.61%,-2.16%,8.71%,-9.22%,38.74%,---,0.00%, +,347,4788,富荣沪深30,9月20日,1.7882,1.7882,-0.08%,-4.23%,-4.71%,-8.02%,-5.56%,-10.72%,6.25%,82.77%,-16.27%,78.82%,-9.84%,0.12%, +,348,5669,前海开源公用,9月19日,2.8404,2.8404,-0.09%,-10.33%,-14.73%,-8.51%,-3.51%,-10.73%,146.46%,167.06%,-22.08%,184.04%,---,0.15%, +,349,368,汇添富沪深3,9月20日,2.2561,2.2561,0.17%,-4.22%,-3.64%,-6.07%,-1.87%,-10.73%,3.84%,43.20%,-12.42%,125.61%,-11.85%,0.12%, +,350,1541,汇添富民营新,9月20日,1.53,1.53,0.99%,-4.91%,-7.78%,-3.04%,-1.10%,-10.74%,-3.53%,60.55%,-18.70%,53.00%,-11.61%,0.15%, +,351,7130,中庚小盘价值,9月20日,2.1574,2.1574,1.01%,-4.78%,-5.99%,-2.66%,0.41%,-10.76%,50.34%,112.09%,-6.97%,115.74%,-12.19%,--, +,352,4789,富荣沪深30,9月20日,1.7794,1.7794,-0.08%,-4.23%,-4.71%,-8.05%,-5.61%,-10.81%,6.04%,82.20%,-16.33%,77.94%,-9.92%,0.00%, +,353,1637,嘉实量化精选,9月20日,1.8099,1.8099,1.17%,-6.57%,-8.15%,-5.36%,-0.65%,-10.82%,14.75%,61.32%,-8.65%,78.90%,-10.62%,0.15%, +,354,4350,汇丰晋信价值,9月19日,1.6554,2.0554,-0.51%,-3.00%,-4.57%,-7.28%,-7.03%,-10.83%,13.70%,57.24%,-21.32%,102.79%,---,0.15%, +,355,519686,交银上证18,9月20日,1.48,1.48,-0.60%,-3.96%,-1.27%,-4.45%,-4.21%,-10.84%,-3.83%,12.29%,-11.11%,48.00%,-10.95%,0.15%, +,356,12930,中庚价值先锋,9月20日,0.9235,0.9235,0.28%,-2.88%,-6.94%,0.95%,-3.36%,-10.87%,---,---,-21.46%,-7.65%,-11.41%,--, +,357,5702,恒生前海港股,9月19日,0.8331,0.8331,-0.57%,-2.39%,-1.76%,-4.34%,-5.10%,-10.93%,-0.11%,-14.06%,-7.24%,-16.69%,---,0.12%, +,358,7903,长城量化小盘,9月20日,1.2471,1.2471,1.23%,-4.64%,-7.92%,-2.78%,-0.63%,-10.93%,-0.01%,---,-14.41%,24.70%,-10.95%,0.15%, +,359,7138,鹏扬元合量化,9月19日,1.5152,1.5152,0.15%,-6.58%,-11.39%,-9.80%,2.89%,-10.95%,23.39%,48.56%,-18.94%,51.52%,---,0.00%, +,360,6502,财通集成电路,9月20日,1.8639,1.8639,-0.31%,-3.68%,-6.37%,7.89%,1.60%,-10.96%,0.74%,39.69%,-13.05%,86.97%,-12.29%,0.15%, +,361,162208,泰达宏利首选,9月20日,1.8885,3.0668,1.33%,-5.08%,-9.52%,-8.71%,0.63%,-10.98%,26.24%,90.57%,-12.50%,375.45%,-11.69%,0.15%, +,362,10141,朱雀企业优选,9月20日,1.0715,1.0715,1.15%,-4.81%,-8.86%,-6.20%,-3.69%,-11.00%,---,---,-20.15%,7.15%,-11.59%,--, +,363,10801,长江量化消费,9月19日,0.7508,0.7508,0.97%,-0.87%,-3.55%,-4.96%,2.79%,-11.01%,---,---,-13.07%,-24.92%,---,0.15%, +,364,4488,嘉实富时中国,9月20日,1.3161,1.3161,0.02%,-2.91%,-3.29%,-6.19%,-2.38%,-11.03%,-10.57%,10.61%,-13.92%,31.61%,-9.75%,0.12%, +,365,100053,富国上证指数,9月20日,1.466,1.466,0.07%,-4.06%,-4.18%,-4.68%,-1.61%,-11.10%,-2.46%,16.16%,-10.99%,46.60%,-11.42%,0.15%, +,366,8295,朱雀企业优胜,9月20日,1.4055,1.4055,1.15%,-4.74%,-7.89%,-4.62%,-2.41%,-11.13%,9.35%,---,-19.98%,40.55%,-11.68%,0.00%, +,367,1044,嘉实新消费股,9月20日,2.08,2.15,0.34%,-2.67%,-2.12%,-4.63%,-2.48%,-11.15%,-8.77%,42.17%,-15.65%,117.95%,-10.65%,0.15%, +,368,13286,富国上证指数,9月20日,1.463,1.463,0.14%,-4.00%,-4.13%,-4.69%,-1.68%,-11.23%,---,---,-11.12%,-7.58%,-11.60%,0.00%, +,369,11043,国泰价值先锋,9月19日,0.8949,0.8949,-0.53%,-7.29%,-10.89%,-6.44%,-0.39%,-11.24%,---,---,-20.45%,-10.51%,-9.67%,0.00%, +,370,10434,红土创新医疗,9月20日,0.8267,0.8267,1.41%,-4.88%,-8.98%,-12.18%,-12.20%,-11.25%,---,---,-15.39%,-17.33%,-11.73%,0.15%, +,371,5229,嘉实富时中国,9月20日,1.1666,1.1666,0.03%,-2.91%,-3.32%,-6.28%,-2.57%,-11.38%,-11.26%,9.29%,-14.17%,14.42%,-10.10%,0.00%, +,372,1178,前海开源再融,9月19日,1.274,1.854,-0.23%,-3.12%,-4.14%,-1.09%,-6.12%,-11.40%,-19.44%,9.09%,-16.07%,90.59%,---,0.15%, +,373,866,华宝制造股票,9月20日,2.344,2.344,0.69%,-4.33%,-2.21%,0.17%,3.44%,-11.45%,21.20%,102.59%,-14.01%,134.40%,-11.85%,0.15%, +,374,519027,海富通上证周,9月19日,1.2334,1.2334,0.11%,-2.91%,0.09%,-4.94%,-5.85%,-11.52%,-11.84%,-7.40%,-9.53%,23.34%,-11.27%,0.12%, +,375,1490,汇添富国企创,9月20日,1.981,1.981,0.51%,-3.22%,-6.11%,-2.41%,-0.80%,-11.52%,12.11%,93.84%,-16.69%,98.10%,-11.09%,0.15%, +,376,10802,长江量化消费,9月19日,0.7435,0.7435,0.98%,-0.88%,-3.58%,-5.11%,2.50%,-11.53%,---,---,-13.44%,-25.65%,---,0.00%, +,377,202021,南方小康ET,9月20日,1.4971,1.5171,-0.43%,-4.71%,-2.76%,-5.09%,-3.43%,-11.54%,2.91%,19.61%,-10.02%,52.67%,-12.52%,0.12%, +,378,5267,嘉实价值精选,9月20日,1.8657,1.8657,-0.40%,-2.46%,1.07%,-2.32%,0.49%,-11.55%,10.70%,74.25%,-12.36%,86.57%,-11.27%,0.15%, +,379,11132,鹏扬沪深30,9月19日,0.9376,0.9376,0.06%,-2.40%,-2.08%,-6.09%,-4.99%,-11.56%,---,---,-12.89%,-6.24%,---,0.12%, +,380,12498,汇添富中证5,9月20日,0.8843,0.8843,0.60%,-5.01%,-7.25%,-3.93%,-1.35%,-11.57%,---,---,-14.91%,-11.57%,---,0.15%, +,381,519671,银河沪深30,9月20日,1.449,1.674,-0.82%,-3.85%,-0.28%,-3.85%,-4.55%,-11.59%,-7.77%,-0.01%,-10.61%,67.68%,-11.43%,0.12%, +,382,162107,金鹰先进制造,9月19日,0.9323,0.9323,-1.69%,-0.47%,-3.48%,2.77%,0.30%,-11.63%,3.47%,22.87%,-15.00%,50.40%,---,0.15%, +,383,13074,银河沪深30,9月20日,0.949,0.949,-0.73%,-3.85%,-0.21%,-3.85%,-4.62%,-11.64%,---,---,-10.72%,-5.10%,-11.47%,0.00%, +,384,6503,财通集成电路,9月20日,1.808,1.808,-0.31%,-3.70%,-6.43%,7.67%,1.20%,-11.66%,-0.86%,36.37%,-13.55%,81.37%,-12.98%,0.00%, +,385,10142,朱雀企业优选,9月20日,1.0546,1.0546,1.15%,-4.82%,-8.92%,-6.38%,-4.08%,-11.72%,---,---,-20.61%,5.46%,-12.29%,0.00%, +,386,310398,申万菱信沪深,9月20日,1.1972,1.7157,-0.76%,-3.86%,-0.58%,-3.70%,-4.78%,-11.73%,-6.33%,1.23%,-10.73%,72.25%,-11.67%,0.12%, +,387,10703,财通智选消费,9月20日,0.7754,0.7754,1.00%,-5.65%,-8.03%,-6.18%,-1.44%,-11.74%,---,---,-17.68%,-23.23%,-10.51%,0.15%, +,388,7817,国泰中证全指,9月19日,0.857,0.857,-1.41%,-6.88%,-10.45%,-3.38%,-7.98%,-11.75%,-25.37%,-14.61%,-21.88%,-14.30%,-11.68%,0.10%, +,389,4812,中欧先进制造,9月20日,3.0778,3.0778,2.96%,-5.23%,-10.67%,-8.77%,-2.42%,-11.80%,61.01%,199.28%,-13.63%,207.78%,-11.01%,0.15%, +,390,5223,广发中证基建,9月19日,0.8009,0.8009,-0.15%,-6.25%,-2.80%,-6.81%,-4.29%,-11.81%,2.18%,2.48%,-9.66%,-19.91%,---,0.10%, +,391,4346,南方小康ET,9月20日,1.4676,1.4876,-0.43%,-4.72%,-2.80%,-5.19%,-3.62%,-11.90%,2.09%,18.19%,-10.28%,19.23%,-12.87%,0.00%, +,392,11133,鹏扬沪深30,9月19日,0.9326,0.9326,0.05%,-2.43%,-2.12%,-6.19%,-5.19%,-11.93%,---,---,-13.14%,-6.74%,---,0.00%, +,393,12080,易方达中证5,9月20日,0.8928,0.8928,0.61%,-4.36%,-5.91%,-3.92%,-1.64%,-11.97%,---,---,-12.80%,-10.72%,-12.38%,0.15%, +,394,7800,申万菱信沪深,9月20日,0.9306,1.0466,-0.77%,-3.86%,-0.61%,-3.77%,-4.92%,-11.99%,-6.87%,0.32%,-10.91%,3.44%,-11.92%,0.00%, +,395,5224,广发中证基建,9月19日,0.7955,0.7955,-0.15%,-6.26%,-2.83%,-6.86%,-4.39%,-12.00%,1.78%,1.88%,-9.79%,-20.45%,---,0.00%, +,396,7818,国泰中证全指,9月19日,0.8485,0.8485,-1.41%,-6.87%,-10.46%,-3.45%,-8.11%,-12.01%,-25.81%,-15.45%,-22.03%,-15.15%,-11.94%,0.00%, +,397,12973,国泰800汽,9月19日,0.8551,0.8551,0.88%,-4.78%,-9.76%,-10.23%,7.99%,-12.05%,---,---,-13.19%,-14.49%,-10.05%,0.10%, +,398,10704,财通智选消费,9月20日,0.7703,0.7703,1.00%,-5.65%,-8.05%,-6.27%,-1.63%,-12.09%,---,---,-17.91%,-23.73%,-10.88%,0.00%, +,399,519193,万家消费成长,9月19日,2.2333,2.2333,0.64%,-3.67%,-1.86%,-3.62%,-3.37%,-12.20%,-5.60%,37.06%,-12.36%,123.33%,-12.03%,0.15%, +,400,6729,万家中证50,9月19日,1.3816,1.7764,-0.41%,-4.99%,-3.82%,2.35%,2.44%,-12.21%,10.79%,57.95%,-9.40%,78.71%,-12.36%,0.10%, +,401,12081,易方达中证5,9月20日,0.8894,0.8894,0.60%,-4.37%,-5.94%,-3.99%,-1.80%,-12.24%,---,---,-12.99%,-11.06%,-12.64%,0.00%, +,402,1736,圆信永丰优加,9月20日,3.0389,3.0389,0.14%,-4.08%,-6.37%,-2.54%,-2.22%,-12.27%,18.29%,94.80%,-15.63%,203.89%,-13.03%,0.15%, +,403,5870,鹏华沪深30,9月19日,1.3187,1.3187,-0.05%,-4.05%,-4.59%,-6.22%,-1.62%,-12.27%,1.45%,28.72%,-10.78%,31.87%,---,0.12%, +,404,12499,汇添富中证5,9月20日,0.8772,0.8772,0.60%,-5.02%,-7.31%,-4.12%,-1.75%,-12.27%,---,---,-15.40%,-12.28%,---,0.00%, +,405,5880,建信上证50,9月19日,1.2369,1.2769,0.07%,-2.61%,-2.29%,-6.37%,-5.03%,-12.29%,-13.22%,6.28%,-14.87%,27.24%,---,0.15%, +,406,12974,国泰800汽,9月19日,0.8523,0.8523,0.88%,-4.79%,-9.79%,-10.29%,7.83%,-12.31%,---,---,-13.38%,-14.77%,-10.31%,0.00%, +,407,3194,汇添富中证上,9月20日,0.7825,0.7825,0.05%,-3.80%,-2.49%,-5.13%,-5.50%,-12.32%,-17.02%,-18.28%,-13.26%,-21.75%,-12.37%,0.12%, +,408,163114,申万菱信中证,9月20日,1.5446,2.6551,2.04%,-6.11%,-12.70%,-8.38%,-4.45%,-12.39%,54.27%,117.57%,-14.44%,125.36%,-13.11%,0.12%, +,409,8240,东财上证50,9月20日,1.1299,1.1299,-0.18%,-3.19%,-2.45%,-6.30%,-5.18%,-12.40%,-9.86%,---,-15.10%,12.99%,-11.28%,0.10%, +,410,671030,西部利得事件,9月19日,2.2494,2.2494,-1.30%,-5.97%,-6.10%,-3.88%,2.88%,-12.41%,34.68%,72.76%,-17.33%,124.94%,-9.99%,0.15%, +,411,2210,创金合信量化,9月19日,1.7913,1.7913,-0.93%,-6.93%,-11.52%,-3.70%,-0.92%,-12.42%,11.43%,58.17%,-14.85%,79.13%,---,0.15%, +,412,2289,华商改革创新,9月19日,2.1136,2.1136,-0.41%,-6.44%,-12.24%,-5.95%,6.72%,-12.46%,19.50%,68.55%,-6.75%,111.36%,---,0.15%, +,413,4813,中欧先进制造,9月20日,3.0276,3.0276,2.97%,-5.24%,-10.74%,-8.95%,-2.81%,-12.50%,58.47%,193.94%,-14.12%,202.76%,-11.72%,0.00%, +,414,7379,易方达上证5,9月20日,1.1112,1.1112,-0.17%,-3.21%,-2.44%,-6.51%,-5.38%,-12.50%,-12.26%,11.12%,-15.34%,11.12%,-11.55%,0.05%, +,415,501060,中金优选30,9月19日,1.6575,1.6575,0.21%,-2.83%,-1.74%,-3.39%,-2.76%,-12.55%,5.57%,35.34%,-9.93%,65.75%,---,0.10%, +,416,6730,万家中证50,9月19日,1.3617,1.7491,-0.41%,-5.00%,-3.84%,2.24%,2.24%,-12.55%,9.92%,56.16%,-9.66%,75.84%,-12.70%,0.00%, +,417,501050,华夏上证50,9月20日,1.309,1.309,0.00%,-3.04%,-2.97%,-7.43%,-6.50%,-12.56%,-10.95%,-0.76%,-14.94%,30.90%,-11.61%,0.15%, +,418,13331,富国中证10,9月20日,1.8717,1.8717,1.20%,-5.88%,-9.92%,-4.96%,-1.60%,-12.56%,---,---,-12.19%,-9.19%,-13.14%,0.00%, +,419,1397,建信精工制造,9月19日,1.8285,1.8285,-0.27%,-5.72%,-10.13%,-3.95%,-2.50%,-12.57%,13.31%,61.67%,-13.53%,82.85%,---,0.15%, +,420,1396,建信互联网+,9月19日,1.262,1.262,-0.24%,-5.75%,-6.52%,-2.17%,2.27%,-12.60%,13.49%,63.90%,-15.07%,26.20%,---,0.15%, +,421,7380,易方达上证5,9月20日,1.1075,1.1075,-0.18%,-3.22%,-2.46%,-6.54%,-5.44%,-12.60%,-12.45%,10.76%,-15.41%,10.75%,-11.65%,0.00%, +,422,502048,易方达上证5,9月20日,1.0269,1.0521,-0.18%,-3.22%,-2.49%,-6.64%,-5.26%,-12.61%,-12.59%,7.57%,-15.15%,7.00%,-11.56%,0.12%, +,423,8241,东财上证50,9月20日,1.1221,1.1221,-0.17%,-3.19%,-2.47%,-6.36%,-5.29%,-12.61%,-10.30%,---,-15.25%,12.21%,-11.50%,0.00%, +,424,5881,建信上证50,9月19日,1.2244,1.2634,0.07%,-2.62%,-2.32%,-6.46%,-5.22%,-12.63%,-13.90%,5.02%,-15.11%,25.88%,---,0.00%, +,425,12754,鹏华内地低碳,9月19日,0.901,0.901,0.02%,-7.71%,-13.81%,-9.54%,-6.54%,-12.63%,---,---,-16.03%,-9.90%,---,0.10%, +,426,13444,建信上证50,9月19日,1.2245,1.2455,0.07%,-2.61%,-2.32%,-6.46%,-5.21%,-12.63%,---,---,-15.10%,-13.79%,---,0.00%, +,427,10419,申万菱信中证,9月20日,1.4977,1.4977,2.04%,-6.12%,-12.73%,-8.44%,-4.60%,-12.66%,---,---,-14.62%,49.77%,-13.37%,0.00%, +,428,161036,富国中证娱乐,9月20日,0.5922,0.5922,0.58%,-3.31%,-4.64%,-6.68%,-6.48%,-12.69%,-32.02%,-9.74%,-23.67%,-40.78%,-11.23%,0.12%, +,429,7994,华夏中证50,9月20日,1.621,1.621,0.96%,-5.08%,-4.91%,-0.06%,2.05%,-12.70%,17.02%,---,-11.04%,62.10%,-13.29%,0.12%, +,430,867,华宝品质生活,9月20日,1.862,1.912,0.38%,-1.53%,-0.27%,-4.32%,2.14%,-12.71%,9.98%,63.62%,-17.65%,92.59%,-11.80%,0.15%, +,431,501061,中金优选30,9月19日,1.6379,1.6379,0.21%,-2.83%,-1.76%,-3.45%,-2.89%,-12.77%,5.05%,34.33%,-10.09%,63.79%,---,0.00%, +,432,6395,华夏上证50,9月20日,1.287,1.287,0.08%,-3.01%,-2.94%,-7.48%,-6.60%,-12.80%,-11.61%,-1.98%,-15.16%,14.81%,-11.91%,0.00%, +,433,12755,鹏华内地低碳,9月19日,0.8989,0.8989,0.02%,-7.72%,-13.82%,-9.59%,-6.63%,-12.81%,---,---,-16.15%,-10.11%,---,0.00%, +,434,1237,博时上证50,9月20日,1.0936,1.0936,-0.17%,-3.18%,-2.44%,-6.50%,-5.59%,-12.83%,-13.90%,2.19%,-15.54%,9.36%,-11.81%,0.12%, +,435,540012,汇丰晋信恒生,9月19日,1.6592,2.2392,0.11%,-3.12%,-2.31%,-5.26%,-3.36%,-12.85%,-10.94%,8.47%,-15.52%,136.94%,---,0.12%, +,436,165525,信诚中证基建,9月19日,0.735,0.743,-0.15%,-6.25%,-2.85%,-7.10%,-4.78%,-12.85%,-0.14%,-3.92%,-10.41%,-25.88%,-15.85%,0.10%, +,437,161026,富国中证国有,9月20日,0.98,1.309,-0.20%,-3.64%,-2.68%,-4.76%,-2.68%,-12.89%,-4.74%,15.93%,-14.49%,16.04%,-12.97%,0.12%, +,438,320010,诺安中证10,9月20日,1.719,1.839,0.17%,-4.13%,-5.76%,-7.68%,-4.76%,-12.92%,-6.17%,21.70%,-16.72%,85.47%,-12.25%,0.12%, +,439,202025,南方上证38,9月19日,1.8713,1.8713,-0.52%,-5.60%,-6.88%,-3.43%,-1.91%,-12.92%,6.04%,34.30%,-13.78%,87.13%,-13.37%,0.12%, +,440,5737,博时上证50,9月20日,1.0887,1.0887,-0.17%,-3.18%,-2.45%,-6.52%,-5.63%,-12.92%,-14.07%,1.88%,-15.60%,16.15%,-11.90%,0.00%, +,441,5829,建信MSCI,9月19日,1.4871,1.4871,-0.12%,-4.10%,-5.14%,-6.03%,-4.11%,-12.95%,0.80%,26.95%,-15.74%,48.70%,---,0.15%, +,442,6341,中金MSCI,9月19日,2.0461,2.0461,0.00%,-2.35%,-2.54%,-6.96%,-4.05%,-12.95%,-9.98%,38.26%,-21.92%,104.61%,---,0.10%, +,443,1548,天弘上证50,9月20日,1.2273,1.2273,-0.19%,-3.25%,-2.52%,-6.76%,-5.79%,-12.96%,-15.21%,0.07%,-15.63%,22.73%,-11.91%,0.10%, +,444,12875,易方达上证5,9月20日,1.0223,1.0223,-0.19%,-3.23%,-2.53%,-6.73%,-5.44%,-12.96%,---,---,-15.37%,-16.57%,-11.91%,0.00%, +,445,690008,民生中证内地,9月20日,1.046,1.046,1.85%,-4.47%,-1.51%,-2.61%,4.18%,-13.05%,60.43%,65.77%,0.97%,4.60%,-14.33%,0.12%, +,446,7995,华夏中证50,9月20日,1.6051,1.6051,0.96%,-5.09%,-4.94%,-0.16%,1.85%,-13.05%,16.09%,---,-11.30%,60.51%,-13.63%,0.00%, +,447,540007,汇丰晋信中小,9月19日,1.8901,1.9101,-0.78%,-4.74%,-4.78%,-3.56%,4.79%,-13.06%,-3.18%,48.23%,-13.28%,92.47%,---,0.15%, +,448,6604,嘉实消费精选,9月20日,1.8332,1.8332,1.10%,-1.14%,-2.19%,-10.23%,-1.21%,-13.06%,3.85%,69.18%,-18.03%,83.32%,-11.62%,0.15%, +,449,3865,创金合信量化,9月19日,1.7198,1.7198,-0.93%,-6.95%,-11.58%,-3.88%,-1.29%,-13.08%,9.77%,54.69%,-15.30%,36.38%,---,0.00%, +,450,11107,九泰天兴量化,9月20日,0.8651,0.8651,0.85%,-3.98%,-5.32%,-5.18%,-2.20%,-13.09%,---,---,-9.69%,-13.49%,-13.13%,0.15%, +,451,11793,建信智能汽车,9月19日,0.8733,0.8733,0.22%,-7.89%,-13.45%,-9.08%,2.00%,-13.09%,---,---,-12.36%,-12.67%,---,0.15%, +,452,502006,易方达中证国,9月20日,1.3312,0.8358,-0.19%,-3.65%,-2.68%,-4.80%,-2.77%,-13.11%,-1.88%,33.60%,-14.53%,-13.41%,-13.21%,0.10%, +,453,1549,天弘上证50,9月20日,1.207,1.207,-0.19%,-3.25%,-2.54%,-6.81%,-5.89%,-13.13%,-15.56%,-0.53%,-15.75%,20.70%,-12.09%,0.00%, +,454,6342,中金MSCI,9月19日,2.0316,2.0316,0.00%,-2.36%,-2.56%,-7.02%,-4.17%,-13.16%,-10.43%,37.25%,-22.06%,103.16%,---,0.00%, +,455,11108,九泰天兴量化,9月20日,0.8642,0.8642,0.85%,-3.99%,-5.32%,-5.20%,-2.25%,-13.17%,---,---,-9.75%,-13.58%,-13.21%,0.00%, +,456,6220,工银上证50,9月20日,1.1796,1.1796,-0.16%,-3.14%,-2.53%,-7.22%,-6.43%,-13.19%,-16.66%,-6.10%,-16.06%,17.96%,-12.16%,0.10%, +,457,13233,华夏中证50,9月20日,0.9027,0.9027,0.97%,-5.07%,-4.85%,-0.14%,1.82%,-13.19%,---,---,-11.00%,-9.73%,-13.74%,0.12%, +,458,13082,信诚中证基建,9月19日,0.732,0.732,-0.16%,-6.26%,-2.89%,-7.20%,-4.98%,-13.21%,---,---,-10.67%,-4.14%,-16.18%,0.00%, +,459,4716,信诚量化阿尔,9月19日,1.4768,1.5577,-0.20%,-3.62%,-4.04%,-6.31%,-5.09%,-13.23%,-2.03%,36.99%,-17.44%,55.16%,-12.75%,0.15%, +,460,11607,民生中证内地,9月20日,1.042,1.042,1.96%,-4.40%,-1.51%,-2.71%,4.10%,-13.24%,---,---,0.87%,27.23%,-14.52%,0.00%, +,461,161211,国投沪深30,9月20日,1.6904,1.6904,-0.94%,-3.93%,-0.56%,-5.02%,-6.64%,-13.27%,-14.82%,-1.13%,-13.13%,69.04%,-12.35%,0.12%, +,462,7571,南方上证38,9月19日,1.8483,1.8483,-0.52%,-5.61%,-6.91%,-3.53%,-2.11%,-13.27%,5.20%,32.71%,-14.03%,40.48%,-13.71%,0.00%, +,463,10331,天弘消费股票,9月20日,0.727,0.727,0.76%,-2.65%,-3.75%,-7.64%,-0.34%,-13.27%,---,---,-17.94%,-27.30%,-11.59%,0.12%, +,464,10351,诺安中证10,9月20日,1.706,1.706,0.24%,-4.10%,-5.75%,-7.73%,-4.91%,-13.27%,---,---,-16.94%,-8.08%,-12.60%,0.00%, +,465,1149,汇丰晋信恒生,9月19日,1.6358,2.1858,0.11%,-3.13%,-2.35%,-5.38%,-3.61%,-13.29%,-11.84%,6.82%,-15.82%,51.61%,---,0.00%, +,466,5830,建信MSCI,9月19日,1.46,1.46,-0.13%,-4.11%,-5.17%,-6.13%,-4.31%,-13.31%,-0.02%,25.40%,-15.98%,45.99%,---,0.00%, +,467,502040,长盛上证50,9月19日,0.9664,0.9664,0.07%,-2.66%,-2.40%,-6.84%,-6.02%,-13.32%,-11.34%,15.85%,-15.97%,61.63%,-12.51%,0.12%, +,468,12873,易方达中证国,9月20日,1.3275,1.3275,-0.19%,-3.66%,-2.70%,-4.86%,-2.90%,-13.33%,---,---,-14.69%,-13.76%,-13.42%,0.00%, +,469,6193,鑫元核心资产,9月19日,1.4299,1.4299,-1.46%,-7.02%,-7.58%,-3.41%,3.05%,-13.39%,-0.36%,34.74%,-11.72%,42.99%,---,0.12%, +,470,11685,创金合信先进,9月19日,1.0361,1.0361,-1.88%,-4.59%,-6.51%,0.06%,-1.06%,-13.39%,---,---,-19.12%,3.61%,---,0.15%, +,471,9106,嘉合同顺智选,9月20日,0.8544,1.0044,0.20%,-4.04%,-5.19%,-5.25%,-3.23%,-13.40%,1.53%,---,-13.94%,-1.61%,-12.66%,0.15%, +,472,161039,富国中证10,9月20日,1.8757,1.8757,1.20%,-7.27%,-10.98%,-5.11%,-2.67%,-13.42%,10.19%,62.36%,-13.11%,85.35%,-12.96%,0.12%, +,473,6221,工银上证50,9月20日,1.1656,1.1656,-0.16%,-3.14%,-2.55%,-7.29%,-6.55%,-13.42%,-17.08%,-6.86%,-16.22%,16.56%,-12.39%,0.00%, +,474,8009,华商高端装备,9月19日,2.3282,2.3282,-1.17%,-3.95%,-7.20%,1.97%,1.87%,-13.44%,49.79%,---,-19.37%,132.82%,---,0.15%, +,475,163407,兴全沪深30,9月20日,2.1636,2.1636,-0.07%,-3.34%,-1.96%,-5.86%,-2.94%,-13.45%,-15.54%,6.75%,-15.56%,116.36%,-12.59%,0.12%, +,476,1051,华夏上证50,9月20日,0.9135,0.9693,-0.17%,-3.19%,-2.49%,-6.91%,-5.96%,-13.48%,-17.36%,-5.51%,-15.99%,-4.00%,-12.48%,0.12%, +,477,310318,申万菱信沪深,9月20日,3.1557,3.9482,0.25%,-3.93%,-3.75%,-5.53%,-2.59%,-13.48%,-5.18%,28.84%,-13.81%,518.80%,-12.76%,0.12%, +,478,6605,嘉实消费精选,9月20日,1.799,1.799,1.10%,-1.15%,-2.23%,-10.35%,-1.46%,-13.49%,2.82%,66.70%,-18.32%,79.90%,-12.06%,0.00%, +,479,8085,海富通先进制,9月19日,1.4013,1.4013,-0.01%,-11.10%,-13.21%,7.09%,13.54%,-13.53%,37.29%,---,-5.25%,40.13%,-11.00%,0.15%, +,480,8916,华夏中证浙江,9月20日,0.9802,0.9802,0.13%,-5.75%,-4.50%,-5.52%,-7.99%,-13.54%,---,---,-15.31%,-1.98%,-13.27%,0.12%, +,481,13234,华夏中证50,9月20日,0.8987,0.8987,0.97%,-5.08%,-4.89%,-0.24%,1.61%,-13.54%,---,---,-11.26%,-10.13%,-14.09%,0.00%, +,482,12503,国泰中证环保,9月19日,1.0271,1.0271,-0.13%,-8.02%,-13.49%,-9.03%,-4.37%,-13.58%,---,---,-14.09%,2.71%,-12.35%,0.10%, +,483,11295,信诚量化阿尔,9月19日,0.8657,0.8657,-0.20%,-3.62%,-4.07%,-6.40%,-5.28%,-13.58%,---,---,-17.68%,-13.43%,-13.11%,0.00%, +,484,162416,华宝港股通恒,9月20日,0.8595,0.8595,1.28%,-0.99%,-4.76%,-3.06%,-3.26%,-13.60%,-5.83%,-13.84%,-9.33%,-14.05%,-12.00%,0.10%, +,485,164304,新华中证环保,9月19日,1.4212,1.4212,-0.17%,-8.39%,-14.44%,-8.53%,-5.91%,-13.61%,49.78%,97.61%,-15.60%,63.13%,---,0.10%, +,486,160634,鹏华中证环保,9月19日,1.479,1.069,-0.20%,-8.42%,-14.46%,-8.70%,-5.98%,-13.61%,51.05%,100.45%,-15.73%,12.69%,---,0.12%, +,487,1188,鹏华改革红利,9月19日,1.187,1.187,-1.00%,-3.73%,-9.46%,-5.19%,-9.32%,-13.61%,2.24%,25.87%,-23.57%,18.70%,---,0.15%, +,488,6704,易方达MSC,9月20日,1.3896,1.3896,0.33%,-4.17%,-4.82%,-6.15%,-3.79%,-13.61%,-0.49%,33.81%,-15.74%,38.96%,-13.37%,0.10%, +,489,10332,天弘消费股票,9月20日,0.7227,0.7227,0.75%,-2.67%,-3.79%,-7.74%,-0.55%,-13.61%,---,---,-18.17%,-27.73%,-11.93%,0.00%, +,490,5960,博时量化价值,9月20日,1.3752,1.3752,-0.23%,-4.19%,-3.81%,-3.09%,-2.91%,-13.63%,3.21%,30.08%,-11.73%,37.52%,-14.67%,0.15%, +,491,12516,国泰中证细分,9月19日,0.9943,0.9943,-0.06%,-7.59%,-13.67%,-8.94%,-5.31%,-13.63%,---,---,-16.97%,-0.57%,-12.13%,0.10%, +,492,7410,浦银安盛中证,9月19日,1.2364,1.2364,-0.95%,-5.50%,-6.52%,-5.75%,-4.39%,-13.64%,-3.62%,---,-15.26%,23.64%,---,0.12%, +,493,2952,建信多因子量,9月19日,1.2109,1.2109,-0.71%,-6.86%,-8.87%,-4.40%,-5.94%,-13.70%,-11.60%,23.38%,-18.96%,21.09%,---,0.15%, +,494,6705,易方达MSC,9月20日,1.3815,1.3815,0.33%,-4.17%,-4.83%,-6.17%,-3.85%,-13.70%,-0.69%,33.38%,-15.80%,38.15%,-13.45%,0.00%, +,495,1163,银华中国梦3,9月20日,1.918,1.918,0.16%,-2.59%,-2.59%,-6.30%,0.47%,-13.72%,-6.26%,62.27%,-18.00%,91.80%,-12.46%,0.15%, +,496,5733,华夏上证50,9月20日,0.9039,0.9592,-0.19%,-3.20%,-2.52%,-6.99%,-6.11%,-13.74%,-17.85%,-6.33%,-16.18%,-2.68%,-12.75%,0.00%, +,497,1409,工银互联网加,9月20日,0.595,0.595,0.34%,-3.57%,-5.10%,-3.41%,0.85%,-13.77%,2.41%,54.15%,-19.49%,-40.50%,-13.39%,0.15%, +,498,1469,广发中证全指,9月19日,0.9493,0.9493,-0.16%,-3.35%,-0.48%,-4.96%,-6.57%,-13.78%,-19.74%,-13.34%,-13.26%,-5.07%,---,0.12%, +,499,519100,长盛中证10,9月19日,1.2074,2.3634,-0.06%,-3.82%,-5.54%,-7.40%,-5.72%,-13.79%,-7.97%,18.36%,-17.17%,168.40%,-12.74%,0.15%, +,500,5009,申万菱信行业,9月20日,1.9547,1.9547,3.05%,-6.02%,-10.45%,-4.38%,6.29%,-13.79%,41.09%,126.13%,-9.08%,95.45%,-14.76%,0.15%, +,501,7230,兴全沪深30,9月20日,2.1401,2.1401,-0.07%,-3.35%,-2.00%,-5.96%,-3.14%,-13.80%,-16.22%,5.64%,-15.81%,16.80%,-12.94%,0.00%, +,502,6286,华泰MSCI,9月19日,1.3023,1.6963,-0.13%,-4.20%,-5.14%,-5.86%,-3.60%,-13.81%,-2.01%,31.80%,-15.96%,65.28%,-13.19%,0.12%, +,503,8917,华夏中证浙江,9月20日,0.9743,0.9743,0.12%,-5.76%,-4.54%,-5.60%,-8.14%,-13.81%,---,---,-15.51%,-2.57%,-13.53%,0.00%, +,504,11686,创金合信先进,9月19日,1.0285,1.0285,-1.89%,-4.59%,-6.55%,-0.06%,-1.30%,-13.82%,---,---,-19.40%,2.85%,---,0.00%, +,505,1714,工银文体产业,9月20日,2.942,3.131,-0.07%,-3.29%,-5.31%,-5.34%,-2.39%,-13.83%,0.51%,70.75%,-19.42%,232.52%,-13.14%,0.15%, +,506,8238,中泰沪深30,9月20日,1.3243,1.3243,0.17%,-3.68%,-4.65%,-6.14%,-3.81%,-13.84%,-3.57%,---,-15.20%,32.20%,-13.03%,0.10%, +,507,7804,申万菱信沪深,9月20日,1.3512,1.3512,0.25%,-3.95%,-3.78%,-5.63%,-2.80%,-13.84%,-5.94%,27.33%,-14.06%,35.12%,-13.12%,0.00%, +,508,7275,银河沪深30,9月20日,1.2936,1.3556,0.19%,-4.06%,-5.14%,-6.62%,-5.26%,-13.84%,-0.97%,32.18%,-15.94%,35.33%,-13.43%,0.15%, +,509,12504,国泰中证环保,9月19日,1.0233,1.0233,-0.14%,-8.03%,-13.51%,-9.10%,-4.52%,-13.84%,---,---,-14.27%,2.33%,-12.61%,0.00%, +,510,9107,嘉合同顺智选,9月20日,0.8429,0.9929,0.20%,-4.06%,-5.24%,-5.37%,-3.50%,-13.85%,0.50%,---,-14.26%,-2.84%,-13.11%,0.00%, +,511,5310,广发电子信息,9月20日,1.6633,1.6633,0.52%,-4.58%,-11.46%,-2.14%,-10.26%,-13.85%,-2.92%,58.30%,-26.17%,66.33%,-13.82%,0.15%, +,512,6251,银华兴盛股票,9月20日,2.0909,---,2.82%,-5.02%,-8.40%,-5.47%,4.96%,-13.87%,33.96%,108.51%,-13.21%,109.09%,-14.24%,0.15%, +,513,8084,海富通先进制,9月19日,1.386,1.386,-0.02%,-11.11%,-13.24%,6.99%,13.31%,-13.88%,36.19%,---,-5.52%,38.60%,-11.35%,0.00%, +,514,5761,招商MSCI,9月20日,1.3482,1.3482,0.35%,-4.15%,-4.81%,-6.14%,-3.58%,-13.89%,-3.59%,20.93%,-15.78%,34.82%,-13.55%,0.12%, +,515,12517,国泰中证细分,9月19日,0.9906,0.9906,-0.06%,-7.59%,-13.70%,-9.01%,-5.46%,-13.89%,---,---,-17.15%,-0.94%,-12.39%,0.00%, +,516,501067,招商富时A-,9月20日,1.1635,1.1635,0.19%,-2.89%,-3.99%,-7.75%,-5.20%,-13.90%,-13.16%,-6.05%,-15.88%,16.35%,-11.62%,0.08%, +,517,11498,富国沪深30,9月20日,0.8365,0.8365,-0.62%,-3.97%,-3.18%,-7.65%,-1.43%,-13.92%,---,---,-16.49%,-16.35%,-12.95%,0.15%, +,518,502020,国金上证50,9月19日,0.9792,0.9792,0.06%,-2.82%,-2.33%,-6.90%,-6.01%,-13.93%,-18.21%,-9.00%,-16.18%,-9.13%,-13.10%,0.12%, +,519,7411,浦银安盛中证,9月19日,1.2265,1.2265,-0.95%,-5.51%,-6.55%,-5.83%,-4.55%,-13.93%,-4.23%,---,-15.44%,22.65%,---,0.00%, +,520,8128,湘财长源股票,9月19日,1.0177,1.5099,-0.79%,-3.54%,-4.32%,-3.75%,-17.66%,-13.93%,9.13%,---,-18.27%,49.16%,---,0.15%, +,521,11104,光大智能汽车,9月20日,0.8803,0.8803,2.97%,-5.80%,-10.68%,-6.28%,-0.58%,-13.94%,---,---,-20.31%,-11.97%,-13.78%,0.15%, +,522,2979,广发中证全指,9月19日,0.9372,0.9372,-0.16%,-3.35%,-0.50%,-5.01%,-6.66%,-13.95%,-20.05%,-13.87%,-13.37%,11.62%,---,0.00%, +,523,20021,国泰上证18,9月19日,0.9993,1.5293,0.06%,-2.35%,0.28%,-5.03%,-7.13%,-13.96%,-20.16%,-15.88%,-12.30%,53.78%,-13.23%,0.12%, +,524,213010,宝盈中证10,9月20日,1.984,1.984,0.15%,-4.11%,-5.48%,-7.55%,-5.16%,-13.96%,-7.20%,20.68%,-16.78%,98.40%,-13.02%,0.12%, +,525,5268,鹏华优势企业,9月19日,1.7617,1.7617,-0.25%,-2.72%,-5.42%,-4.64%,-3.41%,-13.98%,-11.41%,47.20%,-19.89%,76.17%,---,0.15%, +,526,5607,华宝中证50,9月20日,1.161,1.161,0.76%,-4.37%,-3.75%,-1.78%,-1.08%,-13.99%,-4.86%,25.76%,-9.87%,16.10%,-14.48%,0.12%, +,527,6119,银华中证央企,9月20日,1.1335,1.1335,-0.05%,-3.93%,-3.60%,-4.47%,-3.69%,-13.99%,7.91%,11.83%,-14.81%,13.35%,-15.56%,0.12%, +,528,160807,长盛沪深30,9月19日,1.444,1.794,-0.14%,-3.93%,-4.94%,-6.48%,-4.05%,-14.00%,-3.07%,32.53%,-16.24%,85.18%,-13.47%,0.12%, +,529,5802,汇添富智能制,9月20日,1.7339,1.7339,1.84%,-4.14%,-11.24%,-0.07%,3.05%,-14.00%,8.61%,62.00%,-18.71%,73.39%,-13.80%,0.15%, +,530,8326,东财通信A,9月19日,0.8571,0.8571,-1.52%,-6.31%,-10.46%,-5.28%,-8.28%,-14.00%,-20.87%,---,-26.07%,-14.29%,---,0.10%, +,531,12206,中泰沪深30,9月20日,0.8287,0.8287,0.17%,-3.72%,-4.69%,-6.66%,-4.26%,-14.01%,---,---,-15.56%,-17.27%,-13.22%,0.10%, +,532,656,前海开源沪深,9月19日,1.6448,2.0148,-0.12%,-3.83%,-4.97%,-6.51%,-4.54%,-14.02%,-1.86%,33.67%,-16.47%,126.41%,---,0.12%, +,533,9899,上银内需增长,9月19日,0.7944,0.7944,-0.01%,-1.97%,-2.46%,-6.21%,-3.71%,-14.02%,-20.56%,---,-20.56%,-20.56%,-12.54%,0.15%, +,534,6293,华泰MSCI,9月19日,1.2818,1.6758,-0.13%,-4.21%,-5.16%,-5.92%,-3.73%,-14.03%,-2.49%,30.66%,-16.11%,63.11%,-13.41%,0.00%, +,535,4891,华润元大成长,9月20日,1.0047,1.0047,0.38%,-3.82%,-4.57%,-6.18%,-4.92%,-14.06%,-13.10%,---,-16.16%,0.47%,-13.60%,0.15%, +,536,40180,华安上证18,9月19日,1.5016,1.5016,-0.07%,-3.56%,-3.19%,-6.51%,-5.33%,-14.08%,-11.65%,1.68%,-15.02%,50.16%,---,0.12%, +,537,6194,鑫元核心资产,9月19日,1.3947,1.3947,-1.46%,-7.04%,-7.64%,-3.61%,2.63%,-14.09%,-1.94%,31.51%,-12.23%,39.47%,---,0.00%, +,538,5113,平安沪深30,9月19日,1.2549,1.2549,-0.09%,-3.94%,-4.98%,-5.50%,-4.60%,-14.12%,-2.41%,30.30%,-17.14%,25.49%,-13.27%,0.12%, +,539,1186,富国文体健康,9月20日,1.93,1.93,-0.26%,-3.69%,-3.88%,-8.49%,-6.04%,-14.15%,-5.81%,72.01%,-25.14%,93.00%,-13.95%,0.15%, +,540,10128,宝盈发展新动,9月20日,1.1434,1.1434,1.14%,-5.57%,-10.60%,-8.49%,-6.19%,-14.15%,---,---,-23.32%,14.34%,-13.83%,0.15%, +,541,10469,圆信永丰聚优,9月20日,0.9163,0.9163,0.28%,-4.28%,-6.69%,-3.35%,-2.37%,-14.15%,---,---,-16.75%,-8.37%,-14.95%,0.15%, +,542,8239,中泰沪深30,9月20日,1.3113,1.3113,0.18%,-3.69%,-4.68%,-6.25%,-4.00%,-14.18%,-4.34%,---,-15.44%,30.90%,-13.38%,0.00%, +,543,10236,广发电子信息,9月20日,1.6521,1.6521,0.52%,-4.59%,-11.49%,-2.24%,-10.44%,-14.19%,---,---,-26.38%,-1.11%,-14.16%,0.00%, +,544,11934,中航量化阿尔,9月19日,0.8802,0.8802,-0.59%,-5.40%,-6.47%,-4.78%,-1.13%,-14.19%,---,---,-11.09%,-11.98%,-13.82%,0.15%, +,545,202211,南方中证10,9月20日,1.3769,1.852,0.08%,-3.96%,-5.23%,-7.76%,-5.58%,-14.20%,-8.65%,16.94%,-16.83%,111.54%,-13.29%,0.12%, +,546,8327,东财通信C,9月19日,0.8514,0.8514,-1.52%,-6.32%,-10.47%,-5.35%,-8.39%,-14.21%,-21.26%,---,-26.20%,-14.86%,---,0.00%, +,547,13004,国泰价值领航,9月19日,0.8554,0.8554,-0.28%,-6.74%,-12.20%,-8.89%,-1.80%,-14.23%,---,---,-18.47%,-14.46%,---,0.15%, +,548,1015,华夏沪深30,9月20日,1.776,1.776,0.17%,-4.10%,-4.57%,-7.74%,-4.77%,-14.24%,-5.63%,20.49%,-15.67%,77.60%,-13.32%,0.12%, +,549,501068,招商富时A-,9月20日,1.146,1.146,0.19%,-2.90%,-4.02%,-7.84%,-5.39%,-14.24%,-13.85%,-7.17%,-16.12%,14.60%,-11.97%,0.00%, +,550,1050,汇添富成长多,9月20日,1.739,1.739,0.64%,-5.39%,-6.91%,-3.82%,-1.53%,-14.25%,3.08%,48.63%,-12.39%,73.90%,-15.05%,0.15%, +,551,164905,交银国证新能,9月20日,1.3224,1.3889,1.98%,-5.97%,-13.47%,-11.68%,-4.13%,-14.25%,47.01%,111.45%,-16.11%,38.89%,-14.06%,0.12%, +,552,6438,博时央调ET,9月20日,1.2,1.2,-0.07%,-3.97%,-3.63%,-4.41%,-3.40%,-14.26%,7.07%,11.87%,-14.84%,20.00%,-15.83%,0.12%, +,553,1162,前海开源优势,9月19日,1.417,1.417,0.35%,-5.22%,-4.45%,-8.87%,-6.47%,-14.28%,-12.53%,13.36%,-15.95%,41.70%,---,0.15%, +,554,7276,银河沪深30,9月20日,1.2736,1.3346,0.20%,-4.06%,-5.19%,-6.74%,-5.50%,-14.28%,-1.96%,30.17%,-16.25%,33.23%,-13.87%,0.00%, +,555,6196,华夏中证央企,9月19日,1.2077,1.2077,-0.49%,-4.01%,-3.73%,-3.94%,-3.28%,-14.29%,6.52%,12.24%,-14.89%,20.77%,---,0.12%, +,556,4892,华润元大成长,9月20日,0.9983,0.9983,0.37%,-3.82%,-4.59%,-6.24%,-5.05%,-14.29%,-13.54%,---,-16.31%,-0.17%,-13.82%,0.00%, +,557,160620,鹏华资源A,9月19日,1.768,1.259,0.17%,-6.46%,-3.49%,-4.33%,1.55%,-14.30%,52.02%,73.97%,-0.28%,32.80%,---,0.12%, +,558,3232,创金合信金融,9月19日,0.9667,1.049,0.09%,-3.10%,5.21%,-3.07%,-2.44%,-14.30%,-13.25%,1.70%,-16.57%,4.90%,---,0.15%, +,559,5457,景顺长城量化,9月19日,1.5364,1.5364,-0.82%,-7.31%,-10.73%,-6.31%,-5.40%,-14.30%,12.19%,57.06%,-16.33%,53.64%,---,0.15%, +,560,1638,前海开源优势,9月19日,1.551,1.551,0.39%,-5.20%,-4.38%,-8.87%,-6.45%,-14.31%,-12.62%,13.13%,-15.98%,55.10%,---,0.15%, +,561,1539,嘉实中证金融,9月20日,1.1418,1.1418,-0.91%,-4.28%,-1.05%,-5.53%,-7.33%,-14.31%,-18.76%,-12.86%,-13.77%,14.18%,-13.41%,0.12%, +,562,5762,招商MSCI,9月20日,1.3186,1.3186,0.35%,-4.15%,-4.85%,-6.26%,-3.82%,-14.32%,-4.55%,19.13%,-16.08%,31.86%,-13.97%,0.00%, +,563,5608,华宝中证50,9月20日,1.1406,1.1406,0.76%,-4.38%,-3.79%,-1.88%,-1.29%,-14.34%,-5.63%,24.25%,-10.14%,14.06%,-14.82%,0.00%, +,564,5961,博时量化价值,9月20日,1.3289,1.3289,-0.23%,-4.20%,-3.87%,-3.28%,-3.30%,-14.34%,1.54%,26.96%,-12.24%,32.89%,-15.36%,0.00%, +,565,8129,湘财长源股票,9月19日,1.0005,1.488,-0.79%,-3.55%,-4.35%,-3.88%,-17.86%,-14.36%,8.05%,---,-18.56%,46.66%,---,0.00%, +,566,10687,工银文体产业,9月20日,2.911,2.911,-0.10%,-3.32%,-5.36%,-5.49%,-2.71%,-14.36%,---,---,-19.79%,-6.16%,-13.67%,0.00%, +,567,12207,中泰沪深30,9月20日,0.8247,0.8247,0.17%,-3.73%,-4.72%,-6.75%,-4.45%,-14.36%,---,---,-15.81%,-17.67%,-13.56%,0.00%, +,568,12808,鹏华资源C,9月19日,1.109,1.109,0.18%,-6.49%,-3.48%,-4.31%,1.46%,-14.36%,---,---,-0.36%,10.90%,---,0.00%, +,569,1718,工银物流产业,9月20日,3.212,3.212,0.38%,-4.29%,-7.44%,-4.40%,-4.23%,-14.39%,30.46%,106.29%,-22.94%,221.20%,-15.21%,0.15%, +,570,7505,华夏AH经济,9月20日,1.1953,1.1953,0.13%,-3.71%,-2.61%,-5.82%,-3.84%,-14.41%,-2.94%,19.91%,-12.58%,19.53%,-15.17%,0.12%, +,571,3318,景顺中证50,9月19日,1.2466,1.2466,-0.65%,-5.56%,-5.55%,-4.66%,-4.44%,-14.42%,3.87%,29.71%,-13.37%,24.66%,---,0.12%, +,572,11499,富国沪深30,9月20日,0.8298,0.8298,-0.62%,-3.98%,-3.24%,-7.79%,-1.73%,-14.44%,---,---,-16.85%,-17.02%,-13.47%,0.00%, +,573,410008,华富中证10,9月20日,1.3802,1.8802,0.08%,-4.16%,-5.62%,-7.65%,-5.62%,-14.46%,-5.92%,20.85%,-17.34%,86.21%,-13.81%,0.12%, +,574,160814,长盛中证金融,9月19日,0.8109,0.8109,-0.14%,-3.25%,-0.16%,-5.16%,-6.88%,-14.46%,-19.63%,-4.57%,-13.69%,-6.22%,-14.17%,0.12%, +,575,5530,汇添富沪深3,9月20日,1.459,1.459,0.30%,-3.94%,-4.80%,-6.83%,-3.68%,-14.47%,-7.21%,31.57%,-14.82%,45.90%,-14.01%,0.15%, +,576,580008,东吴新产业精,9月19日,3.1325,3.1325,-1.08%,-6.20%,-9.52%,-2.46%,-1.53%,-14.48%,6.74%,51.40%,-16.38%,213.25%,---,0.15%, +,577,10470,圆信永丰聚优,9月20日,0.9111,0.9111,0.29%,-4.29%,-6.72%,-3.44%,-2.56%,-14.49%,---,---,-16.99%,-8.89%,-15.29%,0.00%, +,578,40002,华安中国A股,9月19日,0.849,4.383,-0.35%,-3.96%,-5.35%,-5.35%,-6.29%,-14.50%,-7.59%,18.11%,-19.91%,486.26%,---,0.15%, +,579,1651,工银新蓝筹股,9月20日,2.333,2.333,-0.60%,-3.28%,-2.26%,-4.81%,-5.81%,-14.51%,1.79%,59.03%,-17.09%,133.30%,-14.82%,0.15%, +,580,6486,广发中证10,9月20日,1.4066,1.4066,1.20%,-5.84%,-9.78%,-5.01%,-4.91%,-14.52%,-4.75%,19.67%,-17.73%,40.65%,-14.92%,0.10%, +,581,7809,富国央企创新,9月20日,1.2653,1.2653,-0.02%,-4.30%,-3.87%,-4.30%,-2.43%,-14.52%,19.73%,---,-13.09%,26.53%,-16.39%,0.12%, +,582,13049,兴业能源革新,9月19日,0.8498,0.8498,-0.55%,-5.82%,-9.18%,2.09%,-1.47%,-14.53%,---,---,-18.83%,-15.02%,---,0.15%, +,583,460220,华泰柏瑞上证,9月19日,1.3691,1.3691,-0.39%,-5.26%,-5.11%,-4.90%,-3.20%,-14.55%,2.10%,27.74%,-13.61%,36.91%,-14.95%,0.15%, +,584,5691,南方中证10,9月20日,1.3524,1.8273,0.07%,-3.97%,-5.27%,-7.86%,-5.78%,-14.55%,-9.38%,15.54%,-17.07%,21.11%,-13.64%,0.00%, +,585,6197,华夏中证央企,9月19日,1.1938,1.1938,-0.49%,-4.03%,-3.76%,-4.03%,-3.43%,-14.55%,5.88%,11.24%,-15.07%,19.38%,---,0.00%, +,586,5114,平安沪深30,9月19日,1.2202,1.2202,-0.09%,-3.96%,-5.02%,-5.62%,-4.84%,-14.55%,-3.39%,28.33%,-17.44%,22.02%,-13.70%,0.00%, +,587,8407,恒生沪深港通,9月19日,0.8989,0.8989,-0.27%,-2.90%,-3.00%,-7.82%,-2.79%,-14.55%,-19.43%,---,-13.48%,-10.11%,---,0.12%, +,588,778,鹏华先进制造,9月19日,2.747,2.747,-0.04%,-1.40%,-1.86%,-6.41%,-2.76%,-14.58%,-3.82%,57.51%,-18.94%,174.70%,---,0.15%, +,589,10129,宝盈发展新动,9月20日,1.1322,1.1322,1.14%,-5.57%,-10.65%,-8.61%,-6.43%,-14.58%,---,---,-23.60%,13.22%,-14.26%,0.00%, +,590,10003,景顺长城电子,9月19日,1.0532,1.0532,-1.62%,-4.42%,-11.20%,-2.45%,-8.70%,-14.60%,4.63%,---,-22.45%,5.32%,---,0.15%, +,591,320014,诺安沪深30,9月20日,1.4401,1.4749,0.15%,-3.97%,-4.70%,-6.79%,-4.76%,-14.61%,-2.68%,26.10%,-16.96%,47.49%,-14.26%,0.12%, +,592,6439,博时央调ET,9月20日,1.1836,1.1836,-0.08%,-3.98%,-3.67%,-4.51%,-3.59%,-14.61%,6.20%,10.53%,-15.09%,18.36%,-16.17%,0.00%, +,593,2595,博时工业4.,9月20日,2.049,2.049,1.99%,-4.74%,-9.09%,-6.18%,-3.76%,-14.62%,0.34%,51.89%,-20.30%,104.90%,-14.84%,0.15%, +,594,4854,广发中证全指,9月20日,1.3519,1.3519,0.68%,-4.92%,-8.47%,-13.40%,13.05%,-14.64%,28.32%,73.92%,-10.37%,35.19%,-13.33%,0.10%, +,595,5999,嘉实中证金融,9月20日,1.0478,1.0478,-0.91%,-4.28%,-1.09%,-5.62%,-7.51%,-14.65%,-19.40%,-13.92%,-14.01%,4.99%,-13.75%,0.00%, +,596,11125,富国文体健康,9月20日,1.911,1.911,-0.26%,-3.68%,-3.97%,-8.61%,-6.32%,-14.65%,---,---,-25.44%,-12.02%,-14.46%,0.00%, +,597,7506,华夏AH经济,9月20日,1.1839,1.1839,0.13%,-3.72%,-2.64%,-5.89%,-3.99%,-14.67%,-3.53%,18.83%,-12.77%,18.39%,-15.42%,0.00%, +,598,7580,宝盈中证10,9月20日,1.934,1.934,0.10%,-4.16%,-5.57%,-7.77%,-5.57%,-14.69%,-8.73%,17.85%,-17.28%,22.72%,-13.78%,0.00%, +,599,11935,中航量化阿尔,9月19日,0.8745,0.8745,-0.60%,-5.42%,-6.53%,-4.94%,-1.44%,-14.71%,---,---,-11.48%,-12.55%,-14.34%,0.00%, +,600,1016,华夏沪深30,9月20日,1.709,1.709,0.18%,-4.15%,-4.63%,-7.92%,-5.00%,-14.72%,-6.56%,18.68%,-16.02%,70.90%,-13.77%,0.00%, +,601,7806,建信MSCI,9月19日,1.3611,1.4581,0.26%,-4.25%,-5.56%,-4.36%,-3.38%,-14.72%,5.78%,---,-14.60%,45.10%,---,0.15%, +,602,13005,国泰价值领航,9月19日,0.8501,0.8501,-0.28%,-6.76%,-12.25%,-9.03%,-2.10%,-14.75%,---,---,-18.82%,-14.99%,---,0.00%, +,603,110003,易方达上证5,9月20日,1.8165,3.7665,0.11%,-2.90%,-4.21%,-8.91%,-7.04%,-14.77%,-18.09%,5.94%,-18.22%,479.17%,-13.62%,0.15%, +,604,6034,富国MSCI,9月20日,1.8925,1.8925,0.48%,-4.02%,-5.29%,-6.24%,-2.35%,-14.79%,8.88%,51.85%,-12.34%,89.25%,-14.48%,0.12%, +,605,7523,汇添富内需增,9月20日,1.3341,1.3341,0.96%,-1.69%,-3.90%,-5.48%,2.69%,-14.79%,-12.56%,32.06%,-16.67%,33.41%,-14.31%,0.15%, +,606,11470,东吴新产业精,9月19日,3.1164,3.1164,-1.09%,-6.21%,-9.55%,-2.56%,-1.73%,-14.79%,---,---,-16.62%,-12.38%,---,0.00%, +,607,4855,广发中证全指,9月20日,1.3445,1.3445,0.68%,-4.93%,-8.48%,-13.44%,12.95%,-14.80%,27.82%,72.95%,-10.49%,34.45%,-13.50%,0.00%, +,608,5635,博时量化多策,9月20日,1.4636,1.4636,-0.16%,-4.03%,-3.96%,-1.98%,-0.27%,-14.81%,8.87%,43.11%,-10.63%,46.36%,-15.27%,0.15%, +,609,10556,汇添富沪深3,9月20日,1.4486,1.4486,0.30%,-3.95%,-4.83%,-6.92%,-3.88%,-14.81%,---,---,-15.06%,-9.10%,-14.34%,0.00%, +,610,206012,鹏华价值精选,9月19日,3.15,3.15,-0.54%,-6.08%,-8.54%,-1.32%,-2.51%,-14.86%,22.62%,125.97%,-14.70%,215.00%,---,0.15%, +,611,6487,广发中证10,9月20日,1.3912,1.3912,1.20%,-5.85%,-9.81%,-5.10%,-5.10%,-14.86%,-5.51%,18.30%,-17.97%,39.11%,-15.26%,0.00%, +,612,40190,华安上证龙头,9月19日,1.3,1.3,-0.54%,-4.48%,-3.77%,-6.27%,-6.61%,-14.87%,-14.81%,0.31%,-16.72%,30.00%,---,0.12%, +,613,7810,富国央企创新,9月20日,1.2513,1.2513,-0.02%,-4.30%,-3.89%,-4.39%,-2.62%,-14.87%,18.79%,---,-13.33%,25.13%,-16.72%,0.00%, +,614,3233,创金合信金融,9月19日,0.94,1.0297,0.10%,-3.11%,5.17%,-3.24%,-2.78%,-14.89%,-14.45%,-0.60%,-16.98%,2.97%,---,0.00%, +,615,519180,万家180指,9月19日,0.96,3.3,-0.08%,-3.62%,-3.26%,-6.81%,-5.86%,-14.90%,-13.07%,-0.12%,-15.72%,283.71%,-14.51%,0.15%, +,616,8408,恒生沪深港通,9月19日,0.8905,0.8905,-0.27%,-2.91%,-3.04%,-7.93%,-3.00%,-14.90%,-20.08%,---,-13.73%,-10.95%,---,0.00%, +,617,3016,中金中证50,9月19日,1.6438,1.6438,-0.52%,-5.84%,-7.20%,-4.78%,-4.22%,-14.91%,10.82%,57.17%,-14.75%,64.38%,---,0.10%, +,618,9954,北信瑞丰优选,9月19日,1.2805,1.2805,1.27%,-1.15%,-4.39%,-10.05%,-4.08%,-14.92%,---,---,-22.01%,28.05%,-10.58%,0.15%, +,619,10004,景顺长城电子,9月19日,1.0447,1.0447,-1.61%,-4.43%,-11.23%,-2.56%,-8.88%,-14.94%,3.80%,---,-22.67%,4.47%,---,0.00%, +,620,519032,海富通上证非,9月19日,1.3667,1.3667,-0.15%,-3.68%,-5.11%,-7.61%,-5.12%,-14.95%,-10.32%,12.49%,-18.02%,36.67%,-14.52%,0.12%, +,621,975,MSCI中国,9月20日,1.2189,1.3847,0.31%,-4.20%,-5.00%,-6.55%,-4.20%,-14.96%,-3.29%,23.56%,-16.79%,40.11%,-14.71%,0.12%, +,622,10352,诺安沪深30,9月20日,1.4305,1.4305,0.15%,-3.97%,-4.73%,-6.88%,-4.96%,-14.96%,---,---,-17.21%,-4.44%,-14.61%,0.00%, +,623,11476,工银新蓝筹股,9月20日,2.317,2.317,-0.60%,-3.30%,-2.32%,-4.96%,-6.04%,-14.97%,---,---,-17.40%,-18.59%,-15.25%,0.00%, +,624,13050,兴业能源革新,9月19日,0.8453,0.8453,-0.56%,-5.84%,-9.22%,1.95%,-1.72%,-14.97%,---,---,-19.13%,-15.47%,---,0.00%, +,625,4746,易方达上证5,9月20日,1.7921,1.8921,0.11%,-2.91%,-4.23%,-8.96%,-7.16%,-14.98%,-18.50%,5.15%,-18.37%,62.95%,-13.83%,0.00%, +,626,9374,浦银安盛MS,9月19日,0.9148,1.0248,-0.14%,-4.17%,-5.28%,-6.52%,-4.41%,-14.99%,---,---,-16.60%,0.68%,---,0.12%, +,627,8736,南方高股息股,9月20日,0.9274,0.9274,0.25%,-3.37%,-0.64%,1.96%,-3.69%,-15.00%,-8.98%,---,-16.46%,-7.26%,-14.26%,0.15%, +,628,7143,国投瑞银沪深,9月20日,1.2706,1.3496,0.16%,-3.88%,-5.21%,-7.60%,-5.08%,-15.03%,-0.75%,32.33%,-15.02%,34.44%,-14.69%,0.12%, +,629,399001,中海上证50,9月20日,1.282,1.454,-0.31%,-3.17%,-2.29%,-6.97%,-7.17%,-15.04%,-14.65%,3.03%,-17.77%,46.10%,-14.19%,0.12%, +,630,110021,易方达上证中,9月20日,1.7611,1.7611,0.36%,-4.47%,-4.02%,-5.61%,-4.39%,-15.05%,2.43%,29.05%,-13.73%,76.11%,-15.72%,0.12%, +,631,162907,泰信中证锐联,9月20日,1.1415,1.7016,0.67%,-5.32%,-6.43%,-5.54%,-3.85%,-15.07%,-0.83%,19.18%,-13.74%,73.77%,-16.01%,0.12%, +,632,7807,建信MSCI,9月19日,1.3461,1.4421,0.25%,-4.26%,-5.60%,-4.46%,-3.57%,-15.07%,4.93%,---,-14.85%,43.47%,---,0.00%, +,633,11667,东财高端制造,9月19日,1.0079,1.0079,-0.23%,-6.03%,-11.64%,-7.59%,-7.40%,-15.09%,---,---,-20.50%,0.79%,---,0.15%, +,634,5660,嘉实资源精选,9月20日,2.5598,2.5598,0.40%,-5.21%,-2.50%,3.84%,9.91%,-15.10%,47.14%,126.17%,3.21%,155.98%,-14.86%,0.15%, +,635,163111,申万菱信中小,9月20日,1.3328,2.1317,0.89%,-3.94%,-7.05%,-8.44%,-5.37%,-15.15%,-4.11%,35.67%,-19.84%,80.86%,-14.66%,0.12%, +,636,160638,鹏华中证一带,9月19日,1.634,0.738,-0.43%,-5.98%,-3.77%,-3.26%,-0.06%,-15.16%,25.40%,51.94%,-10.07%,-23.83%,---,0.12%, +,637,7657,东方红中证竞,9月20日,1.2119,1.2119,0.09%,-4.43%,-3.60%,-7.34%,-4.01%,-15.21%,-9.94%,16.82%,-16.40%,21.19%,-14.71%,0.12%, +,638,9479,中银中证10,9月20日,0.7835,0.7835,0.08%,-4.07%,-5.52%,-8.30%,-5.70%,-15.21%,---,---,-17.20%,-21.65%,-14.32%,0.12%, +,639,162413,华宝中证10,9月20日,0.9817,0.4103,1.21%,-5.82%,-9.84%,-5.28%,-5.19%,-15.22%,-6.10%,19.69%,-17.84%,-58.92%,-15.61%,0.10%, +,640,501030,汇添富中证环,9月20日,0.5486,0.5486,1.74%,-5.58%,-9.25%,-2.63%,-7.16%,-15.22%,-12.13%,-5.85%,-18.64%,-45.14%,-16.99%,0.12%, +,641,340006,兴全全球视野,9月20日,2.6598,5.4058,-0.11%,-3.32%,-5.55%,-5.07%,-6.16%,-15.23%,-3.29%,39.71%,-19.63%,902.31%,-15.26%,0.15%, +,642,5735,MSCI中国,9月20日,1.2082,1.3739,0.32%,-4.20%,-5.02%,-6.62%,-4.35%,-15.23%,-3.87%,22.48%,-16.97%,23.14%,-14.97%,0.00%, +,643,9375,浦银安盛MS,9月19日,0.9091,1.0191,-0.14%,-4.16%,-5.29%,-6.59%,-4.55%,-15.24%,---,---,-16.77%,0.09%,---,0.00%, +,644,3578,中金中证50,9月19日,1.6287,1.6287,-0.52%,-5.85%,-7.23%,-4.88%,-4.41%,-15.25%,9.94%,55.04%,-14.99%,50.85%,---,0.00%, +,645,4743,易方达上证中,9月20日,1.7943,1.7943,0.36%,-4.48%,-4.04%,-5.67%,-4.52%,-15.26%,1.91%,28.07%,-13.89%,44.77%,-15.93%,0.00%, +,646,5788,南方MSCI,9月20日,1.532,1.532,0.33%,-4.22%,-5.00%,-6.78%,-4.44%,-15.26%,-4.09%,29.22%,-16.94%,53.20%,-14.91%,0.12%, +,647,1166,建信环保产业,9月19日,1.453,1.453,0.14%,-7.10%,-12.31%,-6.86%,-1.69%,-15.28%,43.01%,112.74%,-14.73%,45.30%,---,0.15%, +,648,165515,信诚沪深30,9月19日,1.0479,1.6852,-0.12%,-3.79%,-4.91%,-7.10%,-5.91%,-15.29%,-3.99%,20.37%,-17.75%,88.33%,-14.64%,0.12%, +,649,7524,汇添富内需增,9月20日,1.3069,1.3069,0.96%,-1.71%,-3.95%,-5.63%,2.37%,-15.30%,-13.65%,29.47%,-17.03%,30.69%,-14.82%,0.00%, +,650,9398,华富成长企业,9月20日,0.9821,1.1231,1.29%,-5.76%,-10.85%,-7.17%,-9.59%,-15.30%,10.41%,---,-21.05%,9.76%,-15.88%,0.15%, +,651,9480,中银中证10,9月20日,0.782,0.782,0.08%,-4.07%,-5.53%,-8.32%,-5.76%,-15.30%,---,---,-17.27%,-21.80%,-14.40%,0.00%, +,652,6783,红土创新中证,9月20日,1.5418,1.5418,1.68%,-3.96%,-5.13%,-2.96%,-1.31%,-15.31%,9.68%,45.98%,-10.43%,54.18%,-15.54%,0.12%, +,653,257060,国联安上证商,9月19日,1.0885,1.0885,-0.21%,-6.65%,-3.49%,-6.06%,2.41%,-15.32%,58.79%,104.41%,0.42%,8.85%,-15.78%,0.15%, +,654,160806,长盛同庆中证,9月19日,1.513,1.727,-0.20%,-4.30%,-5.38%,-6.78%,-5.20%,-15.33%,-1.86%,30.31%,-17.50%,84.83%,-14.78%,0.12%, +,655,6143,恒生前海中证,9月19日,1.2354,1.2354,-1.18%,-5.57%,-4.84%,-3.51%,-5.18%,-15.34%,-10.02%,20.08%,-17.79%,23.54%,---,0.12%, +,656,7792,嘉实央企创新,9月20日,1.1751,1.1751,-0.03%,-4.44%,-3.91%,-4.38%,-2.51%,-15.34%,14.25%,---,-13.28%,17.51%,-17.31%,0.12%, +,657,11328,景顺长城新能,9月19日,1.2923,1.2923,-0.54%,-8.26%,-14.70%,-10.29%,-6.87%,-15.34%,---,---,-21.71%,29.23%,---,0.15%, +,658,162307,海富通中证1,9月19日,1.2858,1.6298,-0.05%,-3.85%,-5.62%,-7.90%,-6.73%,-15.35%,-10.21%,16.00%,-17.53%,70.90%,-14.45%,0.12%, +,659,20011,国泰沪深30,9月19日,0.8607,1.4718,-0.12%,-3.68%,-4.69%,-6.68%,-5.39%,-15.35%,-9.98%,13.53%,-17.55%,-0.55%,-14.71%,0.15%, +,660,162509,国联安中证1,9月19日,0.816,0.816,0.00%,-3.77%,-5.45%,-7.48%,-6.21%,-15.35%,-10.95%,16.83%,-17.82%,86.10%,-14.47%,0.12%, +,661,8382,融通产业趋势,9月20日,0.9851,1.0401,-0.30%,-2.82%,-0.39%,5.05%,5.35%,-15.35%,-11.00%,---,-14.20%,3.31%,-15.07%,0.15%, +,662,11668,东财高端制造,9月19日,1.0035,1.0035,-0.23%,-6.04%,-11.66%,-7.66%,-7.53%,-15.35%,---,---,-20.67%,0.35%,---,0.00%, +,663,1420,南方大数据3,9月19日,1.3199,1.3199,0.11%,-3.96%,-5.33%,-5.92%,-4.45%,-15.36%,-6.74%,21.78%,-16.76%,31.99%,-14.23%,0.12%, +,664,7144,国投瑞银沪深,9月20日,1.2562,1.3317,0.15%,-3.89%,-5.24%,-7.70%,-5.27%,-15.37%,-1.54%,30.74%,-15.26%,32.67%,-15.02%,0.00%, +,665,217017,招商上证消费,9月20日,2.27,2.27,0.50%,-2.48%,-4.11%,-8.30%,-3.26%,-15.38%,-11.91%,35.61%,-19.47%,127.00%,-14.16%,0.15%, +,666,1064,广发中证环保,9月19日,1.0788,1.0788,-0.19%,-8.41%,-14.56%,-9.28%,-6.65%,-15.39%,42.42%,89.83%,-16.52%,7.88%,---,0.12%, +,667,1242,博时中证淘金,9月20日,0.9854,0.9854,1.41%,-4.98%,-7.36%,-1.41%,-2.02%,-15.40%,-13.89%,7.31%,-12.14%,-1.46%,-16.29%,0.12%, +,668,7799,申万菱信中小,9月20日,1.5082,1.5082,0.89%,-3.95%,-7.08%,-8.51%,-5.51%,-15.41%,-4.69%,34.46%,-20.02%,50.82%,-14.92%,0.00%, +,669,10224,海富通中证1,9月19日,1.2872,1.2872,-0.05%,-3.85%,-5.62%,-7.93%,-6.79%,-15.43%,---,---,-17.61%,-15.48%,-14.53%,0.00%, +,670,11329,景顺长城新能,9月19日,1.2902,1.2902,-0.55%,-8.27%,-14.71%,-10.32%,-6.92%,-15.43%,---,---,-21.77%,29.02%,---,0.00%, +,671,920002,中金精选股票,9月20日,1.7291,5.1367,0.55%,-4.13%,-7.84%,-7.01%,-3.63%,-15.44%,-14.02%,---,-16.17%,-9.29%,-14.76%,--, +,672,955,南方产业活力,9月20日,1.428,1.428,0.14%,-3.71%,-5.31%,-6.11%,-2.99%,-15.45%,-17.02%,23.85%,-17.36%,42.80%,-15.40%,0.15%, +,673,1195,工银农业产业,9月20日,1.116,1.116,0.63%,-2.70%,-5.26%,-8.52%,-5.10%,-15.45%,-8.37%,51.63%,-20.79%,11.60%,-15.07%,0.15%, +,674,5868,平安MSCI,9月20日,1.4956,1.4956,0.35%,-4.12%,-4.95%,-6.50%,-5.07%,-15.45%,-1.53%,29.83%,-17.28%,49.56%,-15.17%,0.10%, +,675,7784,广发央企创新,9月20日,1.2886,1.2886,-0.03%,-4.46%,-3.99%,-4.82%,-2.97%,-15.45%,15.84%,---,-14.28%,28.86%,-17.32%,0.12%, +,676,161118,易方达中小企,9月20日,1.3206,1.3206,0.91%,-3.98%,-7.16%,-8.52%,-5.24%,-15.51%,-6.49%,31.52%,-20.04%,71.57%,-15.00%,0.12%, +,677,11545,长江沪深30,9月19日,0.8015,0.8015,0.00%,-4.70%,-5.07%,-7.81%,-4.56%,-15.51%,---,---,-16.78%,-19.85%,---,0.15%, +,678,5636,博时量化多策,9月20日,1.4115,1.4115,-0.16%,-4.04%,-4.03%,-2.18%,-0.68%,-15.52%,7.09%,39.66%,-11.16%,41.15%,-15.97%,0.00%, +,679,5661,嘉实资源精选,9月20日,2.5124,2.5124,0.40%,-5.22%,-2.54%,3.71%,9.63%,-15.52%,45.67%,122.99%,2.84%,151.24%,-15.28%,0.00%, +,680,7793,嘉实央企创新,9月20日,1.1685,1.1685,-0.03%,-4.45%,-3.93%,-4.43%,-2.62%,-15.52%,13.79%,---,-13.41%,16.85%,-17.48%,0.00%, +,681,5869,平安MSCI,9月20日,1.4878,1.4878,0.34%,-4.12%,-4.96%,-6.52%,-5.11%,-15.53%,-1.72%,29.43%,-17.34%,48.78%,-15.25%,0.00%, +,682,7785,广发央企创新,9月20日,1.2848,1.2848,-0.03%,-4.46%,-3.99%,-4.84%,-3.03%,-15.53%,15.60%,---,-14.35%,28.48%,-17.40%,0.00%, +,683,7658,东方红中证竞,9月20日,1.1963,1.1963,0.08%,-4.44%,-3.64%,-7.44%,-4.20%,-15.55%,-10.66%,15.40%,-16.64%,19.63%,-15.05%,0.00%, +,684,2984,广发中证环保,9月19日,1.0637,1.0637,-0.18%,-8.41%,-14.56%,-9.33%,-6.73%,-15.55%,41.86%,88.70%,-16.64%,41.56%,---,0.00%, +,685,501031,汇添富中证环,9月20日,0.5422,0.5422,1.73%,-5.57%,-9.29%,-2.74%,-7.35%,-15.56%,-12.83%,-6.97%,-18.88%,-45.78%,-17.32%,0.00%, +,686,6784,红土创新中证,9月20日,1.5263,1.5263,1.69%,-3.96%,-5.16%,-3.04%,-1.46%,-15.57%,9.02%,44.78%,-10.63%,52.63%,-15.79%,0.00%, +,687,50002,博时沪深30,9月20日,1.5897,3.6236,0.26%,-3.89%,-5.24%,-7.34%,-3.97%,-15.58%,-11.93%,7.94%,-14.24%,423.32%,-15.11%,0.15%, +,688,7538,永赢沪深30,9月20日,1.2751,1.2751,0.12%,-4.06%,-4.83%,-7.19%,-5.30%,-15.58%,-6.91%,22.04%,-17.49%,27.51%,-15.16%,0.10%, +,689,5789,南方MSCI,9月20日,1.5058,1.5058,0.33%,-4.23%,-5.03%,-6.88%,-4.64%,-15.60%,-4.85%,27.68%,-17.18%,50.58%,-15.25%,0.00%, +,690,13134,南方MSCI,9月20日,1.5251,1.5251,0.34%,-4.23%,-5.03%,-6.88%,-4.63%,-15.60%,---,---,-17.18%,-15.37%,-15.25%,0.00%, +,691,6129,华安中证50,9月19日,1.2647,1.2647,-0.62%,-5.47%,-5.60%,-5.23%,-5.81%,-15.62%,-3.47%,13.67%,-14.56%,26.47%,---,0.12%, +,692,8396,博时中证50,9月20日,1.2754,1.2754,0.71%,-5.07%,-6.27%,-4.42%,-3.52%,-15.65%,1.81%,---,-16.07%,27.54%,-16.30%,0.12%, +,693,13120,信诚沪深30,9月19日,1.0434,1.0434,-0.12%,-3.81%,-4.94%,-7.20%,-6.10%,-15.65%,---,---,-18.00%,-14.29%,-15.00%,0.00%, +,694,3986,申万菱信中证,9月19日,1.8079,1.8959,-0.27%,-5.23%,-7.23%,-6.60%,-3.99%,-15.66%,6.64%,50.70%,-13.44%,94.87%,-15.22%,0.12%, +,695,7539,永赢沪深30,9月20日,1.2709,1.2709,0.13%,-4.05%,-4.83%,-7.21%,-5.34%,-15.67%,-7.09%,21.69%,-17.55%,27.09%,-15.24%,0.00%, +,696,6144,恒生前海中证,9月19日,1.2196,1.2196,-1.18%,-5.58%,-4.87%,-3.62%,-5.37%,-15.67%,-10.74%,18.67%,-18.03%,21.96%,---,0.00%, +,697,165521,信诚中证80,9月19日,0.9437,1.6957,-0.21%,-3.34%,-0.96%,-6.37%,-7.66%,-15.69%,-15.33%,-7.25%,-14.75%,75.56%,-14.93%,0.12%, +,698,7593,鹏扬中证50,9月19日,1.6291,1.6291,0.01%,-6.03%,-7.06%,-7.66%,-4.44%,-15.69%,10.79%,61.55%,-13.34%,62.91%,---,0.12%, +,699,8737,南方高股息股,9月20日,0.9115,0.9115,0.24%,-3.38%,-0.71%,1.75%,-4.07%,-15.69%,-10.43%,---,-16.93%,-8.85%,-14.94%,0.00%, +,700,1426,南方大数据3,9月19日,1.2851,1.2851,0.11%,-3.97%,-5.36%,-6.00%,-4.64%,-15.70%,-7.49%,20.34%,-16.99%,28.51%,-14.57%,0.00%, +,701,161607,融通巨潮10,9月20日,0.957,3.132,0.00%,-4.01%,-4.87%,-7.27%,-5.34%,-15.71%,-9.97%,15.94%,-18.39%,428.60%,-14.56%,0.15%, +,702,4407,招商上证消费,9月20日,2.2177,2.2177,0.50%,-2.49%,-4.14%,-8.39%,-3.45%,-15.72%,-12.62%,34.00%,-19.70%,57.84%,-14.50%,0.00%, +,703,9658,汇丰晋信中小,9月19日,0.949,0.949,-0.83%,-5.34%,-4.84%,-0.76%,-0.07%,-15.73%,-4.73%,---,-10.42%,-5.10%,---,0.15%, +,704,8778,嘉实中证50,9月20日,1.319,1.319,1.09%,-6.18%,-7.96%,-4.23%,-2.14%,-15.75%,2.02%,---,-12.06%,30.48%,-15.16%,0.10%, +,705,519300,大成沪深30,9月20日,0.9704,2.8,0.09%,-4.09%,-4.71%,-7.40%,-5.55%,-15.76%,-12.32%,9.29%,-17.55%,237.37%,-15.23%,0.12%, +,706,4513,海富通沪深3,9月19日,1.0853,1.3553,0.17%,-4.63%,-5.26%,-5.45%,-4.22%,-15.76%,-4.82%,20.28%,-17.32%,32.53%,-14.66%,0.10%, +,707,5867,国泰沪深30,9月19日,0.976,0.976,-0.12%,-3.70%,-4.73%,-6.79%,-5.62%,-15.77%,-10.92%,11.80%,-17.85%,19.27%,-15.13%,0.00%, +,708,12872,易方达中小企,9月20日,1.3162,1.3162,0.91%,-3.98%,-7.19%,-8.58%,-5.38%,-15.77%,---,---,-20.22%,-18.61%,-15.26%,0.00%, +,709,82,嘉实研究阿尔,9月20日,1.74,2.575,0.69%,-4.50%,-5.23%,-5.28%,-2.90%,-15.78%,1.99%,47.71%,-17.06%,240.01%,-15.41%,0.15%, +,710,8166,工银消费股票,9月20日,1.1685,1.1685,0.63%,-2.00%,-3.76%,-9.79%,-2.96%,-15.78%,-17.15%,---,-21.95%,16.85%,-13.78%,0.15%, +,711,9491,宝盈创新驱动,9月20日,1.0817,1.0817,0.99%,-5.68%,-10.33%,-8.46%,-6.30%,-15.78%,7.82%,---,-24.56%,8.17%,-15.44%,0.15%, +,712,8944,上投摩根MS,9月19日,0.9541,0.9541,-0.12%,-4.09%,-5.17%,-6.66%,-5.04%,-15.80%,-4.88%,---,-17.47%,-4.59%,---,0.12%, +,713,7096,大成沪深30,9月20日,0.9667,1.206,0.09%,-4.09%,-4.71%,-7.41%,-5.58%,-15.81%,-12.47%,8.82%,-17.59%,14.34%,-15.28%,0.00%, +,714,167503,安信中证一带,9月19日,1.5,0.68,-0.46%,-6.02%,-3.85%,-3.47%,-0.53%,-15.82%,28.42%,43.17%,-10.55%,-32.29%,---,0.05%, +,715,7796,博时央企创新,9月20日,1.212,1.212,-0.07%,-4.39%,-3.91%,-4.62%,-2.81%,-15.82%,14.08%,---,-13.77%,21.20%,-17.75%,0.12%, +,716,4512,海富通沪深3,9月19日,1.134,1.417,0.17%,-4.63%,-5.26%,-5.46%,-4.26%,-15.84%,-5.00%,19.78%,-17.38%,39.24%,-14.75%,0.00%, +,717,11546,长江沪深30,9月19日,0.7974,0.7974,0.00%,-4.71%,-5.09%,-7.89%,-4.74%,-15.84%,---,---,-17.02%,-20.26%,---,0.00%, +,718,5970,国泰消费优选,9月19日,1.3499,1.3499,-2.29%,-5.58%,-7.58%,-10.78%,-12.50%,-15.86%,-26.47%,18.66%,-27.85%,34.99%,-15.95%,0.15%, +,719,920922,中金精选股票,9月20日,1.7109,1.7109,0.55%,-4.14%,-7.87%,-7.13%,-3.87%,-15.86%,-14.88%,---,-16.47%,-11.51%,-15.18%,0.00%, +,720,6600,人保沪深30,9月20日,1.3025,1.3025,0.17%,-4.09%,-4.80%,-7.22%,-5.14%,-15.88%,-8.71%,23.21%,-17.38%,30.25%,-15.26%,0.06%, +,721,8958,嘉实回报精选,9月20日,1.1528,1.1528,0.38%,-2.38%,-2.91%,-5.90%,-2.40%,-15.88%,-0.33%,---,-17.78%,15.27%,-14.42%,0.15%, +,722,3145,中融竞争优势,9月19日,1.8317,1.8317,-0.35%,-7.34%,-9.94%,-0.58%,0.68%,-15.89%,21.17%,118.48%,-19.07%,83.17%,---,0.15%, +,723,8945,上投摩根MS,9月19日,0.952,0.952,-0.12%,-4.10%,-5.18%,-6.68%,-5.08%,-15.89%,-5.08%,---,-17.53%,-4.80%,---,0.00%, +,724,3624,创金合信资源,9月19日,2.4939,2.4939,0.74%,-5.58%,-1.77%,-4.93%,0.87%,-15.91%,61.16%,159.21%,0.25%,149.39%,---,0.15%, +,725,8397,博时中证50,9月20日,1.2651,1.2651,0.72%,-5.07%,-6.29%,-4.49%,-3.66%,-15.91%,1.21%,---,-16.25%,26.51%,-16.54%,0.00%, +,726,2385,博时沪深30,9月20日,1.5679,1.5861,0.26%,-3.90%,-5.27%,-7.44%,-4.16%,-15.92%,-12.63%,6.66%,-14.49%,63.93%,-15.44%,0.00%, +,727,7794,申万菱信中证,9月19日,1.6241,1.6241,-0.28%,-5.24%,-7.25%,-6.67%,-4.14%,-15.92%,6.00%,49.36%,-13.62%,62.41%,-15.46%,0.00%, +,728,163808,中银中证10,9月20日,1.719,1.729,0.12%,-4.07%,-5.50%,-7.68%,-5.76%,-15.94%,-11.71%,11.55%,-17.95%,73.62%,-14.99%,0.15%, +,729,1214,华泰柏瑞中证,9月19日,0.7594,0.7594,-0.65%,-5.99%,-6.89%,-4.69%,-3.78%,-15.94%,1.61%,34.15%,-16.27%,-24.06%,-15.97%,0.10%, +,730,8283,易方达金融行,9月20日,1.1117,1.1117,-0.82%,-4.37%,-1.72%,-8.40%,-7.01%,-15.95%,-6.30%,---,-12.98%,11.17%,-14.08%,0.15%, +,731,6130,华安中证50,9月19日,1.2413,1.2413,-0.62%,-5.48%,-5.63%,-5.32%,-6.00%,-15.96%,-4.24%,12.27%,-14.81%,24.13%,---,0.00%, +,732,10992,东财中证50,9月19日,0.9957,0.9957,-0.66%,-6.00%,-7.00%,-4.49%,-3.52%,-15.97%,---,---,-16.22%,-0.43%,---,0.12%, +,733,1707,诺安高端制造,9月20日,1.54,1.54,0.06%,-5.23%,-10.52%,-6.21%,-3.02%,-15.98%,14.67%,30.07%,-16.94%,54.00%,-17.43%,0.15%, +,734,7594,鹏扬中证50,9月19日,1.6072,1.6072,0.01%,-6.04%,-7.09%,-7.76%,-4.63%,-16.02%,9.92%,59.40%,-13.59%,60.72%,---,0.00%, +,735,13121,信诚中证80,9月19日,0.9398,0.9398,-0.21%,-3.35%,-0.99%,-6.47%,-7.85%,-16.02%,---,---,-15.00%,-13.08%,-15.26%,0.00%, +,736,8390,国联安沪深3,9月20日,1.0428,1.0988,0.12%,-3.72%,-4.87%,-7.20%,-5.90%,-16.05%,-11.02%,---,-17.67%,9.57%,-15.42%,0.08%, +,737,7932,鹏华中证50,9月19日,1.2945,1.2945,-0.65%,-5.90%,-6.83%,-4.62%,-3.84%,-16.07%,2.41%,---,-16.19%,29.45%,---,0.12%, +,738,4874,融通巨潮10,9月20日,0.841,1.566,0.00%,-4.00%,-4.97%,-7.38%,-5.61%,-16.08%,-10.79%,14.46%,-18.64%,38.19%,-14.99%,0.00%, +,739,8779,嘉实中证50,9月20日,1.3051,1.3051,1.08%,-6.18%,-7.99%,-4.33%,-2.32%,-16.08%,1.22%,---,-12.31%,29.11%,-15.48%,0.00%, +,740,165309,建信沪深30,9月19日,1.5184,1.5184,-0.12%,-3.73%,-4.87%,-6.50%,-5.11%,-16.09%,-6.73%,19.72%,-17.28%,51.84%,---,0.12%, +,741,519117,浦银安盛基本,9月19日,1.704,1.704,-0.64%,-6.32%,-6.58%,-5.96%,-4.16%,-16.10%,-2.52%,20.17%,-14.33%,70.40%,---,0.12%, +,742,160133,南方天元新产,9月20日,3.48,3.48,0.17%,-3.89%,-5.84%,-6.70%,-3.49%,-16.10%,-18.62%,24.60%,-18.10%,248.00%,-16.14%,0.15%, +,743,7135,广发中证10,9月20日,1.0835,1.0835,0.08%,-4.12%,-5.52%,-8.48%,-6.86%,-16.12%,-14.96%,5.07%,-18.48%,8.35%,-15.19%,0.12%, +,744,6087,华泰柏瑞中证,9月19日,0.7601,0.7601,-0.65%,-6.00%,-6.91%,-4.75%,-3.89%,-16.14%,1.10%,33.16%,-16.42%,29.75%,-16.17%,0.00%, +,745,160615,鹏华沪深30,9月19日,1.4083,2.1663,-0.10%,-3.76%,-4.89%,-6.98%,-5.65%,-16.17%,-8.25%,19.95%,-17.80%,109.62%,---,0.12%, +,746,7797,博时央企创新,9月20日,1.1983,1.1983,-0.08%,-4.40%,-3.94%,-4.72%,-3.00%,-16.17%,13.18%,---,-14.02%,19.83%,-18.08%,0.00%, +,747,9492,宝盈创新驱动,9月20日,1.0703,1.0703,0.99%,-5.69%,-10.38%,-8.58%,-6.54%,-16.20%,6.75%,---,-24.83%,7.03%,-15.86%,0.00%, +,748,7136,广发中证10,9月20日,1.0789,1.0789,0.07%,-4.13%,-5.53%,-8.51%,-6.91%,-16.21%,-15.13%,4.70%,-18.54%,7.89%,-15.28%,0.00%, +,749,1520,国投瑞银研究,9月20日,1.197,1.892,0.34%,-2.52%,-2.68%,-4.39%,-4.47%,-16.23%,16.52%,83.36%,-14.92%,83.17%,-15.65%,0.15%, +,750,8001,鹏华中证50,9月19日,1.2876,1.2876,-0.66%,-5.90%,-6.84%,-4.67%,-3.93%,-16.23%,2.00%,---,-16.31%,28.76%,---,0.00%, +,751,8391,国联安沪深3,9月20日,1.0385,1.0945,0.12%,-3.73%,-4.89%,-7.25%,-6.00%,-16.24%,-11.40%,---,-17.81%,9.14%,-15.60%,0.00%, +,752,481012,工银深证红利,9月20日,1.2125,1.8473,-0.28%,-3.05%,-1.57%,-5.83%,-4.75%,-16.29%,-18.46%,-0.57%,-19.30%,83.74%,-14.75%,0.10%, +,753,1956,国联安科技动,9月20日,1.9981,1.9981,1.93%,-6.47%,-7.61%,6.36%,-1.68%,-16.29%,7.63%,64.67%,-18.10%,96.02%,-15.56%,0.15%, +,754,1008,工银国企改革,9月20日,2.115,2.115,0.52%,-4.34%,-4.86%,-5.83%,-5.16%,-16.30%,19.90%,76.25%,-17.96%,111.50%,-17.29%,0.15%, +,755,4192,招商中证50,9月20日,1.2832,1.2832,0.94%,-4.26%,-5.85%,-4.26%,-1.66%,-16.30%,-0.33%,33.14%,-14.30%,28.32%,-16.81%,0.12%, +,756,1733,泰达宏利量化,9月20日,1.201,1.201,1.26%,-4.61%,-4.53%,-8.04%,-6.39%,-16.31%,-6.97%,22.80%,-18.35%,20.10%,-15.72%,0.12%, +,757,160925,大成中华沪深,9月20日,1.0182,1.0182,0.51%,-3.05%,-4.28%,-7.83%,-4.45%,-16.31%,-13.98%,2.19%,-15.56%,1.82%,-14.97%,0.12%, +,758,5639,平安300E,9月19日,1.151,1.151,-0.13%,-3.83%,-4.95%,-6.84%,-5.56%,-16.31%,-11.19%,9.70%,-17.79%,15.10%,-15.62%,0.10%, +,759,10993,东财中证50,9月19日,0.9891,0.9891,-0.66%,-6.01%,-7.03%,-4.59%,-3.71%,-16.31%,---,---,-16.45%,-1.09%,---,0.00%, +,760,501302,南方恒指ET,9月20日,0.8196,0.8196,1.04%,-1.66%,-2.64%,-6.72%,-2.13%,-16.32%,-15.26%,-23.07%,-11.23%,-18.04%,-14.40%,0.12%, +,761,202015,南方沪深30,9月20日,1.6775,1.8375,0.12%,-4.12%,-4.86%,-7.58%,-5.49%,-16.32%,-11.53%,11.21%,-17.77%,91.34%,-15.75%,0.12%, +,762,501307,银河中证沪港,9月20日,0.9141,0.9141,0.19%,-3.69%,-2.31%,-5.46%,-5.99%,-16.32%,-6.82%,-3.70%,-9.91%,-8.59%,-16.74%,0.10%, +,763,6939,鹏华沪深30,9月19日,1.1577,1.5817,-0.10%,-3.77%,-4.90%,-7.03%,-5.76%,-16.33%,-8.60%,19.24%,-17.93%,52.27%,---,0.00%, +,764,3625,创金合信资源,9月19日,2.4194,2.4194,0.73%,-5.60%,-1.81%,-5.05%,0.61%,-16.34%,59.54%,157.49%,-0.11%,141.94%,---,0.00%, +,765,1790,国泰智能汽车,9月20日,2.446,2.446,1.62%,-7.10%,-13.69%,-14.86%,-7.21%,-16.35%,47.08%,182.45%,-21.43%,144.60%,-15.57%,0.15%, +,766,481009,工银沪深30,9月20日,0.9809,1.769,0.12%,-4.07%,-4.89%,-7.50%,-5.75%,-16.36%,-12.75%,5.12%,-17.87%,85.69%,-15.80%,0.12%, +,767,270026,广发中小企业,9月20日,1.302,1.302,1.01%,-4.48%,-7.52%,-7.89%,-5.37%,-16.36%,-7.64%,25.24%,-20.21%,30.20%,-16.19%,0.12%, +,768,1781,建信现代服务,9月19日,1.762,1.762,0.51%,-2.60%,-3.87%,-4.81%,-4.40%,-16.37%,3.95%,54.43%,-13.71%,76.20%,---,0.15%, +,769,2861,工银智能制造,9月20日,1.574,1.574,0.64%,-3.61%,-4.26%,-1.56%,-1.01%,-16.37%,5.21%,65.68%,-24.58%,57.40%,-15.78%,0.15%, +,770,5259,建信龙头企业,9月19日,1.821,1.821,0.21%,-2.91%,-3.95%,-4.82%,-3.52%,-16.37%,6.50%,62.13%,-13.18%,82.10%,---,0.15%, +,771,8973,大成中华沪深,9月20日,1.0162,1.0162,0.51%,-3.05%,-4.29%,-7.85%,-4.49%,-16.40%,-14.14%,---,-15.62%,-3.97%,-15.05%,0.00%, +,772,519975,长信量化中小,9月19日,1.499,1.899,-0.33%,-7.07%,-10.56%,-5.55%,-3.23%,-16.40%,15.49%,85.06%,-8.37%,105.78%,-16.06%,0.15%, +,773,502013,长盛中证申万,9月19日,1.2179,1.2179,0.07%,-4.03%,-4.45%,-8.21%,-6.15%,-16.42%,-2.94%,12.75%,-19.25%,-46.73%,-15.82%,0.12%, +,774,9775,汇丰晋信中小,9月19日,0.9331,0.9331,-0.84%,-5.36%,-4.91%,-0.97%,-0.48%,-16.42%,-6.25%,---,-10.94%,-6.69%,---,0.00%, +,775,164508,国富中证10,9月20日,1.124,1.247,0.09%,-4.10%,-5.47%,-8.39%,-6.64%,-16.43%,-16.37%,4.79%,-18.14%,28.68%,-15.49%,0.12%, +,776,7028,易方达中证5,9月19日,1.3337,1.3337,-0.67%,-6.04%,-7.03%,-5.09%,-3.84%,-16.45%,0.17%,31.06%,-16.43%,33.37%,---,0.05%, +,777,948,华夏沪港通恒,9月19日,0.9741,0.9741,-0.63%,-3.07%,-4.09%,-6.62%,-3.26%,-16.47%,-15.60%,-21.78%,-11.27%,-2.59%,-13.54%,0.12%, +,778,8167,工银消费股票,9月20日,1.1449,1.1449,0.62%,-2.01%,-3.82%,-9.98%,-3.36%,-16.47%,-18.48%,---,-22.40%,14.49%,-14.47%,0.00%, +,779,961,天弘沪深30,9月20日,1.3003,1.3003,0.13%,-4.07%,-4.82%,-7.44%,-5.58%,-16.49%,-12.44%,7.80%,-17.82%,30.03%,-15.93%,0.10%, +,780,110020,易方达沪深3,9月19日,1.4768,1.4768,-0.11%,-3.82%,-4.96%,-7.12%,-5.86%,-16.49%,-12.15%,7.91%,-18.06%,47.68%,---,0.12%, +,781,5962,宝盈人工智能,9月20日,2.6322,2.6322,1.02%,-5.79%,-11.00%,-9.55%,-7.20%,-16.50%,3.67%,71.09%,-25.06%,163.22%,-16.13%,0.15%, +,782,6106,景顺长城量化,9月19日,0.8123,0.8123,-0.60%,-2.74%,-3.24%,-5.80%,-3.73%,-16.50%,-14.40%,-16.38%,-9.13%,-18.77%,---,0.15%, +,783,7885,中融中证50,9月19日,1.1535,1.1535,-0.60%,-5.56%,-6.67%,-4.98%,-4.83%,-16.50%,-4.35%,---,-16.39%,15.35%,---,0.10%, +,784,8177,建信高股息主,9月19日,1.1797,1.8562,-0.52%,-7.11%,-7.65%,0.55%,1.41%,-16.52%,41.66%,---,-14.04%,85.64%,---,0.15%, +,785,501308,银河中证沪港,9月20日,0.9043,0.9043,0.19%,-3.69%,-2.33%,-5.52%,-6.10%,-16.53%,-7.28%,-4.42%,-10.07%,-9.57%,-16.95%,0.00%, +,786,7029,易方达中证5,9月19日,1.3286,1.3286,-0.67%,-6.05%,-7.03%,-5.11%,-3.88%,-16.53%,-0.02%,30.68%,-16.49%,32.86%,---,0.00%, +,787,10432,广发中小企业,9月20日,1.2956,1.2956,1.01%,-4.48%,-7.54%,-7.94%,-5.47%,-16.53%,---,---,-20.33%,-7.66%,-16.36%,0.00%, +,788,11824,浙商汇金量化,9月19日,0.8506,0.8506,-0.71%,-5.83%,-9.34%,-5.86%,-4.27%,-16.53%,---,---,-13.32%,-14.94%,---,0.15%, +,789,11142,创金合信新材,9月19日,1.1832,1.1832,-0.33%,-7.61%,-11.06%,-8.96%,-2.33%,-16.57%,---,---,-13.19%,18.32%,---,0.15%, +,790,501086,华宝MSCI,9月20日,1.2448,1.2448,0.18%,-4.14%,-5.17%,-7.83%,-5.89%,-16.59%,-7.06%,23.85%,-18.09%,24.48%,-16.19%,0.10%, +,791,7831,博道伍佰智航,9月19日,1.4712,1.4712,-0.51%,-5.67%,-7.50%,-2.50%,-1.32%,-16.60%,4.87%,---,-13.75%,47.12%,---,0.12%, +,792,160616,鹏华中证50,9月19日,1.634,1.634,-0.61%,-5.88%,-6.84%,-4.67%,-4.16%,-16.63%,2.12%,40.38%,-16.29%,63.40%,---,0.12%, +,793,590007,中邮中证50,9月19日,1.2519,1.7619,-0.28%,-4.96%,-6.76%,-3.57%,-4.43%,-16.63%,-5.42%,28.14%,-14.37%,86.38%,---,0.15%, +,794,6724,工银深证红利,9月20日,1.1991,1.6487,-0.27%,-3.06%,-1.62%,-5.92%,-4.94%,-16.63%,-19.11%,-1.80%,-19.53%,10.71%,-15.09%,0.00%, +,795,4193,招商中证50,9月20日,1.2666,1.2666,0.93%,-4.27%,-5.89%,-4.36%,-1.87%,-16.64%,-1.13%,31.54%,-14.55%,26.66%,-17.14%,0.00%, +,796,501043,汇添富沪深3,9月20日,1.2655,1.2655,0.14%,-4.11%,-5.00%,-7.62%,-5.96%,-16.64%,-9.28%,17.57%,-18.15%,26.55%,-16.09%,0.08%, +,797,660008,农银沪深30,9月20日,1.4502,1.4502,0.14%,-4.06%,-4.87%,-7.61%,-5.84%,-16.65%,-13.10%,6.37%,-17.99%,45.02%,-16.08%,0.12%, +,798,4342,南方沪深30,9月20日,1.6612,1.8212,0.12%,-4.12%,-4.88%,-7.66%,-5.68%,-16.65%,-12.23%,9.89%,-18.01%,33.23%,-16.08%,0.00%, +,799,5640,平安300E,9月19日,1.1305,1.1305,-0.13%,-3.84%,-4.98%,-6.94%,-5.74%,-16.65%,-11.90%,8.39%,-18.03%,13.05%,-15.95%,0.00%, +,800,5659,南方恒指ET,9月20日,0.8048,0.8048,1.03%,-1.66%,-2.67%,-6.81%,-2.33%,-16.66%,-15.94%,-23.98%,-11.48%,-27.20%,-14.75%,0.00%, +,801,5918,天弘沪深30,9月20日,1.1461,1.1461,0.13%,-4.08%,-4.84%,-7.48%,-5.69%,-16.66%,-12.80%,7.16%,-17.94%,14.61%,-16.10%,0.00%, +,802,1588,天弘中证80,9月20日,1.1454,1.1454,0.28%,-4.37%,-5.28%,-6.83%,-5.06%,-16.66%,-9.47%,20.77%,-17.41%,14.54%,-16.39%,0.10%, +,803,7339,易方达沪深3,9月19日,1.4667,1.4667,-0.12%,-3.82%,-4.98%,-7.17%,-5.96%,-16.66%,-12.51%,7.29%,-18.18%,10.25%,---,0.00%, +,804,7886,中融中证50,9月19日,1.148,1.148,-0.61%,-5.57%,-6.68%,-5.03%,-4.92%,-16.66%,-4.70%,---,-16.50%,14.80%,---,0.00%, +,805,11323,国泰智能汽车,9月20日,2.43,2.43,1.63%,-7.07%,-13.71%,-14.92%,-7.39%,-16.67%,---,---,-21.64%,-4.29%,-15.89%,0.00%, +,806,540009,汇丰晋信消费,9月19日,0.8198,1.8128,0.50%,-1.98%,-4.21%,-7.31%,-2.95%,-16.68%,-16.29%,30.08%,-20.55%,89.82%,---,0.15%, +,807,9336,平安中证50,9月19日,1.1704,1.1704,-0.36%,-5.48%,-6.07%,-4.25%,-2.93%,-16.68%,-2.09%,---,-18.14%,17.04%,-17.19%,0.12%, +,808,6937,工银沪深30,9月20日,0.9708,1.191,0.12%,-4.07%,-4.92%,-7.60%,-5.94%,-16.69%,-13.44%,3.86%,-18.11%,15.76%,-16.14%,0.00%, +,809,240014,华宝中证10,9月20日,1.633,1.633,0.06%,-4.17%,-5.68%,-8.98%,-7.33%,-16.70%,-14.06%,9.47%,-19.07%,63.30%,-15.72%,0.12%, +,810,530015,建信深证基本,9月19日,2.3702,2.3702,-0.16%,-3.14%,-2.27%,-4.12%,-4.20%,-16.71%,-10.01%,10.45%,-15.44%,137.02%,---,0.15%, +,811,5734,华夏沪港通恒,9月19日,0.9613,0.9613,-0.63%,-3.08%,-4.10%,-6.69%,-3.40%,-16.71%,-16.09%,-22.47%,-11.45%,-24.54%,-13.79%,0.00%, +,812,5102,工银沪深30,9月19日,0.8259,1.0629,-0.12%,-3.80%,-5.13%,-7.30%,-6.05%,-16.72%,-14.74%,0.80%,-18.16%,1.93%,---,0.10%, +,813,6524,前海开源MS,9月19日,1.3302,1.4302,-0.18%,-4.21%,-5.66%,-7.19%,-5.43%,-16.73%,-7.11%,13.81%,-18.32%,45.58%,---,0.12%, +,814,1236,博时丝路主题,9月20日,1.99,1.99,0.86%,-4.92%,-9.01%,-5.06%,2.00%,-16.74%,19.09%,90.43%,-11.75%,99.00%,-17.77%,0.15%, +,815,5248,新华沪深30,9月19日,1.2183,1.2183,0.12%,-4.20%,-5.28%,-5.71%,-5.09%,-16.74%,-5.84%,---,-16.58%,21.83%,---,0.15%, +,816,613,国寿安保沪深,9月19日,1.0753,1.7953,-0.10%,-3.82%,-4.99%,-7.36%,-6.15%,-16.75%,-12.36%,6.07%,-18.20%,90.17%,---,0.10%, +,817,6911,长江量化匠心,9月19日,1.2025,1.2025,0.13%,-5.10%,-5.24%,-6.70%,-0.23%,-16.75%,-19.38%,8.63%,-11.23%,20.25%,---,0.15%, +,818,12811,华宝MSCI,9月20日,1.2422,1.2422,0.17%,-4.15%,-5.19%,-7.88%,-5.98%,-16.75%,---,---,-18.21%,-18.32%,-16.35%,0.00%, +,819,6611,人保中证50,9月20日,1.4723,1.4723,0.73%,-5.04%,-6.17%,-4.33%,-3.33%,-16.76%,-3.29%,25.90%,-16.15%,47.23%,-17.38%,0.06%, +,820,7685,华商电子行业,9月19日,1.5204,1.5204,-1.47%,-4.81%,-8.62%,-0.90%,-7.90%,-16.76%,-2.50%,52.04%,-24.14%,52.04%,---,0.15%, +,821,8519,中金中证沪港,9月19日,1.0945,1.0945,-0.13%,-2.25%,-3.83%,-8.79%,-0.33%,-16.77%,-13.53%,---,-19.65%,9.45%,---,0.10%, +,822,10953,天弘国证A5,9月19日,0.7749,0.7749,-0.04%,-3.22%,-5.05%,-8.08%,-6.63%,-16.77%,---,---,-19.37%,-22.51%,-15.78%,0.10%, +,823,12275,富国中证沪港,9月20日,0.8107,0.8107,0.48%,-3.19%,-4.31%,-7.20%,-4.84%,-16.79%,---,---,-15.99%,-18.93%,-15.68%,0.12%, +,824,962,天弘中证50,9月20日,1.116,1.116,0.72%,-5.16%,-6.38%,-4.62%,-3.70%,-16.80%,-2.18%,24.43%,-16.44%,11.60%,-17.43%,0.10%, +,825,51,华夏沪深30,9月19日,1.3828,1.3828,-0.12%,-3.86%,-5.03%,-7.21%,-5.87%,-16.81%,-13.40%,5.16%,-18.18%,38.28%,---,0.12%, +,826,4995,广发品牌消费,9月20日,1.5161,1.5161,1.21%,-2.68%,-5.35%,-8.64%,-5.63%,-16.81%,-14.53%,38.60%,-18.70%,51.61%,-15.69%,0.15%, +,827,1589,天弘中证80,9月20日,1.1273,1.1273,0.28%,-4.39%,-5.30%,-6.88%,-5.16%,-16.83%,-9.83%,20.04%,-17.53%,12.73%,-16.56%,0.00%, +,828,161811,银华沪深30,9月20日,0.8703,0.8703,0.26%,-4.11%,-4.47%,-6.38%,-5.05%,-16.85%,-8.69%,19.04%,-17.16%,40.30%,-16.15%,0.12%, +,829,7832,博道伍佰智航,9月19日,1.4582,1.4582,-0.51%,-5.67%,-7.53%,-2.57%,-1.47%,-16.85%,4.25%,---,-13.93%,45.82%,---,0.00%, +,830,501045,汇添富沪深3,9月20日,1.2539,1.2539,0.14%,-4.11%,-5.02%,-7.67%,-6.08%,-16.85%,-9.73%,16.71%,-18.30%,25.39%,-16.30%,0.00%, +,831,7856,易方达中证8,9月20日,1.2251,1.2251,0.26%,-4.33%,-5.23%,-6.86%,-5.50%,-16.86%,-9.06%,---,-17.75%,22.51%,-16.60%,0.06%, +,832,160706,嘉实沪深30,9月20日,0.9892,3.1084,0.12%,-4.12%,-4.93%,-7.70%,-5.94%,-16.87%,-14.01%,3.47%,-18.23%,361.47%,-16.31%,0.15%, +,833,8124,中邮中证50,9月19日,1.2596,1.2596,-0.28%,-4.97%,-6.79%,-3.64%,-4.55%,-16.87%,-5.98%,---,-14.55%,25.96%,---,0.00%, +,834,161123,易方达中证万,9月20日,1.1875,0.4834,1.70%,-4.41%,-6.07%,-0.56%,0.24%,-16.88%,-1.17%,27.49%,-10.71%,-51.22%,-16.93%,0.10%, +,835,164908,交银中证环境,9月20日,0.4714,0.4714,1.73%,-5.59%,-9.52%,-3.50%,-8.47%,-16.89%,-15.82%,-12.54%,-19.97%,-52.86%,-18.65%,0.12%, +,836,6525,前海开源MS,9月19日,1.3198,1.4198,-0.18%,-4.21%,-5.67%,-7.23%,-5.53%,-16.90%,-7.49%,13.11%,-18.44%,44.45%,---,0.00%, +,837,501036,汇添富中证5,9月20日,1.101,1.101,0.71%,-5.08%,-6.39%,-4.91%,-3.95%,-16.91%,0.53%,31.49%,-16.41%,10.10%,-17.50%,0.08%, +,838,6938,鹏华中证50,9月19日,1.715,1.715,-0.64%,-5.98%,-6.95%,-4.78%,-4.30%,-16.91%,1.66%,39.43%,-16.55%,71.50%,---,0.00%, +,839,5103,工银沪深30,9月19日,0.8315,1.0515,-0.12%,-3.79%,-5.16%,-7.35%,-6.17%,-16.92%,-15.16%,-0.04%,-18.30%,1.07%,---,0.00%, +,840,7857,易方达中证8,9月20日,1.2215,1.2215,0.26%,-4.33%,-5.24%,-6.88%,-5.54%,-16.94%,-9.24%,---,-17.80%,22.15%,-16.68%,0.00%, +,841,10954,天弘国证A5,9月19日,0.7725,0.7725,-0.04%,-3.23%,-5.06%,-8.12%,-6.73%,-16.94%,---,---,-19.48%,-22.75%,-15.95%,0.00%, +,842,12831,南方中证新能,9月20日,0.8467,0.8467,2.51%,-8.42%,-14.75%,-11.20%,-6.75%,-16.94%,---,---,-18.01%,-17.40%,-15.34%,0.12%, +,843,110030,易方达沪深3,9月20日,2.6435,2.6435,0.14%,-3.95%,-5.12%,-8.32%,-6.22%,-16.95%,-15.49%,7.86%,-19.83%,164.35%,-15.82%,0.15%, +,844,3884,汇安沪深30,9月19日,1.5122,1.5122,-0.11%,-2.83%,-4.85%,-6.70%,-3.08%,-16.95%,-13.17%,25.62%,-19.02%,51.22%,-15.79%,0.12%, +,845,5662,嘉实金融精选,9月20日,1.1581,1.1581,-0.71%,-5.71%,-1.83%,-9.07%,-9.60%,-16.95%,-17.25%,-3.90%,-16.00%,16.64%,-16.26%,0.15%, +,846,11825,浙商汇金量化,9月19日,0.8454,0.8454,-0.72%,-5.86%,-9.38%,-5.97%,-4.51%,-16.95%,---,---,-13.63%,-15.46%,---,0.00%, +,847,270010,广发沪深30,9月19日,1.9684,2.2584,-0.13%,-3.85%,-5.02%,-7.23%,-6.17%,-16.97%,-12.91%,5.53%,-18.52%,140.57%,---,0.12%, +,848,5919,天弘中证50,9月20日,1.1337,1.1337,0.72%,-5.16%,-6.39%,-4.68%,-3.80%,-16.97%,-2.58%,23.67%,-16.57%,13.37%,-17.60%,0.00%, +,849,13413,交银中证环境,9月20日,0.4709,0.4709,1.73%,-5.59%,-9.51%,-3.52%,-8.51%,-16.98%,---,---,-20.01%,-10.17%,-18.73%,0.00%, +,850,6658,财通中证香港,9月20日,0.7713,0.7713,0.29%,-2.90%,-3.03%,-11.23%,-11.64%,-16.99%,-14.17%,-20.74%,-10.17%,-23.09%,-16.89%,0.12%, +,851,8520,中金中证沪港,9月19日,1.0881,1.0881,-0.13%,-2.26%,-3.85%,-8.85%,-0.46%,-16.99%,-13.96%,---,-19.80%,8.81%,---,0.00%, +,852,8286,易方达研究精,9月20日,1.2515,1.2515,0.74%,-2.97%,-4.63%,-5.32%,0.97%,-16.99%,-10.48%,---,-20.02%,25.15%,-15.36%,0.15%, +,853,167301,方正富邦保险,9月20日,0.674,1.128,-1.32%,-4.40%,2.43%,-3.99%,-6.52%,-17.00%,-31.76%,-25.10%,-14.58%,12.93%,-16.06%,0.08%, +,854,8184,新华沪深30,9月19日,1.2083,1.2083,0.12%,-4.21%,-5.30%,-5.79%,-5.23%,-17.00%,-6.39%,---,-16.76%,20.83%,---,0.00%, +,855,1070,建信信息产业,9月19日,2.838,2.838,-0.32%,-6.21%,-6.24%,-1.32%,-3.99%,-17.02%,35.08%,99.30%,-16.94%,183.80%,---,0.15%, +,856,2311,创金合信中证,9月19日,1.1574,1.378,-0.49%,-5.65%,-7.26%,-6.07%,-2.41%,-17.03%,0.05%,39.81%,-14.23%,36.03%,---,0.15%, +,857,7405,华宝中证10,9月20日,1.6116,1.6116,0.06%,-4.17%,-5.72%,-9.07%,-7.51%,-17.03%,-14.73%,8.18%,-19.30%,18.94%,-16.05%,0.00%, +,858,5658,华夏沪深30,9月19日,1.3629,1.3629,-0.12%,-3.87%,-5.06%,-7.28%,-6.01%,-17.05%,-13.90%,4.28%,-18.35%,-0.81%,---,0.00%, +,859,11143,创金合信新材,9月19日,1.171,1.171,-0.33%,-7.62%,-11.11%,-9.10%,-2.63%,-17.08%,---,---,-13.56%,17.10%,---,0.00%, +,860,309,大摩品质生活,9月20日,3.364,3.364,0.90%,-5.85%,-15.63%,-10.82%,-7.23%,-17.10%,13.84%,71.28%,-21.29%,236.40%,-16.71%,0.15%, +,861,9337,平安中证50,9月19日,1.1569,1.1569,-0.35%,-5.49%,-6.10%,-4.36%,-3.17%,-17.10%,-3.07%,---,-18.43%,15.69%,-17.60%,0.00%, +,862,501037,汇添富中证5,9月20日,1.0882,1.0882,0.72%,-5.08%,-6.40%,-4.97%,-4.07%,-17.11%,0.03%,30.53%,-16.56%,8.82%,-17.70%,0.00%, +,863,42,财通中证ES,9月20日,1.7479,2.2367,-0.14%,-4.00%,-5.13%,-6.38%,-4.69%,-17.11%,4.40%,30.76%,-17.07%,141.58%,-17.14%,0.12%, +,864,6020,广发沪深30,9月19日,1.4705,1.4705,-0.24%,-4.59%,-5.26%,-6.80%,-5.10%,-17.11%,-11.15%,21.57%,-18.23%,47.04%,---,0.15%, +,865,2316,创金合信中证,9月19日,1.1569,1.3777,-0.49%,-5.64%,-7.27%,-6.09%,-2.45%,-17.12%,-0.15%,39.38%,-14.28%,35.98%,---,0.00%, +,866,12276,富国中证沪港,9月20日,0.8069,0.8069,0.50%,-3.19%,-4.33%,-7.28%,-5.03%,-17.12%,---,---,-16.23%,-19.31%,-16.02%,0.00%, +,867,6363,建信深证基本,9月19日,2.3441,2.3441,-0.17%,-3.16%,-2.31%,-4.24%,-4.45%,-17.13%,-10.91%,8.81%,-15.74%,44.87%,---,0.00%, +,868,10245,广发品牌消费,9月20日,1.5055,1.5055,1.22%,-2.68%,-5.38%,-8.72%,-5.81%,-17.13%,---,---,-18.93%,-13.67%,-16.02%,0.00%, +,869,2987,广发沪深30,9月19日,1.9386,1.9386,-0.13%,-3.85%,-5.04%,-7.28%,-6.27%,-17.14%,-13.26%,4.90%,-18.64%,33.87%,---,0.00%, +,870,2556,博时丝路主题,9月20日,1.948,1.948,0.88%,-4.93%,-9.01%,-5.16%,1.78%,-17.14%,17.99%,87.67%,-12.05%,168.69%,-18.15%,0.00%, +,871,5963,宝盈人工智能,9月20日,2.5474,2.5474,1.02%,-5.81%,-11.06%,-9.74%,-7.56%,-17.16%,2.03%,67.05%,-25.47%,154.74%,-16.78%,0.00%, +,872,6063,景顺长城MS,9月19日,1.5122,1.5122,0.18%,-4.41%,-5.07%,-4.59%,-4.27%,-17.16%,-0.17%,34.12%,-13.95%,51.22%,---,0.15%, +,873,161715,招商大宗商品,9月20日,1.5461,1.6846,1.26%,-5.12%,-3.93%,-3.22%,1.60%,-17.17%,48.91%,73.68%,-4.15%,79.96%,-18.63%,0.12%, +,874,3475,前海联合沪深,9月19日,1.341,1.341,-0.07%,-3.71%,-4.87%,-7.33%,-6.34%,-17.19%,-13.86%,13.36%,-18.30%,34.10%,---,0.12%, +,875,7788,易方达中证国,9月19日,1.2537,1.2537,-0.26%,-4.78%,-4.87%,-6.00%,-3.26%,-17.19%,8.83%,---,-11.51%,25.37%,-17.74%,0.06%, +,876,160724,嘉实沪深30,9月20日,0.9136,1.2912,0.12%,-4.11%,-4.96%,-7.78%,-6.13%,-17.20%,-14.70%,2.24%,-18.46%,19.89%,-16.64%,0.00%, +,877,6682,景顺长城中证,9月19日,1.4367,1.4367,-0.43%,-5.69%,-6.10%,-3.37%,-2.79%,-17.20%,9.01%,45.74%,-11.97%,43.67%,---,0.15%, +,878,12832,南方中证新能,9月20日,0.844,0.844,2.50%,-8.43%,-14.77%,-11.27%,-6.89%,-17.20%,---,---,-18.19%,-17.66%,-15.60%,0.00%, +,879,6957,长江量化匠心,9月19日,1.1753,1.1753,0.12%,-5.11%,-5.29%,-6.85%,-0.54%,-17.24%,-20.35%,6.66%,-11.62%,17.53%,---,0.00%, +,880,6659,财通中证香港,9月20日,0.7634,0.7634,0.29%,-2.92%,-3.06%,-11.30%,-11.78%,-17.24%,-14.69%,-21.46%,-10.36%,-23.88%,-17.14%,0.00%, +,881,166802,浙商沪深30,9月19日,1.7314,1.8864,-0.20%,-3.94%,-4.85%,-7.30%,-6.17%,-17.25%,-8.14%,28.49%,-17.76%,99.32%,---,0.15%, +,882,6214,平安500E,9月19日,1.3569,1.3569,-0.67%,-6.00%,-6.95%,-4.92%,-4.27%,-17.25%,-3.04%,23.51%,-16.91%,35.69%,-17.27%,0.10%, +,883,501069,华宝标普中国,9月20日,1.3165,1.3165,1.00%,-5.37%,-6.92%,-5.75%,-8.69%,-17.25%,-9.41%,24.19%,-18.42%,31.64%,-17.73%,0.10%, +,884,3885,汇安沪深30,9月19日,1.3905,1.3905,-0.11%,-2.84%,-4.88%,-6.80%,-3.28%,-17.28%,-13.86%,24.14%,-19.25%,39.05%,-16.12%,0.00%, +,885,7789,易方达中证国,9月19日,1.2488,1.2488,-0.26%,-4.79%,-4.88%,-6.02%,-3.32%,-17.28%,8.60%,---,-11.58%,24.88%,-17.82%,0.00%, +,886,2310,创金合信沪深,9月19日,1.3543,1.4923,-0.14%,-4.73%,-6.70%,-7.73%,-5.61%,-17.29%,-9.64%,22.07%,-17.47%,53.20%,---,0.15%, +,887,7541,新华MSCI,9月19日,1.1379,1.1379,-0.10%,-4.23%,-5.29%,-6.87%,-5.24%,-17.30%,-13.04%,13.42%,-17.50%,13.79%,---,0.12%, +,888,460300,华泰柏瑞沪深,9月19日,0.9354,1.8484,-0.11%,-3.78%,-4.84%,-7.05%,-6.03%,-17.31%,-13.49%,4.95%,-18.25%,88.84%,-16.68%,0.10%, +,889,4403,平安股息精选,9月19日,1.2273,1.2273,-0.13%,-2.31%,2.40%,-0.04%,-8.06%,-17.31%,-13.22%,11.54%,-22.32%,22.73%,-15.89%,0.15%, +,890,1878,嘉实沪港深精,9月20日,1.848,1.916,0.87%,-2.89%,-1.33%,-4.05%,0.05%,-17.32%,-5.08%,22.87%,-16.04%,93.20%,-16.27%,0.15%, +,891,6215,平安500E,9月19日,1.3514,1.3514,-0.67%,-6.00%,-6.96%,-4.94%,-4.31%,-17.33%,-3.24%,23.12%,-16.97%,35.14%,-17.35%,0.00%, +,892,1277,博时国企改革,9月20日,0.79,0.79,1.41%,-4.59%,-7.49%,-9.09%,-3.30%,-17.36%,-19.06%,0.89%,-22.47%,-21.00%,-16.31%,0.15%, +,893,5663,嘉实金融精选,9月20日,1.132,1.132,-0.72%,-5.72%,-1.88%,-9.19%,-9.82%,-17.36%,-18.07%,-5.36%,-16.30%,14.02%,-16.67%,0.00%, +,894,200002,长城久泰沪深,9月20日,2.0498,4.9098,0.28%,-4.38%,-4.73%,-5.60%,-5.07%,-17.37%,-10.10%,13.31%,-16.78%,437.85%,-16.78%,0.15%, +,895,2315,创金合信沪深,9月19日,1.3515,1.4965,-0.15%,-4.74%,-6.72%,-7.76%,-5.67%,-17.38%,-9.83%,21.59%,-17.53%,53.79%,---,0.00%, +,896,1054,工银新金融股,9月20日,2.786,2.786,-0.04%,-2.89%,-3.03%,-5.69%,-4.10%,-17.38%,36.70%,124.14%,-18.61%,178.60%,-17.16%,0.15%, +,897,165511,中信保诚中证,9月19日,1.5451,1.9749,-0.68%,-6.07%,-7.15%,-5.45%,-4.85%,-17.41%,5.25%,43.00%,-17.62%,98.82%,-17.42%,0.12%, +,898,162213,泰达宏利沪深,9月20日,1.9197,2.3597,0.30%,-4.09%,-5.33%,-7.48%,-5.84%,-17.43%,-8.05%,20.32%,-17.31%,173.80%,-17.04%,0.12%, +,899,7044,博道沪深30,9月19日,1.35,1.35,0.01%,-4.20%,-5.69%,-6.35%,-3.57%,-17.43%,-8.76%,25.42%,-16.06%,35.00%,---,0.12%, +,900,1825,建信中国制造,9月19日,2.1224,2.1224,0.14%,-3.61%,-7.40%,-7.65%,-9.83%,-17.44%,30.51%,108.28%,-18.79%,112.24%,---,0.15%, +,901,6021,广发沪深30,9月19日,1.4477,1.4477,-0.25%,-4.60%,-5.29%,-6.89%,-5.30%,-17.44%,-11.85%,20.15%,-18.46%,44.76%,---,0.00%, +,902,8709,银河龙头股票,9月20日,0.6498,0.6498,1.59%,-5.54%,-8.65%,-8.52%,3.18%,-17.48%,-34.28%,---,-13.95%,-35.02%,-16.97%,0.15%, +,903,8236,招商深证10,9月20日,1.0561,1.0561,0.41%,-4.68%,-7.78%,-9.53%,-6.71%,-17.49%,-4.65%,---,-22.05%,5.61%,-16.62%,0.12%, +,904,160119,南方中证50,9月19日,1.6049,1.7049,-0.67%,-6.08%,-7.12%,-5.33%,-4.45%,-17.50%,-4.23%,21.19%,-17.10%,75.29%,---,0.12%, +,905,8926,泰康沪深30,9月19日,0.928,0.928,-0.12%,-3.77%,-5.01%,-7.20%,-5.91%,-17.50%,-14.32%,---,-18.71%,-7.20%,---,0.10%, +,906,884,民生加银优选,9月20日,2.031,2.425,1.96%,-4.42%,-8.92%,-6.36%,1.86%,-17.51%,-0.38%,60.66%,-13.02%,137.30%,-17.47%,0.15%, +,907,3015,中金沪深30,9月19日,1.56,1.56,-0.12%,-3.96%,-4.94%,-6.43%,-5.63%,-17.51%,-10.12%,25.12%,-16.88%,56.00%,---,0.10%, +,908,6131,华泰柏瑞沪深,9月19日,0.9244,1.7574,-0.11%,-3.78%,-4.87%,-7.10%,-6.14%,-17.52%,-13.93%,4.11%,-18.40%,22.12%,-16.89%,0.00%, +,909,7039,前海联合沪深,9月19日,1.3226,1.3226,-0.08%,-3.71%,-4.91%,-7.43%,-6.53%,-17.52%,-14.54%,11.98%,-18.53%,20.85%,---,0.00%, +,910,9513,创金合信同顺,9月19日,1.105,1.105,-0.56%,-6.59%,-12.26%,-5.03%,-4.97%,-17.54%,2.16%,---,-21.41%,10.50%,---,0.15%, +,911,740101,长安沪深30,9月20日,1.311,1.815,0.38%,-4.10%,-6.76%,-8.39%,-6.09%,-17.55%,-13.18%,17.16%,-20.88%,101.23%,-16.97%,0.15%, +,912,450008,国富沪深30,9月20日,1.47,1.813,0.27%,-3.86%,-4.23%,-7.84%,-4.79%,-17.55%,-8.53%,23.53%,-16.24%,94.27%,-17.51%,0.12%, +,913,1677,中银战略新兴,9月20日,2.919,2.919,1.67%,-3.98%,-7.36%,-0.88%,0.10%,-17.57%,31.90%,96.96%,-11.97%,191.90%,-17.19%,0.15%, +,914,10998,博道消费智航,9月19日,0.7677,0.7677,-0.04%,-2.91%,-5.46%,-6.62%,-6.63%,-17.58%,---,---,-21.03%,-23.23%,---,--, +,915,7674,工银产业升级,9月20日,1.3906,1.3906,-1.81%,-4.06%,2.33%,-3.07%,-7.77%,-17.60%,14.31%,---,-14.40%,39.06%,-17.11%,0.15%, +,916,10908,大成沪深30,9月20日,0.7992,0.7992,0.38%,-3.54%,-4.68%,-7.35%,-6.77%,-17.61%,---,---,-15.83%,-20.08%,-16.99%,0.15%, +,917,10736,易方达沪深3,9月20日,0.722,0.722,0.68%,-2.56%,-5.05%,-10.49%,-6.90%,-17.61%,---,---,-20.41%,-27.80%,-16.44%,0.15%, +,918,478,建信中证50,9月19日,2.7063,2.7063,-0.54%,-6.14%,-7.80%,-4.81%,-4.61%,-17.62%,2.20%,33.13%,-15.25%,170.63%,---,0.15%, +,919,6912,长城久泰沪深,9月20日,1.3004,2.0764,0.29%,-4.38%,-4.75%,-5.67%,-5.22%,-17.63%,-10.67%,12.25%,-16.96%,43.63%,-17.04%,0.00%, +,920,3548,泰达宏利沪深,9月20日,1.9114,1.9114,0.30%,-4.09%,-5.36%,-7.55%,-5.99%,-17.68%,-8.61%,19.25%,-17.49%,58.37%,-17.29%,0.00%, +,921,1052,华夏中证50,9月19日,0.6856,0.6856,-0.67%,-6.10%,-7.11%,-5.26%,-4.51%,-17.69%,-4.55%,19.23%,-17.15%,-31.44%,---,0.12%, +,922,165310,建信沪深30,9月19日,1.1639,2.0199,0.11%,-4.32%,-5.54%,-7.13%,-6.54%,-17.72%,-10.93%,22.70%,-17.71%,128.89%,---,0.15%, +,923,8,嘉实中证50,9月19日,1.6717,1.7377,-0.67%,-6.07%,-7.13%,-5.26%,-4.62%,-17.72%,-4.74%,19.16%,-17.13%,75.76%,-17.76%,0.12%, +,924,8237,招商深证10,9月20日,1.0673,1.0673,0.40%,-4.69%,-7.81%,-9.59%,-6.83%,-17.72%,-5.15%,---,-22.20%,6.73%,-16.84%,0.00%, +,925,11645,国泰核心价值,9月19日,0.7732,0.7732,-0.17%,-2.09%,-1.09%,-6.40%,-2.23%,-17.72%,---,---,-20.23%,-22.68%,-16.64%,0.15%, +,926,7943,富安达中证5,9月19日,1.207,1.207,-0.81%,-5.86%,-6.93%,-2.72%,-1.41%,-17.73%,-1.11%,---,-14.08%,20.70%,---,0.12%, +,927,8776,华安沪深30,9月19日,0.901,0.901,-0.19%,-4.00%,-5.17%,-7.35%,-6.34%,-17.75%,-10.27%,---,-19.09%,-9.90%,---,0.05%, +,928,7045,博道沪深30,9月19日,1.3317,1.3317,0.00%,-4.21%,-5.72%,-6.46%,-3.77%,-17.76%,-9.49%,23.91%,-16.31%,33.17%,---,0.00%, +,929,13119,中信保诚中证,9月19日,1.5378,1.5378,-0.68%,-6.08%,-7.18%,-5.54%,-5.05%,-17.79%,---,---,-17.92%,-14.48%,-17.80%,0.00%, +,930,13355,工银新金融股,9月20日,2.772,2.772,-0.04%,-2.87%,-3.08%,-5.78%,-4.35%,-17.79%,---,---,-18.90%,-16.73%,-17.55%,0.00%, +,931,978,景顺长城量化,9月19日,1.731,1.784,-0.46%,-5.46%,-5.77%,-2.75%,-3.08%,-17.81%,7.52%,36.41%,-11.68%,80.43%,---,0.15%, +,932,8927,泰康沪深30,9月19日,0.9198,0.9198,-0.13%,-3.79%,-5.03%,-7.30%,-6.09%,-17.82%,-15.00%,---,-18.94%,-8.02%,---,0.00%, +,933,312,华安沪深30,9月19日,2.1257,2.4857,0.00%,-3.94%,-4.67%,-5.60%,-4.13%,-17.83%,-6.22%,28.44%,-17.86%,161.05%,---,0.12%, +,934,4348,南方中证50,9月19日,1.5772,1.6772,-0.67%,-6.09%,-7.16%,-5.43%,-4.64%,-17.83%,-4.99%,19.75%,-17.34%,-0.10%,---,0.00%, +,935,161017,富国中证50,9月20日,2.22,2.589,0.63%,-4.97%,-7.19%,-6.05%,-4.52%,-17.84%,1.88%,33.17%,-13.92%,175.18%,-18.35%,0.15%, +,936,3579,中金沪深30,9月19日,1.5805,1.5805,-0.11%,-3.97%,-4.96%,-6.52%,-5.82%,-17.84%,-10.84%,23.16%,-17.11%,48.52%,---,0.00%, +,937,5994,国投中证50,9月20日,2.0668,2.0668,0.91%,-4.74%,-7.49%,-5.78%,-5.03%,-17.84%,9.00%,59.98%,-14.09%,106.68%,-18.29%,0.12%, +,938,7786,富国中证国企,9月20日,1.1772,1.1772,0.28%,-4.49%,-4.63%,-5.79%,-3.79%,-17.86%,5.25%,---,-12.08%,17.72%,-18.61%,0.12%, +,939,10737,易方达沪深3,9月20日,0.7182,0.7182,0.67%,-2.58%,-5.09%,-10.56%,-7.05%,-17.86%,---,---,-20.60%,-28.18%,-16.70%,0.00%, +,940,11418,汇添富消费精,9月20日,0.7867,0.7867,1.38%,-1.64%,-4.25%,-7.71%,0.27%,-17.86%,---,---,-18.79%,-21.33%,-16.47%,0.15%, +,941,11224,九泰盈泰量化,9月20日,0.8207,0.8207,0.55%,-3.46%,-3.81%,-7.33%,-2.49%,-17.86%,---,---,-11.71%,-17.93%,-17.85%,0.15%, +,942,10812,中银战略新兴,9月20日,2.899,2.899,1.68%,-3.97%,-7.38%,-0.96%,-0.10%,-17.88%,---,---,-12.20%,26.04%,-17.52%,0.00%, +,943,8777,华安沪深30,9月19日,0.8972,0.8972,-0.19%,-4.00%,-5.19%,-7.40%,-6.43%,-17.91%,-10.62%,---,-19.21%,-10.28%,---,0.00%, +,944,163821,中银沪深30,9月20日,1.729,1.729,0.17%,-4.63%,-5.16%,-7.69%,-6.13%,-17.94%,-12.63%,23.24%,-18.56%,72.90%,-18.13%,0.12%, +,945,5037,银华新能源新,9月20日,1.9252,1.9252,2.81%,-5.66%,-11.61%,-8.47%,-4.15%,-17.94%,68.17%,145.47%,-15.31%,92.50%,-17.83%,0.15%, +,946,164809,工银中证50,9月20日,1.0902,1.4224,0.70%,-5.07%,-6.36%,-4.99%,-4.58%,-17.94%,-6.03%,16.55%,-17.16%,47.23%,-18.40%,0.10%, +,947,10909,大成沪深30,9月20日,0.7942,0.7942,0.38%,-3.55%,-4.70%,-7.44%,-6.95%,-17.94%,---,---,-16.06%,-20.58%,-17.31%,0.00%, +,948,2980,华夏创新前沿,9月20日,2.263,2.263,0.94%,-5.12%,-6.95%,-5.51%,-3.58%,-17.95%,6.14%,81.77%,-20.32%,126.30%,-17.47%,0.15%, +,949,4945,长信中证50,9月19日,1.483,1.483,-0.11%,-4.88%,-5.84%,-4.08%,-4.65%,-17.95%,4.08%,55.99%,-13.32%,48.29%,-17.70%,0.15%, +,950,8258,中银证券中证,9月19日,1.1608,1.1608,-0.63%,-5.89%,-6.88%,-5.16%,-4.66%,-17.95%,-5.95%,---,-17.16%,16.08%,-17.97%,0.12%, +,951,9514,创金合信同顺,9月19日,1.0927,1.0927,-0.56%,-6.61%,-12.30%,-5.16%,-5.21%,-17.96%,1.14%,---,-21.70%,9.27%,---,0.00%, +,952,8832,海富通中证长,9月19日,0.9244,0.9244,-0.28%,-3.62%,-3.58%,-6.96%,-8.57%,-17.96%,-7.24%,---,-16.94%,-7.56%,-18.16%,0.08%, +,953,320022,诺安研究精选,9月20日,2.018,3.506,0.80%,-5.04%,-10.15%,-8.81%,-7.47%,-17.97%,-6.18%,55.59%,-22.56%,250.60%,-17.60%,0.15%, +,954,4404,平安股息精选,9月19日,1.1698,1.1698,-0.13%,-2.33%,2.34%,-0.25%,-8.44%,-17.98%,-14.61%,8.93%,-22.78%,16.98%,-16.56%,0.00%, +,955,501301,华宝香港大盘,9月20日,0.8427,0.8427,0.75%,-2.06%,-2.84%,-7.78%,-2.45%,-17.98%,-21.10%,-26.95%,-12.46%,-15.74%,-15.60%,0.10%, +,956,13332,富国中证50,9月20日,2.217,2.217,0.64%,-4.93%,-7.16%,-6.06%,-4.60%,-17.98%,---,---,-14.04%,-12.37%,-18.49%,0.00%, +,957,6382,华夏中证50,9月19日,0.6751,0.6751,-0.68%,-6.11%,-7.15%,-5.36%,-4.70%,-18.01%,-5.30%,18.02%,-17.39%,29.83%,---,0.00%, +,958,161816,银华中证等权,9月20日,0.8118,0.8118,0.02%,-4.38%,-5.14%,-8.28%,-6.91%,-18.02%,-14.36%,18.19%,-19.41%,15.40%,-17.53%,0.12%, +,959,161227,国投瑞银深证,9月20日,1.309,2.476,0.46%,-4.59%,-7.75%,-9.16%,-6.30%,-18.03%,-7.95%,27.46%,-21.90%,31.77%,-17.15%,0.12%, +,960,5633,建信中证50,9月19日,2.6445,2.6445,-0.55%,-6.15%,-7.83%,-4.94%,-4.85%,-18.03%,1.18%,31.14%,-15.55%,11.51%,---,0.00%, +,961,6656,方正富邦中证,9月20日,1.1564,1.1564,0.68%,-4.93%,-6.20%,-5.11%,-4.63%,-18.03%,-7.12%,16.48%,-17.22%,15.64%,-18.61%,0.12%, +,962,1158,工银新材料新,9月20日,1.499,1.499,1.35%,-4.58%,-7.92%,-8.21%,-2.09%,-18.04%,40.22%,113.23%,-18.62%,49.90%,-18.31%,0.15%, +,963,12835,招商景气精选,9月20日,0.7826,0.7826,0.23%,-0.72%,2.34%,-2.55%,-0.65%,-18.04%,---,---,-13.13%,-21.74%,-18.79%,0.15%, +,964,70039,嘉实中证50,9月19日,1.3059,1.3059,-0.68%,-6.08%,-7.16%,-5.36%,-4.81%,-18.05%,-5.49%,17.74%,-17.37%,30.59%,-18.08%,0.00%, +,965,9208,建信沪深30,9月19日,1.153,1.153,0.10%,-4.33%,-5.57%,-7.23%,-6.73%,-18.06%,-11.65%,---,-17.95%,15.38%,---,0.00%, +,966,7470,博道叁佰智航,9月19日,1.5267,1.5267,0.07%,-4.41%,-6.48%,-6.63%,-4.51%,-18.07%,-3.50%,39.50%,-16.80%,52.67%,---,0.12%, +,967,165806,东吴沪深30,9月19日,1.2366,1.2366,-0.04%,-3.50%,-4.40%,-6.93%,-6.20%,-18.08%,-12.23%,3.98%,-18.92%,23.66%,---,0.12%, +,968,540008,汇丰晋信低碳,9月19日,3.7366,3.8366,0.25%,-5.21%,-11.49%,-12.18%,-2.79%,-18.09%,75.37%,234.85%,-16.55%,307.48%,---,0.15%, +,969,11377,创金合信积极,9月19日,0.891,0.891,-0.80%,-3.76%,-5.26%,2.97%,-5.95%,-18.09%,---,---,-20.16%,-10.90%,---,0.15%, +,970,660011,农银中证50,9月20日,1.5126,1.5126,0.71%,-5.15%,-6.41%,-4.96%,-4.39%,-18.10%,-7.15%,13.76%,-17.26%,51.26%,-18.69%,0.15%, +,971,164811,工银中证京津,9月20日,1.0302,1.5227,0.52%,-5.12%,-6.33%,-7.65%,-13.27%,-18.11%,-8.79%,3.63%,-14.87%,55.98%,-18.62%,0.10%, +,972,513,富国高端制造,9月20日,3.07,3.07,1.02%,-5.94%,-10.76%,-13.06%,-6.00%,-18.11%,-6.54%,57.52%,-25.36%,207.00%,-17.52%,0.15%, +,973,4730,建信量化事件,9月19日,1.4022,1.4022,-0.11%,-4.51%,-6.78%,-6.23%,-4.62%,-18.12%,-1.90%,44.35%,-16.96%,40.22%,---,0.15%, +,974,8259,中银证券中证,9月19日,1.1553,1.1553,-0.64%,-5.90%,-6.91%,-5.22%,-4.76%,-18.12%,-6.33%,---,-17.28%,15.53%,-18.14%,0.00%, +,975,1241,国寿安保中证,9月19日,0.6004,0.6004,-0.65%,-5.98%,-6.99%,-5.34%,-4.91%,-18.13%,-5.37%,17.27%,-17.48%,-39.97%,---,0.12%, +,976,11646,国泰核心价值,9月19日,0.7683,0.7683,-0.17%,-2.10%,-1.13%,-6.52%,-2.48%,-18.13%,---,---,-20.51%,-23.17%,-17.05%,0.00%, +,977,6687,方正富邦深证,9月20日,1.4666,1.4666,0.41%,-4.49%,-7.52%,-9.09%,-6.28%,-18.14%,-7.98%,28.68%,-21.56%,46.66%,-17.26%,0.12%, +,978,9194,泰达宏利中证,9月20日,0.9296,0.9296,0.81%,-4.05%,-5.48%,-10.37%,-7.09%,-18.14%,-8.76%,---,-22.27%,-7.04%,-16.77%,0.12%, +,979,313,华安沪深30,9月19日,2.0211,2.3811,0.00%,-3.95%,-4.70%,-5.69%,-4.32%,-18.15%,-6.96%,26.90%,-18.10%,149.56%,---,0.00%, +,980,7223,工银中证50,9月20日,1.0832,1.0832,0.71%,-5.07%,-6.38%,-5.05%,-4.70%,-18.15%,-6.50%,---,-17.31%,7.79%,-18.61%,0.00%, +,981,457,上投摩根核心,9月19日,2.3164,2.6254,-0.65%,-3.99%,-6.49%,-10.04%,-7.22%,-18.16%,-5.72%,40.56%,-21.86%,170.54%,---,0.15%, +,982,7089,国投中证50,9月20日,2.0376,2.0376,0.91%,-4.75%,-7.52%,-5.88%,-5.22%,-18.17%,8.14%,58.01%,-14.34%,75.20%,-18.61%,0.00%, +,983,7787,富国中证国企,9月20日,1.1644,1.1644,0.28%,-4.49%,-4.67%,-5.88%,-3.98%,-18.19%,4.41%,---,-12.33%,16.44%,-18.93%,0.00%, +,984,11419,汇添富消费精,9月20日,0.7824,0.7824,1.37%,-1.66%,-4.28%,-7.81%,0.05%,-18.19%,---,---,-19.02%,-21.76%,-16.80%,0.00%, +,985,8907,汇添富中证国,9月20日,1.2606,1.2606,0.28%,-4.51%,-4.70%,-6.23%,-4.22%,-18.20%,5.45%,---,-12.82%,26.06%,-18.93%,0.12%, +,986,70030,嘉实中创40,9月20日,1.6775,1.6775,0.91%,-5.52%,-9.34%,-6.15%,-6.52%,-18.21%,-10.39%,19.90%,-21.62%,67.75%,-18.21%,0.12%, +,987,70023,嘉实深证基本,9月20日,2.0003,2.0003,-0.53%,-4.63%,-3.72%,-6.02%,-5.15%,-18.22%,-12.16%,7.80%,-16.55%,100.03%,-18.32%,0.12%, +,988,4190,招商沪深30,9月20日,1.4653,1.4653,0.32%,-3.95%,-5.65%,-8.06%,-5.55%,-18.22%,-11.31%,18.64%,-16.48%,46.53%,-17.92%,0.12%, +,989,100038,富国沪深30,9月20日,1.522,2.108,0.26%,-3.97%,-5.11%,-8.42%,-6.03%,-18.23%,-10.53%,5.95%,-16.90%,116.04%,-17.69%,0.12%, +,990,167601,国金沪深30,9月19日,0.9534,0.9534,-0.15%,-4.02%,-5.78%,-7.13%,-4.76%,-18.23%,-16.99%,-1.09%,-19.05%,61.61%,-17.28%,0.12%, +,991,10322,大摩新兴产业,9月20日,0.9874,0.9874,1.21%,-5.66%,-15.26%,-8.42%,-6.06%,-18.23%,---,---,-22.85%,-1.26%,-17.98%,0.15%, +,992,12728,国泰中证动漫,9月19日,0.7938,0.7938,-2.72%,-6.01%,-9.70%,-9.16%,-13.95%,-18.23%,---,---,-30.09%,-20.62%,---,0.10%, +,993,10561,银华巨潮小盘,9月20日,1.0795,1.0795,0.60%,-4.85%,-4.43%,-4.89%,-4.24%,-18.24%,---,---,-11.86%,7.95%,-19.60%,0.12%, +,994,162711,广发中证50,9月19日,1.3493,1.3493,-0.68%,-6.08%,-7.10%,-5.37%,-5.14%,-18.25%,-5.00%,18.80%,-17.83%,34.93%,---,0.12%, +,995,5038,银华新能源新,9月20日,1.8846,1.8846,2.81%,-5.67%,-11.63%,-8.55%,-4.34%,-18.26%,66.82%,142.52%,-15.55%,88.44%,-18.15%,0.00%, +,996,7675,工银产业升级,9月20日,1.3605,1.3605,-1.81%,-4.07%,2.26%,-3.26%,-8.14%,-18.26%,12.50%,---,-14.89%,36.05%,-17.77%,0.00%, +,997,6355,华宝香港大盘,9月20日,0.83,0.83,0.75%,-2.06%,-2.88%,-7.87%,-2.65%,-18.31%,-21.73%,-27.82%,-12.72%,-29.33%,-15.94%,0.00%, +,998,164825,工银中证京津,9月20日,1.0235,1.0235,0.52%,-5.13%,-6.34%,-7.70%,-13.38%,-18.32%,-9.28%,3.06%,-15.03%,2.50%,-18.82%,0.00%, +,999,5569,中融智选红利,9月19日,1.5249,1.5249,-1.04%,-6.71%,-11.06%,-1.36%,6.42%,-18.32%,13.73%,42.90%,-15.72%,52.49%,---,0.15%, +,1000,5502,华泰紫金智能,9月19日,1.1865,1.1865,-0.13%,-4.63%,-5.35%,-6.82%,-5.54%,-18.33%,-8.16%,23.25%,-18.92%,18.65%,---,0.15%, +,1001,13019,国泰中证全指,9月19日,0.8074,0.8074,-1.88%,-4.97%,-6.33%,-9.22%,-10.32%,-18.33%,---,---,-17.24%,-19.26%,-18.24%,0.10%, +,1002,6657,方正富邦中证,9月20日,1.1376,1.1376,0.68%,-4.94%,-6.23%,-5.21%,-4.84%,-18.36%,-7.87%,14.97%,-17.46%,13.76%,-18.93%,0.00%, +,1003,11225,九泰盈泰量化,9月20日,0.8153,0.8153,0.56%,-3.47%,-3.86%,-7.46%,-2.79%,-18.36%,---,---,-12.09%,-18.47%,-18.35%,0.00%, +,1004,410010,华富中小企业,9月20日,1.3019,1.3019,1.61%,-3.72%,-5.66%,-8.30%,-8.10%,-18.37%,-16.08%,13.23%,-20.02%,30.19%,-17.83%,0.12%, +,1005,13291,富国沪深30,9月20日,1.519,1.749,0.33%,-3.92%,-5.12%,-8.44%,-6.10%,-18.38%,---,---,-17.01%,-17.92%,-17.84%,0.00%, +,1006,409,鹏华环保产业,9月19日,4.633,4.633,-0.39%,-9.53%,-15.47%,-13.69%,-11.80%,-18.39%,47.78%,171.09%,-20.34%,363.30%,---,0.15%, +,1007,540006,汇丰晋信大盘,9月19日,4.1884,4.2484,0.50%,-4.43%,-5.03%,-7.83%,-4.94%,-18.39%,-11.92%,17.25%,-20.46%,343.74%,---,0.15%, +,1008,50013,博时上证超大,9月20日,1.0121,1.0121,-0.30%,-3.88%,-3.83%,-8.65%,-8.78%,-18.40%,-18.77%,-1.56%,-18.69%,1.21%,-17.92%,0.12%, +,1009,7471,博道叁佰智航,9月19日,1.5068,1.5068,0.06%,-4.43%,-6.50%,-6.73%,-4.71%,-18.40%,-4.27%,37.85%,-17.04%,50.68%,---,0.00%, +,1010,519034,海富通中证5,9月19日,1.8718,1.8718,-0.24%,-6.50%,-6.24%,-4.85%,-5.11%,-18.41%,2.31%,44.76%,-17.55%,87.18%,-18.62%,0.12%, +,1011,2903,广发中证50,9月19日,1.0721,1.0721,-0.68%,-6.08%,-7.11%,-5.42%,-5.23%,-18.42%,-5.37%,18.10%,-17.94%,7.21%,---,0.00%, +,1012,1455,景顺长城中证,9月19日,0.876,0.876,-0.57%,-5.91%,-7.01%,-5.19%,-4.78%,-18.44%,-4.89%,21.50%,-17.36%,-12.40%,---,0.12%, +,1013,3416,招商财经大数,9月20日,1.1582,1.1582,0.23%,-0.86%,2.14%,-4.11%,-3.54%,-18.44%,3.74%,41.64%,-16.58%,15.82%,-18.85%,0.15%, +,1014,5495,创金合信科技,9月19日,1.6386,1.6386,-1.53%,-3.33%,-10.96%,1.59%,-2.06%,-18.45%,-6.85%,43.05%,-24.70%,63.86%,---,0.15%, +,1015,8908,汇添富中证国,9月20日,1.251,1.251,0.27%,-4.53%,-4.73%,-6.30%,-4.37%,-18.45%,4.83%,---,-13.00%,25.10%,-19.17%,0.00%, +,1016,6688,方正富邦深证,9月20日,1.4241,1.4241,0.41%,-4.49%,-7.56%,-9.18%,-6.47%,-18.47%,-8.71%,26.35%,-21.79%,42.41%,-17.59%,0.00%, +,1017,9195,泰达宏利中证,9月20日,0.9215,0.9215,0.81%,-4.05%,-5.51%,-10.46%,-7.28%,-18.47%,-9.50%,---,-22.49%,-7.85%,-17.09%,0.00%, +,1018,12729,国泰中证动漫,9月19日,0.7909,0.7909,-2.73%,-6.02%,-9.72%,-9.23%,-14.08%,-18.47%,---,---,-30.24%,-20.91%,---,0.00%, +,1019,13511,汇丰晋信低碳,9月19日,3.719,3.719,0.25%,-5.22%,-11.53%,-12.30%,-3.03%,-18.48%,---,---,-16.84%,-22.64%,---,0.00%, +,1020,6440,中信建投中证,9月20日,1.4142,1.4142,0.93%,-4.91%,-5.71%,-3.40%,-2.64%,-18.50%,8.09%,---,-16.06%,41.42%,-19.00%,0.10%, +,1021,11378,创金合信积极,9月19日,0.8839,0.8839,-0.81%,-3.77%,-5.30%,2.84%,-6.19%,-18.50%,---,---,-20.45%,-11.61%,---,0.00%, +,1022,673090,西部利得个股,9月19日,1.4725,1.5945,-0.95%,-4.68%,-5.90%,-9.50%,-9.18%,-18.53%,-5.45%,28.51%,-20.67%,64.94%,-17.50%,0.12%, +,1023,5727,嘉实中创40,9月20日,1.0073,1.0073,0.91%,-5.52%,-9.37%,-6.25%,-6.71%,-18.53%,-11.09%,18.48%,-21.84%,0.73%,-18.53%,0.00%, +,1024,1705,泓德战略转型,9月20日,1.3461,2.2461,0.19%,-4.15%,-6.93%,-10.24%,-7.49%,-18.54%,-1.05%,71.30%,-24.15%,129.40%,-17.67%,0.15%, +,1025,5998,嘉实深证基本,9月20日,1.2326,1.2326,-0.52%,-4.63%,-3.75%,-6.11%,-5.34%,-18.54%,-12.85%,6.52%,-16.78%,22.40%,-18.63%,0.00%, +,1026,6346,安信量化优选,9月19日,1.8832,1.8832,-0.45%,-6.27%,-9.73%,-2.52%,-2.02%,-18.54%,-4.95%,42.46%,-19.02%,88.32%,---,0.15%, +,1027,8112,中泰中证50,9月20日,1.3115,1.3115,0.68%,-5.57%,-6.42%,-5.04%,-4.75%,-18.54%,-0.84%,---,-16.28%,30.26%,-18.31%,0.10%, +,1028,700002,平安深证30,9月19日,2.345,2.425,-0.30%,-5.06%,-8.29%,-9.04%,-7.42%,-18.55%,-1.68%,41.18%,-23.32%,152.61%,-17.76%,0.12%, +,1029,9126,嘉实基础产业,9月20日,1.1498,1.1498,-0.17%,-4.56%,-3.02%,-6.99%,-3.46%,-18.55%,-0.44%,---,-14.82%,14.98%,-19.49%,0.15%, +,1030,11630,东财有色金属,9月19日,1.3341,1.3341,0.02%,-6.60%,-6.55%,-9.29%,-3.54%,-18.55%,---,---,-9.53%,33.41%,---,0.15%, +,1031,4191,招商沪深30,9月20日,1.4367,1.4367,0.32%,-3.96%,-5.69%,-8.15%,-5.74%,-18.55%,-12.02%,17.22%,-16.72%,43.67%,-18.25%,0.00%, +,1032,9059,南方沪深30,9月20日,1.1862,1.1862,1.00%,-4.28%,-4.70%,-7.70%,-6.96%,-18.56%,-11.33%,---,-19.59%,18.62%,-18.11%,0.12%, +,1033,13020,国泰中证全指,9月19日,0.8048,0.8048,-1.89%,-4.96%,-6.34%,-9.29%,-10.45%,-18.57%,---,---,-17.42%,-19.52%,-18.49%,0.00%, +,1034,217016,招商深证10,9月20日,1.8401,1.8401,0.42%,-4.73%,-7.82%,-9.54%,-6.52%,-18.58%,-7.26%,29.12%,-22.23%,84.01%,-17.68%,0.12%, +,1035,3834,华夏能源革新,9月20日,3.335,3.335,1.93%,-4.19%,-8.10%,-10.59%,-1.59%,-18.58%,103.48%,232.83%,-14.16%,233.50%,-17.47%,0.15%, +,1036,4790,富荣中证50,9月20日,1.2966,1.2966,0.49%,-4.53%,-6.45%,-5.75%,-4.15%,-18.61%,1.67%,32.83%,-12.35%,29.66%,-19.26%,0.12%, +,1037,9004,海富通中证5,9月19日,1.8602,1.8602,-0.24%,-6.50%,-6.26%,-4.91%,-5.23%,-18.62%,1.81%,---,-17.69%,22.33%,-18.82%,0.00%, +,1038,161037,富国中证高端,9月20日,1.8033,1.8033,0.95%,-5.85%,-11.50%,-11.31%,-10.11%,-18.64%,-2.40%,49.56%,-22.10%,80.33%,-18.63%,0.12%, +,1039,4791,富荣中证50,9月20日,1.2904,1.2904,0.49%,-4.53%,-6.45%,-5.77%,-4.20%,-18.68%,1.47%,32.40%,-12.41%,29.04%,-19.34%,0.00%, +,1040,12911,同泰沪深30,9月19日,0.828,0.828,0.02%,-4.53%,-6.71%,-7.05%,-6.51%,-18.70%,---,---,-18.54%,-17.20%,---,0.12%, +,1041,12836,招商景气精选,9月20日,0.7753,0.7753,0.23%,-0.73%,2.27%,-2.75%,-1.06%,-18.71%,---,---,-13.63%,-22.47%,-19.44%,0.00%, +,1042,4189,华商消费行业,9月19日,1.2823,0.9128,0.78%,-0.50%,-1.78%,-7.48%,-4.96%,-18.72%,-19.69%,24.09%,-23.47%,-8.72%,---,0.15%, +,1043,696,汇添富环保行,9月20日,2.413,2.413,2.20%,-4.59%,-11.29%,-14.55%,-5.34%,-18.73%,44.23%,128.72%,-18.15%,141.30%,-18.06%,0.15%, +,1044,5288,海富通创业板,9月19日,1.687,1.687,-0.66%,-7.43%,-12.24%,-7.47%,-7.67%,-18.73%,1.55%,57.87%,-23.30%,68.70%,-17.83%,0.15%, +,1045,5570,中融智选红利,9月19日,1.4935,1.4935,-1.05%,-6.72%,-11.10%,-1.48%,6.16%,-18.73%,12.64%,40.66%,-16.02%,49.35%,---,0.00%, +,1046,5965,安信中证50,9月19日,1.7575,1.7575,-0.09%,-4.91%,-5.59%,-4.21%,-4.33%,-18.73%,0.72%,39.62%,-15.32%,75.75%,---,0.12%, +,1047,6441,中信建投中证,9月20日,1.4042,1.4042,0.93%,-4.91%,-5.73%,-3.47%,-2.78%,-18.75%,7.45%,---,-16.23%,40.42%,-19.24%,0.00%, +,1048,11269,中银证券优势,9月19日,1.0851,1.0851,0.38%,-6.91%,-2.66%,7.31%,20.98%,-18.77%,---,---,13.24%,8.51%,---,0.15%, +,1049,11631,东财有色金属,9月19日,1.3281,1.3281,0.02%,-6.60%,-6.57%,-9.36%,-3.68%,-18.80%,---,---,-9.73%,32.81%,---,0.00%, +,1050,519935,长信创新驱动,9月19日,1.712,1.712,-0.93%,-5.99%,-8.74%,-0.93%,-0.93%,-18.82%,-2.06%,37.73%,-18.05%,71.20%,-18.42%,0.15%, +,1051,5496,创金合信科技,9月19日,1.5961,1.5961,-1.52%,-3.34%,-10.99%,1.46%,-2.30%,-18.85%,-7.78%,40.68%,-24.96%,59.61%,---,0.00%, +,1052,8113,中泰中证50,9月20日,1.297,1.297,0.68%,-5.58%,-6.46%,-5.13%,-4.94%,-18.86%,-1.63%,---,-16.52%,28.82%,-18.63%,0.00%, +,1053,6347,安信量化优选,9月19日,1.8556,1.8556,-0.45%,-6.28%,-9.76%,-2.63%,-2.22%,-18.87%,-5.72%,40.74%,-19.26%,85.56%,---,0.00%, +,1054,13262,西部利得个股,9月19日,1.4659,1.4659,-0.95%,-4.69%,-5.93%,-9.61%,-9.38%,-18.87%,---,---,-20.92%,-19.90%,-17.84%,0.00%, +,1055,512,国泰沪深30,9月20日,1.1879,2.1408,0.40%,-4.08%,-4.66%,-6.85%,-4.54%,-18.88%,-11.17%,18.28%,-16.07%,121.23%,-18.96%,0.10%, +,1056,9060,南方沪深30,9月20日,1.1748,1.1748,0.99%,-4.29%,-4.74%,-7.79%,-7.15%,-18.89%,-12.05%,---,-19.82%,17.48%,-18.43%,0.00%, +,1057,4408,招商深证10,9月20日,1.8039,1.8039,0.41%,-4.74%,-7.85%,-9.64%,-6.72%,-18.91%,-8.01%,27.57%,-22.46%,42.04%,-18.01%,0.00%, +,1058,110019,易方达深证1,9月20日,1.4833,1.4833,0.43%,-4.67%,-7.81%,-9.63%,-6.69%,-18.91%,-9.84%,23.98%,-22.35%,48.33%,-18.03%,0.12%, +,1059,7952,招商财经大数,9月20日,1.1428,1.1428,0.23%,-0.88%,2.08%,-4.27%,-3.84%,-18.93%,2.53%,39.66%,-16.95%,40.77%,-19.33%,0.00%, +,1060,9127,嘉实基础产业,9月20日,1.136,1.136,-0.18%,-4.56%,-3.06%,-7.11%,-3.70%,-18.96%,-1.42%,---,-15.12%,13.60%,-19.89%,0.00%, +,1061,12912,同泰沪深30,9月19日,0.8252,0.8252,0.01%,-4.55%,-6.75%,-7.13%,-6.66%,-18.96%,---,---,-18.72%,-17.48%,---,0.00%, +,1062,2063,国泰沪深30,9月20日,1.1644,2.1134,0.40%,-4.08%,-4.67%,-6.87%,-4.59%,-18.97%,-11.36%,17.91%,-16.13%,31.96%,-19.05%,0.00%, +,1063,519116,浦银安盛沪深,9月19日,1.119,1.968,0.00%,-3.45%,-4.44%,-8.32%,-8.07%,-18.98%,-11.91%,14.49%,-20.13%,86.85%,---,0.12%, +,1064,586,景顺长城中小,9月19日,2.183,2.183,-2.54%,-5.25%,-9.83%,-7.93%,-13.44%,-19.03%,-7.03%,39.85%,-29.03%,118.30%,---,0.15%, +,1065,5287,海富通创业板,9月19日,1.6588,1.6588,-0.66%,-7.44%,-12.27%,-7.57%,-7.86%,-19.06%,0.74%,55.99%,-23.53%,65.88%,-18.16%,0.00%, +,1066,5966,安信中证50,9月19日,1.7309,1.7309,-0.09%,-4.92%,-5.62%,-4.30%,-4.52%,-19.06%,-0.08%,37.96%,-15.56%,73.09%,---,0.00%, +,1067,164401,前海开源中证,9月19日,0.8676,0.8676,-0.90%,-5.87%,-7.00%,-9.09%,-10.58%,-19.07%,-15.02%,42.13%,-21.55%,57.75%,---,0.12%, +,1068,11270,中银证券优势,9月19日,1.08,1.08,0.38%,-6.90%,-2.68%,7.21%,20.75%,-19.09%,---,---,12.94%,8.00%,---,0.00%, +,1069,13188,华夏能源革新,9月20日,3.312,3.312,1.91%,-4.22%,-8.18%,-10.73%,-1.92%,-19.10%,---,---,-14.55%,-15.53%,-17.98%,0.00%, +,1070,4742,易方达深证1,9月20日,1.4766,1.4766,0.42%,-4.68%,-7.83%,-9.69%,-6.82%,-19.12%,-10.29%,23.04%,-22.49%,45.12%,-18.23%,0.00%, +,1071,6048,长城中证50,9月20日,1.4919,1.4919,1.19%,-4.16%,-6.81%,-6.91%,-4.73%,-19.16%,5.35%,43.25%,-15.85%,49.19%,-19.38%,0.12%, +,1072,5137,长信沪深30,9月19日,1.1285,1.4091,-0.01%,-4.72%,-5.86%,-6.37%,-4.57%,-19.18%,-2.80%,34.00%,-15.80%,36.57%,-18.45%,0.08%, +,1073,162216,泰达中证50,9月20日,1.3741,2.5524,1.04%,-5.09%,-6.91%,-5.63%,-4.15%,-19.18%,3.01%,41.34%,-14.33%,225.22%,-19.87%,0.12%, +,1074,594,大摩进取优选,9月20日,2.603,2.603,0.23%,-3.52%,-8.15%,-6.70%,-2.40%,-19.21%,5.64%,51.34%,-20.93%,160.30%,-18.58%,0.15%, +,1075,12655,博时5G50,9月20日,0.7309,0.7309,-0.12%,-6.07%,-10.66%,-8.18%,-13.65%,-19.24%,---,---,-30.39%,-26.91%,---,0.12%, +,1076,10892,中银证券精选,9月19日,0.7549,0.7549,0.16%,-8.06%,-15.70%,-11.14%,-5.19%,-19.25%,---,---,-18.13%,-24.51%,-16.52%,0.15%, +,1077,9608,广发中证50,9月20日,1.0067,1.0067,1.01%,-4.42%,-6.39%,-3.96%,-2.85%,-19.26%,---,---,-14.75%,0.67%,-20.17%,0.15%, +,1078,163109,申万菱信深证,9月20日,0.609,0.8273,0.64%,-5.04%,-8.12%,-8.57%,-7.09%,-19.27%,-11.84%,16.12%,-22.02%,-15.04%,-18.84%,0.12%, +,1079,13132,创金合信文娱,9月19日,0.8044,0.8044,-1.37%,-3.84%,-4.06%,-7.09%,-7.38%,-19.27%,---,---,-24.37%,-19.56%,---,0.15%, +,1080,8399,华泰柏瑞中证,9月19日,1.1828,1.2503,-0.94%,-5.34%,-10.30%,-8.74%,-11.21%,-19.28%,-5.53%,---,-24.80%,24.29%,-19.56%,0.10%, +,1081,8854,南方内需增长,9月20日,1.0693,1.0693,0.83%,-4.06%,-4.59%,-4.83%,-1.89%,-19.29%,-16.76%,---,-17.29%,6.93%,-18.82%,0.15%, +,1082,411,景顺长城优质,9月19日,1.471,1.821,-0.34%,-1.74%,-2.58%,-9.42%,-6.78%,-19.31%,-9.48%,21.57%,-16.85%,86.22%,---,0.15%, +,1083,8768,创金合信上证,9月19日,0.889,0.889,0.18%,-2.94%,-3.04%,-7.47%,-8.50%,-19.35%,-20.84%,---,-18.86%,-11.10%,---,0.15%, +,1084,1974,景顺长城量化,9月19日,2.048,2.048,0.24%,-4.21%,-4.74%,-5.40%,-5.58%,-19.37%,-3.21%,33.33%,-15.16%,104.80%,---,0.15%, +,1085,9043,九泰久信量化,9月20日,1.2132,1.2132,0.98%,-5.81%,-9.02%,-6.82%,-6.14%,-19.37%,-0.80%,---,-13.43%,21.32%,-19.71%,0.15%, +,1086,3053,嘉实文体娱乐,9月20日,1.534,1.534,0.26%,-4.07%,-7.48%,-1.16%,-5.72%,-19.39%,1.25%,24.31%,-22.41%,53.40%,-19.43%,0.15%, +,1087,1039,嘉实先进制造,9月20日,1.862,1.862,1.03%,-3.62%,-9.52%,-8.28%,-4.32%,-19.39%,13.54%,99.36%,-19.91%,86.20%,-19.18%,0.15%, +,1088,7413,长城中证50,9月20日,1.4685,1.4685,1.20%,-4.16%,-6.83%,-6.99%,-4.87%,-19.40%,4.73%,41.75%,-16.03%,57.53%,-19.62%,0.00%, +,1089,1028,华安物联网主,9月19日,1.19,1.19,-0.58%,-5.41%,-9.85%,-7.68%,-8.18%,-19.43%,1.28%,45.48%,-23.03%,19.00%,---,0.15%, +,1090,5554,南方H股联接,9月20日,0.6786,0.6786,0.97%,-2.16%,-2.16%,-8.56%,-2.70%,-19.45%,-25.15%,-30.11%,-13.04%,-32.14%,-17.11%,0.12%, +,1091,8618,永赢医药健康,9月20日,0.9423,0.9423,0.19%,-6.29%,-7.45%,-8.51%,-14.71%,-19.46%,-20.86%,---,-24.41%,-5.77%,-19.61%,0.15%, +,1092,8400,华泰柏瑞中证,9月19日,1.1749,1.2424,-0.94%,-5.34%,-10.32%,-8.79%,-11.32%,-19.48%,-6.00%,---,-24.93%,23.50%,-19.76%,0.00%, +,1093,7448,长信沪深30,9月19日,1.1116,1.3888,-0.01%,-4.73%,-5.90%,-6.46%,-4.77%,-19.51%,-3.59%,32.17%,-16.05%,42.01%,-18.78%,0.00%, +,1094,4784,招商稳健优选,9月20日,2.7311,2.7311,2.05%,-5.26%,-11.55%,-5.08%,-7.44%,-19.52%,49.35%,149.60%,-24.56%,173.11%,-20.39%,0.15%, +,1095,162510,国联安中小综,9月19日,0.915,1.703,-0.44%,-5.18%,-8.50%,-7.95%,-9.41%,-19.53%,-16.82%,4.57%,-23.04%,65.65%,-18.72%,0.12%, +,1096,1628,招商体育文化,9月20日,1.298,1.298,0.85%,-4.42%,-8.01%,-11.82%,-15.55%,-19.53%,0.70%,34.23%,-32.92%,29.80%,-20.37%,0.15%, +,1097,530018,建信深证10,9月19日,2.1383,2.1383,-0.10%,-4.63%,-7.94%,-8.36%,-7.66%,-19.55%,-11.09%,25.20%,-22.46%,113.83%,---,0.15%, +,1098,1975,景顺长城环保,9月19日,3.066,3.066,-1.26%,-6.87%,-13.00%,-8.80%,-8.94%,-19.55%,7.13%,68.93%,-27.60%,206.60%,---,0.15%, +,1099,12658,博时5G50,9月20日,0.7274,0.7274,-0.12%,-6.08%,-10.69%,-8.27%,-13.83%,-19.56%,---,---,-30.59%,-27.26%,---,0.00%, +,1100,13437,财通资管中证,9月20日,0.8178,0.8178,2.44%,-4.66%,-3.55%,-6.84%,-2.84%,-19.56%,---,---,-8.78%,-18.22%,-19.70%,0.10%, +,1101,10893,中银证券精选,9月19日,0.7499,0.7499,0.15%,-8.08%,-15.74%,-11.23%,-5.39%,-19.58%,---,---,-18.36%,-25.01%,-16.85%,0.00%, +,1102,1879,长城创业板指,9月20日,2.051,2.051,1.10%,-6.95%,-10.92%,-9.50%,-6.72%,-19.58%,2.81%,70.76%,-21.97%,105.10%,-18.96%,0.15%, +,1103,502000,西部利得中证,9月19日,1.5945,1.0253,-0.26%,-6.18%,-5.82%,-5.76%,-2.39%,-19.59%,20.59%,74.06%,-14.32%,9.08%,-19.83%,0.10%, +,1104,9609,广发中证50,9月20日,0.9989,0.9989,1.01%,-4.43%,-6.42%,-4.05%,-3.04%,-19.59%,---,---,-14.99%,-0.11%,-20.48%,0.00%, +,1105,9874,九泰久睿量化,9月20日,0.8568,0.8568,0.99%,-5.85%,-8.82%,-7.16%,-6.37%,-19.59%,-10.16%,---,-12.71%,-14.32%,-20.11%,--, +,1106,161604,融通深证10,9月20日,1.456,3.084,0.41%,-4.71%,-7.91%,-9.79%,-7.14%,-19.60%,-11.82%,21.37%,-22.87%,424.88%,-18.66%,0.15%, +,1107,3957,安信量化精选,9月19日,1.4845,1.4845,-0.01%,-3.96%,-4.69%,-7.06%,-6.33%,-19.62%,-9.84%,27.72%,-19.18%,48.45%,---,0.08%, +,1108,8619,永赢医药健康,9月20日,0.9379,0.9379,0.19%,-6.29%,-7.47%,-8.56%,-14.79%,-19.62%,-21.17%,---,-24.52%,-6.21%,-19.77%,0.00%, +,1109,10771,天弘国证消费,9月20日,0.7665,0.7665,0.31%,-3.98%,-7.14%,-9.77%,-6.68%,-19.64%,---,---,-22.25%,-23.35%,-18.58%,0.15%, +,1110,10815,农银新兴消费,9月20日,0.7084,0.7084,0.64%,-1.56%,-2.91%,-8.96%,-3.49%,-19.64%,---,---,-21.65%,-29.16%,-18.69%,0.15%, +,1111,13438,财通资管中证,9月20日,0.817,0.817,2.45%,-4.66%,-3.56%,-6.86%,-2.89%,-19.64%,---,---,-8.84%,-18.30%,-19.78%,0.00%, +,1112,9384,大摩MSCI,9月20日,0.9084,0.9084,0.36%,-3.94%,-6.25%,-5.78%,-4.01%,-19.65%,-8.68%,---,-17.67%,-9.16%,-19.79%,0.15%, +,1113,202017,南方深证成份,9月20日,1.0096,1.0096,0.65%,-5.10%,-8.21%,-8.83%,-7.23%,-19.66%,-10.80%,20.77%,-22.37%,0.96%,-19.21%,0.12%, +,1114,13195,招商中证新能,9月20日,0.8073,0.8073,2.31%,-4.69%,-11.31%,-15.14%,-2.86%,-19.66%,---,---,-16.12%,-19.27%,-18.74%,0.12%, +,1115,8592,天弘沪深30,9月20日,1.204,1.204,0.19%,-4.05%,-4.98%,-7.31%,-4.96%,-19.67%,-4.51%,---,-18.43%,20.40%,-19.30%,0.15%, +,1116,164818,工银中证传媒,9月20日,0.6772,0.2209,0.39%,-4.86%,-8.20%,-12.17%,-14.81%,-19.67%,-37.97%,-25.50%,-32.86%,-80.34%,-18.92%,0.10%, +,1117,11882,招商蓝筹精选,9月20日,0.8018,0.8018,0.63%,-4.54%,-6.90%,-9.11%,-4.27%,-19.67%,---,---,-17.56%,-19.82%,---,0.15%, +,1118,13133,创金合信文娱,9月19日,0.8,0.8,-1.38%,-3.86%,-4.09%,-7.20%,-7.61%,-19.67%,---,---,-24.64%,-20.00%,---,0.00%, +,1119,519965,长信量化多策,9月19日,1.6945,1.6945,0.27%,-4.95%,-6.64%,-8.26%,-7.70%,-19.74%,0.80%,45.45%,-15.08%,69.45%,-19.00%,0.15%, +,1120,8769,创金合信上证,9月19日,0.877,0.877,0.18%,-2.95%,-3.07%,-7.59%,-8.73%,-19.75%,-21.63%,---,-19.16%,-12.30%,---,0.00%, +,1121,803,工银研究精选,9月20日,2.94,2.94,-0.91%,-5.86%,-2.23%,-7.34%,-9.26%,-19.76%,17.55%,95.35%,-22.02%,194.00%,-19.50%,0.15%, +,1122,13013,华夏中证新能,9月20日,0.8027,0.8027,2.31%,-4.89%,-11.59%,-15.91%,-4.05%,-19.76%,---,---,-17.26%,-19.73%,---,0.12%, +,1123,5555,南方H股联接,9月20日,0.6662,0.6662,0.95%,-2.17%,-2.20%,-8.65%,-2.90%,-19.77%,-25.75%,-30.94%,-13.30%,-33.38%,-17.45%,0.00%, +,1124,3958,安信量化精选,9月19日,1.4625,1.4625,-0.01%,-3.97%,-4.71%,-7.11%,-6.42%,-19.78%,-10.20%,26.94%,-19.29%,46.25%,---,0.00%, +,1125,8855,南方内需增长,9月20日,1.0528,1.0528,0.82%,-4.07%,-4.65%,-4.99%,-2.20%,-19.79%,-17.76%,---,-17.66%,5.28%,-19.31%,0.00%, +,1126,13218,国泰中证有色,9月19日,0.8842,0.8842,-0.01%,-6.34%,-6.02%,-9.66%,-4.11%,-19.81%,---,---,-9.78%,-11.58%,-17.90%,0.10%, +,1127,6928,长城创业板指,9月20日,2.0175,2.0175,1.10%,-6.96%,-10.94%,-9.56%,-6.86%,-19.82%,2.19%,68.83%,-22.14%,132.08%,-19.20%,0.00%, +,1128,3054,嘉实文体娱乐,9月20日,1.492,1.492,0.27%,-4.05%,-7.50%,-1.26%,-5.99%,-19.83%,0.20%,22.40%,-22.69%,49.20%,-19.83%,0.00%, +,1129,5189,海富通量化前,9月19日,1.4109,1.7249,-0.35%,-6.71%,-9.30%,-8.41%,-8.84%,-19.86%,3.59%,57.08%,-19.50%,66.52%,-19.10%,0.15%, +,1130,8199,华夏创新10,9月20日,0.9979,0.9979,0.05%,-4.14%,-5.58%,-9.56%,-8.73%,-19.87%,-18.55%,---,-21.54%,-0.21%,-19.67%,0.12%, +,1131,10677,工银中证传媒,9月20日,0.6742,0.6742,0.39%,-4.85%,-8.22%,-12.22%,-14.92%,-19.87%,---,---,-32.99%,-32.78%,-19.11%,0.00%, +,1132,10772,天弘国证消费,9月20日,0.7625,0.7625,0.32%,-3.99%,-7.16%,-9.84%,-6.82%,-19.88%,---,---,-22.42%,-23.75%,-18.83%,0.00%, +,1133,161812,银华深证10,9月20日,1.156,1.156,0.35%,-4.70%,-7.89%,-9.76%,-7.15%,-19.89%,-13.99%,17.81%,-22.78%,54.48%,-18.99%,0.12%, +,1134,4876,融通深证10,9月20日,1.426,1.676,0.42%,-4.68%,-7.88%,-9.86%,-7.34%,-19.89%,-12.53%,19.87%,-23.12%,25.55%,-18.98%,0.00%, +,1135,11512,天弘中证新能,9月20日,1.2302,1.2302,2.35%,-4.72%,-11.47%,-15.41%,-3.16%,-19.89%,---,---,-16.25%,23.02%,-18.97%,0.10%, +,1136,3876,华宝沪深30,9月20日,1.6913,1.6913,0.23%,-4.06%,-4.67%,-7.41%,-6.26%,-19.89%,-11.77%,22.26%,-18.86%,69.13%,-19.68%,0.12%, +,1137,1496,工银聚焦30,9月20日,1.513,1.513,1.95%,-4.36%,-9.73%,-7.69%,-5.02%,-19.90%,22.81%,109.27%,-19.22%,51.30%,-19.56%,0.15%, +,1138,9300,西部利得中证,9月19日,1.5794,1.5794,-0.27%,-6.19%,-5.85%,-5.86%,-2.59%,-19.91%,19.63%,---,-14.56%,69.72%,-20.15%,0.00%, +,1139,8593,天弘沪深30,9月20日,1.1943,1.1943,0.19%,-4.05%,-5.00%,-7.37%,-5.10%,-19.92%,-5.07%,---,-18.60%,19.43%,-19.54%,0.00%, +,1140,162714,广发深证10,9月20日,1.3567,1.5759,0.41%,-4.40%,-7.64%,-9.60%,-7.30%,-19.97%,-12.57%,17.55%,-22.91%,69.00%,-19.08%,0.12%, +,1141,167302,方正富邦大湾,9月20日,0.8576,0.8576,0.23%,-2.95%,-3.83%,-8.11%,-7.40%,-19.97%,-24.36%,---,-18.24%,-14.24%,-18.96%,0.10%, +,1142,4345,南方深证成份,9月20日,0.9889,0.9889,0.65%,-5.11%,-8.24%,-8.92%,-7.42%,-19.98%,-11.51%,19.33%,-22.60%,16.22%,-19.52%,0.00%, +,1143,13196,招商中证新能,9月20日,0.8039,0.8039,2.30%,-4.69%,-11.35%,-15.23%,-3.06%,-19.99%,---,---,-16.36%,-19.61%,-19.06%,0.00%, +,1144,13014,华夏中证新能,9月20日,0.8002,0.8002,2.29%,-4.91%,-11.62%,-15.98%,-4.20%,-20.00%,---,---,-17.44%,-19.98%,---,0.00%, +,1145,12768,华夏中证动漫,9月19日,0.7872,0.7872,-2.85%,-6.36%,-10.21%,-9.40%,-14.92%,-20.03%,---,---,-32.41%,-21.28%,---,0.12%, +,1146,11513,天弘中证新能,9月20日,1.2266,1.2266,2.34%,-4.72%,-11.49%,-15.46%,-3.26%,-20.05%,---,---,-16.38%,22.66%,-19.14%,0.00%, +,1147,13219,国泰中证有色,9月19日,0.8813,0.8813,-0.01%,-6.34%,-6.03%,-9.73%,-4.26%,-20.06%,---,---,-9.98%,-11.87%,-18.14%,0.00%, +,1148,4858,长信量化多策,9月19日,1.6358,1.6358,0.26%,-4.97%,-6.68%,-8.35%,-7.88%,-20.07%,-0.01%,43.37%,-15.32%,35.75%,-19.34%,0.00%, +,1149,7590,华宝绿色领先,9月20日,1.4552,1.4552,0.86%,-5.76%,-10.67%,-10.21%,-4.97%,-20.07%,10.82%,45.52%,-21.84%,45.52%,-20.77%,0.15%, +,1150,8200,华夏创新10,9月20日,0.9908,0.9908,0.05%,-4.14%,-5.60%,-9.62%,-8.86%,-20.10%,-19.03%,---,-21.70%,-0.92%,-19.91%,0.00%, +,1151,9601,招商科技动力,9月20日,0.7999,0.7999,0.21%,-0.73%,2.24%,-3.07%,-1.65%,-20.10%,-19.96%,---,-14.48%,-20.01%,-20.17%,0.15%, +,1152,854,鹏华养老产业,9月19日,2.952,2.952,-0.44%,-3.24%,-4.99%,-6.32%,-5.54%,-20.13%,-11.91%,38.46%,-22.78%,195.20%,---,0.15%, +,1153,10202,天弘中证科技,9月20日,0.9917,0.9917,0.06%,-5.88%,-10.74%,-8.75%,-9.33%,-20.13%,---,---,-24.13%,-0.83%,-20.34%,0.15%, +,1154,9472,广发深证10,9月20日,1.3509,1.3509,0.42%,-4.40%,-7.65%,-9.64%,-7.40%,-20.13%,-12.92%,---,-23.02%,8.45%,-19.24%,0.00%, +,1155,161612,融通深证成份,9月20日,1.037,1.118,0.58%,-5.12%,-8.31%,-8.71%,-7.16%,-20.15%,-8.45%,26.62%,-22.41%,10.76%,-19.73%,0.12%, +,1156,176,嘉实沪深30,9月20日,1.5438,1.5438,0.18%,-4.25%,-4.83%,-7.48%,-6.52%,-20.16%,-15.03%,9.10%,-18.32%,54.38%,-19.59%,0.10%, +,1157,8898,国寿创精选8,9月19日,0.9896,0.9896,-0.99%,-6.08%,-11.33%,-6.54%,-12.25%,-20.17%,-14.44%,---,-26.45%,-1.05%,---,0.12%, +,1158,9981,万家创业板指,9月19日,0.8709,0.8709,-0.53%,-7.26%,-12.45%,-8.52%,-8.07%,-20.17%,---,---,-21.04%,-12.91%,-19.38%,0.15%, +,1159,5188,海富通量化前,9月19日,1.4131,1.7271,-0.35%,-6.72%,-9.33%,-8.51%,-9.03%,-20.18%,2.77%,55.22%,-19.73%,66.66%,-19.42%,0.00%, +,1160,290010,泰信中证20,9月20日,1.143,1.163,0.26%,-4.99%,-5.93%,-8.12%,-6.69%,-20.18%,-10.84%,15.69%,-19.79%,16.59%,-20.24%,0.12%, +,1161,7404,华宝沪深30,9月20日,1.6696,1.6696,0.22%,-4.07%,-4.70%,-7.51%,-6.45%,-20.21%,-12.48%,20.80%,-19.09%,41.96%,-20.00%,0.00%, +,1162,501057,汇添富中证新,9月20日,2.5285,2.5285,2.37%,-4.71%,-11.52%,-15.53%,-3.80%,-20.22%,71.76%,183.53%,-17.25%,152.85%,-19.43%,0.12%, +,1163,1938,中欧时代先锋,9月20日,1.492,2.8168,1.39%,-4.70%,-6.84%,-3.85%,-2.62%,-20.23%,9.08%,52.67%,-20.07%,253.32%,-20.24%,0.15%, +,1164,9079,南方粤港澳大,9月20日,0.9919,0.9919,0.05%,-4.19%,-5.65%,-9.70%,-8.78%,-20.23%,-20.27%,---,-21.70%,-0.81%,-19.39%,0.12%, +,1165,8261,招商研究优选,9月20日,1.1111,1.1111,0.26%,-0.72%,2.57%,-2.38%,-0.96%,-20.23%,-15.24%,---,-14.81%,11.11%,-20.12%,0.15%, +,1166,161031,富国中证工业,9月20日,0.922,0.762,0.22%,-5.34%,-9.52%,-6.59%,-10.92%,-20.24%,-0.23%,52.10%,-26.83%,-16.70%,-19.83%,0.12%, +,1167,10153,中加中证50,9月20日,0.9536,0.9536,0.67%,-6.01%,-6.85%,-5.96%,-5.36%,-20.24%,---,---,-16.09%,-5.27%,-19.69%,0.10%, +,1168,3745,广发多元新兴,9月20日,2.0913,2.0913,1.66%,-5.75%,-10.58%,-7.75%,-9.23%,-20.28%,-8.87%,59.96%,-21.97%,109.13%,-19.33%,0.15%, +,1169,12769,华夏中证动漫,9月19日,0.7847,0.7847,-2.86%,-6.38%,-10.24%,-9.48%,-15.07%,-20.28%,---,---,-32.55%,-21.53%,---,0.00%, +,1170,9080,南方粤港澳大,9月20日,0.9895,0.9895,0.05%,-4.19%,-5.65%,-9.72%,-8.83%,-20.30%,-20.43%,---,-21.76%,-1.05%,-19.47%,0.00%, +,1171,9852,银华品质消费,9月20日,0.7834,0.7834,1.08%,-1.14%,-2.74%,-9.19%,-2.37%,-20.30%,---,---,-17.72%,-21.66%,-19.55%,0.15%, +,1172,11883,招商蓝筹精选,9月20日,0.7945,0.7945,0.63%,-4.54%,-6.96%,-9.29%,-4.67%,-20.30%,---,---,-18.03%,-20.55%,---,0.00%, +,1173,311,景顺长城沪深,9月19日,2.243,2.583,0.18%,-3.86%,-4.59%,-6.19%,-6.66%,-20.32%,-14.32%,4.91%,-17.66%,163.86%,---,0.12%, +,1174,1167,金鹰科技创新,9月19日,1.109,1.109,-2.80%,-5.46%,-14.95%,-3.73%,-13.83%,-20.33%,15.40%,67.27%,-29.32%,10.90%,---,0.15%, +,1175,7354,创金合信港股,9月19日,0.6903,0.6903,-0.76%,-3.40%,-4.01%,-8.16%,-6.39%,-20.34%,---,---,-13.54%,-30.97%,---,0.15%, +,1176,160629,鹏华中证传媒,9月19日,0.713,1.133,-1.66%,-5.19%,-8.47%,-11.76%,-14.61%,-20.34%,-40.29%,-23.26%,-32.86%,-48.72%,---,0.12%, +,1177,2076,浙商中证50,9月19日,1.5031,1.5031,-0.50%,-5.66%,-6.99%,-5.78%,-6.83%,-20.36%,7.39%,59.51%,-17.67%,50.31%,---,0.15%, +,1178,2621,中欧消费主题,9月20日,2.0588,2.0588,1.19%,-3.06%,-5.86%,-8.70%,-3.66%,-20.36%,-31.53%,26.38%,-25.08%,105.88%,-18.85%,0.15%, +,1179,7983,申万菱信中证,9月20日,1.457,1.457,0.17%,-4.48%,-8.90%,-10.32%,-10.74%,-20.37%,-1.12%,---,-25.55%,45.70%,-19.92%,0.12%, +,1180,10203,天弘中证科技,9月20日,0.9861,0.9861,0.07%,-5.88%,-10.76%,-8.81%,-9.47%,-20.37%,---,---,-24.29%,-1.39%,-20.57%,0.00%, +,1181,519706,交银深证30,9月20日,1.731,1.731,-0.35%,-4.94%,-3.46%,-6.89%,-7.63%,-20.38%,-18.73%,-3.67%,-19.11%,73.10%,-19.86%,0.15%, +,1182,6265,红土创新新科,9月20日,3.6248,3.6748,4.98%,-3.53%,-10.85%,-7.10%,0.37%,-20.38%,55.71%,155.11%,-12.96%,267.99%,-20.29%,0.15%, +,1183,470068,汇添富深证3,9月20日,1.5647,1.5647,0.57%,-4.90%,-8.02%,-9.24%,-7.26%,-20.40%,-13.80%,15.33%,-22.61%,56.47%,-19.75%,0.10%, +,1184,4875,融通深证成份,9月20日,1.052,1.07,0.57%,-5.05%,-8.28%,-8.76%,-7.31%,-20.41%,-9.15%,25.18%,-22.60%,23.30%,-19.99%,0.00%, +,1185,8899,国寿创精选8,9月19日,0.9826,0.9826,-0.99%,-6.09%,-11.35%,-6.61%,-12.38%,-20.41%,-14.95%,---,-26.60%,-1.75%,---,0.00%, +,1186,12543,嘉实中证新能,9月20日,0.794,0.794,2.33%,-4.74%,-11.40%,-15.20%,-2.83%,-20.41%,---,---,-16.09%,-20.60%,-19.50%,0.10%, +,1187,180003,银华-道琼斯,9月20日,1.265,3.4983,0.15%,-2.80%,-2.08%,-3.29%,-3.47%,-20.42%,-4.41%,27.28%,-21.92%,584.73%,-19.94%,0.15%, +,1188,9147,建信新能源行,9月19日,2.2833,2.2833,-0.17%,-8.39%,-11.84%,-8.50%,-3.00%,-20.42%,91.06%,---,-16.77%,128.33%,---,0.15%, +,1189,501058,汇添富中证新,9月20日,2.4984,2.4984,2.37%,-4.72%,-11.55%,-15.58%,-3.92%,-20.43%,70.90%,181.41%,-17.40%,149.84%,-19.63%,0.00%, +,1190,11285,民生价值优选,9月20日,0.7604,0.7604,0.33%,-2.58%,-3.08%,-6.71%,-1.43%,-20.43%,---,---,-18.71%,-23.96%,-20.24%,0.15%, +,1191,10154,中加中证50,9月20日,0.9476,0.9476,0.67%,-6.02%,-6.88%,-6.03%,-5.49%,-20.48%,---,---,-16.27%,-5.87%,-19.93%,0.00%, +,1192,1193,中金消费升级,9月19日,1.0332,1.0332,-0.29%,-2.88%,-5.40%,-12.42%,-8.10%,-20.49%,-21.67%,25.08%,-24.18%,3.32%,---,0.15%, +,1193,9982,万家创业板指,9月19日,0.8652,0.8652,-0.53%,-7.27%,-12.48%,-8.61%,-8.26%,-20.49%,---,---,-21.26%,-13.48%,-19.69%,0.00%, +,1194,5612,嘉实核心优势,9月20日,1.3806,1.3806,0.52%,-2.09%,-3.31%,-7.04%,-3.03%,-20.52%,-13.41%,27.67%,-19.99%,38.06%,-18.86%,0.15%, +,1195,1245,工银生态环境,9月20日,2.289,2.289,2.55%,-5.61%,-11.07%,-10.20%,-5.49%,-20.55%,77.30%,201.18%,-18.34%,128.90%,-20.41%,0.15%, +,1196,11122,汇添富ESG,9月20日,0.7726,0.7726,1.87%,-3.39%,-6.99%,-5.20%,-6.37%,-20.55%,---,---,-21.92%,-22.74%,-20.00%,0.15%, +,1197,10872,博时沪深30,9月20日,0.7264,0.7264,0.39%,-3.93%,-5.08%,-7.76%,-5.66%,-20.57%,---,---,-15.59%,-27.36%,-20.54%,0.12%, +,1198,1351,诺安中证50,9月20日,0.916,0.916,0.58%,-5.22%,-7.27%,-7.44%,-6.78%,-20.58%,-6.46%,28.29%,-19.38%,-8.40%,-20.93%,0.12%, +,1199,11722,前海开源深圳,9月19日,0.7319,0.7319,-0.58%,-3.15%,-3.55%,-9.40%,-7.88%,-20.58%,---,---,-21.09%,-26.81%,---,0.15%, +,1200,7984,申万菱信中证,9月20日,1.4447,1.4447,0.18%,-4.48%,-8.92%,-10.38%,-10.87%,-20.60%,-1.71%,---,-25.71%,44.47%,-20.16%,0.00%, +,1201,12544,嘉实中证新能,9月20日,0.7918,0.7918,2.33%,-4.75%,-11.42%,-15.25%,-2.95%,-20.61%,---,---,-16.24%,-20.82%,-19.70%,0.00%, +,1202,7386,浙商中证50,9月19日,1.4872,1.4872,-0.52%,-5.68%,-7.03%,-5.87%,-7.00%,-20.64%,6.64%,57.74%,-17.87%,51.82%,---,0.00%, +,1203,10013,易方达信息行,9月20日,0.8788,0.8788,-0.27%,-4.49%,-6.36%,-1.60%,-1.93%,-20.64%,-11.86%,---,-23.50%,-12.12%,-20.69%,0.15%, +,1204,592,建信改革红利,9月19日,5.349,5.349,-0.56%,-7.20%,-9.09%,-2.48%,-2.55%,-20.66%,44.22%,138.05%,-18.22%,434.90%,---,0.15%, +,1205,11723,前海开源深圳,9月19日,0.7309,0.7309,-0.58%,-3.15%,-3.56%,-9.42%,-7.94%,-20.67%,---,---,-21.15%,-26.91%,---,0.00%, +,1206,12244,广发金融地产,9月20日,0.8206,0.8206,-1.22%,-4.15%,0.85%,-6.58%,-4.48%,-20.67%,---,---,-15.96%,-17.94%,-19.85%,0.15%, +,1207,9904,民生加银中证,9月20日,0.8592,0.8592,0.12%,-5.34%,-6.08%,-8.92%,-5.31%,-20.69%,---,---,-18.42%,-14.08%,-20.67%,0.15%, +,1208,9602,招商科技动力,9月20日,0.7871,0.7871,0.22%,-0.74%,2.17%,-3.26%,-2.05%,-20.74%,-21.23%,---,-14.97%,-21.29%,-20.81%,0.00%, +,1209,11286,民生价值优选,9月20日,0.7566,0.7566,0.34%,-2.58%,-3.10%,-6.79%,-1.61%,-20.74%,---,---,-18.93%,-24.34%,-20.54%,0.00%, +,1210,1230,鹏华医药科技,9月19日,0.993,0.993,-0.50%,-4.89%,-6.41%,-5.97%,-1.88%,-20.81%,-1.68%,58.88%,-19.07%,-0.70%,---,0.15%, +,1211,10873,博时沪深30,9月20日,0.7226,0.7226,0.38%,-3.94%,-5.11%,-7.83%,-5.80%,-20.81%,---,---,-15.78%,-27.74%,-20.78%,0.00%, +,1212,163209,诺安创业板指,9月20日,1.6823,1.7059,0.57%,-6.42%,-11.67%,-10.98%,-7.99%,-20.84%,12.67%,62.26%,-23.81%,32.59%,-20.24%,0.12%, +,1213,4241,中欧时代先锋,9月20日,1.4418,2.532,1.39%,-4.72%,-6.91%,-4.05%,-3.03%,-20.87%,7.35%,49.07%,-20.53%,156.74%,-20.88%,0.00%, +,1214,8262,招商研究优选,9月20日,1.0899,1.0899,0.27%,-0.73%,2.49%,-2.57%,-1.37%,-20.87%,-16.59%,---,-15.30%,8.99%,-20.75%,0.00%, +,1215,90012,大成深证成长,9月20日,1.083,1.083,0.28%,-6.48%,-11.95%,-11.95%,-11.52%,-20.89%,-26.08%,16.83%,-27.27%,8.30%,-20.31%,0.12%, +,1216,10355,诺安中证50,9月20日,0.9093,0.9093,0.59%,-5.22%,-7.31%,-7.54%,-6.97%,-20.90%,---,---,-19.62%,-2.96%,-21.24%,0.00%, +,1217,9905,民生加银中证,9月20日,0.8546,0.8546,0.11%,-5.35%,-6.11%,-9.00%,-5.46%,-20.94%,---,---,-18.60%,-14.54%,-20.91%,0.00%, +,1218,4752,广发中证传媒,9月20日,0.5678,0.5678,0.37%,-4.88%,-8.27%,-12.47%,-15.29%,-20.95%,-41.92%,-27.93%,-33.40%,-43.22%,-20.21%,0.12%, +,1219,4432,南方有色金属,9月20日,1.2039,1.2039,2.84%,-4.77%,-3.80%,-8.68%,-3.66%,-20.96%,43.37%,72.78%,-10.51%,20.39%,-21.14%,0.12%, +,1220,7842,南华中证杭州,9月20日,1.1062,1.1062,1.06%,-4.76%,-6.65%,-8.34%,-6.92%,-20.96%,-9.00%,---,-21.68%,10.62%,-20.66%,0.12%, +,1221,2697,中欧消费主题,9月20日,2.0215,2.0215,1.19%,-3.08%,-5.93%,-8.90%,-4.06%,-20.97%,-32.64%,23.49%,-25.52%,102.15%,-19.49%,0.00%, +,1222,7357,创金合信港股,9月19日,0.6811,0.6811,-0.76%,-3.42%,-4.07%,-8.34%,-6.76%,-20.98%,---,---,-14.02%,-31.89%,---,0.00%, +,1223,12245,广发金融地产,9月20日,0.8166,0.8166,-1.21%,-4.14%,0.81%,-6.67%,-4.67%,-20.98%,---,---,-16.19%,-18.34%,-20.16%,0.00%, +,1224,9988,信澳蓝筹精选,9月20日,0.8702,0.8702,1.27%,-2.37%,-3.13%,-8.79%,-5.01%,-20.99%,-14.92%,---,-21.63%,-14.07%,-18.79%,0.15%, +,1225,5628,汇安趋势动力,9月19日,1.4256,1.4256,-1.12%,-6.25%,-9.70%,-7.87%,-6.27%,-21.05%,13.18%,25.04%,-22.65%,42.56%,-19.15%,0.15%, +,1226,13179,广发国证新能,9月20日,0.8189,0.8189,2.02%,-5.46%,-13.57%,-17.37%,-5.00%,-21.07%,---,---,-17.78%,-18.11%,-20.31%,0.12%, +,1227,4753,广发中证传媒,9月20日,0.5666,0.5666,0.37%,-4.89%,-8.29%,-12.51%,-15.37%,-21.11%,-42.15%,-28.32%,-33.49%,-43.34%,-20.35%,0.00%, +,1228,697,汇添富移动互,9月20日,1.619,1.619,0.19%,-4.82%,-7.01%,-5.04%,-10.11%,-21.14%,-13.70%,28.09%,-29.97%,61.90%,-21.06%,0.15%, +,1229,2510,申万菱信中证,9月20日,1.5275,1.5275,0.94%,-4.65%,-7.29%,-4.24%,-2.93%,-21.15%,-3.30%,42.07%,-15.65%,52.75%,-22.11%,0.12%, +,1230,8860,民生加银龙头,9月20日,1.2474,1.2474,0.34%,-2.57%,-3.26%,-6.69%,-1.40%,-21.16%,-9.39%,---,-19.12%,24.74%,-20.80%,0.15%, +,1231,10356,诺安创业板指,9月20日,1.6693,1.6693,0.57%,-6.43%,-11.70%,-11.07%,-8.18%,-21.17%,---,---,-24.03%,8.24%,-20.56%,0.00%, +,1232,11123,汇添富ESG,9月20日,0.7647,0.7647,1.86%,-3.41%,-7.06%,-5.39%,-6.76%,-21.19%,---,---,-22.37%,-23.53%,-20.63%,0.00%, +,1233,974,安信消费医药,9月19日,1.331,1.391,0.00%,-4.18%,-8.08%,-12.43%,-12.78%,-21.20%,-26.71%,-3.13%,-24.42%,38.70%,---,0.15%, +,1234,7474,华夏创成长E,9月19日,1.9989,1.9989,-0.85%,-7.79%,-11.98%,-8.17%,-9.64%,-21.21%,15.23%,80.62%,-23.52%,99.89%,---,0.12%, +,1235,968,广发养老指数,9月20日,0.8776,0.8776,0.30%,-4.36%,-4.52%,-11.28%,-9.42%,-21.23%,-32.24%,-10.43%,-24.64%,-12.24%,-20.38%,0.12%, +,1236,13180,广发国证新能,9月20日,0.8171,0.8171,2.02%,-5.47%,-13.59%,-17.42%,-5.10%,-21.23%,---,---,-17.90%,-18.29%,-20.47%,0.00%, +,1237,828,泰达转型机遇,9月20日,3.036,3.256,3.58%,-6.04%,-13.13%,-2.66%,2.67%,-21.27%,90.94%,196.48%,-11.41%,264.82%,-22.87%,0.12%, +,1238,10990,南方有色金属,9月20日,1.1956,1.1956,2.83%,-4.77%,-3.84%,-8.77%,-3.86%,-21.27%,---,---,-10.77%,23.42%,-21.46%,0.00%, +,1239,4433,南方有色金属,9月20日,1.18,1.18,2.83%,-4.78%,-3.83%,-8.77%,-3.86%,-21.28%,42.22%,70.72%,-10.78%,18.00%,-21.46%,0.00%, +,1240,7843,南华中证杭州,9月20日,1.0956,1.0956,1.06%,-4.76%,-6.67%,-8.43%,-7.11%,-21.28%,-9.72%,---,-21.90%,9.56%,-20.98%,0.00%, +,1241,2900,南方中证50,9月20日,0.9603,0.9603,0.05%,-5.35%,-10.37%,-4.32%,-11.73%,-21.30%,-27.20%,-8.84%,-28.24%,-4.02%,-21.55%,0.12%, +,1242,12698,平安中证新能,9月19日,0.8416,0.8416,1.11%,-6.33%,-13.45%,-15.81%,-5.62%,-21.30%,---,---,-18.62%,-15.84%,-18.68%,0.10%, +,1243,217019,招商深证TM,9月20日,1.6081,1.6081,-0.15%,-4.72%,-8.67%,-8.42%,-10.57%,-21.35%,-17.99%,12.60%,-28.63%,60.81%,-21.09%,0.15%, +,1244,8052,工银湾创10,9月20日,0.8795,0.8795,0.07%,-4.66%,-6.95%,-10.22%,-10.17%,-21.35%,-23.41%,---,-22.77%,-12.05%,-20.52%,0.10%, +,1245,2982,广发养老指数,9月20日,0.8652,0.8652,0.30%,-4.36%,-4.52%,-11.33%,-9.52%,-21.38%,-32.51%,-10.96%,-24.75%,-9.42%,-20.54%,0.00%, +,1246,7795,申万菱信中证,9月20日,1.6269,1.6269,0.93%,-4.66%,-7.31%,-4.32%,-3.08%,-21.39%,-3.90%,40.80%,-15.84%,62.69%,-22.35%,0.00%, +,1247,1528,诺安先进制造,9月20日,2.09,2.09,0.24%,-2.79%,-6.99%,-7.81%,-10.80%,-21.40%,-2.25%,37.50%,-25.89%,109.00%,-21.43%,0.15%, +,1248,501007,汇添富中证互,9月20日,0.8886,0.8886,0.09%,-5.77%,-8.71%,-6.39%,-10.69%,-21.43%,-29.77%,-11.98%,-29.32%,-11.14%,-20.70%,0.08%, +,1249,5629,汇安趋势动力,9月19日,1.3907,1.3907,-1.13%,-6.26%,-9.74%,-7.98%,-6.51%,-21.44%,12.06%,23.18%,-22.93%,39.07%,-19.55%,0.00%, +,1250,168701,合煦智远金融,9月19日,0.8183,0.8183,-2.80%,-5.57%,-6.51%,-8.73%,-16.66%,-21.45%,-28.07%,---,-27.27%,-18.17%,-21.33%,0.15%, +,1251,12800,泰达转型机遇,9月20日,3.026,3.026,3.59%,-6.02%,-13.15%,-2.70%,2.54%,-21.48%,---,---,-11.60%,-15.57%,-23.08%,0.00%, +,1252,7853,华商计算机行,9月19日,0.9758,0.9758,-1.61%,-3.39%,-5.46%,-5.23%,-9.94%,-21.53%,-25.21%,---,-25.84%,-2.42%,---,0.15%, +,1253,7475,华夏创成长E,9月19日,1.9735,1.9735,-0.85%,-7.79%,-12.00%,-8.26%,-9.82%,-21.53%,14.32%,78.48%,-23.74%,97.35%,---,0.00%, +,1254,1421,南方量化成长,9月20日,1.311,1.311,1.39%,-4.72%,-12.42%,-9.15%,-5.68%,-21.54%,-6.22%,31.36%,-16.55%,31.10%,-21.92%,0.15%, +,1255,501309,国泰恒生港股,9月19日,0.8802,0.8802,-0.79%,-3.60%,-4.39%,-8.22%,-6.32%,-21.54%,-19.97%,-10.81%,-15.14%,-11.98%,-19.15%,0.12%, +,1256,4616,中欧电子信息,9月20日,2.103,2.103,-0.58%,-2.68%,-5.15%,1.02%,-9.29%,-21.56%,4.92%,46.29%,-28.14%,110.30%,-21.62%,0.15%, +,1257,4347,南方中证50,9月20日,0.9416,0.9416,0.05%,-5.55%,-10.36%,-5.10%,-11.87%,-21.57%,-27.74%,-10.50%,-28.41%,0.88%,-21.86%,0.00%, +,1258,12699,平安中证新能,9月19日,0.8375,0.8375,1.11%,-6.34%,-13.48%,-15.91%,-5.81%,-21.62%,---,---,-18.85%,-16.25%,-19.00%,0.00%, +,1259,4409,招商深证TM,9月20日,1.578,1.578,-0.15%,-4.73%,-8.70%,-8.52%,-10.75%,-21.66%,-18.65%,11.25%,-28.84%,5.13%,-21.41%,0.00%, +,1260,549,华安大国新经,9月19日,2.954,2.954,-0.57%,-5.71%,-10.43%,-8.57%,-9.36%,-21.67%,-0.07%,44.73%,-24.02%,195.40%,---,0.15%, +,1261,11651,招商港股通核,9月20日,0.7549,0.7549,0.75%,-3.79%,-5.99%,-6.74%,-3.07%,-21.67%,---,---,-16.73%,-24.51%,-20.69%,0.15%, +,1262,950,易方达沪深3,9月20日,0.7884,0.7884,-0.54%,-6.82%,-3.31%,-8.37%,-8.69%,-21.68%,-32.09%,-24.27%,-21.49%,-21.16%,-20.88%,0.10%, +,1263,8053,工银湾创10,9月20日,0.8697,0.8697,0.06%,-4.68%,-6.99%,-10.31%,-10.38%,-21.69%,-24.04%,---,-23.01%,-13.03%,-20.86%,0.00%, +,1264,501008,汇添富中证互,9月20日,0.8725,0.8725,0.09%,-5.78%,-8.74%,-6.48%,-10.88%,-21.73%,-30.33%,-13.03%,-29.52%,-12.75%,-21.02%,0.00%, +,1265,7882,易方达沪深3,9月20日,0.7859,0.7859,-0.54%,-6.82%,-3.32%,-8.39%,-8.73%,-21.75%,-32.23%,-24.49%,-21.54%,-22.10%,-20.95%,0.00%, +,1266,12926,民生加银中证,9月20日,0.8122,0.8122,0.76%,-5.08%,-6.84%,-6.95%,-6.60%,-21.75%,---,---,-17.93%,-18.78%,-22.20%,0.15%, +,1267,7737,诺德研发创新,9月19日,1.3424,1.3424,-0.67%,-4.25%,-9.29%,-9.63%,-10.96%,-21.76%,-10.46%,27.53%,-26.35%,34.24%,-21.13%,0.15%, +,1268,5813,华安CES港,9月19日,0.7937,0.7937,-0.90%,-3.81%,-5.49%,-7.78%,-5.16%,-21.78%,-21.62%,-18.12%,-15.60%,-20.63%,---,0.12%, +,1269,8889,银华中证5G,9月20日,0.6532,0.6532,-0.40%,-6.98%,-12.38%,-9.45%,-13.39%,-21.80%,-31.02%,---,-32.32%,-34.68%,-21.50%,0.12%, +,1270,12862,汇添富中证电,9月20日,0.8132,0.8132,2.43%,-6.31%,-14.86%,-11.20%,-7.29%,-21.83%,---,---,-19.95%,-18.68%,-21.26%,0.10%, +,1271,168702,合煦智远金融,9月19日,0.8083,0.8083,-2.80%,-5.58%,-6.55%,-8.85%,-16.88%,-21.85%,-28.79%,---,-27.53%,-19.17%,-21.72%,0.00%, +,1272,746,招商行业精选,9月20日,3.185,3.185,0.22%,-0.69%,2.28%,-3.13%,-1.94%,-21.86%,-17.10%,61.35%,-15.13%,218.50%,-21.96%,0.15%, +,1273,3853,金鹰信息产业,9月19日,3.3368,3.6733,0.13%,-6.74%,-12.89%,-12.24%,-8.01%,-21.88%,44.91%,142.97%,-24.65%,270.63%,---,0.12%, +,1274,826,广发百发10,9月20日,1.232,1.592,1.82%,-6.24%,-11.37%,-14.33%,-13.18%,-21.88%,-16.53%,9.71%,-26.88%,57.69%,-21.38%,0.12%, +,1275,827,广发百发10,9月20日,1.23,1.59,1.82%,-6.18%,-11.38%,-14.35%,-13.14%,-21.90%,-16.55%,9.72%,-26.87%,57.46%,-21.36%,0.06%, +,1276,360001,光大量化股票,9月20日,1.0254,3.4471,0.90%,-4.28%,-5.30%,-5.59%,-7.30%,-21.92%,-9.64%,15.79%,-17.75%,406.14%,-21.97%,0.15%, +,1277,6593,博道中证50,9月19日,1.8636,1.9636,-0.20%,-5.64%,-6.71%,-4.22%,-3.20%,-21.94%,12.86%,62.49%,-16.45%,102.87%,---,0.12%, +,1278,10805,东财新能源车,9月19日,1.249,1.249,1.14%,-6.27%,-13.45%,-15.95%,-5.31%,-21.95%,---,---,-18.47%,24.90%,---,0.12%, +,1279,10120,九泰久福量化,9月20日,0.8564,0.8564,0.98%,-5.83%,-8.84%,-6.22%,-6.22%,-21.95%,---,---,-13.35%,-14.36%,-22.25%,0.12%, +,1280,5062,博时中证50,9月20日,1.2817,1.2817,0.76%,-4.65%,-5.44%,-3.90%,-3.73%,-21.96%,-6.95%,28.88%,-14.03%,28.17%,-22.97%,0.12%, +,1281,688,景顺长城研究,9月19日,1.477,1.497,0.00%,-3.59%,-4.71%,-7.69%,-7.11%,-21.98%,-22.34%,-3.34%,-26.70%,50.49%,---,0.15%, +,1282,12927,民生加银中证,9月20日,0.8095,0.8095,0.76%,-5.09%,-6.87%,-7.03%,-6.75%,-21.98%,---,---,-18.11%,-19.05%,-22.43%,0.00%, +,1283,1556,天弘中证50,9月20日,1.2212,1.2212,0.84%,-4.73%,-5.78%,-5.82%,-3.19%,-21.99%,4.21%,50.04%,-15.74%,22.12%,-22.17%,0.15%, +,1284,4532,民生加银中证,9月20日,0.8979,0.8979,0.31%,-3.90%,-4.65%,-8.56%,-8.22%,-22.02%,-6.91%,-1.76%,-13.37%,-10.21%,-20.22%,0.12%, +,1285,6257,信澳先进智造,9月20日,2.0591,2.6794,2.83%,-6.59%,-12.84%,-6.06%,-3.22%,-22.02%,22.91%,108.40%,-20.54%,162.27%,-20.62%,0.15%, +,1286,10524,银华中证5G,9月20日,0.6501,0.6501,-0.38%,-6.97%,-12.39%,-9.52%,-13.52%,-22.03%,---,---,-32.46%,-29.29%,-21.73%,0.00%, +,1287,12863,汇添富中证电,9月20日,0.811,0.811,2.44%,-6.31%,-14.87%,-11.26%,-7.40%,-22.03%,---,---,-20.09%,-18.90%,-21.45%,0.00%, +,1288,9805,国泰医药健康,9月19日,0.7354,0.7354,-0.78%,-6.18%,-10.40%,-8.40%,-14.16%,-22.05%,-25.42%,---,-24.41%,-26.46%,-20.65%,0.15%, +,1289,5814,华安CES港,9月19日,0.796,0.796,-0.90%,-3.82%,-5.53%,-7.88%,-5.36%,-22.09%,-22.24%,-18.98%,-15.84%,-20.40%,---,0.00%, +,1290,1361,景顺中证科技,9月19日,0.56,0.56,-1.41%,-4.60%,-8.94%,-6.82%,-13.31%,-22.11%,-24.02%,-2.27%,-29.20%,-44.00%,---,0.12%, +,1291,10341,招商产业精选,9月20日,0.7778,0.7778,0.34%,-0.59%,2.23%,-2.99%,-2.19%,-22.13%,---,---,-15.17%,-22.22%,-22.24%,0.15%, +,1292,9067,国泰中证新能,9月19日,2.2462,2.2462,1.04%,-6.19%,-13.30%,-16.08%,-5.97%,-22.14%,62.56%,---,-18.54%,124.62%,-19.54%,0.10%, +,1293,4686,华夏研究精选,9月20日,1.4324,1.4324,0.35%,-4.72%,-5.84%,-8.14%,-4.86%,-22.16%,-9.08%,33.58%,-20.04%,43.24%,-21.43%,0.15%, +,1294,160127,南方新兴消费,9月19日,0.7342,0.7342,0.01%,-1.96%,-2.65%,-4.36%,-0.73%,-22.17%,-19.31%,31.71%,-17.81%,247.17%,---,0.15%, +,1295,163110,申万菱信量化,9月20日,2.0113,3.0949,0.99%,-5.63%,-8.70%,-7.75%,-6.44%,-22.18%,-3.42%,28.42%,-17.67%,240.88%,-22.78%,0.15%, +,1296,6594,博道中证50,9月19日,1.843,1.943,-0.21%,-5.64%,-6.73%,-4.29%,-3.34%,-22.18%,12.19%,61.05%,-16.63%,100.64%,---,0.00%, +,1297,5763,中欧电子信息,9月20日,2.1635,2.1635,-0.58%,-2.70%,-5.22%,0.82%,-9.66%,-22.19%,3.25%,42.87%,-28.55%,97.60%,-22.24%,0.00%, +,1298,5885,金鹰信息产业,9月19日,3.3165,3.635,0.12%,-6.75%,-12.92%,-12.33%,-8.19%,-22.20%,43.75%,140.22%,-24.87%,251.31%,---,0.00%, +,1299,1557,天弘中证50,9月20日,1.1937,1.1937,0.84%,-4.73%,-5.81%,-5.89%,-3.34%,-22.22%,3.58%,48.69%,-15.92%,19.37%,-22.40%,0.00%, +,1300,4533,民生加银中证,9月20日,0.886,0.886,0.31%,-3.91%,-4.68%,-8.63%,-8.35%,-22.22%,-7.38%,-2.51%,-13.54%,-11.40%,-20.42%,0.00%, +,1301,10806,东财新能源车,9月19日,1.2403,1.2403,1.14%,-6.28%,-13.48%,-16.04%,-5.50%,-22.26%,---,---,-18.70%,24.03%,---,0.00%, +,1302,5795,博时中证50,9月20日,1.2591,1.2591,0.76%,-4.66%,-5.48%,-4.00%,-3.92%,-22.29%,-7.72%,27.31%,-14.28%,30.05%,-23.28%,0.00%, +,1303,11652,招商港股通核,9月20日,0.7459,0.7459,0.74%,-3.79%,-6.05%,-6.94%,-3.46%,-22.31%,---,---,-17.21%,-25.41%,-21.32%,0.00%, +,1304,10121,九泰久福量化,9月20日,0.8492,0.8492,0.98%,-5.83%,-8.88%,-6.35%,-6.47%,-22.34%,---,---,-13.66%,-15.08%,-22.65%,0.00%, +,1305,9265,易方达消费精,9月20日,0.927,0.927,0.83%,-1.27%,-0.95%,-6.67%,2.81%,-22.35%,-22.47%,---,-21.69%,-7.30%,-19.80%,0.15%, +,1306,11326,国泰医药健康,9月19日,0.7306,0.7306,-0.79%,-6.19%,-10.44%,-8.50%,-14.34%,-22.37%,---,---,-24.63%,-27.02%,-20.97%,0.00%, +,1307,10777,浙商智选家居,9月19日,0.6794,0.6794,-0.76%,-4.44%,-5.35%,-13.20%,-4.38%,-22.37%,---,---,-24.75%,-32.06%,---,0.15%, +,1308,160137,南方中证互联,9月20日,0.7694,0.7694,0.08%,-5.54%,-8.03%,-8.20%,-14.13%,-22.38%,-26.53%,-4.45%,-29.98%,-16.45%,-21.95%,0.12%, +,1309,9068,国泰中证新能,9月19日,2.23,2.23,1.04%,-6.20%,-13.32%,-16.15%,-6.12%,-22.38%,61.58%,---,-18.72%,123.00%,-19.78%,0.00%, +,1310,10749,浙商创业板指,9月19日,0.8831,0.8831,-0.67%,-7.59%,-13.02%,-10.64%,-10.73%,-22.41%,---,---,-23.71%,-11.69%,---,0.12%, +,1311,10495,创金合信创新,9月19日,0.763,0.763,-1.20%,-4.45%,-9.60%,-6.24%,-9.31%,-22.44%,---,---,-29.44%,-23.70%,---,0.15%, +,1312,761,国富健康优质,9月20日,1.4994,1.4994,1.05%,-2.80%,-2.43%,-11.61%,-9.05%,-22.45%,-14.68%,28.04%,-22.99%,49.94%,-22.00%,0.15%, +,1313,4476,景顺长城沪港,9月19日,1.648,1.648,-1.26%,-4.41%,-7.26%,-6.20%,-5.23%,-22.45%,-17.02%,30.59%,-22.52%,64.80%,---,0.15%, +,1314,160225,国泰国证新能,9月19日,1.8378,1.8378,1.17%,-6.42%,-14.44%,-15.32%,-6.28%,-22.46%,48.02%,128.55%,-19.23%,81.94%,---,0.12%, +,1315,4856,广发中证全指,9月20日,1.2017,1.2017,-0.41%,-5.71%,-7.10%,-13.00%,-12.51%,-22.50%,-18.73%,24.31%,-21.13%,20.17%,-22.11%,0.10%, +,1316,10391,易方达战略新,9月20日,0.7886,0.7886,-0.32%,-5.00%,-9.58%,-10.58%,-6.32%,-22.50%,---,---,-24.46%,-21.14%,-21.97%,0.15%, +,1317,13177,华宝深证创新,9月20日,0.7673,0.7673,0.34%,-4.92%,-9.41%,-11.00%,-9.50%,-22.54%,---,---,-25.03%,-23.27%,-21.86%,0.10%, +,1318,233010,大摩深证30,9月20日,1.8,1.8,0.56%,-4.81%,-8.16%,-9.09%,-7.07%,-22.61%,-10.85%,23.37%,-23.70%,80.00%,-22.04%,0.12%, +,1319,12605,东财证券保险,9月19日,0.8285,0.8285,-0.89%,-6.66%,-3.72%,-7.79%,-7.39%,-22.62%,---,---,-21.07%,-17.15%,---,0.12%, +,1320,8056,南方上证50,9月19日,1.0613,1.0613,0.69%,-3.92%,-3.30%,-12.19%,-9.47%,-22.63%,-17.03%,---,-19.40%,6.13%,-22.69%,0.15%, +,1321,160144,南方新兴消费,9月19日,0.7265,0.7265,0.00%,-1.98%,-2.71%,-4.51%,-1.04%,-22.64%,---,---,-18.16%,-26.33%,-20.50%,0.00%, +,1322,8086,华夏中证5G,9月20日,0.837,0.837,-0.40%,-7.15%,-12.62%,-9.54%,-13.88%,-22.64%,-31.97%,---,-33.23%,-16.30%,-22.36%,0.12%, +,1323,4857,广发中证全指,9月20日,1.2034,1.2034,-0.41%,-5.71%,-7.11%,-13.04%,-12.59%,-22.65%,-19.05%,23.64%,-21.24%,20.34%,-22.26%,0.00%, +,1324,10361,嘉实品质优选,9月20日,0.7217,0.7217,1.79%,-4.96%,-7.26%,-5.05%,-4.94%,-22.65%,---,---,-20.02%,-27.83%,-22.11%,0.15%, +,1325,370023,上投摩根中证,9月19日,1.6244,1.8044,-0.34%,-3.23%,-4.62%,-9.15%,-9.29%,-22.69%,-25.69%,-2.03%,-23.09%,90.10%,---,0.12%, +,1326,5707,富国港股通量,9月20日,0.81,0.81,0.96%,-2.04%,-3.32%,-6.78%,-2.76%,-22.70%,-20.25%,-15.61%,-13.93%,-19.00%,-20.87%,0.15%, +,1327,8072,景顺长城创业,9月19日,1.2852,1.2852,-0.77%,-7.15%,-14.09%,-10.16%,-12.35%,-22.73%,2.19%,---,-25.37%,28.52%,---,0.15%, +,1328,10342,招商产业精选,9月20日,0.7668,0.7668,0.33%,-0.61%,2.16%,-3.19%,-2.59%,-22.76%,---,---,-15.67%,-23.32%,-22.87%,0.00%, +,1329,10778,浙商智选家居,9月19日,0.6744,0.6744,-0.77%,-4.45%,-5.39%,-13.31%,-4.61%,-22.76%,---,---,-25.01%,-32.56%,---,0.00%, +,1330,9046,东财创业板A,9月20日,1.3639,1.3639,0.67%,-6.78%,-12.69%,-11.56%,-11.05%,-22.78%,-1.95%,---,-26.28%,36.39%,-22.13%,0.12%, +,1331,13178,华宝深证创新,9月20日,0.7649,0.7649,0.34%,-4.92%,-9.44%,-11.06%,-9.64%,-22.78%,---,---,-25.19%,-23.51%,-22.10%,0.00%, +,1332,1416,嘉实事件驱动,9月20日,1.058,1.058,0.76%,-4.72%,-4.81%,-5.91%,-5.15%,-22.79%,-7.65%,31.25%,-20.81%,5.00%,-22.43%,0.15%, +,1333,9613,上银中证50,9月19日,0.9738,0.9738,-0.68%,-5.77%,-7.34%,-8.44%,-9.19%,-22.80%,1.81%,---,-19.09%,-2.62%,-23.27%,0.12%, +,1334,10750,浙商创业板指,9月19日,0.8765,0.8765,-0.67%,-7.60%,-13.05%,-10.76%,-10.95%,-22.80%,---,---,-23.97%,-12.35%,---,0.00%, +,1335,10392,易方达战略新,9月20日,0.7833,0.7833,-0.32%,-5.01%,-9.61%,-10.67%,-6.52%,-22.81%,---,---,-24.68%,-21.67%,-22.28%,0.00%, +,1336,161028,富国中证新能,9月19日,1.19,1.422,1.10%,-6.23%,-13.45%,-16.37%,-5.93%,-22.83%,62.16%,168.07%,-18.99%,81.43%,-20.14%,0.12%, +,1337,12636,国泰中证全指,9月19日,0.74,0.74,-2.17%,-2.68%,-3.63%,-5.14%,-13.14%,-22.86%,---,---,-27.41%,-26.00%,-23.19%,0.10%, +,1338,8087,华夏中证5G,9月20日,0.8301,0.8301,-0.41%,-7.16%,-12.65%,-9.61%,-14.01%,-22.88%,-32.37%,---,-33.37%,-16.99%,-22.59%,0.00%, +,1339,12606,东财证券保险,9月19日,0.8247,0.8247,-0.88%,-6.67%,-3.75%,-7.88%,-7.57%,-22.93%,---,---,-21.29%,-17.53%,---,0.00%, +,1340,6603,嘉实互融精选,9月20日,0.9335,0.9335,0.56%,-4.74%,-8.62%,-14.29%,-8.61%,-22.94%,-25.27%,-7.00%,-26.62%,-6.65%,-22.28%,0.15%, +,1341,8057,南方上证50,9月19日,1.0512,1.0512,0.69%,-3.92%,-3.33%,-12.28%,-9.64%,-22.94%,-17.69%,---,-19.63%,5.12%,-22.99%,0.00%, +,1342,3069,光大创业板量,9月20日,1.4926,1.4926,0.48%,-6.73%,-11.82%,-10.34%,-10.52%,-22.95%,-1.07%,53.51%,-23.91%,49.26%,-22.59%,0.15%, +,1343,9047,东财创业板C,9月20日,1.3554,1.3554,0.67%,-6.78%,-12.71%,-11.61%,-11.17%,-22.98%,-2.44%,---,-26.41%,35.54%,-22.32%,0.00%, +,1344,13048,富国中证新能,9月19日,1.187,1.187,1.11%,-6.24%,-13.48%,-16.41%,-6.09%,-23.02%,---,---,-19.20%,-15.76%,-20.28%,0.00%, +,1345,751,嘉实新兴产业,9月20日,3.795,3.795,-0.21%,-3.67%,-5.56%,-7.72%,-2.51%,-23.03%,-19.56%,35.19%,-22.94%,280.30%,-23.36%,0.15%, +,1346,9614,上银中证50,9月19日,0.9674,0.9674,-0.68%,-5.78%,-7.35%,-8.50%,-9.32%,-23.03%,1.21%,---,-19.26%,-3.26%,-23.50%,0.00%, +,1347,160221,国泰国证有色,9月19日,1.3234,0.9579,-0.09%,-7.23%,-7.23%,-10.47%,-7.83%,-23.04%,45.48%,74.38%,-12.84%,0.34%,-21.47%,0.00%, +,1348,10496,创金合信创新,9月19日,0.7526,0.7526,-1.21%,-4.46%,-9.65%,-6.43%,-9.67%,-23.05%,---,---,-29.85%,-24.74%,---,0.00%, +,1349,160625,鹏华中证80,9月19日,0.657,1.795,-0.90%,-6.68%,-4.37%,-8.62%,-9.13%,-23.07%,-29.81%,-17.64%,-22.34%,44.98%,---,0.12%, +,1350,165522,信诚中证TM,9月19日,0.6734,1.5193,-1.77%,-4.48%,-8.84%,-7.13%,-13.09%,-23.07%,-28.97%,-7.10%,-29.34%,-2.19%,-23.38%,0.10%, +,1351,12637,国泰中证全指,9月19日,0.7373,0.7373,-2.16%,-2.68%,-3.66%,-5.21%,-13.26%,-23.09%,---,---,-27.56%,-26.27%,-23.43%,0.00%, +,1352,11013,长城消费30,9月20日,0.7133,0.7133,0.79%,-1.26%,-2.27%,-10.01%,-5.67%,-23.11%,---,---,-22.94%,-28.67%,-22.09%,0.15%, +,1353,10362,嘉实品质优选,9月20日,0.716,0.716,1.79%,-4.98%,-7.30%,-5.19%,-5.23%,-23.12%,---,---,-20.36%,-28.40%,-22.58%,0.00%, +,1354,165524,信诚中证智能,9月19日,0.718,0.5442,-1.41%,-6.80%,-13.87%,-5.80%,-10.17%,-23.16%,-27.96%,-8.31%,-28.21%,-52.21%,-22.70%,0.10%, +,1355,501303,广发恒生中型,9月20日,0.7881,0.7881,0.56%,-3.84%,-4.07%,-7.21%,-5.38%,-23.16%,-18.88%,-10.07%,-16.25%,-21.19%,-22.03%,0.12%, +,1356,3070,光大创业板量,9月20日,1.3662,1.3662,0.48%,-6.74%,-11.85%,-10.45%,-10.73%,-23.25%,-1.52%,---,-24.13%,36.62%,-22.88%,0.00%, +,1357,1552,天弘中证证券,9月19日,0.7747,0.7747,-0.91%,-6.80%,-4.48%,-8.84%,-9.14%,-23.27%,-30.72%,-15.71%,-22.34%,-22.53%,-22.83%,0.10%, +,1358,7664,永赢创业板指,9月20日,1.5297,1.5297,0.64%,-6.70%,-12.52%,-11.57%,-11.48%,-23.29%,-1.16%,52.92%,-26.60%,52.97%,-22.67%,0.10%, +,1359,5873,建信创业板E,9月19日,1.5296,1.5296,-0.74%,-6.92%,-12.62%,-9.95%,-11.33%,-23.31%,-4.42%,43.81%,-25.98%,52.96%,---,0.15%, +,1360,9550,汇添富开放优,9月20日,0.8003,0.8003,0.31%,-3.86%,-6.11%,-9.29%,-12.05%,-23.31%,-21.10%,---,-26.21%,-19.97%,-23.00%,0.15%, +,1361,7665,永赢创业板指,9月20日,1.5253,1.5253,0.65%,-6.70%,-12.53%,-11.59%,-11.52%,-23.36%,-1.35%,52.50%,-26.65%,52.53%,-22.75%,0.00%, +,1362,996,中银新动力股,9月20日,0.997,0.997,0.40%,-3.20%,-4.50%,-10.74%,-7.34%,-23.37%,-13.23%,40.82%,-27.49%,-0.30%,-22.47%,0.15%, +,1363,13122,信诚中证TM,9月19日,0.6706,0.6706,-1.77%,-4.50%,-8.87%,-7.23%,-13.27%,-23.38%,---,---,-29.54%,-26.05%,-23.67%,0.00%, +,1364,50021,博时创业板E,9月20日,2.0164,2.0164,0.67%,-6.76%,-12.61%,-11.57%,-11.28%,-23.39%,-5.15%,42.60%,-26.37%,101.62%,-22.74%,0.08%, +,1365,519673,银河康乐股票,9月20日,2.499,2.499,0.81%,-4.94%,-7.48%,-11.23%,-13.20%,-23.39%,-19.26%,43.54%,-26.91%,149.90%,-23.51%,0.15%, +,1366,10161,广发瑞安精选,9月20日,0.8488,0.8488,-0.20%,-5.53%,-6.24%,-9.32%,-13.14%,-23.39%,---,---,-28.59%,-15.12%,-22.66%,0.15%, +,1367,6733,博时创业板E,9月20日,2.0145,2.0145,0.67%,-6.76%,-12.61%,-11.57%,-11.29%,-23.41%,-5.20%,42.49%,-26.38%,85.50%,-22.76%,0.00%, +,1368,1553,天弘中证证券,9月19日,0.7623,0.7623,-0.91%,-6.80%,-4.49%,-8.87%,-9.22%,-23.42%,-30.99%,-16.21%,-22.44%,-23.77%,-22.98%,0.00%, +,1369,11316,天弘创业板3,9月20日,0.7889,0.7889,0.78%,-6.50%,-12.34%,-9.92%,-11.66%,-23.43%,---,---,-26.67%,-21.11%,-22.87%,0.10%, +,1370,13084,信诚中证智能,9月19日,0.7151,0.7151,-1.41%,-6.82%,-13.90%,-5.90%,-10.34%,-23.45%,---,---,-28.40%,-26.80%,-22.99%,0.00%, +,1371,11035,嘉实中证稀土,9月19日,0.8445,0.8445,-1.10%,-8.79%,-13.63%,-11.52%,-5.88%,-23.55%,---,---,-20.99%,-15.55%,---,0.10%, +,1372,673100,西部利得沪深,9月19日,1.6406,1.7606,-0.25%,-4.68%,-6.75%,-9.03%,-8.75%,-23.56%,-10.02%,32.64%,-22.10%,81.96%,-22.65%,0.08%, +,1373,1703,银华沪港深增,9月20日,2.049,2.119,1.54%,-1.77%,-3.30%,-4.56%,0.84%,-23.57%,-10.41%,31.43%,-22.68%,118.02%,-22.82%,0.15%, +,1374,11014,长城消费30,9月20日,0.7075,0.7075,0.80%,-1.27%,-2.32%,-10.15%,-5.96%,-23.57%,---,---,-23.27%,-29.25%,-22.56%,0.00%, +,1375,7839,汇添富中证长,9月20日,1,1,0.61%,-4.85%,-6.84%,-9.31%,-10.11%,-23.58%,-17.04%,---,-23.12%,0.00%,-23.21%,0.12%, +,1376,11317,天弘创业板3,9月20日,0.7867,0.7867,0.77%,-6.50%,-12.36%,-9.97%,-11.75%,-23.58%,---,---,-26.77%,-21.33%,-23.02%,0.00%, +,1377,5874,建信创业板E,9月19日,1.5114,1.5114,-0.74%,-6.93%,-12.66%,-10.05%,-11.51%,-23.61%,-5.18%,42.12%,-26.19%,51.14%,---,0.00%, +,1378,13443,建信创业板E,9月19日,1.5117,1.5117,-0.74%,-6.93%,-12.65%,-10.04%,-11.51%,-23.61%,---,---,-26.19%,-24.20%,---,0.00%, +,1379,4996,广发恒生中型,9月20日,0.7706,0.7706,0.56%,-3.86%,-4.12%,-7.35%,-5.67%,-23.62%,-19.85%,-11.64%,-16.61%,-22.94%,-22.49%,0.00%, +,1380,11036,嘉实中证稀土,9月19日,0.8436,0.8436,-1.10%,-8.79%,-13.64%,-11.54%,-5.92%,-23.62%,---,---,-21.05%,-15.64%,---,0.00%, +,1381,6781,汇丰晋信港股,9月19日,0.8244,0.8244,-1.69%,-4.44%,-5.49%,-5.28%,-3.48%,-23.65%,-24.43%,-17.01%,-16.19%,-17.56%,---,0.15%, +,1382,10162,广发瑞安精选,9月20日,0.8429,0.8429,-0.19%,-5.53%,-6.27%,-9.40%,-13.31%,-23.70%,---,---,-28.79%,-15.71%,-22.96%,0.00%, +,1383,3366,浙商汇金中证,9月19日,0.861,0.861,-0.23%,-5.70%,-10.03%,-10.78%,-8.79%,-23.81%,-25.39%,2.26%,-17.45%,-13.90%,---,0.15%, +,1384,7840,汇添富中证长,9月20日,0.991,0.991,0.61%,-4.86%,-6.86%,-9.38%,-10.26%,-23.81%,-17.54%,---,-23.29%,-0.90%,-23.45%,0.00%, +,1385,5035,银华信息科技,9月20日,0.9987,0.9987,0.34%,-6.09%,-10.73%,-6.33%,-10.91%,-23.83%,-17.97%,9.58%,-27.37%,-0.13%,-24.04%,0.15%, +,1386,9414,中银大健康股,9月20日,0.9934,0.9934,0.61%,-4.21%,-6.94%,-10.08%,0.08%,-23.84%,-14.41%,---,-17.23%,-0.66%,-24.00%,0.15%, +,1387,673101,西部利得沪深,9月19日,1.6205,1.6205,-0.25%,-4.69%,-6.78%,-9.12%,-8.94%,-23.87%,-10.74%,30.99%,-22.32%,75.17%,-22.96%,0.00%, +,1388,2906,南方中证50,9月19日,1.14,1.14,-0.26%,-6.86%,-9.81%,-8.95%,-7.92%,-23.90%,-10.31%,21.93%,-20.78%,14.00%,---,0.15%, +,1389,9012,平安创业板E,9月19日,1.2421,1.2421,-0.68%,-7.22%,-13.05%,-10.42%,-11.93%,-23.90%,-5.02%,---,-26.81%,24.21%,-22.76%,0.06%, +,1390,9551,汇添富开放优,9月20日,0.7865,0.7865,0.31%,-3.87%,-6.18%,-9.48%,-12.41%,-23.93%,-22.36%,---,-26.64%,-21.35%,-23.61%,0.00%, +,1391,1105,信澳转型创新,9月20日,1.036,1.036,3.60%,-9.50%,-16.39%,-7.32%,-2.25%,-23.95%,-2.06%,40.85%,-16.81%,0.00%,-21.57%,0.15%, +,1392,160223,国泰创业板指,9月19日,1.2061,1.2061,-0.56%,-7.01%,-13.11%,-10.17%,-11.89%,-23.95%,-1.01%,47.93%,-27.27%,20.60%,-22.91%,0.12%, +,1393,10253,兴银中证50,9月19日,0.9411,0.9411,-0.45%,-5.64%,-7.72%,-5.46%,-6.24%,-23.95%,---,---,-18.72%,-5.89%,---,0.15%, +,1394,59,国联安中证医,9月20日,1.0258,1.5858,0.20%,-5.98%,-7.67%,-11.56%,-14.52%,-24.04%,-25.58%,9.14%,-25.88%,57.09%,-23.78%,0.12%, +,1395,10157,汇安中证50,9月19日,0.9069,0.9069,-0.53%,-5.79%,-7.46%,-5.85%,-6.98%,-24.05%,---,---,-20.73%,-9.31%,-23.90%,0.12%, +,1396,11205,兴银中证50,9月19日,0.9377,0.9377,-0.46%,-5.64%,-7.73%,-5.50%,-6.33%,-24.10%,---,---,-18.83%,-6.23%,---,0.00%, +,1397,167702,德邦量化优选,9月20日,1.2754,1.4854,1.62%,-5.74%,-6.85%,-4.14%,-2.17%,-24.13%,-20.65%,11.44%,-15.77%,43.22%,-23.34%,--, +,1398,5036,银华信息科技,9月20日,0.9909,---,0.33%,-6.10%,-10.77%,-6.43%,-11.10%,-24.14%,-18.62%,8.30%,-27.59%,-0.91%,-24.34%,0.00%, +,1399,10321,中银大健康股,9月20日,0.986,0.986,0.59%,-4.23%,-6.98%,-10.18%,-0.13%,-24.15%,---,---,-17.48%,-18.48%,-24.31%,0.00%, +,1400,9013,平安创业板E,9月19日,1.2298,1.2298,-0.68%,-7.23%,-13.08%,-10.51%,-12.11%,-24.21%,-5.78%,---,-27.02%,22.98%,-23.06%,0.00%, +,1401,2907,南方中证50,9月19日,1.12,1.12,-0.27%,-6.90%,-9.82%,-9.09%,-8.05%,-24.22%,-10.97%,20.43%,-21.02%,12.00%,---,0.00%, +,1402,160637,鹏华创业板指,9月19日,1.064,0.688,-0.75%,-7.24%,-13.14%,-10.51%,-12.07%,-24.22%,-4.87%,43.70%,-27.27%,-28.64%,---,0.12%, +,1403,165520,信诚中证80,9月19日,1.5884,1.5953,0.11%,-6.65%,-6.40%,-11.61%,-5.53%,-24.27%,50.59%,90.14%,-11.25%,73.09%,-22.31%,0.12%, +,1404,167703,德邦量化优选,9月20日,1.2452,1.4552,1.62%,-5.75%,-6.87%,-4.20%,-2.30%,-24.32%,-21.05%,10.60%,-15.91%,40.16%,-23.54%,0.00%, +,1405,6569,国联安中证医,9月20日,1.0406,1.0406,0.19%,-5.98%,-7.70%,-11.65%,-14.69%,-24.35%,-26.17%,7.87%,-26.09%,36.33%,-24.08%,0.00%, +,1406,1592,天弘创业板E,9月20日,0.9498,0.9498,0.67%,-6.84%,-12.77%,-11.94%,-11.75%,-24.36%,-7.20%,38.62%,-27.14%,-5.02%,-23.70%,0.10%, +,1407,10158,汇安中证50,9月19日,0.9002,0.9002,-0.52%,-5.80%,-7.49%,-5.95%,-7.17%,-24.36%,---,---,-20.96%,-9.98%,-24.21%,0.00%, +,1408,9557,申万菱信创业,9月20日,0.8492,0.8492,0.86%,-6.73%,-10.76%,-11.18%,-10.86%,-24.40%,-14.44%,---,-23.49%,-15.08%,-24.17%,0.15%, +,1409,1877,宝盈国家安全,9月20日,1.3308,1.3308,1.17%,-4.66%,-12.25%,-4.13%,-6.90%,-24.43%,13.16%,51.06%,-28.30%,33.08%,-25.19%,0.15%, +,1410,5390,工银创业板E,9月20日,1.2914,1.2914,0.64%,-6.74%,-12.64%,-11.92%,-11.86%,-24.43%,-8.23%,36.83%,-27.01%,29.14%,-23.76%,0.10%, +,1411,7472,华夏创蓝筹E,9月20日,1.3034,1.3034,-0.21%,-4.81%,-7.45%,-8.11%,-10.38%,-24.44%,-19.81%,19.29%,-27.57%,30.62%,-24.83%,0.12%, +,1412,161613,融通创业板指,9月20日,0.874,2.486,0.81%,-6.82%,-12.51%,-11.27%,-10.54%,-24.45%,-6.82%,40.34%,-25.71%,177.35%,-23.94%,0.12%, +,1413,942,广发信息技术,9月19日,0.9696,0.9696,-1.77%,-5.05%,-11.23%,-6.83%,-15.18%,-24.46%,-28.62%,-8.36%,-30.58%,-3.04%,---,0.12%, +,1414,6248,华夏创业板E,9月20日,1.6949,1.6949,0.67%,-7.32%,-13.26%,-10.72%,-12.21%,-24.47%,-4.65%,43.53%,-27.43%,68.37%,-23.31%,0.12%, +,1415,1593,天弘创业板E,9月20日,0.9325,0.9325,0.66%,-6.84%,-12.79%,-12.00%,-11.84%,-24.52%,-7.57%,37.78%,-27.25%,-6.75%,-23.86%,0.00%, +,1416,7133,嘉实长青竞争,9月20日,1.1502,1.1502,0.21%,-4.11%,-4.55%,-6.47%,-10.37%,-24.52%,-19.13%,8.60%,-28.92%,15.02%,-23.99%,0.15%, +,1417,10001,创金合信研究,9月19日,0.8964,0.8964,-0.76%,-6.76%,-11.14%,-10.69%,-9.44%,-24.53%,---,---,-21.59%,-10.36%,---,0.15%, +,1418,13081,信诚中证80,9月19日,1.5819,1.5819,0.10%,-6.66%,-6.44%,-11.70%,-5.72%,-24.57%,---,---,-11.51%,-22.59%,-22.62%,0.00%, +,1419,2974,广发信息技术,9月19日,0.9594,0.9594,-1.77%,-5.06%,-11.25%,-6.87%,-15.25%,-24.61%,-28.91%,-8.92%,-30.68%,-16.31%,---,0.00%, +,1420,161022,富国创业板指,9月20日,0.998,1.75,0.60%,-6.73%,-12.76%,-11.92%,-11.84%,-24.62%,-8.27%,37.33%,-27.31%,54.20%,-23.99%,0.12%, +,1421,161030,富国中证体育,9月19日,0.707,0.559,-2.48%,-5.73%,-10.05%,-12.50%,-17.79%,-24.63%,-39.39%,-16.00%,-34.23%,-52.32%,-23.17%,0.12%, +,1422,1410,信澳新能源产,9月20日,4.257,4.319,2.93%,-7.62%,-16.00%,-9.67%,-6.95%,-24.65%,18.68%,102.94%,-22.89%,333.00%,-23.13%,0.15%, +,1423,585001,东吴中证新兴,9月19日,1.4061,1.4061,-1.10%,-5.49%,-8.71%,-11.28%,-10.97%,-24.69%,-9.49%,27.60%,-25.41%,40.61%,---,0.12%, +,1424,5391,工银创业板E,9月20日,1.2645,1.2645,0.64%,-6.74%,-12.66%,-11.99%,-12.02%,-24.69%,-8.87%,35.36%,-27.19%,26.45%,-24.02%,0.00%, +,1425,6249,华夏创业板E,9月20日,1.6742,1.6742,0.66%,-7.33%,-13.28%,-10.78%,-12.34%,-24.69%,-5.23%,42.25%,-27.58%,66.33%,-23.55%,0.00%, +,1426,110026,易方达创业板,9月20日,2.405,2.405,0.65%,-6.83%,-12.76%,-11.99%,-11.91%,-24.70%,-8.06%,36.25%,-27.34%,140.50%,-24.05%,0.12%, +,1427,10505,申万菱信创业,9月20日,0.8025,0.8025,0.87%,-6.73%,-10.78%,-11.26%,-11.04%,-24.70%,---,---,-23.69%,-19.75%,-24.46%,0.00%, +,1428,1319,农银信息传媒,9月20日,0.9353,0.9353,-0.37%,-3.49%,-4.27%,-10.32%,-11.29%,-24.71%,-23.01%,14.73%,-28.73%,-6.47%,-24.16%,0.15%, +,1429,13278,富国中证体育,9月19日,0.706,0.706,-2.49%,-5.74%,-10.06%,-12.62%,-17.91%,-24.73%,---,---,-34.33%,-18.57%,-23.25%,0.00%, +,1430,4870,融通创业板指,9月20日,0.823,1.215,0.73%,-6.90%,-12.54%,-11.41%,-10.74%,-24.74%,-7.57%,38.58%,-25.90%,27.10%,-24.25%,0.00%, +,1431,4925,长信低碳环保,9月19日,2.2486,2.2486,0.50%,-7.71%,-13.32%,-14.42%,-7.35%,-24.75%,66.35%,153.74%,-16.55%,124.86%,-22.51%,0.15%, +,1432,7473,华夏创蓝筹E,9月20日,1.2866,1.2866,-0.22%,-4.82%,-7.48%,-8.21%,-10.56%,-24.75%,-20.45%,17.86%,-27.78%,28.94%,-25.13%,0.00%, +,1433,13277,富国创业板指,9月20日,0.997,0.997,0.61%,-6.82%,-12.77%,-12.00%,-12.00%,-24.75%,---,---,-27.44%,-25.43%,-24.07%,0.00%, +,1434,2168,嘉实智能汽车,9月20日,3.35,3.35,1.24%,-4.37%,-9.46%,-18.31%,-12.03%,-24.87%,24.26%,96.94%,-24.80%,235.00%,-24.74%,0.15%, +,1435,3765,广发创业板E,9月20日,1.2802,1.2802,0.68%,-6.76%,-12.66%,-11.80%,-12.25%,-24.88%,-9.02%,33.19%,-27.59%,28.02%,-24.24%,0.12%, +,1436,1550,天弘中证医药,9月19日,0.8183,0.8183,-1.22%,-6.22%,-7.95%,-12.05%,-15.03%,-24.89%,-29.72%,5.66%,-26.52%,-18.17%,-24.60%,0.10%, +,1437,4744,易方达创业板,9月20日,2.3748,2.3748,0.66%,-6.83%,-12.78%,-12.04%,-12.02%,-24.89%,-8.51%,35.20%,-27.47%,31.25%,-24.24%,0.00%, +,1438,2656,南方创业板E,9月19日,1.1729,1.1729,-0.69%,-7.35%,-13.32%,-10.79%,-12.55%,-24.90%,-7.24%,40.10%,-27.71%,17.29%,-23.74%,0.12%, +,1439,7134,嘉实长青竞争,9月20日,1.1285,1.1285,0.20%,-4.11%,-4.58%,-6.58%,-10.60%,-24.90%,-19.94%,6.96%,-29.17%,12.85%,-24.37%,0.00%, +,1440,10002,创金合信研究,9月19日,0.8873,0.8873,-0.77%,-6.79%,-11.19%,-10.81%,-9.67%,-24.92%,---,---,-21.87%,-11.27%,---,0.00%, +,1441,11614,工银科创板5,9月20日,0.7943,0.7943,0.93%,-4.16%,-10.56%,-9.15%,-12.65%,-24.92%,---,---,-27.54%,-20.57%,-25.21%,0.10%, +,1442,10854,汇添富沪深3,9月20日,0.6746,0.6746,0.10%,-4.26%,-6.45%,-8.31%,-8.75%,-24.94%,---,---,-23.55%,-32.54%,-24.84%,0.15%, +,1443,13151,长信低碳环保,9月19日,2.2395,2.2395,0.49%,-7.72%,-13.35%,-14.51%,-7.55%,-25.02%,---,---,-16.79%,-19.66%,-22.79%,0.00%, +,1444,8749,富国中证科技,9月20日,0.8769,0.8769,0.26%,-4.76%,-9.13%,-9.66%,-14.44%,-25.03%,-21.36%,---,-30.23%,-12.31%,-24.65%,0.12%, +,1445,3766,广发创业板E,9月20日,1.2739,1.2739,0.67%,-6.77%,-12.68%,-11.85%,-12.34%,-25.04%,-9.39%,32.38%,-27.70%,27.39%,-24.39%,0.00%, +,1446,1551,天弘中证医药,9月19日,0.8055,0.8055,-1.23%,-6.23%,-7.97%,-12.10%,-15.12%,-25.05%,-30.01%,5.02%,-26.63%,-19.45%,-24.76%,0.00%, +,1447,1849,前海开源强势,9月19日,1.396,1.396,-0.71%,-6.75%,-10.46%,-10.05%,-9.47%,-25.11%,-14.51%,26.33%,-22.40%,39.60%,---,0.15%, +,1448,12645,建信中证全指,9月19日,0.7441,0.7441,-0.91%,-7.67%,-7.13%,-10.27%,-10.94%,-25.12%,---,---,-24.99%,-25.59%,---,0.15%, +,1449,11832,西部利得人工,9月19日,0.7344,0.7344,-1.42%,-3.91%,-9.63%,-7.87%,-18.31%,-25.13%,---,---,-30.03%,-26.56%,-24.47%,0.12%, +,1450,12696,同泰数字经济,9月19日,0.715,0.715,-0.85%,-6.01%,-9.96%,0.21%,-4.03%,-25.13%,---,---,-19.46%,-28.50%,---,0.15%, +,1451,10785,博时创业板指,9月20日,0.8045,0.8045,0.64%,-6.74%,-12.67%,-12.31%,-12.41%,-25.18%,---,---,-27.69%,-19.55%,-24.52%,0.08%, +,1452,4343,南方创业板E,9月19日,1.171,1.171,-0.70%,-7.35%,-13.34%,-10.88%,-12.72%,-25.20%,-7.97%,38.43%,-27.92%,28.58%,-24.05%,0.00%, +,1453,10183,南方创业板E,9月19日,1.1638,1.1638,-0.70%,-7.36%,-13.34%,-10.88%,-12.72%,-25.20%,-7.96%,---,-27.92%,-6.48%,-24.04%,0.00%, +,1454,11615,工银科创板5,9月20日,0.7894,0.7894,0.92%,-4.18%,-10.59%,-9.24%,-12.83%,-25.22%,---,---,-27.75%,-21.06%,-25.51%,0.00%, +,1455,1643,汇丰晋信智造,9月19日,3.0933,3.0933,-0.49%,-4.64%,-12.17%,-10.75%,-10.56%,-25.26%,47.11%,179.30%,-28.42%,209.24%,---,0.15%, +,1456,8750,富国中证科技,9月20日,0.8678,0.8678,0.25%,-4.77%,-9.17%,-9.76%,-14.62%,-25.33%,-22.00%,---,-30.43%,-13.22%,-24.96%,0.00%, +,1457,163116,申万中证申万,9月20日,0.7675,0.8949,-0.21%,-6.37%,-12.42%,-8.01%,-16.07%,-25.36%,-17.92%,19.32%,-32.03%,-10.51%,-25.19%,0.12%, +,1458,12646,建信中证全指,9月19日,0.7411,0.7411,-0.90%,-7.67%,-7.15%,-10.37%,-11.12%,-25.41%,---,---,-25.20%,-25.89%,---,0.00%, +,1459,11833,西部利得人工,9月19日,0.7307,0.7307,-1.43%,-3.92%,-9.67%,-7.96%,-18.48%,-25.43%,---,---,-30.23%,-26.93%,-24.76%,0.00%, +,1460,12697,同泰数字经济,9月19日,0.7117,0.7117,-0.85%,-6.02%,-9.99%,0.11%,-4.23%,-25.43%,---,---,-19.70%,-28.83%,---,0.00%, +,1461,4805,长信消费精选,9月19日,1.3681,1.3681,1.28%,-1.64%,-5.18%,-11.71%,-7.01%,-25.46%,-24.28%,11.12%,-24.88%,36.81%,-22.87%,0.15%, +,1462,10786,博时创业板指,9月20日,0.799,0.799,0.63%,-6.75%,-12.71%,-12.40%,-12.60%,-25.53%,---,---,-27.91%,-20.10%,-24.86%,0.00%, +,1463,10855,汇添富沪深3,9月20日,0.6657,0.6657,0.11%,-4.26%,-6.50%,-8.48%,-9.12%,-25.55%,---,---,-23.98%,-33.43%,-25.43%,0.00%, +,1464,10531,申万中证申万,9月20日,0.807,0.807,-0.22%,-6.39%,-12.44%,-8.08%,-16.21%,-25.59%,---,---,-32.18%,-19.30%,-25.42%,0.00%, +,1465,12116,中银创业板E,9月19日,0.8018,0.8018,-0.68%,-7.16%,-13.04%,-10.95%,-12.52%,-25.59%,---,---,-27.69%,-19.82%,-24.46%,0.10%, +,1466,1644,汇丰晋信智造,9月19日,2.9903,2.9903,-0.49%,-4.65%,-12.21%,-10.86%,-10.79%,-25.63%,45.65%,175.27%,-28.67%,198.94%,---,0.00%, +,1467,8537,兴银研究精选,9月19日,0.9882,0.9882,-0.27%,-5.24%,-8.79%,-8.43%,-5.98%,-25.69%,-1.31%,---,-20.12%,-1.18%,---,0.15%, +,1468,4292,鹏华沪深港互,9月19日,1.7769,1.7769,-0.80%,-7.55%,-9.54%,-7.59%,-4.97%,-25.72%,-1.01%,57.72%,-26.06%,77.69%,---,0.15%, +,1469,7750,广发优势增长,9月20日,1.3647,1.3647,2.19%,-4.54%,-7.84%,-2.49%,-3.85%,-25.73%,-9.12%,---,-19.59%,36.47%,-25.85%,0.15%, +,1470,12117,中银创业板E,9月19日,0.7993,0.7993,-0.68%,-7.16%,-13.04%,-10.99%,-12.61%,-25.74%,---,---,-27.79%,-20.07%,-24.61%,0.00%, +,1471,13152,长信消费精选,9月19日,1.3618,1.3618,1.28%,-1.65%,-5.21%,-11.80%,-7.21%,-25.77%,---,---,-25.11%,-28.70%,-23.19%,0.00%, +,1472,3984,嘉实新能源新,9月20日,2.7287,2.7287,1.37%,-5.41%,-10.37%,-16.66%,-12.21%,-25.79%,26.20%,114.61%,-24.67%,169.19%,-24.82%,0.15%, +,1473,7300,国联安中证半,9月20日,1.7778,1.7778,-0.13%,-4.77%,-10.28%,-6.29%,-17.79%,-25.82%,-11.18%,39.88%,-29.13%,78.00%,-26.64%,0.08%, +,1474,6909,华夏战略新兴,9月20日,1.6978,1.6978,1.13%,-6.82%,-11.93%,-13.57%,-13.10%,-25.83%,-7.56%,48.01%,-26.51%,67.89%,-24.48%,0.12%, +,1475,11608,易方达科创板,9月20日,0.7772,0.7772,0.92%,-4.23%,-10.80%,-9.64%,-13.48%,-25.84%,---,---,-28.34%,-22.28%,-26.12%,0.06%, +,1476,502053,长盛中证全指,9月19日,0.8383,0.8383,-0.97%,-7.91%,-7.25%,-10.40%,-10.67%,-25.85%,-22.47%,-0.81%,-25.23%,-5.37%,-25.30%,0.12%, +,1477,11612,华夏科创板5,9月19日,0.7858,0.7858,-2.18%,-5.39%,-11.37%,-10.01%,-13.59%,-25.87%,---,---,-28.29%,-21.42%,---,0.12%, +,1478,11609,易方达科创板,9月20日,0.776,0.776,0.92%,-4.23%,-10.80%,-9.66%,-13.53%,-25.91%,---,---,-28.39%,-22.40%,-26.19%,0.00%, +,1479,160516,博时中证全指,9月20日,1.0623,0.7353,0.08%,-7.79%,-7.24%,-9.51%,-10.33%,-25.92%,-22.67%,-10.15%,-24.85%,-26.47%,-25.45%,0.10%, +,1480,160626,鹏华信息A,9月19日,0.763,1.834,-1.55%,-4.15%,-8.73%,-7.18%,-15.88%,-25.92%,-29.55%,-1.51%,-32.18%,67.47%,---,0.12%, +,1481,12040,鹏华信息C,9月19日,0.786,0.786,-1.63%,-4.26%,-8.82%,-7.31%,-15.94%,-25.99%,---,---,-32.24%,-21.40%,---,0.00%, +,1482,12552,天弘中证芯片,9月20日,0.6699,0.6699,-0.49%,-6.10%,-11.76%,-8.45%,-19.60%,-25.99%,---,---,-30.74%,-33.01%,-26.75%,0.10%, +,1483,7301,国联安中证半,9月20日,1.7592,1.7592,-0.14%,-4.77%,-10.30%,-6.35%,-17.89%,-26.00%,-11.62%,38.69%,-29.26%,76.14%,-26.82%,0.00%, +,1484,7464,交银创业板5,9月20日,1.5964,1.5964,1.06%,-7.12%,-14.04%,-13.08%,-12.26%,-26.01%,0.36%,---,-27.99%,59.64%,-25.31%,0.12%, +,1485,1616,嘉实环保低碳,9月20日,3.014,3.014,0.84%,-2.55%,-6.69%,-14.71%,-11.72%,-26.02%,14.99%,103.37%,-25.82%,201.40%,-25.47%,0.15%, +,1486,8538,兴银研究精选,9月19日,0.9755,0.9755,-0.28%,-5.25%,-8.81%,-8.54%,-6.22%,-26.05%,-2.51%,---,-20.41%,-2.45%,---,0.00%, +,1487,6910,华夏战略新兴,9月20日,1.6811,1.6811,---,-6.83%,-11.96%,-13.64%,-13.23%,-26.06%,-8.12%,46.69%,-26.67%,66.24%,-24.71%,0.00%, +,1488,11613,华夏科创板5,9月19日,0.7828,0.7828,-2.17%,-5.39%,-11.39%,-10.06%,-13.69%,-26.06%,---,---,-28.41%,-21.72%,---,0.00%, +,1489,12553,天弘中证芯片,9月20日,0.6684,0.6684,-0.48%,-6.10%,-11.77%,-8.49%,-19.67%,-26.13%,---,---,-30.84%,-33.16%,-26.89%,0.00%, +,1490,3985,嘉实新能源新,9月20日,2.672,2.672,1.36%,-5.42%,-10.40%,-16.77%,-12.43%,-26.16%,24.95%,111.43%,-24.93%,163.61%,-25.19%,0.00%, +,1491,501016,国泰中证申万,9月19日,0.9324,0.9324,-0.98%,-7.88%,-7.29%,-10.12%,-10.50%,-26.16%,-28.67%,-11.54%,-24.98%,-6.76%,-25.62%,0.12%, +,1492,13306,国泰中证科创,9月19日,0.7332,0.7332,-0.56%,-5.48%,-11.21%,-8.79%,-12.85%,-26.22%,---,---,-25.84%,-26.68%,-25.51%,0.10%, +,1493,7465,交银创业板5,9月20日,1.5786,1.5786,1.06%,-7.12%,-14.07%,-13.17%,-12.44%,-26.31%,-0.44%,---,-28.20%,57.86%,-25.61%,0.00%, +,1494,11610,华泰柏瑞科创,9月19日,0.7778,0.7778,-2.15%,-5.28%,-11.42%,-9.90%,-13.72%,-26.32%,---,---,-28.52%,-22.22%,-25.92%,0.10%, +,1495,5826,华夏潜龙精选,9月20日,1.8358,1.8358,1.71%,-4.90%,-9.07%,-4.92%,-6.84%,-26.40%,5.16%,37.10%,-23.07%,83.58%,-26.81%,0.15%, +,1496,8264,南方ESG股,9月20日,1.3167,1.3167,1.86%,-3.52%,-4.70%,-3.64%,-3.68%,-26.41%,-9.96%,---,-18.83%,31.67%,-25.92%,0.15%, +,1497,7992,华夏中证全指,9月20日,0.9749,0.9749,0.09%,-7.78%,-7.25%,-9.56%,-10.51%,-26.41%,-25.01%,---,-25.13%,-2.51%,-25.99%,0.12%, +,1498,13307,国泰中证科创,9月19日,0.7308,0.7308,-0.56%,-5.50%,-11.24%,-8.88%,-12.99%,-26.46%,---,---,-26.00%,-26.92%,-25.75%,0.00%, +,1499,1617,天弘中证电子,9月19日,1.1083,1.1083,-1.47%,-5.34%,-11.58%,-8.18%,-16.83%,-26.49%,-22.99%,10.58%,-32.53%,10.83%,-26.59%,0.10%, +,1500,5777,广发科技动力,9月20日,1.3828,1.3828,0.14%,-5.70%,-8.89%,-6.88%,-0.72%,-26.49%,-31.31%,9.94%,-23.56%,38.28%,-26.27%,0.15%, +,1501,11611,华泰柏瑞科创,9月19日,0.7748,0.7748,-2.16%,-5.29%,-11.44%,-9.96%,-13.83%,-26.50%,---,---,-28.64%,-22.52%,-26.12%,0.00%, +,1502,161631,融通人工智能,9月20日,1.0717,1.0717,0.38%,-4.20%,-10.30%,-12.65%,-21.60%,-26.52%,-29.60%,-6.82%,-33.62%,7.17%,-26.33%,0.12%, +,1503,8281,国泰CES半,9月19日,1.3691,1.3691,-1.72%,-5.29%,-10.83%,-6.27%,-18.54%,-26.52%,-9.76%,---,-30.97%,36.91%,---,0.10%, +,1504,3956,南方产业智选,9月20日,1.7942,1.7942,2.32%,-7.34%,-13.34%,-0.12%,-1.03%,-26.59%,-2.36%,51.07%,-26.31%,79.42%,-25.97%,0.15%, +,1505,11146,创金合信气候,9月19日,1.2531,1.2531,0.83%,-4.50%,-10.18%,-13.47%,-5.80%,-26.59%,---,---,-17.62%,25.31%,---,0.15%, +,1506,7993,华夏中证全指,9月20日,0.9677,0.9677,0.09%,-7.79%,-7.28%,-9.63%,-10.65%,-26.63%,-25.46%,---,-25.29%,-3.23%,-26.21%,0.00%, +,1507,1618,天弘中证电子,9月19日,1.0904,1.0904,-1.46%,-5.34%,-11.59%,-8.23%,-16.92%,-26.63%,-23.29%,9.93%,-32.63%,9.04%,-26.74%,0.00%, +,1508,8590,天弘中证全指,9月20日,0.9486,0.9486,0.08%,-7.79%,-7.32%,-9.80%,-10.74%,-26.66%,-25.64%,---,-25.45%,-5.14%,-26.21%,0.10%, +,1509,160420,华安创业板5,9月19日,1.2282,0.4687,-0.65%,-8.12%,-14.99%,-12.18%,-13.19%,-26.69%,-1.20%,65.03%,-28.63%,-53.13%,---,0.12%, +,1510,161628,融通中证云计,9月20日,0.7849,0.5945,-0.43%,-4.27%,-7.09%,-7.13%,-14.99%,-26.71%,-32.22%,-11.10%,-29.69%,-41.81%,-26.51%,0.12%, +,1511,8282,国泰CES半,9月19日,1.3576,1.3576,-1.73%,-5.29%,-10.85%,-6.35%,-18.66%,-26.74%,-10.29%,---,-31.12%,35.76%,---,0.00%, +,1512,160633,鹏华券商A,9月19日,0.884,0.559,-1.01%,-7.92%,-7.43%,-10.53%,-10.89%,-26.76%,-27.42%,-6.93%,-25.40%,-46.30%,---,0.12%, +,1513,163113,申万菱信中证,9月20日,0.7469,1.7858,0.09%,-7.74%,-7.26%,-9.73%,-10.81%,-26.77%,-31.12%,-17.78%,-25.37%,13.36%,-26.30%,0.12%, +,1514,160422,华安创业板5,9月19日,1.6951,1.3929,-0.64%,-8.05%,-14.80%,-11.96%,-12.85%,-26.78%,-1.42%,60.22%,-28.53%,32.85%,---,0.12%, +,1515,160224,国泰中证计算,9月19日,0.6624,0.8501,-1.92%,-2.44%,-5.48%,-7.17%,-14.79%,-26.81%,-28.97%,-0.71%,-29.79%,-26.21%,-26.67%,0.10%, +,1516,8591,天弘中证全指,9月20日,0.9434,0.9434,0.08%,-7.79%,-7.33%,-9.84%,-10.82%,-26.81%,-25.93%,---,-25.56%,-5.66%,-26.35%,0.00%, +,1517,9239,融通人工智能,9月20日,1.0615,1.0615,0.38%,-4.21%,-10.33%,-12.75%,-21.75%,-26.81%,-30.15%,---,-33.81%,-13.11%,-26.62%,0.00%, +,1518,12044,鹏华券商C,9月19日,0.855,0.855,-1.04%,-7.97%,-7.47%,-10.56%,-10.94%,-26.86%,---,---,-25.46%,-14.50%,---,0.00%, +,1519,8265,南方ESG股,9月20日,1.295,1.295,1.85%,-3.53%,-4.76%,-3.80%,-3.98%,-26.87%,-11.05%,---,-19.18%,29.50%,-26.37%,0.00%, +,1520,11147,创金合信气候,9月19日,1.2445,1.2445,0.83%,-4.51%,-10.22%,-13.56%,-6.00%,-26.89%,---,---,-17.86%,24.45%,---,0.00%, +,1521,161720,招商中证全指,9月20日,0.9241,0.6626,0.09%,-7.85%,-7.33%,-9.87%,-10.93%,-26.89%,-27.86%,-11.23%,-25.55%,-45.49%,-26.41%,0.10%, +,1522,6818,安信盈利驱动,9月19日,1.0929,1.0929,-0.16%,-5.66%,-10.89%,-11.13%,-17.16%,-26.89%,-33.65%,2.25%,-24.37%,9.29%,---,0.15%, +,1523,13317,华宝中证科创,9月20日,0.7214,0.7214,0.60%,-4.92%,-10.93%,-9.99%,-13.12%,-26.90%,---,---,-26.49%,-27.86%,-26.55%,0.10%, +,1524,160419,华安中证全指,9月19日,0.8937,0.6019,-1.00%,-7.93%,-7.39%,-10.47%,-11.00%,-26.92%,-25.82%,-3.26%,-25.62%,-39.81%,---,0.12%, +,1525,12321,东财云计算A,9月20日,0.6897,0.6897,-0.62%,-3.91%,-7.21%,-8.66%,-13.64%,-26.95%,---,---,-30.86%,-31.03%,---,0.15%, +,1526,502010,易方达中证全,9月19日,0.9998,0.6849,-0.99%,-7.95%,-7.36%,-10.39%,-10.70%,-26.95%,-26.02%,-4.00%,-25.53%,-31.62%,-26.41%,0.10%, +,1527,13597,招商中证全指,9月20日,0.9233,0.9233,0.08%,-7.85%,-7.35%,-9.90%,-10.98%,-26.95%,---,---,-25.61%,-29.84%,-26.47%,0.00%, +,1528,1626,国泰央企改革,9月20日,1.639,1.639,-0.30%,-2.38%,-5.97%,-2.96%,-4.99%,-26.96%,-20.90%,27.55%,-24.89%,63.90%,-27.61%,0.15%, +,1529,4683,建信高端医疗,9月19日,1.7562,1.7562,-1.20%,-5.61%,-6.89%,-11.41%,-11.37%,-26.97%,-5.73%,59.51%,-25.14%,75.62%,---,0.15%, +,1530,13445,东财芯片A,9月19日,0.7303,0.7303,-1.89%,-5.69%,-11.25%,-6.37%,-17.96%,-26.97%,---,---,-29.66%,-26.97%,---,0.12%, +,1531,160424,华安创业板5,9月19日,1.6808,1.9608,-0.64%,-8.06%,-14.82%,-12.02%,-12.96%,-26.97%,-1.91%,59.00%,-28.66%,82.99%,---,0.00%, +,1532,6098,华宝券商ET,9月20日,1.2147,1.2147,0.09%,-7.85%,-7.32%,-9.99%,-11.04%,-26.98%,-28.19%,-10.21%,-25.44%,21.47%,-26.51%,0.10%, +,1533,161027,富国中证全指,9月19日,0.825,0.557,-0.96%,-7.92%,-7.41%,-10.23%,-10.71%,-26.99%,-27.15%,-10.87%,-25.61%,-47.81%,-26.38%,0.12%, +,1534,12898,兴银中证科创,9月19日,0.6587,0.6587,-0.62%,-5.67%,-11.48%,-8.75%,-12.96%,-27.01%,---,---,-26.58%,-34.13%,---,0.12%, +,1535,10210,国泰中证计算,9月19日,0.6589,0.6589,-1.93%,-2.44%,-5.49%,-7.24%,-14.91%,-27.02%,---,---,-29.94%,-30.11%,-26.89%,0.00%, +,1536,12362,国泰中证全指,9月19日,0.799,0.799,-0.94%,-7.91%,-7.34%,-10.36%,-11.07%,-27.03%,---,---,-25.48%,-20.10%,-26.59%,0.10%, +,1537,12969,鹏华国证半导,9月19日,0.6996,0.6996,-1.60%,-5.96%,-11.58%,-7.41%,-20.68%,-27.07%,---,---,-31.51%,-30.04%,---,0.12%, +,1538,501047,汇添富中证全,9月20日,0.8982,0.8982,0.08%,-7.90%,-7.34%,-10.02%,-10.93%,-27.08%,-29.26%,-10.07%,-25.53%,-10.18%,-26.62%,0.08%, +,1539,12550,华宝中证电子,9月20日,0.6599,0.6599,-0.42%,-4.41%,-10.14%,-8.82%,-17.86%,-27.08%,---,---,-32.67%,-33.73%,-27.11%,0.10%, +,1540,12899,兴银中证科创,9月19日,0.6579,0.6579,-0.62%,-5.68%,-11.49%,-8.79%,-13.01%,-27.08%,---,---,-26.64%,-34.21%,---,0.00%, +,1541,13276,富国中证全指,9月19日,0.823,0.823,-0.96%,-7.94%,-7.42%,-10.35%,-10.83%,-27.10%,---,---,-25.72%,-19.55%,-26.56%,0.00%, +,1542,13318,华宝中证科创,9月20日,0.719,0.719,0.59%,-4.94%,-10.97%,-10.08%,-13.26%,-27.13%,---,---,-26.66%,-28.10%,-26.78%,0.00%, +,1543,6671,广发消费升级,9月20日,1.3354,1.3354,1.45%,-3.75%,-5.36%,-11.81%,-4.41%,-27.15%,-22.86%,19.31%,-22.05%,33.54%,-26.26%,0.15%, +,1544,12322,东财云计算C,9月20日,0.6874,0.6874,-0.62%,-3.91%,-7.25%,-8.72%,-13.77%,-27.17%,---,---,-31.02%,-31.26%,---,0.00%, +,1545,6819,安信盈利驱动,9月19日,1.0772,1.0772,-0.17%,-5.67%,-10.92%,-11.22%,-17.32%,-27.18%,-34.20%,1.00%,-24.58%,7.72%,---,0.00%, +,1546,12629,广发国证半导,9月19日,0.667,0.667,-1.58%,-5.96%,-11.55%,-7.30%,-20.69%,-27.19%,---,---,-31.53%,-33.30%,---,0.12%, +,1547,12970,鹏华国证半导,9月19日,0.6981,0.6981,-1.61%,-5.97%,-11.60%,-7.45%,-20.76%,-27.21%,---,---,-31.60%,-30.19%,---,0.00%, +,1548,1104,华安新丝路主,9月19日,1.837,1.946,-0.16%,-3.67%,-6.66%,-9.01%,-7.69%,-27.22%,-12.90%,41.66%,-24.31%,92.24%,---,0.15%, +,1549,4069,南方中证全指,9月19日,0.9122,0.9122,-0.98%,-7.99%,-7.41%,-10.63%,-11.13%,-27.23%,-28.18%,-8.63%,-25.75%,-8.78%,-26.71%,0.12%, +,1550,12551,华宝中证电子,9月20日,0.6584,0.6584,-0.41%,-4.42%,-10.18%,-8.88%,-17.96%,-27.23%,---,---,-32.77%,-33.89%,-27.25%,0.00%, +,1551,11077,汇丰晋信创新,9月19日,0.7626,0.7626,-0.37%,-5.36%,-12.17%,-13.08%,-13.16%,-27.24%,---,---,-29.07%,-23.74%,---,0.15%, +,1552,12874,易方达中证全,9月19日,0.9953,0.9953,-0.98%,-7.96%,-7.38%,-10.48%,-10.88%,-27.24%,---,---,-25.75%,-18.62%,-26.71%,0.00%, +,1553,12363,国泰中证全指,9月19日,0.796,0.796,-0.93%,-7.90%,-7.36%,-10.42%,-11.20%,-27.25%,---,---,-25.63%,-20.40%,-26.80%,0.00%, +,1554,3760,国泰中证50,9月19日,1.0368,1.1006,-0.75%,-5.71%,-7.77%,-6.60%,-4.48%,-27.26%,-17.22%,1.64%,-14.57%,9.18%,-27.40%,0.10%, +,1555,501048,汇添富中证全,9月20日,0.8976,0.8976,0.09%,-7.89%,-7.36%,-10.08%,-11.04%,-27.26%,-29.61%,-10.73%,-25.66%,-10.24%,-26.80%,0.00%, +,1556,13302,招商中证科创,9月20日,0.7297,0.7297,0.61%,-5.05%,-11.09%,-10.10%,-13.04%,-27.26%,---,---,-26.54%,-27.03%,-26.91%,0.12%, +,1557,7531,华宝券商ET,9月20日,1.1992,1.1992,0.08%,-7.87%,-7.36%,-10.08%,-11.22%,-27.27%,-28.76%,-11.27%,-25.65%,-5.04%,-26.80%,0.00%, +,1558,13446,东财芯片C,9月19日,0.7273,0.7273,-1.90%,-5.72%,-11.29%,-6.48%,-18.13%,-27.27%,---,---,-29.87%,-27.27%,---,0.00%, +,1559,3761,国泰中证50,9月19日,1.023,1.0859,-0.75%,-5.71%,-7.78%,-6.61%,-4.49%,-27.28%,-17.24%,1.48%,-14.59%,7.73%,-27.42%,0.00%, +,1560,1180,广发医药卫生,9月20日,0.8858,0.8858,0.23%,-5.16%,-7.61%,-13.39%,-15.79%,-27.30%,-27.32%,4.58%,-26.55%,-11.42%,-27.43%,0.12%, +,1561,12907,鹏扬中证科创,9月19日,0.6843,0.6843,-0.61%,-5.69%,-11.60%,-9.01%,-13.55%,-27.31%,---,---,-27.21%,-31.57%,---,0.10%, +,1562,12837,华安CES半,9月19日,0.6294,0.6294,-1.76%,-5.52%,-11.11%,-7.15%,-19.44%,-27.31%,---,---,-31.82%,-37.06%,---,0.05%, +,1563,519606,国泰金鑫股票,9月19日,1.9775,2.1469,-0.55%,-6.84%,-14.44%,-6.05%,-1.52%,-27.35%,-19.09%,30.44%,-20.04%,97.75%,-25.80%,0.15%, +,1564,12326,天弘中证全指,9月20日,0.6517,0.6517,-0.08%,-6.08%,-5.89%,-11.85%,-9.59%,-27.36%,---,---,-25.21%,-34.83%,-27.75%,0.10%, +,1565,12894,天弘中证科创,9月20日,0.6567,0.6567,0.60%,-5.03%,-11.08%,-10.05%,-13.19%,-27.40%,---,---,-26.91%,-34.33%,-27.05%,0.10%, +,1566,12630,广发国证半导,9月19日,0.6648,0.6648,-1.58%,-5.97%,-11.57%,-7.37%,-20.81%,-27.41%,---,---,-31.68%,-33.52%,---,0.00%, +,1567,161025,富国中证移动,9月20日,0.698,1.564,-0.29%,-5.55%,-9.59%,-10.40%,-15.50%,-27.44%,-25.68%,8.36%,-32.88%,7.15%,-27.14%,0.12%, +,1568,8585,华夏中证人工,9月20日,0.6377,0.6377,0.36%,-4.18%,-10.07%,-12.72%,-22.29%,-27.44%,-30.91%,---,-34.08%,-36.23%,-27.30%,0.12%, +,1569,2978,广发医药卫生,9月20日,0.8748,0.8748,0.22%,-5.17%,-7.63%,-13.45%,-15.88%,-27.45%,-27.62%,3.94%,-26.66%,11.77%,-27.58%,0.00%, +,1570,12327,天弘中证全指,9月20日,0.6501,0.6501,-0.09%,-6.10%,-5.92%,-11.89%,-9.68%,-27.51%,---,---,-25.32%,-34.99%,-27.89%,0.00%, +,1571,4070,南方中证全指,9月19日,0.8921,0.8921,-0.98%,-8.00%,-7.44%,-10.72%,-11.31%,-27.52%,-28.75%,-9.72%,-25.96%,-10.79%,-27.01%,0.00%, +,1572,12838,华安CES半,9月19日,0.6273,0.6273,-1.77%,-5.53%,-11.13%,-7.22%,-19.57%,-27.53%,---,---,-31.96%,-37.27%,---,0.00%, +,1573,12895,天弘中证科创,9月20日,0.6551,0.6551,0.60%,-5.04%,-11.10%,-10.10%,-13.29%,-27.56%,---,---,-27.02%,-34.49%,-27.20%,0.00%, +,1574,13303,招商中证科创,9月20日,0.7265,0.7265,0.60%,-5.06%,-11.12%,-10.20%,-13.22%,-27.56%,---,---,-26.76%,-27.35%,-27.21%,0.00%, +,1575,11839,天弘中证人工,9月20日,0.6848,0.6848,0.35%,-4.22%,-10.13%,-12.90%,-22.67%,-27.58%,---,---,-34.18%,-31.52%,-27.39%,0.10%, +,1576,12908,鹏扬中证科创,9月19日,0.6811,0.6811,-0.61%,-5.70%,-11.63%,-9.11%,-13.73%,-27.60%,---,---,-27.41%,-31.89%,---,0.00%, +,1577,13505,华安新丝路主,9月19日,1.827,1.827,-0.16%,-3.69%,-6.69%,-9.15%,-7.96%,-27.61%,---,---,-24.60%,-25.70%,---,0.00%, +,1578,160636,鹏华中证移动,9月19日,0.689,0.843,-1.57%,-4.83%,-9.22%,-9.10%,-15.25%,-27.63%,-27.91%,6.92%,-32.71%,-25.78%,---,0.12%, +,1579,8075,招商核心优选,9月20日,0.9825,0.9825,1.06%,-3.74%,-7.03%,-6.98%,-5.93%,-27.66%,-27.34%,---,-20.39%,-1.75%,-27.55%,0.15%, +,1580,161718,招商沪深30,9月20日,0.895,1.2119,0.62%,-5.15%,-8.50%,-13.45%,-10.93%,-27.67%,-23.45%,-3.53%,-27.13%,1.42%,-27.31%,0.10%, +,1581,8586,华夏中证人工,9月20日,0.6333,0.6333,0.35%,-4.19%,-10.11%,-12.79%,-22.42%,-27.67%,-31.33%,---,-34.23%,-36.67%,-27.53%,0.00%, +,1582,1291,大摩量化多策,9月20日,1.099,1.099,0.37%,-3.93%,-6.63%,-8.72%,-12.08%,-27.70%,-5.42%,30.52%,-26.24%,9.90%,-28.08%,0.15%, +,1583,11840,天弘中证人工,9月20日,0.6833,0.6833,0.35%,-4.22%,-10.14%,-12.94%,-22.75%,-27.73%,---,---,-34.28%,-31.67%,-27.53%,0.00%, +,1584,5620,中欧品质消费,9月20日,1.3634,1.6184,0.97%,-3.61%,-7.53%,-14.98%,-11.50%,-27.79%,-36.93%,19.70%,-32.87%,53.43%,-26.86%,0.15%, +,1585,13304,易方达中证科,9月20日,0.7178,0.7178,0.60%,-5.05%,-11.10%,-10.20%,-13.41%,-27.79%,---,---,-27.08%,-28.22%,-27.52%,0.06%, +,1586,7815,嘉实新兴科技,9月20日,0.9671,0.9671,0.05%,-4.74%,-9.29%,-13.05%,-18.37%,-27.82%,-28.77%,---,-31.26%,-3.30%,-27.66%,0.12%, +,1587,165523,信诚中证信息,9月19日,0.6362,0.5035,-2.36%,-3.52%,-6.55%,-6.32%,-17.94%,-27.84%,-36.44%,-25.69%,-30.70%,-58.19%,-27.74%,0.10%, +,1588,5968,创金合信工业,9月19日,2.5595,2.5595,-0.36%,-8.57%,-14.24%,-14.01%,-14.92%,-27.86%,23.17%,154.30%,-26.50%,155.95%,---,0.15%, +,1589,8887,华夏国证半导,9月20日,0.9809,0.9809,-0.29%,-6.25%,-12.05%,-8.99%,-21.09%,-27.86%,-8.47%,---,-32.20%,-1.91%,-28.60%,0.12%, +,1590,5927,创金合信新能,9月19日,2.7156,2.7156,0.98%,-4.75%,-10.54%,-14.47%,-7.40%,-27.89%,83.29%,172.40%,-18.58%,171.56%,---,0.15%, +,1591,9864,招商景气优选,9月20日,0.6525,0.6525,0.99%,-3.63%,-6.72%,-6.95%,-5.90%,-27.89%,---,---,-20.30%,-34.75%,-27.78%,0.15%, +,1592,13310,华夏科创创业,9月20日,0.6993,0.6993,0.60%,-5.05%,-11.09%,-10.15%,-13.33%,-27.93%,---,---,-26.99%,-30.07%,---,0.12%, +,1593,7816,嘉实新兴科技,9月20日,0.9615,0.9615,0.05%,-4.74%,-9.29%,-13.09%,-18.45%,-27.96%,-29.06%,---,-31.36%,-3.86%,-27.80%,0.00%, +,1594,12401,天弘中证医药,9月20日,0.745,0.745,0.15%,-5.02%,-7.81%,-12.60%,-13.42%,-27.96%,---,---,-28.32%,-25.50%,-28.28%,0.15%, +,1595,13305,易方达中证科,9月20日,0.7154,0.7154,0.59%,-5.07%,-11.13%,-10.27%,-13.56%,-28.02%,---,---,-27.24%,-28.46%,-27.75%,0.00%, +,1596,8154,嘉实医药健康,9月20日,0.6127,0.6127,0.00%,-4.80%,-7.74%,-13.01%,-15.68%,-28.06%,---,---,-27.78%,-38.73%,-28.48%,0.12%, +,1597,8888,华夏国证半导,9月20日,0.9742,0.9742,-0.30%,-6.25%,-12.07%,-9.06%,-21.20%,-28.07%,-9.02%,---,-32.34%,-2.58%,-28.82%,0.00%, +,1598,13083,信诚中证信息,9月19日,0.6336,0.6336,-2.37%,-3.52%,-6.58%,-6.41%,-18.11%,-28.13%,---,---,-30.90%,-30.61%,-28.03%,0.00%, +,1599,1629,天弘中证计算,9月20日,0.6239,0.6239,-0.21%,-3.51%,-5.77%,-9.09%,-16.28%,-28.14%,-34.84%,-20.39%,-31.44%,-37.61%,-27.81%,0.10%, +,1600,13311,华夏科创创业,9月20日,0.6971,0.6971,0.62%,-5.04%,-11.11%,-10.20%,-13.46%,-28.14%,---,---,-27.14%,-30.29%,---,0.00%, +,1601,12402,天弘中证医药,9月20日,0.7426,0.7426,0.15%,-5.04%,-7.84%,-12.67%,-13.56%,-28.18%,---,---,-28.47%,-25.74%,-28.49%,0.00%, +,1602,6923,前海开源沪港,9月19日,1.0116,1.0116,-0.76%,-10.31%,-11.47%,-7.79%,-0.75%,-28.22%,-20.68%,-3.79%,-28.94%,1.16%,---,0.15%, +,1603,8155,嘉实医药健康,9月20日,0.6109,0.6109,-0.02%,-4.81%,-7.76%,-13.06%,-15.77%,-28.22%,---,---,-27.89%,-38.91%,-28.63%,0.00%, +,1604,6981,中金新医药股,9月19日,1.5145,1.5145,-1.62%,-5.02%,-5.18%,-9.43%,-6.64%,-28.23%,-18.94%,31.90%,-22.18%,51.45%,---,0.15%, +,1605,13298,南方中证科创,9月20日,0.7118,0.7118,0.61%,-5.08%,-11.14%,-10.25%,-13.51%,-28.23%,---,---,-27.20%,-28.82%,-28.06%,0.12%, +,1606,8076,招商核心优选,9月20日,0.9611,0.9611,1.05%,-3.76%,-7.10%,-7.18%,-6.32%,-28.24%,-28.49%,---,-20.85%,-3.89%,-28.13%,0.00%, +,1607,1630,天弘中证计算,9月20日,0.6143,0.6143,-0.21%,-3.50%,-5.80%,-9.14%,-16.36%,-28.29%,-35.10%,-20.87%,-31.54%,-38.57%,-27.96%,0.00%, +,1608,13315,嘉实中证科创,9月19日,0.6812,0.6812,-0.63%,-5.64%,-11.54%,-8.98%,-13.85%,-28.34%,---,---,-27.37%,-31.88%,-27.55%,0.10%, +,1609,5621,中欧品质消费,9月20日,1.3233,1.5623,0.97%,-3.62%,-7.59%,-15.16%,-11.86%,-28.37%,-37.94%,16.84%,-33.25%,48.13%,-27.43%,0.00%, +,1610,5969,创金合信工业,9月19日,2.4855,2.4855,-0.36%,-8.58%,-14.29%,-14.17%,-15.23%,-28.37%,21.46%,149.02%,-26.87%,148.55%,---,0.00%, +,1611,10420,民生加银成长,9月20日,0.7306,0.7306,1.19%,-5.17%,-8.19%,-12.46%,-12.58%,-28.39%,---,---,-28.79%,-26.94%,-27.64%,0.15%, +,1612,1749,招商中国机遇,9月20日,1.951,1.951,1.67%,-5.20%,-8.02%,1.04%,-1.76%,-28.40%,8.03%,84.75%,-20.46%,95.10%,-28.59%,0.15%, +,1613,5928,创金合信新能,9月19日,2.6318,2.6318,0.97%,-4.77%,-10.60%,-14.63%,-7.73%,-28.40%,80.74%,166.89%,-18.99%,163.18%,---,0.00%, +,1614,6924,前海开源沪港,9月19日,1.0027,1.0027,-0.76%,-10.32%,-11.49%,-7.87%,-0.88%,-28.40%,-21.08%,-4.51%,-29.07%,0.27%,---,0.00%, +,1615,13299,南方中证科创,9月20日,0.7095,0.7095,0.62%,-5.07%,-11.16%,-10.31%,-13.64%,-28.45%,---,---,-27.36%,-29.05%,-28.27%,0.00%, +,1616,1113,南方大数据1,9月19日,0.9397,0.9397,-0.87%,-8.93%,-15.64%,-5.54%,-3.99%,-28.47%,-1.99%,43.66%,-19.70%,-6.03%,---,0.12%, +,1617,9865,招商景气优选,9月20日,0.6429,0.6429,0.99%,-3.64%,-6.79%,-7.14%,-6.28%,-28.47%,---,---,-20.76%,-35.71%,-28.35%,0.00%, +,1618,7005,中金新医药股,9月19日,1.489,1.489,-1.63%,-5.03%,-5.21%,-9.52%,-6.83%,-28.51%,-19.58%,30.25%,-22.40%,48.90%,---,0.00%, +,1619,13316,嘉实中证科创,9月19日,0.6794,0.6794,-0.63%,-5.64%,-11.56%,-9.04%,-13.96%,-28.52%,---,---,-27.50%,-32.06%,-27.72%,0.00%, +,1620,1899,东海社会安全,9月19日,0.518,0.518,-0.96%,-4.60%,-7.17%,-5.82%,-9.76%,-28.55%,-33.93%,-25.04%,-26.21%,-48.20%,-27.88%,0.12%, +,1621,4450,嘉实前沿科技,9月20日,1.6103,1.6103,1.26%,-4.01%,-9.99%,-9.16%,-13.47%,-28.59%,-18.38%,21.95%,-28.04%,61.01%,-28.58%,0.15%, +,1622,1072,华安智能装备,9月19日,1.838,1.891,-1.13%,-5.60%,-13.30%,-6.61%,-10.91%,-28.65%,1.16%,58.86%,-28.62%,93.16%,---,0.15%, +,1623,13313,富国中证科创,9月19日,0.7114,0.7114,-0.60%,-5.60%,-11.51%,-9.06%,-13.78%,-28.73%,---,---,-27.19%,-28.86%,---,0.12%, +,1624,4344,南方大数据1,9月19日,0.92,0.92,-0.86%,-8.94%,-15.67%,-5.63%,-4.19%,-28.75%,-2.77%,41.98%,-19.92%,1.51%,---,0.00%, +,1625,4606,上投摩根优选,9月19日,1.0843,1.0843,-0.03%,-5.77%,-9.11%,-9.66%,-10.25%,-28.78%,-21.93%,-2.09%,-24.64%,8.43%,---,0.15%, +,1626,13314,富国中证科创,9月19日,0.7106,0.7106,-0.60%,-5.62%,-11.52%,-9.10%,-13.82%,-28.81%,---,---,-27.24%,-28.94%,---,0.00%, +,1627,10585,创金合信医药,9月19日,0.7007,0.7007,-0.37%,-5.71%,-7.50%,-10.17%,-11.81%,-28.87%,---,---,-26.38%,-29.93%,---,0.90%, +,1628,4352,北信瑞丰研究,9月19日,1.393,1.393,-0.43%,-7.55%,-11.23%,-6.83%,-5.53%,-28.95%,2.28%,32.15%,-20.85%,39.30%,-29.07%,0.15%, +,1629,540010,汇丰晋信科技,9月19日,2.2713,2.2713,-0.58%,-4.99%,-12.53%,-11.87%,-16.45%,-28.96%,-31.71%,9.21%,-32.82%,127.13%,---,0.15%, +,1630,11254,长江量化科技,9月19日,0.7131,0.7131,-1.22%,-6.01%,-10.72%,-7.78%,-10.31%,-28.96%,---,---,-26.36%,-28.69%,---,0.15%, +,1631,7965,民生加银品质,9月20日,1.0295,1.0295,1.06%,-3.54%,-8.59%,-9.13%,-12.03%,-28.98%,-17.28%,---,-28.28%,2.95%,-28.72%,0.15%, +,1632,167506,安信深圳科技,9月19日,0.9705,0.9705,-1.04%,-6.98%,-13.76%,-9.62%,-11.61%,-29.09%,-30.56%,---,-31.12%,-2.95%,---,0.12%, +,1633,10586,创金合信医药,9月19日,0.6944,0.6944,-0.37%,-5.72%,-7.54%,-10.28%,-12.03%,-29.23%,---,---,-26.64%,-30.56%,---,0.00%, +,1634,167507,安信深圳科技,9月19日,0.9638,0.9638,-1.04%,-6.98%,-13.77%,-9.67%,-11.72%,-29.26%,-30.91%,---,-31.24%,-3.62%,---,0.00%, +,1635,7966,民生加银品质,9月20日,1.0196,1.0196,1.06%,-3.55%,-8.62%,-9.22%,-12.21%,-29.26%,-17.93%,---,-28.49%,1.96%,-29.01%,0.00%, +,1636,10112,广发研究精选,9月20日,0.7269,0.7269,1.89%,-5.70%,-10.18%,-7.09%,-12.16%,-29.27%,---,---,-27.79%,-27.31%,-29.12%,0.15%, +,1637,7873,华宝科技ET,9月20日,1.0233,1.0233,-0.48%,-4.78%,-8.05%,-9.64%,-18.99%,-29.34%,-30.18%,-2.76%,-32.90%,2.33%,-28.95%,0.10%, +,1638,11255,长江量化科技,9月19日,0.7077,0.7077,-1.23%,-6.03%,-10.77%,-7.92%,-10.59%,-29.39%,---,---,-26.67%,-29.23%,---,0.00%, +,1639,12882,工银科技龙头,9月20日,0.6375,0.6375,-0.47%,-4.74%,-8.00%,-9.55%,-18.91%,-29.47%,---,---,-32.85%,-36.25%,-29.08%,0.10%, +,1640,8551,东财医药A,9月19日,0.9505,0.9505,-1.11%,-6.31%,-7.78%,-12.69%,-15.16%,-29.48%,-24.86%,---,-26.98%,-4.95%,---,0.10%, +,1641,10113,广发研究精选,9月20日,0.7215,0.7215,1.91%,-5.70%,-10.21%,-7.18%,-12.32%,-29.55%,---,---,-28.00%,-27.85%,-29.40%,0.00%, +,1642,12883,工银科技龙头,9月20日,0.6367,0.6367,-0.48%,-4.76%,-8.02%,-9.59%,-18.96%,-29.55%,---,---,-32.90%,-36.33%,-29.15%,0.00%, +,1643,11425,广发优势成长,9月20日,0.6638,0.6638,2.12%,-4.46%,-8.35%,-4.13%,-7.93%,-29.57%,---,---,-22.84%,-33.62%,-29.74%,0.15%, +,1644,7874,华宝科技ET,9月20日,1.0109,1.0109,-0.48%,-4.80%,-8.09%,-9.74%,-19.15%,-29.63%,-30.74%,-3.92%,-33.10%,1.09%,-29.24%,0.00%, +,1645,8552,东财医药C,9月19日,0.9447,0.9447,-1.12%,-6.32%,-7.81%,-12.75%,-15.27%,-29.66%,-25.24%,---,-27.11%,-5.53%,---,0.00%, +,1646,12730,国泰中证细分,9月19日,0.8324,0.8324,0.10%,-6.10%,-6.97%,-12.05%,-7.15%,-29.80%,---,---,-17.42%,-16.76%,-28.79%,0.10%, +,1647,11426,广发优势成长,9月20日,0.6595,0.6595,2.11%,-4.48%,-8.39%,-4.24%,-8.12%,-29.86%,---,---,-23.06%,-34.05%,-30.02%,0.00%, +,1648,12537,华宝中证细分,9月20日,0.8089,0.8089,1.26%,-4.32%,-6.14%,-10.68%,-6.80%,-29.93%,---,---,-17.62%,-19.11%,-29.62%,0.10%, +,1649,12731,国泰中证细分,9月19日,0.8294,0.8294,0.11%,-6.10%,-7.00%,-12.11%,-7.29%,-30.01%,---,---,-17.60%,-17.06%,-29.01%,0.00%, +,1650,12538,华宝中证细分,9月20日,0.807,0.807,1.27%,-4.32%,-6.14%,-10.72%,-6.89%,-30.06%,---,---,-17.74%,-19.30%,-29.75%,0.00%, +,1651,12045,大成医药健康,9月20日,0.7,0.7,0.13%,-5.72%,-11.08%,-17.19%,-22.61%,-30.06%,---,---,-31.83%,-30.00%,---,0.15%, +,1652,5894,华夏优势精选,9月19日,1.6772,1.6772,-1.37%,-9.19%,-11.63%,-0.38%,-3.65%,-30.09%,6.04%,34.24%,-16.22%,67.72%,-28.10%,0.15%, +,1653,12046,大成医药健康,9月20日,0.6972,0.6972,0.13%,-5.73%,-11.11%,-17.27%,-22.76%,-30.34%,---,---,-32.03%,-30.28%,---,0.00%, +,1654,5770,信澳中证沪港,9月20日,0.7441,0.7441,0.35%,-5.64%,-7.58%,-10.11%,-14.83%,-30.40%,-34.15%,-24.94%,-24.46%,-25.86%,-30.30%,0.10%, +,1655,160635,鹏华中证医药,9月19日,1.004,0.911,-1.08%,-6.08%,-7.55%,-12.62%,-15.20%,-30.42%,-31.19%,-2.90%,-27.19%,-8.99%,---,0.12%, +,1656,8020,华富中证人工,9月20日,0.6529,0.6529,0.08%,-4.27%,-9.91%,-11.82%,-21.01%,-30.42%,-35.67%,---,-35.71%,-34.71%,-30.09%,0.10%, +,1657,6786,泰康港股通大,9月19日,1.0272,1.0272,-1.51%,-5.55%,-8.73%,-8.47%,3.99%,-30.44%,-23.00%,5.70%,-20.20%,2.72%,---,0.10%, +,1658,10366,鹏华中证医药,9月19日,0.671,0.671,-1.03%,-6.02%,-7.58%,-12.52%,-15.17%,-30.47%,---,---,-27.22%,-32.90%,---,0.00%, +,1659,1605,国富沪港深成,9月20日,1.843,1.843,0.93%,-4.26%,-7.34%,-8.90%,-9.83%,-30.53%,-15.92%,32.49%,-26.19%,84.30%,-30.24%,0.15%, +,1660,160219,国泰国证医药,9月19日,0.6293,2.0143,-1.13%,-6.09%,-7.85%,-12.00%,-15.04%,-30.61%,-29.30%,9.33%,-27.63%,70.29%,-30.64%,0.10%, +,1661,8021,华富中证人工,9月20日,0.6481,0.6481,0.08%,-4.28%,-9.94%,-11.90%,-21.15%,-30.63%,-36.07%,---,-35.85%,-35.19%,-30.30%,0.00%, +,1662,6787,泰康港股通大,9月19日,1.0132,1.0132,-1.52%,-5.56%,-8.75%,-8.56%,3.79%,-30.72%,-23.61%,4.45%,-20.42%,1.32%,---,0.00%, +,1663,780,鹏华医疗保健,9月19日,1.942,1.942,-0.15%,-4.94%,-5.91%,-8.01%,-9.97%,-30.74%,-18.23%,38.22%,-25.68%,94.20%,---,0.15%, +,1664,163118,申万菱信中证,9月20日,0.7228,1.175,-0.06%,-4.96%,-8.12%,-13.64%,-15.94%,-30.79%,-26.52%,15.75%,-28.33%,17.50%,-30.88%,0.12%, +,1665,168203,中融国证钢铁,9月19日,1.135,1.135,-1.30%,-6.04%,-6.35%,-9.49%,-8.91%,-30.96%,27.10%,36.19%,-17.27%,-51.35%,---,0.00%, +,1666,165519,信诚中证80,9月19日,0.9491,1.6541,-0.62%,-6.61%,-9.62%,-14.56%,-19.21%,-31.10%,-26.63%,12.53%,-29.68%,75.43%,-30.66%,0.12%, +,1667,793,工银高端制造,9月20日,1.673,1.673,0.97%,-5.43%,-9.52%,-6.64%,-5.91%,-31.18%,5.62%,95.22%,-22.90%,67.30%,-31.60%,0.15%, +,1668,5112,银华中证全指,9月20日,1.4391,1.4391,1.12%,-5.22%,-6.46%,-16.01%,-11.04%,-31.18%,-27.81%,15.25%,-28.41%,43.91%,-31.66%,0.12%, +,1669,7076,汇添富中证医,9月19日,1.0198,1.0198,-1.10%,-6.24%,-7.80%,-13.13%,-16.07%,-31.30%,-32.34%,-3.82%,-27.91%,1.98%,-31.07%,0.10%, +,1670,13080,信诚中证80,9月19日,0.9453,0.9453,-0.62%,-6.62%,-9.64%,-14.65%,-19.37%,-31.37%,---,---,-29.87%,-29.44%,-30.94%,0.00%, +,1671,13162,广发沪港深科,9月20日,0.684,0.684,0.25%,-4.44%,-8.67%,-12.62%,-13.35%,-31.51%,---,---,-30.52%,-31.60%,-30.82%,0.12%, +,1672,376510,上投摩根大盘,9月19日,2.4529,2.4529,-0.61%,-5.38%,-13.72%,-13.68%,-14.82%,-31.52%,-10.48%,36.12%,-28.09%,145.29%,---,0.15%, +,1673,1915,宝盈医疗健康,9月20日,1.465,1.465,0.41%,-5.48%,-7.69%,-9.51%,-9.34%,-31.57%,-25.71%,31.86%,-22.69%,46.50%,-32.40%,0.15%, +,1674,7077,汇添富中证医,9月19日,1.0058,1.0058,-1.10%,-6.25%,-7.83%,-13.23%,-16.23%,-31.57%,-32.88%,-4.96%,-28.12%,0.58%,-31.34%,0.00%, +,1675,13163,广发沪港深科,9月20日,0.6825,0.6825,0.23%,-4.45%,-8.70%,-12.67%,-13.44%,-31.65%,---,---,-30.63%,-31.75%,-30.96%,0.00%, +,1676,8189,国泰中证钢铁,9月19日,1.231,1.431,-1.27%,-6.16%,-6.39%,-9.01%,-7.79%,-31.67%,23.46%,---,-16.00%,40.18%,-31.63%,0.10%, +,1677,4075,交银医药创新,9月20日,2.5489,2.5489,0.56%,-3.87%,-7.07%,-13.45%,-8.83%,-31.71%,-14.18%,55.55%,-25.01%,154.89%,-32.16%,0.15%, +,1678,12315,创金合信港股,9月19日,0.6255,0.6255,-2.39%,-7.59%,-6.81%,-9.91%,-4.05%,-31.83%,---,---,-26.88%,-37.45%,---,0.15%, +,1679,373,华安中证细分,9月19日,1.26,1.26,-0.87%,-6.74%,-9.68%,-14.17%,-19.08%,-31.86%,-35.35%,-6.94%,-28.97%,26.00%,---,0.12%, +,1680,8190,国泰中证钢铁,9月19日,1.2204,1.4204,-1.27%,-6.17%,-6.41%,-9.07%,-7.93%,-31.88%,22.72%,---,-16.18%,39.08%,-31.83%,0.00%, +,1681,524,上投摩根民生,9月19日,2.3855,2.8245,-0.60%,-4.42%,-10.91%,-9.52%,-8.97%,-31.89%,-8.88%,52.04%,-26.51%,198.29%,---,0.15%, +,1682,3230,创金合信医疗,9月19日,2.2149,2.144,-0.85%,-7.02%,-9.20%,-10.47%,-12.83%,-32.02%,-16.94%,51.77%,-28.23%,114.40%,---,0.15%, +,1683,11236,上投摩根行业,9月19日,0.8082,0.8082,0.00%,-5.26%,-9.34%,-10.39%,-6.95%,-32.04%,---,---,-23.55%,-19.18%,---,0.15%, +,1684,9877,中银内核驱动,9月20日,0.7746,0.7746,1.37%,-2.65%,-5.12%,-14.96%,-10.72%,-32.08%,-21.39%,---,-27.79%,-22.54%,-30.89%,0.15%, +,1685,8314,上投摩根慧选,9月19日,1.2909,1.2909,-0.07%,-4.62%,-10.33%,-13.81%,-13.33%,-32.12%,-16.82%,---,-29.54%,29.09%,---,0.15%, +,1686,376,华安中证细分,9月19日,1.22,1.22,-0.81%,-6.73%,-9.70%,-14.27%,-19.26%,-32.15%,-35.86%,-7.99%,-29.19%,22.00%,---,0.00%, +,1687,12316,创金合信港股,9月19日,0.6216,0.6216,-2.40%,-7.61%,-6.85%,-10.03%,-4.30%,-32.17%,---,---,-27.14%,-37.84%,---,0.00%, +,1688,9733,创金合信港股,9月19日,0.5761,0.5761,-0.95%,-3.03%,-6.81%,-6.61%,-7.62%,-32.20%,-40.98%,---,-26.75%,-42.39%,---,0.15%, +,1689,2332,汇丰晋信沪港,9月19日,1.2062,1.2612,-0.22%,-6.26%,-10.99%,-12.40%,-6.05%,-32.27%,-23.20%,1.50%,-26.56%,26.81%,---,0.15%, +,1690,12600,中银内核驱动,9月20日,0.7712,0.7712,1.38%,-2.66%,-5.15%,-15.05%,-10.90%,-32.33%,---,---,-28.01%,-38.23%,-31.16%,0.00%, +,1691,11237,上投摩根行业,9月19日,0.8019,0.8019,0.00%,-5.28%,-9.38%,-10.50%,-7.20%,-32.38%,---,---,-23.82%,-19.81%,---,0.00%, +,1692,3231,创金合信医疗,9月19日,2.1646,1.9771,-0.86%,-7.03%,-9.25%,-10.63%,-13.13%,-32.49%,-18.10%,48.44%,-28.58%,97.71%,---,0.00%, +,1693,160322,华夏港股通精,9月20日,0.9987,1.0487,1.00%,-2.71%,-5.76%,-10.89%,-8.16%,-32.49%,-29.02%,-5.55%,-25.38%,4.16%,-32.61%,0.15%, +,1694,502023,鹏华国证钢铁,9月19日,1.532,1.074,-1.35%,-6.13%,-6.41%,-9.88%,-9.78%,-32.51%,23.85%,36.11%,-18.34%,9.29%,---,0.12%, +,1695,12810,鹏华国证钢铁,9月19日,0.843,0.843,-1.29%,-6.12%,-6.33%,-9.94%,-9.74%,-32.56%,---,---,-18.39%,-15.70%,---,0.00%, +,1696,2333,汇丰晋信沪港,9月19日,1.1663,1.2213,-0.22%,-6.28%,-11.03%,-12.53%,-6.31%,-32.62%,-23.97%,-0.03%,-26.83%,22.68%,---,0.00%, +,1697,1126,上投摩根卓越,9月19日,1.3012,1.4307,-0.09%,-4.72%,-10.83%,-13.83%,-15.18%,-32.64%,-7.85%,64.56%,-30.65%,40.54%,---,0.15%, +,1698,8107,华商医药医疗,9月19日,0.9613,0.9613,-2.34%,-8.28%,-9.00%,-11.32%,-14.85%,-32.66%,-30.42%,---,-29.14%,-3.87%,---,0.15%, +,1699,8315,上投摩根慧选,9月19日,1.264,1.264,-0.07%,-4.64%,-10.39%,-13.98%,-13.68%,-32.67%,-18.13%,---,-29.94%,26.40%,---,0.00%, +,1700,9734,创金合信港股,9月19日,0.5679,0.5679,-0.94%,-3.04%,-6.86%,-6.78%,-7.94%,-32.67%,-41.80%,---,-27.11%,-43.21%,---,0.00%, +,1701,1009,上投摩根安全,9月19日,1.574,1.8206,-0.40%,-7.30%,-12.40%,-4.04%,-3.60%,-32.71%,-0.14%,59.30%,-19.86%,78.42%,---,0.15%, +,1702,1685,汇添富沪港深,9月20日,1.141,1.141,0.97%,-2.48%,-4.68%,-8.72%,-8.06%,-32.88%,-18.96%,8.36%,-23.83%,14.10%,-32.20%,0.15%, +,1703,12884,华夏港股通精,9月20日,0.9915,0.9915,0.99%,-2.73%,-5.81%,-11.03%,-8.47%,-32.91%,---,---,-25.72%,-39.84%,-33.02%,0.00%, +,1704,9017,银华港股通精,9月20日,0.9365,0.9365,0.80%,-1.54%,-5.63%,-9.34%,-2.26%,-33.07%,-24.80%,---,-18.68%,-6.35%,-32.10%,0.15%, +,1705,161035,富国中证医药,9月20日,1.393,1.393,0.07%,-4.91%,-9.19%,-15.11%,-16.84%,-33.19%,-28.08%,16.57%,-29.75%,39.30%,-33.32%,0.12%, +,1706,5626,富国中证医药,9月20日,1.39,1.39,0.07%,-4.92%,-9.21%,-15.19%,-16.92%,-33.30%,---,---,-29.87%,-32.59%,-33.46%,0.00%, +,1707,1313,上投摩根智慧,9月19日,0.9722,0.9722,-0.13%,-8.69%,-15.84%,-14.91%,-8.09%,-33.32%,-15.83%,29.80%,-24.03%,-2.78%,---,0.15%, +,1708,900029,中信证券量化,9月19日,0.94,1.939,-0.17%,-5.24%,-8.12%,-5.27%,-11.46%,-33.39%,-15.12%,---,-28.37%,9.15%,---,0.10%, +,1709,7388,上投摩根研究,9月19日,1.0311,1.0311,-0.78%,-5.53%,-13.97%,-13.77%,-13.80%,-33.55%,-7.65%,---,-27.13%,3.11%,---,0.15%, +,1710,12371,东财沪港深互,9月20日,0.5605,0.5605,0.48%,-5.10%,-6.96%,-13.84%,-9.51%,-33.61%,---,---,-30.70%,-43.95%,-32.75%,0.12%, +,1711,1171,工银养老产业,9月20日,1.547,1.547,0.98%,-3.19%,-8.35%,-13.09%,-14.62%,-33.80%,-1.65%,68.70%,-26.65%,54.70%,-34.37%,0.15%, +,1712,1344,易方达沪深3,9月20日,1.0739,1.0739,-0.19%,-5.31%,-8.04%,-14.77%,-15.29%,-33.84%,-31.75%,-3.92%,-28.96%,7.39%,-33.60%,0.10%, +,1713,12372,东财沪港深互,9月20日,0.5576,0.5576,0.49%,-5.11%,-6.99%,-13.92%,-9.69%,-33.87%,---,---,-30.90%,-44.24%,-33.02%,0.00%, +,1714,10204,中银港股通优,9月20日,0.6053,0.6053,1.39%,-2.26%,-5.70%,-8.98%,-7.98%,-33.88%,---,---,-21.93%,-39.47%,-32.79%,0.15%, +,1715,7883,易方达沪深3,9月20日,1.0704,1.0704,-0.18%,-5.31%,-8.05%,-14.79%,-15.34%,-33.91%,-31.88%,-4.21%,-29.01%,2.85%,-33.67%,0.00%, +,1716,960,招商医药健康,9月20日,2.077,2.077,0.19%,-4.02%,-8.46%,-14.63%,-19.03%,-33.92%,-24.42%,33.14%,-26.74%,107.70%,-34.33%,0.15%, +,1717,900030,中信证券量化,9月19日,0.9228,1.9218,-0.17%,-5.27%,-8.19%,-5.46%,-11.82%,-33.92%,-16.46%,---,-28.78%,7.15%,---,0.00%, +,1718,1482,上投摩根新兴,9月19日,1.6803,1.6803,-0.60%,-4.13%,-10.34%,-9.69%,-8.88%,-34.00%,-11.70%,47.65%,-26.86%,68.03%,---,0.15%, +,1719,399011,中海医疗保健,9月20日,1.349,2.929,0.67%,-4.73%,-7.41%,-12.52%,-10.37%,-34.04%,-21.63%,12.00%,-27.59%,239.85%,-34.73%,0.15%, +,1720,7389,上投摩根研究,9月19日,1.0124,1.0124,-0.79%,-5.56%,-14.04%,-13.96%,-14.15%,-34.08%,-9.13%,---,-27.55%,1.24%,---,0.00%, +,1721,12238,工银养老产业,9月20日,1.535,1.535,0.99%,-3.22%,-8.36%,-13.23%,-14.86%,-34.18%,---,---,-26.94%,-29.88%,-34.74%,0.00%, +,1722,5237,银华医疗健康,9月20日,1.2741,1.2741,0.47%,-5.38%,-7.73%,-13.65%,-15.42%,-34.42%,-23.64%,13.67%,-28.13%,27.41%,-34.81%,0.15%, +,1723,5238,银华医疗健康,9月20日,1.2534,1.2534,0.47%,-5.40%,-7.76%,-13.74%,-15.60%,-34.69%,-24.26%,12.32%,-28.34%,25.34%,-35.06%,0.00%, +,1724,6756,国泰中证生物,9月19日,1.0999,1.0999,-0.55%,-5.07%,-8.22%,-12.80%,-20.72%,-34.87%,-41.05%,-4.85%,-29.37%,9.99%,-35.04%,0.10%, +,1725,6757,国泰中证生物,9月19日,1.0846,1.0846,-0.55%,-5.08%,-8.25%,-12.88%,-20.84%,-35.07%,-41.40%,-5.75%,-29.53%,8.46%,-35.23%,0.00%, +,1726,2121,广发沪港深新,9月20日,1.2426,1.3276,0.92%,-4.40%,-7.72%,-14.70%,-8.39%,-35.08%,-30.89%,-6.71%,-27.60%,33.01%,-33.91%,0.15%, +,1727,9896,广发港股通成,9月20日,0.621,0.621,0.98%,-4.21%,-8.76%,-14.74%,-5.28%,-35.20%,-37.86%,---,-27.42%,-37.90%,-34.20%,0.15%, +,1728,1764,广发沪港深新,9月20日,1.039,1.176,1.86%,-5.46%,-9.26%,-15.46%,-7.07%,-35.22%,-32.53%,5.80%,-24.27%,16.02%,-34.37%,0.15%, +,1729,5303,嘉实医药健康,9月20日,1.6454,1.6454,0.40%,-5.56%,-11.00%,-15.86%,-7.53%,-35.33%,-23.71%,38.50%,-24.40%,64.54%,-35.68%,0.15%, +,1730,10024,广发沪港深新,9月20日,1.233,1.233,0.92%,-4.40%,-7.75%,-14.78%,-8.58%,-35.34%,---,---,-27.81%,-29.54%,-34.18%,0.00%, +,1731,12634,国泰中证医疗,9月19日,0.5748,0.5748,-2.16%,-7.63%,-9.15%,-14.97%,-13.55%,-35.36%,---,---,-29.12%,-42.52%,-35.59%,0.10%, +,1732,9897,广发港股通成,9月20日,0.616,0.616,0.98%,-4.21%,-8.78%,-14.82%,-5.48%,-35.46%,-38.36%,---,-27.62%,-38.40%,-34.45%,0.00%, +,1733,12635,国泰中证医疗,9月19日,0.5727,0.5727,-2.17%,-7.64%,-9.18%,-15.04%,-13.69%,-35.55%,---,---,-29.28%,-42.73%,-35.78%,0.00%, +,1734,5433,申万菱信医药,9月20日,0.6658,0.6658,1.39%,-3.72%,-4.16%,-12.60%,-15.42%,-35.60%,---,---,-31.34%,-33.42%,-36.10%,0.15%, +,1735,5402,广发资源优选,9月20日,1.8831,1.8831,3.27%,-3.37%,-3.70%,-8.24%,2.64%,-35.84%,6.82%,86.06%,-21.23%,88.31%,-35.64%,0.15%, +,1736,5304,嘉实医药健康,9月20日,1.5847,1.5847,0.40%,-5.57%,-11.07%,-16.03%,-7.91%,-35.85%,-24.92%,35.29%,-24.83%,58.47%,-36.18%,0.00%, +,1737,831,工银医疗保健,9月20日,2.687,2.687,0.83%,-3.52%,-8.61%,-13.85%,-13.49%,-35.93%,-6.44%,52.50%,-26.64%,168.70%,-36.42%,0.15%, +,1738,11373,招商前沿医疗,9月20日,0.614,0.614,0.28%,-3.94%,-9.45%,-15.17%,-18.76%,-35.95%,---,---,-29.30%,-38.60%,-36.23%,0.15%, +,1739,711,嘉实医疗保健,9月20日,2.148,2.148,0.14%,-9.11%,-11.18%,-16.44%,-10.44%,-36.05%,-25.05%,19.97%,-25.52%,114.50%,-36.43%,0.15%, +,1740,10235,广发资源优选,9月20日,1.8692,1.8692,3.27%,-3.38%,-3.73%,-8.34%,2.43%,-36.10%,---,---,-21.46%,10.71%,-35.90%,0.00%, +,1741,161122,易方达中证万,9月20日,0.6439,0.934,0.34%,-5.11%,-10.94%,-17.13%,-21.89%,-36.21%,-29.45%,19.25%,-29.50%,-4.29%,-35.74%,0.10%, +,1742,10572,易方达中证万,9月20日,0.6405,0.6405,0.34%,-5.11%,-10.97%,-17.19%,-22.01%,-36.40%,---,---,-29.65%,-30.30%,-35.94%,0.00%, +,1743,11374,招商前沿医疗,9月20日,0.6079,0.6079,0.28%,-3.97%,-9.51%,-15.35%,-19.10%,-36.47%,---,---,-29.70%,-39.21%,-36.74%,0.00%, +,1744,1717,工银前沿医疗,9月20日,3.06,3.06,0.79%,-3.50%,-8.03%,-13.73%,-14.95%,-36.61%,-6.28%,70.57%,-28.24%,206.00%,-37.22%,0.15%, +,1745,12737,广发中证创新,9月20日,0.5986,0.5986,0.17%,-4.44%,-10.64%,-15.87%,-20.50%,-36.66%,---,---,-30.42%,-40.14%,-36.44%,0.12%, +,1746,12738,广发中证创新,9月20日,0.5972,0.5972,0.17%,-4.43%,-10.67%,-15.90%,-20.57%,-36.78%,---,---,-30.52%,-40.28%,-36.56%,0.00%, +,1747,10685,工银前沿医疗,9月20日,3.028,3.028,0.77%,-3.54%,-8.08%,-13.88%,-15.23%,-37.00%,---,---,-28.57%,-7.68%,-37.61%,0.00%, +,1748,2387,工银沪港深股,9月20日,0.903,0.971,0.86%,-4.43%,-11.78%,-16.64%,-13.41%,-37.03%,-30.42%,-15.69%,-32.17%,-4.18%,-35.94%,0.15%, +,1749,162412,华宝医疗ET,9月20日,0.7889,0.5445,-0.01%,-5.35%,-8.95%,-16.07%,-14.10%,-37.30%,-30.65%,13.02%,-30.07%,-49.78%,-37.64%,0.12%, +,1750,7512,工银沪港深股,9月20日,0.8914,0.8914,0.86%,-4.44%,-11.82%,-16.77%,-13.68%,-37.41%,-31.25%,-16.98%,-32.46%,-18.49%,-36.32%,0.00%, +,1751,12323,华宝医疗ET,9月20日,0.7869,0.7869,-0.01%,-5.35%,-8.97%,-16.12%,-14.19%,-37.42%,---,---,-30.17%,-43.74%,-37.76%,0.00%, +,1752,502056,广发中证医疗,9月20日,0.891,0.9264,-0.02%,-5.32%,-8.89%,-16.09%,-14.17%,-37.49%,-28.91%,11.89%,-30.27%,-5.00%,-37.81%,0.05%, +,1753,5983,上投摩根核心,9月19日,2.0684,2.0684,-0.81%,-9.26%,-15.76%,-14.47%,-10.05%,-37.50%,7.72%,74.08%,-24.79%,106.84%,---,0.15%, +,1754,9881,广发中证医疗,9月20日,0.8875,0.8875,-0.02%,-5.33%,-8.91%,-16.12%,-14.26%,-37.61%,-29.19%,---,-30.37%,-33.02%,-37.93%,0.00%, +,1755,501311,嘉实港股通新,9月20日,0.8315,0.8315,1.50%,-4.44%,-10.88%,-14.15%,-6.47%,-37.70%,-36.22%,-16.60%,-29.27%,-16.85%,-36.51%,0.10%, +,1756,6002,工银医药健康,9月20日,1.9283,2.4759,0.47%,-3.82%,-10.59%,-15.27%,-13.69%,-37.73%,-14.82%,57.34%,-28.49%,131.55%,-38.28%,0.15%, +,1757,501009,汇添富中证生,9月20日,1.4488,1.4488,-0.45%,-4.67%,-10.52%,-16.53%,-23.25%,-37.81%,-34.40%,11.96%,-31.79%,44.88%,-37.74%,0.10%, +,1758,6614,嘉实港股通新,9月20日,0.8193,0.8193,1.50%,-4.44%,-10.92%,-14.25%,-6.65%,-37.95%,-36.72%,-17.58%,-29.47%,-18.07%,-36.76%,0.00%, +,1759,501010,汇添富中证生,9月20日,1.4371,1.4371,-0.46%,-4.69%,-10.56%,-16.62%,-23.41%,-38.07%,-34.93%,10.68%,-31.99%,43.71%,-37.99%,0.00%, +,1760,6003,工银医药健康,9月20日,1.8743,2.4175,0.46%,-3.83%,-10.64%,-15.41%,-13.96%,-38.11%,-15.84%,54.47%,-28.80%,125.58%,-38.65%,0.00%, +,1761,9162,富国医药成长,9月20日,0.8201,0.8201,0.31%,-3.98%,-9.25%,-15.68%,-11.75%,-38.12%,-22.30%,---,-28.35%,-17.99%,-38.65%,0.15%, +,1762,501005,汇添富中证精,9月20日,0.9815,0.9815,-0.44%,-4.19%,-11.68%,-18.39%,-25.16%,-38.17%,-41.01%,-0.35%,-33.19%,-1.85%,-37.93%,0.08%, +,1763,1766,上投摩根医疗,9月19日,1.6961,1.6961,-2.04%,-7.23%,-8.91%,-14.09%,-14.67%,-38.41%,-23.15%,29.37%,-30.51%,69.61%,---,0.15%, +,1764,501006,汇添富中证精,9月20日,0.9572,0.9572,-0.44%,-4.19%,-11.71%,-18.47%,-25.31%,-38.42%,-41.48%,-1.50%,-33.38%,-4.28%,-38.18%,0.00%, +,1765,5328,前海开源价值,9月19日,1.0824,1.0824,0.68%,-5.02%,-5.69%,-7.77%,-13.03%,-38.43%,-19.27%,10.58%,-35.17%,8.24%,---,0.15%, +,1766,12496,同泰行业优选,9月19日,0.6163,0.6163,-1.42%,-9.10%,-13.73%,-13.20%,-10.13%,-39.08%,---,---,-33.60%,-38.37%,---,0.15%, +,1767,9898,民生加银医药,9月20日,0.533,0.533,0.70%,-5.18%,-9.54%,-12.32%,-24.18%,-39.13%,-46.79%,---,-33.37%,-46.70%,-38.86%,0.15%, +,1768,12497,同泰行业优选,9月19日,0.6137,0.6137,-1.43%,-9.11%,-13.76%,-13.28%,-10.32%,-39.33%,---,---,-33.79%,-38.63%,---,0.00%, +,1769,5209,东吴双三角股,9月19日,0.6957,0.6957,-1.68%,-8.75%,-10.73%,-17.23%,-13.79%,-39.45%,-42.71%,-24.52%,-29.92%,-30.43%,---,0.15%, +,1770,5210,东吴双三角股,9月19日,0.6779,0.6779,-1.70%,-8.76%,-10.77%,-17.34%,-14.03%,-39.76%,-43.28%,-25.64%,-30.18%,-32.21%,---,0.00%, +,1771,9537,太平行业优选,9月19日,0.6806,0.7306,-0.06%,-7.24%,-13.10%,-14.96%,-17.44%,-39.79%,-27.81%,---,-35.04%,-28.71%,-39.10%,0.12%, +,1772,11040,天弘国证生物,9月20日,0.5784,0.5784,0.24%,-5.30%,-12.32%,-19.01%,-23.38%,-39.96%,---,---,-31.74%,-42.16%,-39.49%,0.10%, +,1773,9538,太平行业优选,9月19日,0.6735,0.7235,-0.06%,-7.24%,-13.13%,-15.06%,-17.64%,-40.08%,-28.52%,---,-35.27%,-29.44%,-39.40%,0.00%, +,1774,12598,华安国证生物,9月19日,0.6274,0.6274,-1.04%,-7.72%,-12.41%,-18.05%,-23.18%,-40.08%,---,---,-31.56%,-37.26%,---,0.05%, +,1775,11041,天弘国证生物,9月20日,0.5765,0.5765,0.24%,-5.31%,-12.33%,-19.05%,-23.46%,-40.09%,---,---,-31.84%,-42.35%,-39.61%,0.00%, +,1776,12599,华安国证生物,9月19日,0.6255,0.6255,-1.03%,-7.72%,-12.42%,-18.11%,-23.29%,-40.25%,---,---,-31.71%,-37.45%,---,0.00%, +,1777,10387,易方达医药生,9月20日,0.5395,0.5395,1.35%,-4.68%,-8.70%,-11.53%,-10.20%,-40.79%,---,---,-31.20%,-46.05%,-40.69%,0.15%, +,1778,161726,招商国证生物,9月20日,0.565,1.0672,0.23%,-5.31%,-12.39%,-19.27%,-23.86%,-40.84%,-38.12%,7.97%,-32.42%,-9.91%,-40.38%,0.10%, +,1779,12417,招商国证生物,9月20日,0.5644,0.5644,0.25%,-5.32%,-12.39%,-19.29%,-23.89%,-40.89%,---,---,-32.46%,-49.29%,-40.43%,0.00%, +,1780,10388,易方达医药生,9月20日,0.5355,0.5355,1.34%,-4.68%,-8.73%,-11.60%,-10.38%,-41.02%,---,---,-31.40%,-46.45%,-40.93%,0.00%, +,1781,8412,长盛竞争优势,9月19日,0.6668,0.6668,-2.06%,-6.43%,-8.54%,-16.23%,-21.96%,-41.10%,-30.46%,---,-32.12%,-33.32%,---,0.15%, +,1782,8413,长盛竞争优势,9月19日,0.6554,0.6554,-2.06%,-6.45%,-8.60%,-16.39%,-22.26%,-41.57%,-31.57%,---,-32.50%,-34.46%,---,0.00%, +,1783,9863,富国创新趋势,9月20日,0.6571,0.6571,0.47%,-5.10%,-9.29%,-17.83%,-23.86%,-42.44%,-35.13%,---,-39.35%,-34.29%,-42.79%,0.15%, +,1784,6228,中欧医疗创新,9月20日,1.5763,1.5763,0.24%,-4.58%,-10.78%,-18.21%,-15.30%,-42.75%,-24.16%,36.43%,-32.63%,57.63%,-43.14%,0.15%, +,1785,4851,广发医疗保健,9月20日,2.0465,2.0465,0.49%,-5.32%,-10.42%,-19.21%,-19.96%,-42.89%,-29.31%,28.73%,-34.57%,104.65%,-43.30%,0.15%, +,1786,4040,金鹰医疗健康,9月19日,1.2342,1.5342,-2.00%,-7.38%,-9.30%,-17.97%,-28.53%,-43.09%,-21.78%,17.74%,-36.73%,39.01%,---,0.12%, +,1787,9163,广发医疗保健,9月20日,2.0266,2.0266,0.49%,-5.33%,-10.45%,-19.29%,-20.12%,-43.12%,-29.88%,---,-34.76%,9.53%,-43.52%,0.00%, +,1788,6229,中欧医疗创新,9月20日,1.5331,1.5331,0.24%,-4.60%,-10.84%,-18.37%,-15.65%,-43.21%,-25.37%,33.43%,-33.02%,53.31%,-43.59%,0.00%, +,1789,4041,金鹰医疗健康,9月19日,1.2823,1.5923,-1.99%,-7.40%,-9.33%,-18.05%,-28.67%,-43.32%,-22.40%,16.33%,-36.91%,44.25%,---,0.00%, +,1790,913,农银医疗保健,9月20日,1.7176,1.7176,0.33%,-4.14%,-9.27%,-15.80%,-18.92%,-43.56%,-31.14%,16.17%,-34.25%,71.76%,-43.70%,0.15%, +,1791,2300,长盛医疗行业,9月19日,1.502,1.502,-2.28%,-7.63%,-9.68%,-18.85%,-26.59%,-43.64%,-29.28%,22.51%,-34.87%,50.20%,---,0.15%, +,1792,10592,南方医药创新,9月20日,0.5084,0.5084,0.04%,-4.22%,-9.20%,-14.25%,-16.79%,-43.85%,---,---,-33.03%,-49.16%,-44.02%,0.15%, +,1793,10593,南方医药创新,9月20日,0.5037,0.5037,0.04%,-7.29%,-9.28%,-14.22%,-17.08%,-44.21%,---,---,-33.34%,-49.65%,-44.35%,0.00%, +,1794,11601,前海开源公共,9月19日,0.5083,0.5083,-1.82%,-7.09%,-8.00%,-13.21%,-19.98%,-48.52%,---,---,-36.81%,-49.17%,---,0.15%, +,1795,11602,前海开源公共,9月19日,0.5053,0.5053,-1.83%,-7.10%,-8.03%,-13.30%,-20.12%,-48.73%,---,---,-37.00%,-49.47%,---,0.00%, +,1796,12596,汇添富中证8,9月20日,0.9168,0.9168,0.26%,-4.33%,-5.14%,-6.79%,-3.46%,---,---,---,-10.43%,-8.32%,-8.32%,0.05%, +,1797,11589,九泰天利量化,9月20日,0.8321,0.8321,1.34%,-4.73%,-7.19%,-3.78%,-5.84%,---,---,---,-17.07%,-16.79%,-16.79%,0.15%, +,1798,12416,德邦上证G6,9月20日,0.7239,0.7239,2.27%,-7.87%,-16.27%,-14.17%,-19.34%,---,---,---,-27.95%,-29.22%,-27.61%,0.00%, +,1799,12597,汇添富中证8,9月20日,0.9157,0.9157,0.26%,-4.33%,-5.15%,-6.81%,-3.46%,---,---,---,-10.44%,-8.43%,-8.43%,0.00%, +,1800,11861,南方中证10,9月19日,0.8627,0.8627,-1.21%,-7.13%,-11.01%,-5.17%,-6.20%,---,---,---,-19.22%,-13.73%,-12.68%,0.00%, +,1801,11836,银华智能建造,9月20日,0.8213,---,-1.46%,-3.35%,0.05%,-9.12%,-14.13%,---,---,---,-18.58%,-17.87%,-17.87%,0.15%, +,1802,13473,华宝中证新材,9月20日,0.7907,0.7907,1.11%,-5.41%,-10.88%,-12.99%,-6.78%,---,---,---,-18.47%,-20.93%,-20.93%,0.10%, +,1803,13606,华夏中证内地,9月20日,0.8148,0.8148,1.94%,-8.00%,-14.36%,-10.36%,-7.57%,---,---,---,-17.37%,-20.07%,-18.52%,0.00%, +,1804,12319,东财消费电子,9月19日,0.735,0.735,-0.64%,-6.13%,-12.11%,-7.17%,-17.24%,---,---,---,-33.22%,-26.50%,---,0.15%, +,1805,12320,东财消费电子,9月19日,0.7329,0.7329,-0.64%,-6.15%,-12.13%,-7.25%,-17.36%,---,---,---,-33.37%,-26.71%,---,0.00%, +,1806,13242,北信瑞丰优势,9月19日,0.7814,0.7814,-0.70%,-8.36%,-13.56%,-8.80%,-7.48%,---,---,---,-22.08%,-21.86%,-20.44%,0.15%, +,1807,13031,民生加银中证,9月20日,0.8182,0.8182,0.39%,-4.21%,-5.53%,-8.40%,-7.60%,---,---,---,-17.93%,-18.18%,-18.18%,0.15%, +,1808,12901,招商创业板指,9月20日,0.6651,0.6651,0.64%,-6.76%,-14.20%,-15.84%,-16.53%,---,---,---,-30.38%,-33.49%,-33.49%,0.00%, +,1809,13474,华宝中证新材,9月20日,0.7885,0.7885,1.12%,-5.42%,-10.90%,-13.05%,-6.92%,---,---,---,-18.64%,-21.15%,-21.15%,0.00%, +,1810,12213,天弘中证高端,9月20日,0.8283,0.8283,0.98%,-5.68%,-11.18%,-8.60%,-5.00%,---,---,---,-17.20%,-17.17%,-17.17%,0.00%, +,1811,12559,天弘中证沪港,9月20日,0.6895,0.6895,0.28%,-4.47%,-8.77%,-12.53%,-13.22%,---,---,---,-30.46%,-31.05%,-31.05%,0.10%, +,1812,12394,农银中证新华,9月20日,0.7794,0.7794,0.31%,-3.72%,-6.19%,-9.17%,-7.04%,---,---,---,-23.08%,-22.07%,-22.07%,0.12%, +,1813,12212,天弘中证高端,9月20日,0.8301,0.8301,0.97%,-5.67%,-11.16%,-8.53%,-4.85%,---,---,---,-17.02%,-16.99%,-16.99%,0.15%, +,1814,13642,博道成长智航,9月19日,0.8267,0.8267,-0.95%,-6.52%,-11.22%,-2.90%,-3.93%,---,---,---,-17.84%,-17.33%,---,0.00%, +,1815,12157,汇添富上证5,9月20日,0.8945,0.8945,-0.30%,-3.00%,-2.61%,-6.96%,-5.88%,---,---,---,---,-10.55%,-10.55%,0.15%, +,1816,12405,天弘国证建筑,9月20日,0.847,0.847,-0.40%,-5.34%,-7.24%,-14.48%,-12.66%,---,---,---,-23.75%,-15.30%,-15.30%,0.10%, +,1817,12151,华泰柏瑞中证,9月19日,0.846,0.846,-0.74%,-3.53%,-4.67%,-7.15%,-4.47%,---,---,---,-11.34%,-15.40%,-14.51%,0.12%, +,1818,13129,汇添富消费龙,9月20日,0.8308,0.8308,0.70%,-1.34%,-0.31%,-3.59%,2.37%,---,---,---,-17.13%,-16.92%,-16.92%,0.10%, +,1819,12152,华泰柏瑞中证,9月19日,0.8452,0.8452,-0.73%,-3.53%,-4.66%,-7.16%,-4.52%,---,---,---,-11.40%,-15.48%,-14.60%,0.00%, +,1820,13613,宝盈国家安全,9月20日,1.3237,1.3237,1.16%,-4.67%,-12.30%,-4.28%,-7.20%,---,---,---,-28.62%,-23.17%,-23.17%,0.00%, +,1821,13319,华安中证新能,9月19日,0.7424,0.7424,1.12%,-6.08%,-13.20%,-16.15%,-5.70%,---,---,---,-18.28%,-25.76%,---,0.05%, +,1822,13320,华安中证新能,9月19日,0.741,0.741,1.12%,-6.10%,-13.22%,-16.20%,-5.80%,---,---,---,-18.40%,-25.90%,---,0.00%, +,1823,12180,浦银安盛创业,9月19日,0.7617,0.7617,-0.72%,-7.00%,-12.88%,-10.28%,-12.66%,---,---,---,-23.83%,-23.83%,---,0.00%, +,1824,12215,民生加银核心,9月20日,0.8719,0.8719,0.33%,-2.56%,-3.06%,-6.54%,-0.50%,---,---,---,-11.63%,-12.81%,-12.81%,0.00%, +,1825,13816,汇添富中证光,9月20日,0.8853,0.8853,3.28%,-6.18%,-12.78%,4.53%,11.79%,---,---,---,-1.57%,-11.47%,-11.47%,0.12%, +,1826,13093,汇添富科技龙,9月20日,0.6764,0.6764,0.24%,-4.40%,-8.67%,-12.33%,-12.92%,---,---,---,-30.05%,-32.36%,-32.36%,0.10%, +,1827,12560,天弘中证沪港,9月20日,0.6882,0.6882,0.26%,-4.50%,-8.80%,-12.59%,-13.31%,---,---,---,-30.56%,-31.18%,-31.18%,0.00%, +,1828,13340,创金合信芯片,9月19日,0.8463,0.8463,-2.52%,-4.19%,-11.14%,2.86%,-7.32%,---,---,---,-22.66%,-15.37%,---,0.00%, +,1829,13125,华夏中证细分,9月20日,0.88,0.88,1.02%,-0.48%,-2.16%,-6.71%,1.93%,---,---,---,-15.51%,-12.00%,-12.00%,0.12%, +,1830,13448,华宝中证智能,9月20日,0.7943,0.7943,0.62%,-4.89%,-10.49%,-5.04%,-10.51%,---,---,---,-24.19%,-20.57%,-20.57%,0.00%, +,1831,13130,汇添富消费龙,9月20日,0.8289,0.8289,0.70%,-1.34%,-0.32%,-3.65%,2.22%,---,---,---,-17.28%,-17.11%,-17.11%,0.00%, +,1832,12619,嘉实中证软件,9月20日,0.6987,0.6987,-0.75%,-3.88%,-3.77%,-6.89%,-12.05%,---,---,---,-29.69%,-30.13%,-30.13%,0.10%, +,1833,13465,博时智选量化,9月20日,0.9479,0.9479,1.48%,-5.53%,-8.90%,-1.12%,3.08%,---,---,---,-5.82%,-5.21%,-5.21%,0.15%, +,1834,13475,华宝中证智能,9月20日,0.729,0.729,1.65%,-5.31%,-11.82%,-14.74%,-7.10%,---,---,---,-23.24%,-27.10%,-27.10%,0.10%, +,1835,13094,汇添富科技龙,9月20日,0.6748,0.6748,0.25%,-4.39%,-8.68%,-12.38%,-13.03%,---,---,---,-30.17%,-32.52%,-32.52%,0.00%, +,1836,12179,浦银安盛创业,9月19日,0.7634,0.7634,-0.72%,-6.98%,-12.85%,-10.21%,-12.52%,---,---,---,-23.66%,-23.66%,---,0.12%, +,1837,12214,民生加银核心,9月20日,0.875,0.875,0.33%,-2.55%,-3.01%,-6.44%,-0.30%,---,---,---,-11.37%,-12.50%,-12.50%,0.15%, +,1838,12809,鹏华中证沪港,9月19日,0.6991,0.6991,-1.26%,-6.17%,-8.97%,-12.10%,-13.64%,---,---,---,-30.25%,-30.09%,---,0.00%, +,1839,11860,南方中证10,9月19日,0.8636,0.8636,-1.21%,-7.11%,-11.00%,-5.13%,-6.15%,---,---,---,-19.16%,-13.64%,-12.60%,0.12%, +,1840,13453,交银国证新能,9月20日,1.3203,1.3203,1.99%,-5.97%,-13.49%,-11.73%,-4.23%,---,---,---,-16.24%,-12.06%,-12.06%,0.00%, +,1841,13810,广发科创板5,9月19日,0.7187,0.7187,-2.16%,-5.33%,-11.35%,-10.08%,-14.07%,---,---,---,-28.76%,-28.13%,---,0.12%, +,1842,12761,华泰柏瑞上证,9月19日,1.057,1.057,0.26%,-3.80%,1.14%,2.63%,4.60%,---,---,---,4.93%,5.70%,5.48%,0.12%, +,1843,13490,同泰金融精选,9月19日,0.8394,0.8394,-0.45%,-4.73%,-1.97%,-7.00%,-9.04%,---,---,---,-16.03%,-16.06%,---,0.15%, +,1844,13491,同泰金融精选,9月19日,0.8366,0.8366,-0.45%,-4.74%,-2.00%,-7.10%,-9.21%,---,---,---,-16.27%,-16.34%,---,0.00%, +,1845,13803,财通资管中证,9月20日,0.815,0.815,0.75%,-5.14%,-5.74%,-8.98%,-7.21%,---,---,---,-15.12%,-18.50%,-18.50%,0.00%, +,1846,13476,华宝中证智能,9月20日,0.727,0.727,1.64%,-5.33%,-11.85%,-14.81%,-7.26%,---,---,---,-23.42%,-27.30%,-27.30%,0.00%, +,1847,12419,天弘国证建筑,9月20日,0.8455,0.8455,-0.40%,-5.34%,-7.25%,-14.53%,-12.75%,---,---,---,-23.86%,-15.45%,-15.45%,0.00%, +,1848,12900,招商创业板指,9月20日,0.6672,0.6672,0.63%,-6.76%,-14.19%,-15.77%,-16.37%,---,---,---,-30.18%,-33.28%,-33.28%,0.12%, +,1849,13339,创金合信芯片,9月19日,0.8504,0.8504,-2.51%,-4.17%,-11.10%,2.99%,-7.08%,---,---,---,-22.38%,-14.96%,---,0.15%, +,1850,13126,华夏中证细分,9月20日,0.8776,0.8776,1.02%,-0.49%,-2.18%,-6.79%,1.77%,---,---,---,-15.70%,-12.24%,-12.24%,0.00%, +,1851,13639,光大保德信中,9月20日,0.8994,0.8994,0.97%,-3.60%,-3.96%,-1.69%,-1.14%,---,---,---,-10.02%,-10.06%,-10.06%,--, +,1852,12555,西部利得创业,9月19日,0.7043,0.7043,-0.79%,-7.48%,-12.82%,-11.79%,-11.46%,---,---,---,-28.66%,-29.57%,-29.07%,0.00%, +,1853,11590,九泰天利量化,9月20日,0.8282,0.8282,1.33%,-4.74%,-7.24%,-3.90%,-6.08%,---,---,---,-17.37%,-17.18%,-17.18%,0.00%, +,1854,14118,国泰中证沪港,9月19日,0.6556,0.6556,-1.56%,-8.98%,-12.26%,-12.93%,-17.24%,---,---,---,-29.87%,-34.44%,-34.23%,0.00%, +,1855,14185,招商专精特新,9月20日,1.0855,1.0855,0.14%,-1.07%,-2.83%,6.71%,15.74%,---,---,---,8.69%,8.55%,8.55%,0.15%, +,1856,12561,天弘中证新材,9月20日,0.7903,0.7903,1.13%,-5.53%,-11.12%,-13.36%,-6.86%,---,---,---,-18.48%,-20.97%,-20.97%,0.10%, +,1857,12695,汇添富国证生,9月20日,0.7635,0.7635,0.22%,-5.36%,-12.05%,-19.19%,-24.46%,---,---,---,---,-23.65%,-23.65%,0.00%, +,1858,14380,建信中国制造,9月19日,2.116,2.116,0.14%,-3.62%,-7.43%,-7.74%,-10.01%,---,---,---,-19.02%,-18.67%,---,0.00%, +,1859,12620,嘉实中证软件,9月20日,0.6973,0.6973,-0.75%,-3.87%,-3.79%,-6.94%,-12.16%,---,---,---,-29.82%,-30.27%,-30.27%,0.00%, +,1860,14133,工银中证50,9月20日,0.873,0.873,0.85%,-5.23%,-5.56%,-2.69%,1.43%,---,---,---,-13.33%,-12.70%,-12.70%,0.15%, +,1861,14028,招商中证银行,9月20日,1.0922,1.0922,-0.92%,-2.20%,0.56%,-4.26%,-5.27%,---,---,---,-6.20%,-9.62%,-9.62%,0.00%, +,1862,14173,富国中证移动,9月20日,0.697,0.697,-0.29%,-5.56%,-9.60%,-10.53%,-15.62%,---,---,---,-32.98%,-31.53%,-31.53%,0.00%, +,1863,14172,富国中证工业,9月20日,0.921,0.921,0.22%,-5.44%,-9.62%,-6.69%,-11.01%,---,---,---,-26.90%,-24.57%,-24.57%,0.00%, +,1864,13918,申万菱信量化,9月20日,2.0039,2.0039,0.99%,-5.64%,-8.73%,-7.85%,-6.63%,---,---,---,-17.91%,-17.87%,-17.87%,0.00%, +,1865,12759,工银沪港深互,9月20日,0.6987,0.6987,0.46%,-4.94%,-6.79%,-13.50%,-8.98%,---,---,---,-30.33%,-30.13%,-30.13%,0.10%, +,1866,13471,华宝中证全指,9月20日,0.9765,0.9765,-0.64%,-5.07%,-4.48%,-2.56%,-0.28%,---,---,---,-6.34%,-2.35%,-2.35%,0.10%, +,1867,13879,圆信永丰中证,9月20日,0.8551,0.8551,1.17%,-4.28%,-5.55%,-3.44%,-4.06%,---,---,---,-14.97%,-14.49%,-14.49%,0.00%, +,1868,14063,景顺长城专精,9月19日,0.7666,0.7666,-1.20%,-5.83%,-12.86%,-4.81%,-4.73%,---,---,---,-21.09%,-23.34%,---,0.00%, +,1869,14111,嘉实中证稀有,9月20日,0.8336,0.8336,3.50%,-3.20%,-4.83%,-10.25%,-0.02%,---,---,---,-9.57%,-16.64%,-16.64%,0.00%, +,1870,14126,华夏中证10,9月19日,0.8927,0.8927,-0.70%,-5.84%,-8.99%,-3.87%,-1.84%,---,---,---,-12.89%,-10.73%,---,0.00%, +,1871,14100,博时中证科创,9月20日,0.7016,0.7016,0.59%,-4.80%,-10.75%,-10.27%,-14.30%,---,---,---,-27.61%,-29.84%,-29.84%,0.00%, +,1872,12664,国寿安保沪港,9月19日,0.9603,0.9603,-0.25%,-3.58%,-4.75%,-7.37%,-3.98%,---,---,---,---,-3.97%,---,0.00%, +,1873,12718,易方达中证科,9月20日,0.8512,0.8512,0.34%,-5.32%,-9.95%,-11.25%,-15.06%,---,---,---,---,-14.88%,-14.88%,0.00%, +,1874,14193,汇添富中证芯,9月20日,0.7192,0.7192,-0.58%,-4.39%,-7.42%,-0.46%,-11.92%,---,---,---,-25.50%,-28.08%,-28.08%,0.15%, +,1875,13869,创金合信物联,9月19日,0.75,0.75,-0.19%,-5.52%,-10.64%,-4.14%,-2.10%,---,---,---,-25.54%,-25.00%,---,0.15%, +,1876,13447,华宝中证智能,9月20日,0.7965,0.7965,0.63%,-4.87%,-10.47%,-4.97%,-10.36%,---,---,---,-24.02%,-20.35%,-20.35%,0.10%, +,1877,12694,汇添富国证生,9月20日,0.764,0.764,0.21%,-5.36%,-12.03%,-19.14%,-24.36%,---,---,---,---,-23.60%,-23.60%,0.10%, +,1878,14304,华安中证内地,9月19日,0.8227,0.8227,0.32%,-7.85%,-14.25%,-13.45%,-10.64%,---,---,---,-18.33%,-17.73%,---,0.00%, +,1879,13878,圆信永丰中证,9月20日,0.8571,0.8571,1.17%,-4.28%,-5.53%,-3.37%,-3.92%,---,---,---,-14.79%,-14.29%,-14.29%,0.12%, +,1880,14331,华泰柏瑞中证,9月19日,0.7937,0.7937,-1.05%,-8.71%,-13.30%,-11.07%,-6.02%,---,---,---,-19.71%,-20.63%,-18.57%,0.12%, +,1881,14130,融通中证云计,9月20日,0.7824,0.7824,-0.43%,-4.27%,-7.12%,-7.23%,-15.17%,---,---,---,-29.91%,-29.19%,-29.19%,0.00%, +,1882,13899,上投摩根全景,9月19日,0.8858,0.8858,-0.54%,-4.92%,-11.89%,-10.56%,-7.77%,---,---,---,---,-11.42%,---,0.15%, +,1883,14431,华夏中证新材,9月20日,0.9083,0.9083,1.11%,-5.52%,-11.03%,-13.06%,-6.54%,---,---,---,---,-9.17%,-9.17%,0.12%, +,1884,14201,天弘中证10,9月20日,0.9519,0.9519,1.27%,-5.58%,-10.12%,-4.08%,3.38%,---,---,---,---,-4.81%,-4.81%,0.15%, +,1885,12644,招商中证红利,9月20日,0.9863,0.9863,0.10%,-3.63%,-0.64%,1.62%,2.06%,---,---,---,---,-1.37%,-1.37%,0.00%, +,1886,13926,易米国证消费,9月19日,0.8794,0.8794,-0.19%,-4.07%,-7.66%,-7.92%,-5.30%,---,---,---,-12.14%,-12.06%,---,0.12%, +,1887,14300,泰达宏利先进,9月20日,0.9664,0.9664,1.68%,-4.18%,-8.19%,-3.03%,3.21%,---,---,---,---,-3.36%,-3.36%,0.00%, +,1888,14052,银华港股通精,9月20日,0.9338,0.9338,0.80%,-1.55%,-5.68%,-9.44%,-2.47%,---,---,---,-18.91%,-24.89%,-24.89%,0.00%, +,1889,8924,建信医疗健康,9月19日,0.8954,0.8954,-2.01%,-6.48%,-7.99%,-9.56%,-5.54%,---,---,---,-10.50%,-10.46%,---,0.00%, +,1890,13622,华安智能装备,9月19日,1.826,1.826,-1.14%,-5.63%,-13.38%,-6.79%,-11.27%,---,---,---,-29.00%,-25.04%,---,0.00%, +,1891,14237,东财新能源A,9月19日,0.8622,0.8622,0.20%,-8.15%,-14.35%,-11.78%,-6.21%,---,---,---,-13.00%,-13.78%,---,0.15%, +,1892,13478,华宝中证金融,9月20日,0.7601,0.7601,-0.24%,-6.99%,-7.77%,-7.70%,-18.51%,---,---,---,-26.47%,-23.99%,-23.99%,0.00%, +,1893,14155,国泰君安中证,9月19日,0.8987,0.8987,-0.60%,-6.10%,-7.40%,-5.58%,-2.48%,---,---,---,-11.45%,-10.13%,---,--, +,1894,14156,国泰君安中证,9月19日,0.8959,0.8959,-0.61%,-6.12%,-7.44%,-5.68%,-2.68%,---,---,---,-11.71%,-10.41%,---,0.00%, +,1895,14099,博时中证科创,9月20日,0.7022,0.7022,0.59%,-4.80%,-10.73%,-10.24%,-14.25%,---,---,---,-27.56%,-29.78%,-29.78%,0.12%, +,1896,12717,易方达中证科,9月20日,0.8526,0.8526,0.34%,-5.31%,-9.93%,-11.19%,-14.93%,---,---,---,---,-14.74%,-14.74%,0.06%, +,1897,14046,交银医药创新,9月20日,2.5367,2.5367,0.56%,-3.88%,-7.12%,-13.59%,-9.11%,---,---,---,-25.33%,-24.80%,-24.80%,0.00%, +,1898,14188,华夏量化优选,9月19日,0.8969,0.8969,-0.21%,-5.64%,-7.05%,-5.57%,-5.02%,---,---,---,-11.11%,-10.31%,-9.52%,0.00%, +,1899,12663,国寿安保沪港,9月19日,0.9579,0.9579,-0.24%,-3.57%,-4.72%,-7.29%,-4.24%,---,---,---,---,-4.21%,---,0.12%, +,1900,14419,西部利得CE,9月19日,0.6999,0.6999,-1.86%,-3.67%,-9.85%,-4.53%,-17.82%,---,---,---,-30.02%,-30.01%,-30.46%,0.00%, +,1901,13919,建信中小盘先,9月19日,4.318,4.318,0.00%,-7.28%,-6.50%,4.40%,15.98%,---,---,---,2.27%,9.43%,---,0.00%, +,1902,14299,泰达宏利先进,9月20日,0.9684,0.9684,1.69%,-4.18%,-8.17%,-2.96%,3.37%,---,---,---,---,-3.16%,-3.16%,0.15%, +,1903,13477,华宝中证金融,9月20日,0.7619,0.7619,-0.24%,-6.98%,-7.75%,-7.64%,-18.38%,---,---,---,-26.32%,-23.81%,-23.81%,0.10%, +,1904,14117,国泰中证沪港,9月19日,0.6572,0.6572,-1.57%,-8.98%,-12.24%,-12.86%,-17.12%,---,---,---,-29.73%,-34.28%,-34.06%,0.10%, +,1905,13881,长信中证50,9月19日,1.4779,1.4779,-0.11%,-4.89%,-5.87%,-4.19%,-4.85%,---,---,---,-13.57%,-9.21%,-8.57%,0.00%, +,1906,14125,华夏中证10,9月19日,0.8955,0.8955,-0.70%,-5.84%,-8.96%,-3.76%,-1.64%,---,---,---,-12.63%,-10.45%,---,0.12%, +,1907,13941,东吴医疗服务,9月19日,0.7184,0.7184,-1.54%,-8.51%,-10.66%,-15.06%,-17.06%,---,---,---,-26.62%,-28.16%,---,0.00%, +,1908,13802,财通资管中证,9月20日,0.8174,0.8174,0.75%,-5.14%,-5.73%,-8.96%,-7.16%,---,---,---,-15.05%,-18.26%,-18.26%,0.10%, +,1909,13817,汇添富中证光,9月20日,0.8833,0.8833,3.27%,-6.19%,-12.79%,4.47%,11.65%,---,---,---,-1.75%,-11.67%,-11.67%,0.00%, +,1910,12562,天弘中证新材,9月20日,0.7889,0.7889,1.12%,-5.54%,-11.14%,-13.41%,-6.96%,---,---,---,-18.61%,-21.11%,-21.11%,0.00%, +,1911,13032,民生加银中证,9月20日,0.8158,0.8158,0.39%,-4.22%,-5.56%,-8.46%,-7.74%,---,---,---,-18.11%,-18.42%,-18.42%,0.00%, +,1912,12762,华泰柏瑞上证,9月19日,1.0561,1.0561,0.26%,-3.81%,1.13%,2.60%,4.54%,---,---,---,4.87%,5.61%,5.39%,0.00%, +,1913,12801,富国中证医药,9月20日,0.8354,0.8354,-0.06%,-5.41%,-7.59%,-13.95%,-15.62%,---,---,---,-16.46%,-16.46%,-16.46%,0.12%, +,1914,13870,创金合信物联,9月19日,0.747,0.747,-0.19%,-5.53%,-10.67%,-4.27%,-2.35%,---,---,---,-25.81%,-25.30%,---,0.00%, +,1915,14187,华夏量化优选,9月19日,0.9018,0.9018,-0.20%,-5.62%,-6.98%,-5.39%,-4.67%,---,---,---,-10.65%,-9.82%,-9.03%,0.15%, +,1916,13811,广发科创板5,9月19日,0.7167,0.7167,-2.17%,-5.35%,-11.38%,-10.15%,-14.21%,---,---,---,-28.92%,-28.33%,---,0.00%, +,1917,14198,华夏智胜先锋,9月20日,1.0099,1.0099,1.37%,-4.76%,-5.38%,2.48%,9.38%,---,---,---,-0.42%,0.99%,0.99%,0.00%, +,1918,13942,华宝中证稀有,9月20日,0.9275,0.9275,4.00%,-3.17%,-3.88%,-9.44%,1.69%,---,---,---,-7.69%,-7.25%,-7.25%,0.10%, +,1919,12757,易方达中证龙,9月20日,0.954,0.954,0.18%,-3.27%,-3.27%,-8.25%,-3.62%,---,---,---,---,-4.60%,-4.60%,0.00%, +,1920,12643,招商中证红利,9月20日,0.9867,0.9867,0.09%,-3.64%,-0.63%,1.64%,2.11%,---,---,---,---,-1.33%,-1.33%,0.06%, +,1921,14418,西部利得CE,9月19日,0.702,0.702,-1.86%,-3.65%,-9.82%,-4.42%,-17.65%,---,---,---,-29.82%,-29.80%,-30.25%,0.12%, +,1922,13908,永赢深创10,9月20日,0.7277,0.7277,0.33%,-4.91%,-9.37%,-11.00%,-9.73%,---,---,---,-25.61%,-27.23%,-27.23%,0.00%, +,1923,14382,博时国企改革,9月20日,0.788,0.788,1.29%,-4.72%,-7.62%,-9.22%,-3.43%,---,---,---,-22.67%,-23.64%,-23.64%,0.00%, +,1924,13900,上投摩根全景,9月19日,0.8827,0.8827,-0.55%,-4.93%,-11.92%,-10.68%,-8.00%,---,---,---,---,-11.73%,---,0.00%, +,1925,14416,泰康研究精选,9月19日,0.7911,0.7911,-0.86%,-5.83%,-10.42%,-10.45%,-10.70%,---,---,---,-21.97%,-20.89%,---,0.15%, +,1926,13640,光大保德信中,9月20日,0.8954,0.8954,0.97%,-3.61%,-4.00%,-1.80%,-1.34%,---,---,---,-10.30%,-10.46%,-10.46%,0.00%, +,1927,14345,鹏华中证50,9月19日,0.9425,0.9425,-0.42%,-5.41%,-6.30%,-4.06%,-0.12%,---,---,---,---,-5.75%,---,0.00%, +,1928,14238,东财新能源C,9月19日,0.8602,0.8602,0.20%,-8.15%,-14.37%,-11.85%,-6.36%,---,---,---,-13.18%,-13.98%,---,0.00%, +,1929,14064,银华农业产业,9月20日,2.034,---,-0.49%,-5.47%,-5.77%,-3.85%,3.00%,---,---,---,-10.16%,-8.57%,-8.57%,0.00%, +,1930,14163,富国港股通量,9月20日,0.8087,0.8087,0.96%,-2.05%,-3.35%,-6.83%,-2.87%,---,---,---,-14.06%,-15.13%,-15.13%,0.00%, +,1931,14186,招商专精特新,9月20日,1.0787,1.0787,0.13%,-1.08%,-2.89%,6.50%,15.27%,---,---,---,8.08%,7.87%,7.87%,0.00%, +,1932,14377,东吴新能源汽,9月19日,0.9936,0.9936,0.71%,-7.23%,-11.81%,-11.44%,-0.70%,---,---,---,---,-0.64%,---,0.00%, +,1933,13415,永赢中证全指,9月20日,0.7664,0.7664,-0.31%,-5.69%,-6.04%,-11.33%,-12.96%,---,---,---,-24.50%,-23.35%,-23.35%,0.10%, +,1934,14154,天弘华证沪深,9月20日,0.8549,0.8549,0.64%,-3.69%,-5.90%,-9.60%,-7.03%,---,---,---,---,-14.51%,-14.51%,0.00%, +,1935,14303,华安中证内地,9月19日,0.8238,0.8238,0.30%,-7.85%,-14.24%,-13.40%,-10.55%,---,---,---,-18.23%,-17.62%,---,0.05%, +,1936,501219,华夏智胜先锋,9月20日,1.013,1.013,1.37%,-4.76%,-5.34%,2.58%,9.61%,---,---,---,-0.14%,1.30%,1.30%,0.15%, +,1937,13907,永赢深创10,9月20日,0.7289,0.7289,0.33%,-4.89%,-9.34%,-10.95%,-9.63%,---,---,---,-25.49%,-27.11%,-27.11%,0.10%, +,1938,14432,华夏中证新材,9月20日,0.9064,0.9064,1.12%,-5.53%,-11.05%,-13.13%,-6.68%,---,---,---,---,-9.36%,-9.36%,0.00%, +,1939,14533,易方达MSC,9月20日,0.8451,0.8451,0.24%,-4.05%,-5.29%,-8.69%,-6.18%,---,---,---,-15.51%,-15.49%,-15.49%,0.00%, +,1940,14332,华泰柏瑞中证,9月19日,0.7922,0.7922,-1.05%,-8.71%,-13.32%,-11.13%,-6.14%,---,---,---,-19.86%,-20.78%,-18.73%,0.00%, +,1941,14531,华夏MSCI,9月19日,0.8262,0.8262,0.22%,-3.94%,-5.53%,-8.36%,-6.17%,---,---,---,-17.50%,-17.38%,---,0.00%, +,1942,14364,银华沪港深增,9月20日,2.04,2.04,1.54%,-1.78%,-3.36%,-4.67%,0.49%,---,---,---,-23.02%,-23.34%,-23.34%,0.00%, +,1943,13834,国泰中证50,9月20日,0.873,0.873,0.72%,-5.06%,-6.28%,-4.54%,-3.88%,---,---,---,-15.79%,-12.70%,-12.70%,0.00%, +,1944,13174,银华华证ES,9月20日,0.8294,0.8294,0.11%,-3.54%,-3.51%,-5.59%,-3.77%,---,---,---,-17.15%,-17.06%,-17.06%,0.12%, +,1945,13472,华宝中证全指,9月20日,0.9743,0.9743,-0.63%,-5.07%,-4.50%,-2.63%,-0.42%,---,---,---,-6.53%,-2.57%,-2.57%,0.00%, +,1946,13601,国泰中证光伏,9月19日,0.8804,0.8804,-0.93%,-9.47%,-16.05%,-3.18%,2.09%,---,---,---,-7.60%,-11.96%,-9.52%,0.10%, +,1947,13602,国泰中证光伏,9月19日,0.8781,0.8781,-0.94%,-9.47%,-16.07%,-3.26%,1.93%,---,---,---,-7.79%,-12.19%,-9.76%,0.00%, +,1948,14256,富国中证娱乐,9月20日,0.5914,0.5914,0.56%,-3.33%,-4.66%,-6.75%,-6.59%,---,---,---,-23.77%,-18.61%,-18.61%,0.00%, +,1949,14530,华夏MSCI,9月19日,0.828,0.828,0.22%,-3.93%,-5.51%,-8.30%,-6.03%,---,---,---,-17.32%,-17.20%,---,0.12%, +,1950,160646,鹏华中证沪港,9月19日,0.7008,0.7008,-1.25%,-6.17%,-8.95%,-12.03%,-13.51%,---,---,---,-30.10%,-29.92%,---,0.12%, +,1951,14134,工银中证50,9月20日,0.8704,0.8704,0.86%,-5.23%,-5.59%,-2.78%,1.23%,---,---,---,-13.57%,-12.96%,-12.96%,0.00%, +,1952,14376,东吴新能源汽,9月19日,0.9962,0.9962,0.71%,-7.22%,-11.79%,-11.35%,-0.50%,---,---,---,---,-0.38%,---,0.15%, +,1953,13416,永赢中证全指,9月20日,0.7651,0.7651,-0.33%,-5.69%,-6.08%,-11.39%,-13.06%,---,---,---,-24.61%,-23.48%,-23.48%,0.00%, +,1954,14344,鹏华中证50,9月19日,0.9451,0.9451,-0.42%,-5.40%,-6.28%,-3.96%,0.08%,---,---,---,---,-5.49%,---,0.12%, +,1955,12989,天弘国证港股,9月20日,1.0033,1.0033,1.07%,-2.25%,-4.35%,-8.77%,---,---,---,---,---,0.33%,0.33%,0.10%, +,1956,12340,东财食品饮料,9月19日,0.8283,0.8283,0.83%,-0.24%,-3.11%,-7.50%,-0.46%,---,---,---,-17.51%,-17.17%,---,0.15%, +,1957,12341,东财食品饮料,9月19日,0.8261,0.8261,0.82%,-0.25%,-3.14%,-7.57%,-0.61%,---,---,---,-17.69%,-17.39%,---,0.00%, +,1958,14110,嘉实中证稀有,9月20日,0.8353,0.8353,3.51%,-3.20%,-4.80%,-10.19%,0.11%,---,---,---,-9.40%,-16.47%,-16.47%,0.10%, +,1959,14194,汇添富中证芯,9月20日,0.7175,0.7175,-0.58%,-4.40%,-7.43%,-0.54%,-12.05%,---,---,---,-25.66%,-28.25%,-28.25%,0.00%, +,1960,12990,天弘国证港股,9月20日,1.0095,1.0095,1.07%,-2.26%,-4.36%,-8.82%,---,---,---,---,---,0.95%,0.95%,0.00%, +,1961,13833,国泰中证50,9月20日,0.8753,0.8753,0.71%,-5.04%,-6.25%,-4.46%,-3.73%,---,---,---,-15.60%,-12.47%,-12.47%,0.10%, +,1962,14153,天弘华证沪深,9月20日,0.8563,0.8563,0.63%,-3.69%,-5.88%,-9.55%,-6.90%,---,---,---,---,-14.37%,-14.37%,0.10%, +,1963,14528,汇添富MSC,9月20日,0.861,0.861,0.23%,-3.94%,-5.54%,-8.37%,-5.98%,---,---,---,---,-14.10%,-13.90%,0.10%, +,1964,12855,英大中证ES,9月19日,0.9953,0.9953,-0.11%,-2.68%,-4.28%,-4.50%,-0.48%,---,---,---,---,-0.47%,-0.45%,0.00%, +,1965,13927,易米国证消费,9月19日,0.8764,0.8764,-0.19%,-4.08%,-7.69%,-8.01%,-5.49%,---,---,---,-12.39%,-12.36%,---,0.00%, +,1966,14532,易方达MSC,9月20日,0.847,0.847,0.25%,-4.04%,-5.27%,-8.61%,-6.04%,---,---,---,-15.32%,-15.30%,-15.30%,0.12%, +,1967,13053,天弘国证龙头,9月19日,1.0223,1.0223,-0.13%,-3.36%,-2.29%,-0.98%,2.22%,---,---,---,---,2.23%,2.35%,0.10%, +,1968,13054,天弘国证龙头,9月19日,1.0168,1.0168,-0.13%,-3.37%,-2.32%,-1.04%,1.67%,---,---,---,---,1.68%,1.80%,0.00%, +,1969,14529,汇添富MSC,9月20日,0.8596,0.8596,0.23%,-3.93%,-5.55%,-8.42%,-6.09%,---,---,---,---,-14.24%,-14.04%,0.00%, +,1970,13011,工银中证创新,9月20日,0.7809,0.7809,0.17%,-4.38%,-10.51%,-15.40%,-19.92%,---,---,---,-21.91%,-21.91%,-21.91%,0.10%, +,1971,13940,东吴医疗服务,9月19日,0.7195,0.7195,-1.53%,-8.51%,-10.65%,-15.02%,-16.98%,---,---,---,-26.51%,-28.05%,---,0.15%, +,1972,14129,东财沪港深创,9月19日,0.6808,0.6808,-1.66%,-9.65%,-13.02%,-13.38%,-17.79%,---,---,---,-30.90%,-31.92%,---,0.00%, +,1973,12708,东方红中证红,9月20日,0.9801,0.9801,-0.02%,-3.37%,0.14%,-1.57%,0.96%,---,---,---,-1.99%,-1.99%,-1.99%,0.12%, +,1974,14544,汇添富中证沪,9月20日,0.6801,0.6801,-0.25%,-3.95%,-7.32%,-12.46%,-18.02%,---,---,---,-31.99%,-31.99%,-31.99%,0.00%, +,1975,12760,工银沪港深互,9月20日,0.6981,0.6981,0.46%,-4.94%,-6.80%,-13.52%,-9.03%,---,---,---,-30.38%,-30.19%,-30.19%,0.00%, +,1976,14128,东财沪港深创,9月19日,0.6831,0.6831,-1.66%,-9.64%,-12.98%,-13.29%,-17.62%,---,---,---,-30.70%,-31.69%,---,0.12%, +,1977,14062,景顺长城专精,9月19日,0.7692,0.7692,-1.18%,-5.80%,-12.82%,-4.70%,-4.53%,---,---,---,-20.86%,-23.08%,---,0.15%, +,1978,13012,工银中证创新,9月20日,0.7803,0.7803,0.15%,-4.39%,-10.52%,-15.43%,-19.97%,---,---,---,-21.97%,-21.97%,-21.97%,0.00%, +,1979,14407,富国中证新华,9月20日,0.9129,0.9129,0.30%,-3.69%,-6.16%,-8.45%,-7.35%,---,---,---,---,-8.71%,-8.71%,0.00%, +,1980,12415,德邦上证G6,9月20日,0.7256,0.7256,2.27%,-7.87%,-16.25%,-14.12%,-19.25%,---,---,---,-27.82%,-29.05%,-27.44%,0.12%, +,1981,14602,嘉实中证医疗,9月20日,0.7786,0.7786,-0.01%,-5.45%,-9.03%,-16.53%,-14.61%,---,---,---,---,-22.14%,-22.14%,0.10%, +,1982,14737,创金合信专精,9月19日,1.0701,1.0701,-0.85%,-2.42%,-1.93%,16.38%,11.27%,---,---,---,---,7.01%,---,0.00%, +,1983,13466,博时智选量化,9月20日,0.9437,0.9437,1.47%,-5.54%,-8.94%,-1.25%,2.81%,---,---,---,-6.16%,-5.63%,-5.63%,0.00%, +,1984,14932,上投摩根医疗,9月19日,1.6897,1.6897,-2.05%,-7.24%,-8.96%,-14.22%,-14.92%,---,---,---,---,-22.82%,---,0.00%, +,1985,14967,建信潜力新蓝,9月19日,3.96,3.96,0.03%,-7.11%,-6.25%,4.40%,18.95%,---,---,---,---,22.15%,---,0.00%, +,1986,14536,诺安高端制造,9月20日,1.534,1.534,0.07%,-5.25%,-10.61%,-6.35%,-3.40%,---,---,---,-17.30%,-15.99%,-15.99%,0.00%, +,1987,12782,银华中证创新,9月20日,0.7,0.7,0.14%,-4.40%,-10.68%,-15.62%,-20.43%,---,---,---,-29.84%,-30.00%,-30.00%,0.00%, +,1988,12158,汇添富上证5,9月20日,0.8917,0.8917,-0.29%,-3.00%,-2.64%,-7.05%,-6.07%,---,---,---,---,-10.83%,-10.83%,0.00%, +,1989,14690,国泰中证港股,9月19日,0.879,0.879,-0.73%,-3.46%,-4.37%,-6.78%,-4.32%,---,---,---,-12.10%,-12.10%,-11.20%,0.00%, +,1990,14902,长城新能源股,9月20日,1.1788,1.1788,2.77%,-4.64%,-9.37%,6.10%,18.71%,---,---,---,---,17.88%,17.88%,0.15%, +,1991,13605,华夏中证内地,9月20日,0.8171,0.8171,1.95%,-7.99%,-14.33%,-10.30%,-7.44%,---,---,---,-17.19%,-19.85%,-18.29%,0.12%, +,1992,14942,鹏华中证细分,9月19日,0.9231,0.9231,0.16%,-5.86%,-6.66%,-11.87%,-8.00%,---,---,---,---,-7.69%,---,0.10%, +,1993,14534,南方MSCI,9月20日,0.9071,0.9071,0.23%,-4.10%,-5.28%,-8.71%,-5.61%,---,---,---,---,-9.29%,-9.29%,0.12%, +,1994,13503,易方达中证内,9月20日,1.11,1.11,1.89%,-5.80%,-11.74%,-8.89%,---,---,---,---,---,11.00%,11.00%,0.00%, +,1995,14831,兴银中证10,9月19日,0.8853,0.8853,-1.07%,-6.98%,-13.05%,-11.10%,-7.69%,---,---,---,---,-11.47%,---,0.15%, +,1996,12554,西部利得创业,9月19日,0.706,0.706,-0.79%,-7.47%,-12.80%,-11.72%,-11.33%,---,---,---,-28.50%,-29.40%,-28.90%,0.10%, +,1997,14689,国泰中证港股,9月19日,0.8806,0.8806,-0.73%,-3.44%,-4.34%,-6.70%,-4.17%,---,---,---,-11.94%,-11.94%,-11.04%,0.10%, +,1998,15075,上投摩根卓越,9月19日,1.2983,1.2983,-0.11%,-4.73%,-10.89%,-13.97%,-15.44%,---,---,---,---,-19.13%,---,0.00%, +,1999,14632,华安中证电子,9月19日,0.9367,0.9367,-1.46%,-4.49%,-10.31%,-9.64%,---,---,---,---,---,-6.33%,---,0.05%, +,2000,14535,南方MSCI,9月20日,0.9052,0.9052,0.22%,-4.11%,-5.30%,-8.78%,-5.76%,---,---,---,---,-9.48%,-9.48%,0.00%, +,2001,15002,工银生态环境,9月20日,2.283,2.283,2.51%,-5.66%,-11.13%,-10.33%,-5.74%,---,---,---,---,0.44%,0.44%,0.00%, +,2002,5627,富国中证高端,9月20日,1.8007,1.8007,0.94%,-5.86%,-11.52%,-11.38%,-10.21%,---,---,---,---,-13.77%,-13.77%,0.00%, +,2003,13641,博道成长智航,9月19日,0.8305,0.8305,-0.94%,-6.50%,-11.18%,-2.77%,-3.68%,---,---,---,-17.54%,-16.95%,---,--, +,2004,15038,天弘MSCI,9月19日,0.9365,0.9365,0.22%,-3.96%,-5.40%,-7.62%,-4.63%,---,---,---,---,-6.35%,-6.12%,0.00%, +,2005,14191,广发先进制造,9月19日,1.1647,1.1647,-0.67%,-9.95%,-14.48%,-0.61%,18.40%,---,---,---,---,16.47%,---,0.15%, +,2006,10144,国泰国证医药,9月19日,0.6278,0.6278,-1.15%,-6.12%,-7.88%,-12.07%,-15.23%,---,---,---,---,-16.25%,-16.24%,0.00%, +,2007,14979,华安上证18,9月19日,1.4999,1.4999,-0.07%,-3.57%,-3.20%,-6.55%,-5.43%,---,---,---,---,-11.02%,---,0.00%, +,2008,14202,天弘中证10,9月20日,0.9499,0.9499,1.27%,-5.60%,-10.15%,-4.15%,3.22%,---,---,---,---,-5.01%,-5.01%,0.00%, +,2009,15123,汇添富国企创,9月20日,1.972,1.972,0.51%,-3.29%,-6.18%,-2.67%,-1.20%,---,---,---,---,-3.66%,-3.66%,0.00%, +,2010,12781,银华中证创新,9月20日,0.7005,0.7005,0.16%,-4.39%,-10.67%,-15.60%,-20.39%,---,---,---,-29.79%,-29.95%,-29.95%,0.12%, +,2011,12962,招商创业板大,9月20日,0.9218,0.9218,0.66%,-6.68%,-11.83%,-12.78%,---,---,---,---,---,-7.82%,-7.82%,0.00%, +,2012,14736,创金合信专精,9月19日,1.0736,1.0736,-0.85%,-2.40%,-1.89%,16.53%,11.57%,---,---,---,---,7.36%,---,0.15%, +,2013,14192,广发先进制造,9月19日,1.1621,1.1621,-0.68%,-9.97%,-14.51%,-0.71%,18.16%,---,---,---,---,16.21%,---,0.00%, +,2014,13481,华宝国证治理,9月20日,0.8343,0.8343,-0.60%,-2.93%,-0.69%,-5.59%,-4.54%,---,---,---,-16.34%,-16.57%,-16.57%,0.00%, +,2015,15060,华夏节能环保,9月20日,2.2412,2.2412,3.51%,-7.14%,-14.59%,1.51%,2.14%,---,---,---,---,-0.74%,-0.74%,0.00%, +,2016,8923,建信医疗健康,9月19日,0.8981,0.8981,-2.02%,-6.47%,-7.96%,-9.46%,-5.33%,---,---,---,-10.23%,-10.19%,---,0.15%, +,2017,15225,汇添富中证细,9月20日,0.9556,0.9556,1.33%,-4.44%,-5.92%,-8.45%,-4.53%,---,---,---,---,-4.44%,-4.44%,0.12%, +,2018,13479,金鹰先进制造,9月19日,0.9297,0.9297,-1.69%,-0.47%,-3.51%,2.67%,0.19%,---,---,---,---,-11.98%,---,0.00%, +,2019,14605,嘉实中证光伏,9月20日,1.0749,1.0749,2.86%,-6.97%,-14.23%,-2.19%,2.96%,---,---,---,---,7.49%,7.49%,0.00%, +,2020,14930,富国高端制造,9月20日,3.059,3.059,1.02%,-5.93%,-10.79%,-13.15%,-6.25%,---,---,---,---,-17.81%,-17.81%,0.00%, +,2021,14630,汇添富中证人,9月20日,0.9763,0.9763,0.35%,-4.06%,-9.88%,-12.95%,---,---,---,---,---,-2.37%,-2.37%,0.10%, +,2022,12709,东方红中证红,9月20日,0.9773,0.9773,-0.02%,-3.38%,0.10%,-1.67%,0.76%,---,---,---,-2.27%,-2.27%,-2.27%,0.00%, +,2023,14543,汇添富中证沪,9月20日,0.6813,0.6813,-0.25%,-3.95%,-7.29%,-12.41%,-17.92%,---,---,---,-31.87%,-31.87%,-31.87%,0.10%, +,2024,14455,中银成长优选,9月20日,1.0535,1.0535,2.57%,-5.61%,-12.10%,-0.89%,3.86%,---,---,---,-12.57%,-11.50%,-11.50%,0.00%, +,2025,14863,建信信息产业,9月19日,2.832,2.832,-0.32%,-6.23%,-6.29%,-1.43%,-4.19%,---,---,---,---,-3.80%,---,0.00%, +,2026,14661,天弘上海金E,9月20日,0.9367,0.9367,0.24%,-0.94%,-0.37%,-2.99%,-3.34%,---,---,---,---,-6.33%,-6.33%,0.10%, +,2027,14453,中银新动力股,9月20日,0.993,0.993,0.40%,-3.22%,-4.52%,-10.86%,-7.54%,---,---,---,-27.78%,-29.22%,-29.22%,0.00%, +,2028,14417,泰康研究精选,9月19日,0.7882,0.7882,-0.88%,-5.84%,-10.46%,-10.56%,-10.93%,---,---,---,-22.25%,-21.18%,---,0.00%, +,2029,14937,上投摩根核心,9月19日,2.061,2.061,-0.81%,-9.27%,-15.81%,-14.59%,-10.31%,---,---,---,---,-18.60%,---,0.00%, +,2030,14984,华安中证全指,9月19日,0.8929,0.8929,-0.99%,-7.93%,-7.39%,-10.50%,-11.08%,---,---,---,---,-16.36%,---,0.00%, +,2031,14985,华安创业板5,9月19日,1.2269,1.2269,-0.66%,-8.13%,-15.00%,-12.22%,-13.28%,---,---,---,---,-14.05%,---,0.00%, +,2032,15278,东财沪深30,9月19日,0.9959,0.9959,-0.11%,-3.83%,-5.03%,-7.19%,-0.67%,---,---,---,---,-0.41%,---,0.12%, +,2033,15157,申万菱信行业,9月20日,1.9515,1.9515,3.04%,-6.03%,-10.49%,-4.48%,6.08%,---,---,---,---,-2.92%,-2.92%,0.00%, +,2034,15337,嘉实中证芯片,9月20日,0.8305,0.8305,-0.50%,-6.06%,-11.88%,-9.31%,---,---,---,---,---,-16.95%,-16.95%,0.00%, +,2035,15046,前海开源中航,9月19日,1.1423,1.1423,-1.41%,-0.58%,-0.18%,8.99%,10.47%,---,---,---,---,5.19%,---,0.00%, +,2036,15177,申万菱信深证,9月20日,0.6081,0.6081,0.65%,-5.04%,-8.16%,-8.65%,-7.23%,---,---,---,---,-14.60%,-14.60%,0.00%, +,2037,13922,华夏中证10,9月19日,0.9049,0.9049,-1.21%,-7.08%,-10.81%,-5.31%,-6.02%,---,---,---,---,-9.51%,---,0.12%, +,2038,14406,富国中证新华,9月20日,0.9134,0.9134,0.30%,-3.69%,-6.14%,-8.43%,-7.31%,---,---,---,---,-8.66%,-8.66%,0.12%, +,2039,15178,申万菱信中证,9月20日,0.7456,0.7456,0.09%,-7.75%,-7.29%,-9.82%,-10.97%,---,---,---,---,-15.05%,-15.05%,0.00%, +,2040,13923,华夏中证10,9月19日,0.9032,0.9032,-1.21%,-7.10%,-10.83%,-5.37%,-6.16%,---,---,---,---,-9.68%,---,0.00%, +,2041,15170,上投摩根核心,9月19日,2.3104,2.3104,-0.65%,-4.00%,-6.52%,-10.15%,-7.45%,---,---,---,---,-17.11%,---,0.00%, +,2042,14907,国泰中证消费,9月19日,0.7619,0.7619,-0.95%,-6.02%,-12.72%,-10.48%,-17.52%,---,---,---,---,-23.81%,-23.77%,0.00%, +,2043,14994,国泰上证18,9月19日,0.9976,0.9976,0.06%,-2.35%,0.25%,-5.10%,-7.27%,---,---,---,---,-13.99%,-14.68%,0.00%, +,2044,14997,国泰国证新能,9月19日,1.8347,1.8347,1.16%,-6.42%,-14.46%,-15.39%,-6.43%,---,---,---,---,-9.46%,---,0.00%, +,2045,14864,建信食品饮料,9月19日,1.1292,1.1292,0.90%,1.01%,-0.77%,-1.41%,5.57%,---,---,---,---,-0.49%,---,0.00%, +,2046,14820,华安创新医药,9月19日,0.8514,0.8514,-1.57%,-7.69%,-11.05%,-16.51%,---,---,---,---,---,-14.86%,---,--, +,2047,14776,富国中证芯片,9月20日,0.7856,0.7856,-0.46%,-5.97%,-11.29%,-7.42%,-17.78%,---,---,---,---,-21.44%,-21.44%,0.12%, +,2048,14908,国泰中证新材,9月19日,0.8893,0.8893,-0.01%,-6.70%,-11.64%,-13.34%,-8.60%,---,---,---,---,-11.07%,-10.05%,0.10%, +,2049,15052,东方红医疗升,9月20日,0.9219,0.9219,0.51%,-4.26%,-6.57%,-9.35%,---,---,---,---,---,-7.81%,-7.81%,--, +,2050,15053,东方红医疗升,9月20日,0.9197,0.9197,0.51%,-4.27%,-6.61%,-9.47%,---,---,---,---,---,-8.03%,-8.03%,0.00%, +,2051,15176,申万菱信中证,9月20日,0.7218,0.7218,-0.04%,-4.95%,-8.13%,-13.69%,-16.05%,---,---,---,---,-17.47%,-17.47%,0.00%, +,2052,15140,泰康医疗健康,9月19日,0.9302,0.9302,-1.81%,-6.63%,-8.70%,-9.63%,-9.00%,---,---,---,---,-6.98%,---,0.00%, +,2053,15114,汇添富高端制,9月20日,2.507,2.507,0.36%,-2.72%,-3.80%,-1.65%,5.38%,---,---,---,---,-0.40%,-0.40%,0.00%, +,2054,15226,汇添富中证细,9月20日,0.9543,0.9543,1.33%,-4.45%,-5.93%,-8.50%,-4.65%,---,---,---,---,-4.57%,-4.57%,0.00%, +,2055,15159,申万菱信智能,9月20日,3.408,3.408,0.90%,-5.60%,-10.52%,-4.34%,-6.66%,---,---,---,---,-10.94%,-10.94%,0.00%, +,2056,15139,泰康医疗健康,9月19日,0.9327,0.9327,-1.80%,-6.61%,-8.66%,-9.51%,-8.76%,---,---,---,---,-6.73%,---,0.15%, +,2057,15279,东财沪深30,9月19日,0.9938,0.9938,-0.12%,-3.85%,-5.07%,-7.29%,-0.88%,---,---,---,---,-0.62%,---,0.00%, +,2058,15388,中欧沪深30,9月20日,0.9639,0.9639,0.14%,-4.11%,-5.04%,-5.78%,---,---,---,---,---,-3.74%,-3.61%,0.00%, +,2059,14674,富国中证港股,9月20日,0.7376,0.7376,1.25%,-4.81%,-8.11%,-17.17%,-7.31%,---,---,---,---,-26.24%,-26.24%,0.00%, +,2060,12802,富国中证医药,9月20日,0.8347,0.8347,-0.07%,-5.42%,-7.60%,-13.97%,-15.67%,---,---,---,-16.53%,-16.53%,-16.53%,0.00%, +,2061,14519,博时恒生高股,9月20日,0.8224,0.8224,0.35%,-1.71%,2.14%,-16.65%,-17.64%,---,---,---,---,-17.76%,-17.76%,0.12%, +,2062,15040,国泰国证食品,9月19日,1.0372,1.0372,0.77%,-0.67%,-3.58%,-7.66%,-0.38%,---,---,---,---,-11.55%,-10.70%,0.00%, +,2063,13873,平安中证医药,9月19日,0.813,0.813,-1.37%,-7.58%,-9.64%,-16.31%,-20.32%,---,---,---,---,-18.70%,-18.73%,0.12%, +,2064,14629,华泰紫金智能,9月19日,1.2349,1.2349,-0.13%,-4.63%,-5.37%,-6.86%,-5.63%,---,---,---,---,-13.58%,---,0.00%, +,2065,15048,建信新能源行,9月19日,2.2769,2.2769,-0.18%,-8.40%,-11.87%,-8.59%,-3.20%,---,---,---,---,-7.48%,---,0.00%, +,2066,15172,上投摩根安全,9月19日,1.5694,1.5694,-0.41%,-7.32%,-12.44%,-4.16%,-3.84%,---,---,---,---,-7.46%,---,0.00%, +,2067,15171,申万菱信医药,9月20日,0.6642,0.6642,1.39%,-3.74%,-4.20%,-12.69%,-15.57%,---,---,---,---,-18.01%,-18.01%,0.00%, +,2068,14866,大摩MSCI,9月20日,0.8979,0.8979,0.36%,-3.95%,-6.28%,-5.97%,-5.04%,---,---,---,---,-13.09%,-13.09%,0.00%, +,2069,14906,国泰中证消费,9月19日,0.7631,0.7631,-0.95%,-6.01%,-12.69%,-10.40%,-17.40%,---,---,---,---,-23.69%,-23.65%,0.10%, +,2070,14415,招商中证畜牧,9月20日,1.0995,1.0995,-0.61%,-4.99%,-3.37%,-1.74%,---,---,---,---,---,9.95%,9.95%,0.00%, +,2071,15336,嘉实中证芯片,9月20日,0.8316,0.8316,-0.49%,-6.06%,-11.86%,-9.25%,---,---,---,---,---,-16.84%,-16.84%,0.10%, +,2072,14604,嘉实中证光伏,9月20日,1.0766,1.0766,2.87%,-6.96%,-14.21%,-2.14%,3.09%,---,---,---,---,7.66%,7.66%,0.10%, +,2073,14821,华安创新医药,9月19日,0.8498,0.8498,-1.58%,-7.70%,-11.09%,-16.60%,---,---,---,---,---,-15.02%,---,0.00%, +,2074,12734,易方达中证人,9月20日,0.789,0.789,0.34%,-4.17%,-10.01%,-12.41%,-21.14%,---,---,---,---,-21.10%,-21.10%,0.00%, +,2075,12700,易方达中证全,9月20日,0.9112,0.9112,0.08%,-7.75%,-7.25%,-9.92%,-8.87%,---,---,---,---,-8.88%,-8.88%,0.00%, +,2076,15196,汇添富智能制,9月20日,1.7274,1.7274,1.84%,-4.15%,-11.29%,-0.21%,2.67%,---,---,---,---,4.63%,4.63%,0.00%, +,2077,13480,华宝国证治理,9月20日,0.8362,0.8362,-0.59%,-2.93%,-0.67%,-5.52%,-4.40%,---,---,---,-16.15%,-16.38%,-16.38%,0.10%, +,2078,14832,兴银中证10,9月19日,0.8841,0.8841,-1.09%,-7.00%,-13.08%,-11.15%,-7.80%,---,---,---,---,-11.59%,---,0.00%, +,2079,14372,浙商沪深30,9月19日,1.7256,1.7256,-0.20%,-3.96%,-4.89%,-7.42%,-6.40%,---,---,---,---,-14.12%,---,0.00%, +,2080,15198,汇添富移动互,9月20日,1.613,1.613,0.19%,-4.89%,-7.09%,-5.23%,-10.39%,---,---,---,---,-11.47%,-11.47%,0.00%, +,2081,14520,博时恒生高股,9月20日,0.8201,0.8201,0.34%,-1.73%,2.09%,-16.75%,-17.81%,---,---,---,---,-17.99%,-17.99%,0.00%, +,2082,14903,长城新能源股,9月20日,1.1743,1.1743,2.77%,-4.64%,-9.42%,5.96%,18.37%,---,---,---,---,17.43%,17.43%,0.00%, +,2083,14943,鹏华中证细分,9月19日,0.9216,0.9216,0.16%,-5.87%,-6.68%,-11.94%,-8.14%,---,---,---,---,-7.84%,---,0.00%, +,2084,15395,招商体育文化,9月20日,1.292,1.292,0.86%,-4.44%,-8.04%,-12.05%,-15.89%,---,---,---,---,-13.17%,-13.17%,0.00%, +,2085,14983,华安中证银行,9月19日,0.8836,0.8836,0.36%,-0.53%,1.46%,-4.30%,-4.91%,---,---,---,---,-11.35%,---,0.00%, +,2086,15037,天弘MSCI,9月19日,0.9379,0.9379,0.24%,-3.95%,-5.38%,-7.55%,-4.50%,---,---,---,---,-6.21%,-5.99%,0.10%, +,2087,14777,富国中证芯片,9月20日,0.7845,0.7845,-0.47%,-5.98%,-11.31%,-7.48%,-17.87%,---,---,---,---,-21.55%,-21.55%,0.00%, +,2088,14662,天弘上海金E,9月20日,0.9349,0.9349,0.24%,-0.94%,-0.40%,-3.08%,-3.52%,---,---,---,---,-6.51%,-6.51%,0.00%, +,2089,15001,工银物流产业,9月20日,3.223,3.223,0.40%,-4.28%,-7.44%,-4.50%,-4.45%,---,---,---,---,-13.62%,-13.62%,0.00%, +,2090,14631,汇添富中证人,9月20日,0.9759,0.9759,0.34%,-4.07%,-9.90%,-13.01%,---,---,---,---,---,-2.41%,-2.41%,0.00%, +,2091,14980,华安上证龙头,9月19日,1.297,1.297,-0.54%,-4.49%,-3.85%,-6.35%,-6.76%,---,---,---,---,-12.42%,---,0.00%, +,2092,14174,富国中证国有,9月20日,0.977,0.977,-0.20%,-3.74%,-2.79%,-4.78%,-2.79%,---,---,---,-14.75%,-12.69%,-12.69%,0.00%, +,2093,14218,添富中证科创,9月20日,0.9854,0.9854,0.97%,-5.05%,-11.01%,-6.65%,---,---,---,---,---,-1.46%,-1.46%,0.12%, +,2094,14219,添富中证科创,9月20日,0.9843,0.9843,0.97%,-5.05%,-11.03%,-6.70%,---,---,---,---,---,-1.57%,-1.57%,0.00%, +,2095,14603,嘉实中证医疗,9月20日,0.7773,0.7773,-0.03%,-5.46%,-9.06%,-16.59%,-14.72%,---,---,---,---,-22.27%,-22.27%,0.00%, +,2096,12961,招商创业板大,9月20日,0.9257,0.9257,0.66%,-6.67%,-11.80%,-12.70%,---,---,---,---,---,-7.43%,-7.43%,0.12%, +,2097,15194,汇添富新兴消,9月20日,1.622,1.622,1.76%,-4.98%,-11.46%,-7.89%,-1.46%,---,---,---,---,-2.70%,-2.70%,0.00%, +,2098,12590,易方达中证全,9月20日,0.9121,0.9121,0.08%,-7.76%,-7.24%,-9.89%,-8.83%,---,---,---,---,-8.79%,-8.79%,0.06%, +,2099,15085,中欧核心消费,9月20日,1.0327,1.0327,0.87%,-0.47%,-1.72%,-3.23%,---,---,---,---,---,3.27%,3.27%,0.15%, +,2100,15086,中欧核心消费,9月20日,1.0303,1.0303,0.87%,-0.48%,-1.76%,-3.36%,---,---,---,---,---,3.03%,3.03%,0.00%, +,2101,14909,国泰中证新材,9月19日,0.8878,0.8878,-0.02%,-6.72%,-11.66%,-13.40%,-8.74%,---,---,---,---,-11.22%,-10.19%,0.00%, +,2102,14206,长城中证医药,9月20日,0.9169,0.9169,0.58%,-5.86%,-7.74%,-8.30%,---,---,---,---,---,-8.31%,-8.31%,0.00%, +,2103,15577,国联安上证商,9月20日,1.1028,1.1028,1.28%,-6.66%,-3.51%,-6.11%,---,---,---,---,---,6.73%,8.10%,0.00%, +,2104,15773,招商移动互联,9月20日,1.5654,1.5654,-0.36%,-3.21%,-4.51%,13.03%,---,---,---,---,---,30.66%,30.66%,0.00%, +,2105,14139,易方达高质量,9月20日,0.9481,0.9481,-0.26%,-2.88%,-4.96%,-6.88%,---,---,---,---,---,-5.19%,-5.19%,0.15%, +,2106,12854,英大中证ES,9月19日,0.9492,0.9492,-0.09%,-2.68%,-4.27%,-6.09%,-5.09%,---,---,---,---,-5.08%,-5.07%,0.08%, +,2107,13888,天弘新华沪港,9月20日,0.883,0.883,0.82%,-3.07%,-3.41%,---,---,---,---,---,---,-11.70%,-11.70%,--, +,2108,15598,国泰中证申万,9月19日,0.9325,0.9325,-0.98%,-7.89%,-7.32%,-10.16%,---,---,---,---,---,-0.57%,-0.47%,0.00%, +,2109,15618,永赢卓越臻选,9月20日,0.9157,0.9157,0.18%,-3.53%,-6.27%,-8.79%,---,---,---,---,---,-8.43%,-8.43%,0.00%, +,2110,14305,华泰柏瑞中证,9月20日,1.0064,1.0064,0.07%,-0.62%,-0.66%,-0.06%,---,---,---,---,---,0.57%,0.64%,0.15%, +,2111,15466,太平中证10,9月19日,1.0276,1.0276,-1.10%,-7.23%,-11.61%,-4.80%,---,---,---,---,---,2.76%,4.04%,0.12%, +,2112,12929,银华中证光伏,9月20日,1.2991,1.2991,2.83%,-6.74%,-13.80%,-2.59%,---,---,---,---,---,29.91%,29.91%,0.00%, +,2113,15364,汇丰晋信价值,9月19日,1.6511,2.0511,-0.51%,-3.01%,-4.62%,-7.41%,---,---,---,---,---,-7.54%,---,0.00%, +,2114,12733,易方达中证人,9月20日,0.7894,0.7894,0.34%,-4.16%,-10.00%,-12.38%,-21.09%,---,---,---,---,-21.06%,-21.06%,0.06%, +,2115,15495,景顺长城中证,9月19日,0.9908,0.9908,-0.91%,-7.73%,-11.49%,-7.60%,---,---,---,---,---,-0.92%,---,0.12%, +,2116,15468,嘉实农业产业,9月20日,0.9434,0.9434,-0.16%,-4.13%,-6.33%,-5.03%,---,---,---,---,---,-5.66%,-5.66%,0.00%, +,2117,15454,中欧中证50,9月20日,0.9566,0.9566,0.61%,-4.28%,-6.71%,-5.20%,---,---,---,---,---,-4.34%,-4.34%,0.00%, +,2118,15497,华泰紫金中证,9月19日,0.9448,0.9448,-1.41%,-6.45%,-7.62%,-13.09%,---,---,---,---,---,-5.52%,---,0.06%, +,2119,15498,华泰紫金中证,9月19日,0.9444,0.9444,-1.41%,-6.45%,-7.63%,-13.11%,---,---,---,---,---,-5.56%,---,0.00%, +,2120,14673,富国中证港股,9月20日,0.7386,0.7386,1.25%,-4.81%,-8.10%,-17.13%,-7.22%,---,---,---,---,-26.14%,-26.14%,0.12%, +,2121,14205,长城中证医药,9月20日,0.9177,0.9177,0.58%,-5.86%,-7.71%,-8.23%,---,---,---,---,---,-8.23%,-8.23%,0.12%, +,2122,11965,泰康中证50,9月19日,0.9234,0.9234,-0.71%,-6.14%,-7.41%,---,---,---,---,---,---,-7.66%,---,0.00%, +,2123,14414,招商中证畜牧,9月20日,1.1024,1.1024,-0.61%,-4.98%,-3.32%,-1.64%,---,---,---,---,---,10.24%,10.24%,0.12%, +,2124,15671,前海开源沪深,9月19日,1.6428,1.6428,-0.13%,-3.84%,-5.00%,-6.61%,---,---,---,---,---,0.69%,---,0.00%, +,2125,14588,华安中证50,9月19日,0.9456,0.9456,-0.61%,-6.01%,-6.97%,-7.16%,---,---,---,---,---,-5.44%,---,0.00%, +,2126,15596,国泰国证有色,9月19日,1.3233,1.3233,-0.09%,-7.23%,-7.26%,-10.57%,---,---,---,---,---,1.38%,3.46%,0.00%, +,2127,15674,鹏华中证80,9月19日,1.009,1.009,0.50%,-2.98%,4.67%,6.66%,---,---,---,---,---,0.90%,---,0.00%, +,2128,15675,鹏华中证传媒,9月19日,0.957,0.957,-1.75%,-5.15%,-8.51%,-11.80%,---,---,---,---,---,-4.30%,---,0.00%, +,2129,13889,天弘新华沪港,9月20日,0.88,0.88,0.82%,-3.08%,-3.43%,---,---,---,---,---,---,-12.00%,-12.00%,0.00%, +,2130,15507,兴业中证50,9月19日,0.9539,0.9539,-0.43%,-5.67%,-6.35%,-4.65%,---,---,---,---,---,-4.61%,-3.83%,0.12%, +,2131,15673,鹏华创业板指,9月19日,1.037,1.037,-0.77%,-7.25%,-13.15%,-10.60%,---,---,---,---,---,3.70%,---,0.00%, +,2132,15678,鹏华中证高铁,9月19日,0.964,0.964,-0.31%,-4.74%,-4.65%,-3.89%,---,---,---,---,---,-3.60%,---,0.00%, +,2133,15387,中欧沪深30,9月20日,0.9667,0.9667,0.13%,-4.10%,-5.00%,-5.64%,---,---,---,---,---,-3.46%,-3.33%,0.15%, +,2134,14306,华泰柏瑞中证,9月20日,1.0048,1.0048,0.07%,-0.63%,-0.69%,-0.17%,---,---,---,---,---,0.41%,0.48%,0.00%, +,2135,12928,银华中证光伏,9月20日,1.3004,1.3004,2.82%,-6.74%,-13.78%,-2.53%,---,---,---,---,---,30.04%,30.04%,0.12%, +,2136,14140,易方达高质量,9月20日,0.9466,0.9466,-0.27%,-2.88%,-5.00%,-7.00%,---,---,---,---,---,-5.34%,-5.34%,0.00%, +,2137,13502,易方达中证内,9月20日,1.1124,1.1124,1.90%,-5.78%,-11.72%,-8.81%,---,---,---,---,---,11.24%,11.24%,0.12%, +,2138,15484,前海开源中证,9月19日,1.219,1.219,0.47%,-3.15%,-4.35%,-7.05%,---,---,---,---,---,-0.55%,---,0.00%, +,2139,12756,易方达中证龙,9月20日,0.9562,0.9562,0.19%,-3.26%,-3.24%,-8.16%,-3.42%,---,---,---,---,-4.38%,-4.38%,0.10%, +,2140,15558,万家中证红利,9月19日,2.2823,2.2823,0.08%,-4.35%,-0.40%,1.27%,---,---,---,---,---,7.35%,7.47%,0.00%, +,2141,13893,国联安上证科,9月20日,0.9016,0.9016,0.87%,-5.06%,-10.93%,-10.69%,---,---,---,---,---,-10.62%,-9.84%,0.10%, +,2142,15676,鹏华中证移动,9月19日,0.998,0.998,-1.67%,-4.86%,-9.27%,-9.19%,---,---,---,---,---,-0.20%,---,0.00%, +,2143,14564,天弘恒生沪深,9月19日,0.904,0.904,-1.90%,-9.99%,-12.29%,-13.95%,---,---,---,---,---,-9.60%,---,0.10%, +,2144,15599,国泰国证航天,9月19日,1.3079,1.3079,-1.75%,-3.02%,-4.11%,3.51%,---,---,---,---,---,9.21%,9.24%,0.00%, +,2145,13894,国联安上证科,9月20日,0.9,0.9,0.87%,-5.08%,-10.96%,-10.82%,---,---,---,---,---,-10.78%,-10.00%,0.00%, +,2146,15467,太平中证10,9月19日,1.0259,1.0259,-1.10%,-7.24%,-11.64%,-4.89%,---,---,---,---,---,2.59%,3.87%,0.00%, +,2147,15328,华泰紫金中证,9月19日,0.9697,0.9697,0.15%,-6.44%,-7.53%,-12.97%,---,---,---,---,---,-3.03%,---,0.06%, +,2148,15329,华泰紫金中证,9月19日,0.9692,0.9692,0.17%,-6.44%,-7.54%,-12.99%,---,---,---,---,---,-3.08%,---,0.00%, +,2149,15508,兴业中证50,9月19日,0.9531,0.9531,-0.43%,-5.67%,-6.38%,-4.72%,---,---,---,---,---,-4.69%,-3.91%,0.00%, +,2150,15739,国泰中证港股,9月20日,0.8307,0.8307,1.58%,-4.01%,-11.04%,-16.60%,---,---,---,---,---,-16.93%,-16.93%,0.10%, +,2151,15475,天弘文化新兴,9月20日,1.014,1.014,0.53%,-1.73%,-1.89%,-6.89%,---,---,---,---,---,1.40%,1.40%,0.00%, +,2152,15686,富国新兴产业,9月20日,2.082,2.082,1.22%,-3.52%,-6.38%,14.58%,---,---,---,---,---,18.23%,18.23%,0.00%, +,2153,15998,大成中证电池,9月20日,0.8721,0.8721,2.41%,-5.93%,-14.04%,---,---,---,---,---,---,-12.79%,-12.79%,0.00%, +,2154,14958,华富消费成长,9月20日,0.9687,0.9687,1.21%,-1.15%,-1.99%,---,---,---,---,---,---,-3.13%,-3.13%,0.00%, +,2155,14589,招商成长先导,9月20日,0.8898,0.8898,0.49%,-3.62%,-8.81%,---,---,---,---,---,---,-11.02%,-11.02%,0.15%, +,2156,14590,招商成长先导,9月20日,0.8885,0.8885,0.47%,-3.64%,-8.89%,---,---,---,---,---,---,-11.15%,-11.15%,0.00%, +,2157,15872,景顺长城国证,9月19日,0.8643,0.8643,1.29%,-6.57%,-14.71%,-16.89%,---,---,---,---,---,-13.57%,---,0.00%, +,2158,14634,景顺长城ES,9月19日,0.9478,0.9478,0.15%,-3.63%,-3.67%,-6.16%,---,---,---,---,---,-5.22%,---,0.15%, +,2159,14635,景顺长城ES,9月19日,0.9462,0.9462,0.15%,-3.64%,-3.71%,-6.30%,---,---,---,---,---,-5.38%,---,0.00%, +,2160,15922,申万菱信国证,9月20日,0.95,0.95,1.26%,-5.14%,-8.13%,---,---,---,---,---,---,-5.00%,-5.00%,0.00%, +,2161,15894,平安中证消费,9月19日,0.9178,0.9178,-0.96%,-6.07%,-12.88%,---,---,---,---,---,---,-8.22%,-8.18%,0.10%, +,2162,14633,华安中证电子,9月19日,0.9361,0.9361,-1.46%,-4.49%,-10.33%,-9.68%,---,---,---,---,---,-6.39%,---,0.00%, +,2163,15496,景顺长城中证,9月19日,0.9891,0.9891,-0.92%,-7.74%,-11.52%,-7.71%,---,---,---,---,---,-1.09%,---,0.00%, +,2164,15871,景顺长城国证,9月19日,0.8647,0.8647,1.30%,-6.57%,-14.71%,-16.86%,---,---,---,---,---,-13.53%,---,0.12%, +,2165,14854,嘉实中证半导,9月20日,1.2672,1.2672,-0.52%,-4.37%,-4.81%,6.83%,---,---,---,---,---,26.72%,26.72%,0.10%, +,2166,15641,银华数字经济,9月20日,1.0236,1.0236,1.98%,-3.80%,-6.28%,-5.55%,---,---,---,---,---,2.36%,2.36%,0.15%, +,2167,15642,银华数字经济,9月20日,1.0228,1.0228,1.97%,-3.81%,-6.31%,-5.61%,---,---,---,---,---,2.28%,2.28%,0.00%, +,2168,14121,大成品质医疗,9月19日,0.848,0.848,-2.55%,-7.61%,-10.77%,-15.82%,---,---,---,---,---,-15.20%,-15.14%,0.15%, +,2169,15877,富国中证消费,9月20日,0.8883,0.8883,0.03%,-6.63%,-13.35%,-11.17%,---,---,---,---,---,-11.17%,-11.17%,0.00%, +,2170,15997,大成中证电池,9月20日,0.8726,0.8726,2.41%,-5.93%,-14.02%,---,---,---,---,---,---,-12.74%,-12.74%,0.15%, +,2171,14855,嘉实中证半导,9月20日,1.266,1.266,-0.52%,-4.37%,-4.83%,6.76%,---,---,---,---,---,26.60%,26.60%,0.00%, +,2172,13943,华宝中证稀有,9月20日,0.9254,0.9254,4.01%,-3.18%,-3.90%,-9.51%,1.54%,---,---,---,-7.88%,-7.46%,-7.46%,0.00%, +,2173,15679,景顺长城沪深,9月19日,2.242,2.242,0.22%,-3.82%,-4.60%,-6.19%,---,---,---,---,---,-0.18%,---,0.00%, +,2174,15808,汇添富中证电,9月20日,0.8124,0.8124,2.45%,-6.31%,-14.88%,-11.28%,---,---,---,---,---,6.74%,6.74%,0.00%, +,2175,15895,平安中证消费,9月19日,0.9171,0.9171,-0.97%,-6.08%,-12.91%,---,---,---,---,---,---,-8.29%,-8.25%,0.00%, +,2176,15994,博时中证光伏,9月20日,0.9173,0.9173,2.72%,-6.85%,-8.28%,---,---,---,---,---,---,-8.27%,-8.27%,0.00%, +,2177,15876,富国中证消费,9月20日,0.8887,0.8887,0.03%,-6.64%,-13.34%,-11.13%,---,---,---,---,---,-11.13%,-11.13%,0.12%, +,2178,15149,华安中证10,9月19日,0.9241,0.9241,-1.16%,-6.87%,-10.03%,---,---,---,---,---,---,-7.59%,---,0.00%, +,2179,16020,招商中证电池,9月20日,0.8446,0.8446,2.39%,-6.20%,-14.67%,---,---,---,---,---,---,-15.54%,-15.54%,0.00%, +,2180,14587,华安中证50,9月19日,0.9469,0.9469,-0.60%,-6.00%,-6.93%,-7.05%,---,---,---,---,---,-5.31%,---,0.12%, +,2181,16019,招商中证电池,9月20日,0.8454,0.8454,2.40%,-6.19%,-14.64%,---,---,---,---,---,---,-15.46%,-15.46%,0.12%, +,2182,15588,国泰大农业股,9月20日,2.3136,2.3136,0.07%,-4.12%,-5.76%,-5.44%,---,---,---,---,---,0.01%,0.01%,0.00%, +,2183,15617,永赢卓越臻选,9月20日,0.9169,0.9169,0.16%,-3.52%,-6.24%,-8.68%,---,---,---,---,---,-8.31%,-8.31%,0.15%, +,2184,12329,天弘中证新能,9月20日,0.8876,0.8876,2.52%,-6.01%,-11.79%,---,---,---,---,---,---,-11.24%,-11.24%,0.00%, +,2185,15453,中欧中证50,9月20日,0.9588,0.9588,0.61%,-4.26%,-6.66%,-5.05%,---,---,---,---,---,-4.12%,-4.12%,0.15%, +,2186,14565,天弘恒生沪深,9月19日,0.9032,0.9032,-1.90%,-10.00%,-12.31%,-14.00%,---,---,---,---,---,-9.68%,---,0.00%, +,2187,15897,天弘中证细分,9月20日,0.899,0.899,1.26%,-4.46%,-6.18%,-10.10%,---,---,---,---,---,-10.10%,-10.10%,0.00%, +,2188,15896,天弘中证细分,9月20日,0.8994,0.8994,1.25%,-4.46%,-6.18%,-10.06%,---,---,---,---,---,-10.06%,-10.06%,0.10%, +,2189,15608,信澳转型创新,9月20日,1.035,1.035,3.71%,-9.60%,-16.49%,-7.59%,---,---,---,---,---,2.89%,6.70%,0.00%, +,2190,14122,大成品质医疗,9月19日,0.8467,0.8467,-2.55%,-7.61%,-10.80%,-15.90%,---,---,---,---,---,-15.33%,-15.28%,0.00%, +,2191,15873,工银国证新能,9月20日,0.8554,0.8554,1.98%,-5.33%,-13.33%,---,---,---,---,---,---,-14.46%,-14.46%,0.10%, +,2192,15860,宝盈国证证券,9月20日,0.9372,0.9372,---,---,-7.30%,---,---,---,---,---,---,-6.28%,-6.28%,0.00%, +,2193,14957,华富消费成长,9月20日,0.97,0.97,1.22%,-1.13%,-1.92%,---,---,---,---,---,---,-3.00%,-3.00%,0.15%, +,2194,15677,鹏华中证一带,9月19日,1.01,1.01,-0.49%,-6.05%,-3.81%,-3.35%,---,---,---,---,---,1.00%,---,0.00%, +,2195,15740,国泰中证港股,9月20日,0.8329,0.8329,1.57%,-4.02%,-11.06%,-16.65%,---,---,---,---,---,-16.71%,-16.71%,0.00%, +,2196,16018,银河康乐股票,9月20日,2.498,2.498,0.77%,-4.95%,-7.55%,---,---,---,---,---,---,-9.72%,-9.72%,0.00%, +,2197,15761,银华中证基建,9月20日,0.9382,---,-0.13%,-6.29%,-5.14%,-7.37%,---,---,---,---,---,-6.18%,-6.18%,0.12%, +,2198,16033,华宝中证10,9月20日,0.9812,0.9812,1.20%,-5.83%,-9.86%,---,---,---,---,---,---,-7.19%,-7.19%,0.00%, +,2199,15600,国泰创业板指,9月19日,1.2062,1.2062,-0.56%,-7.02%,-13.10%,-10.21%,---,---,---,---,---,-1.91%,-1.37%,0.00%, +,2200,15754,上银内需增长,9月19日,0.7929,0.7929,-0.03%,-1.99%,-2.51%,-6.37%,---,---,---,---,---,-2.22%,-2.53%,0.00%, +,2201,15762,银华中证基建,9月20日,0.9373,0.9373,-0.14%,-6.31%,-5.17%,-7.45%,---,---,---,---,---,-6.27%,-6.27%,0.00%, +,2202,16129,景顺长城中证,9月19日,1.0027,1.0027,-0.30%,-3.61%,-1.11%,---,---,---,---,---,---,0.27%,---,0.00%, +,2203,13874,平安中证医药,9月19日,0.8114,0.8114,-1.37%,-7.59%,-9.66%,-16.39%,-20.47%,---,---,---,---,-18.86%,-18.90%,0.00%, +,2204,850788,海通智选一年,9月19日,0.8422,1.5372,-1.24%,-6.63%,-10.95%,---,---,---,---,---,---,-10.95%,---,0.12%, +,2205,15148,华安中证10,9月19日,0.9247,0.9247,-1.17%,-6.87%,-10.01%,---,---,---,---,---,---,-7.53%,---,0.12%, +,2206,16185,广发中证全指,9月19日,0.9518,0.9518,-1.04%,-8.97%,-7.21%,---,---,---,---,---,---,-4.82%,---,0.12%, +,2207,16186,广发中证全指,9月19日,0.9513,0.9513,-1.05%,-8.98%,-7.24%,---,---,---,---,---,---,-4.87%,---,0.00%, +,2208,16496,景顺长城中证,9月19日,0.9337,0.9337,-2.14%,-8.14%,---,---,---,---,---,---,---,-6.63%,---,0.00%, +,2209,15874,工银国证新能,9月20日,0.8548,0.8548,1.98%,-5.34%,-13.36%,---,---,---,---,---,---,-14.52%,-14.52%,0.00%, +,2210,16399,九泰久睿量化,9月20日,0.8563,0.8563,0.99%,-5.86%,-8.88%,---,---,---,---,---,---,-3.96%,-3.96%,0.00%, +,2211,16113,华宝高端装备,9月20日,0.9893,0.9893,-0.16%,---,-0.81%,---,---,---,---,---,---,-1.07%,-1.07%,0.10%, +,2212,16114,华宝高端装备,9月20日,0.989,0.989,-0.16%,---,-0.84%,---,---,---,---,---,---,-1.10%,-1.10%,0.00%, +,2213,15042,国泰国证房地,9月20日,0.895,0.895,-2.01%,-3.61%,1.07%,0.98%,-1.61%,---,---,---,---,-4.38%,-4.38%,0.00%, +,2214,12328,天弘中证新能,9月20日,0.8881,0.8881,2.50%,-6.01%,-11.76%,---,---,---,---,---,---,-11.19%,-11.19%,0.12%, +,2215,16401,上投摩根大盘,9月19日,2.4524,2.4524,-0.62%,-5.40%,-13.76%,---,---,---,---,---,---,-14.39%,---,0.00%, +,2216,850799,海通智选一年,9月19日,0.8421,1.5371,-1.24%,-6.63%,-10.96%,---,---,---,---,---,---,-10.96%,---,0.00%, +,2217,15879,富国中证农业,9月20日,0.9505,0.9505,-0.29%,-4.68%,-4.95%,---,---,---,---,---,---,-4.95%,-4.95%,0.00%, +,2218,16134,嘉实沪深30,9月20日,0.8951,0.8951,0.19%,-4.26%,-4.85%,---,---,---,---,---,---,-10.49%,-10.49%,0.00%, +,2219,16123,华富中证科创,9月20日,0.9908,0.9908,0.14%,-0.96%,---,---,---,---,---,---,---,-1.06%,-0.92%,0.00%, +,2220,16291,华安大国新经,9月19日,2.952,2.952,-0.61%,-5.75%,-10.49%,---,---,---,---,---,---,-11.54%,---,0.00%, +,2221,16128,景顺长城中证,9月19日,1.0028,1.0028,-0.30%,-3.61%,-1.10%,---,---,---,---,---,---,0.28%,---,0.12%, +,2222,16052,华商改革创新,9月19日,2.1117,2.1117,-0.41%,-6.45%,-12.28%,---,---,---,---,---,---,-11.90%,---,0.00%, 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2.0', + 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11', + 'User-Agent:Opera/9.80 (Windows NT 6.1; U; en) Presto/2.8.131 Version/11.11', + 'Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/20100101 Firefox/4.0.1', + 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0)', + 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50', + 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0', + ' Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1', + 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1', + ' Mozilla/5.0 (Macintosh; Intel Mac OS X 10.6; rv:2.0.1) Gecko/20100101 Firefox/4.0.1', +] \ No newline at end of file diff --git "a/\346\210\221\347\232\204\345\244\251.html" "b/\346\210\221\347\232\204\345\244\251.html" new file mode 100644 index 000000000..ef3e12f1b --- /dev/null +++ "b/\346\210\221\347\232\204\345\244\251.html" @@ -0,0 +1,6659 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +我的天_百度搜索 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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故事:老婆离世,尸体已经腐烂,几天后我却收到她发的求救微信

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+ 2022年7月25日 第二天早上的时候，新发来的消息又消失不见。我确定这不是幻觉，她肯定是出了什么事儿，我要救她；不管付出多少代价，我都要救她。于是我请了个长假，打算先从她工作的出租车公司开...
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我的天用英语怎么说

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+ 2022年4月14日 英语是语言的一种。语言是一种符号系统。割裂地去学习任何一种语言都是不对的。许多人在学习英语的时候,都将单词识记强行分割出来识记。这种学习英语的方法是错误的低效的。这就难...
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张敬轩《我的天》2020盛乐×中乐团红馆演唱会现场版【中文字幕...

视频时长 04:21

https://weibo.com/2793921830/JvvBAjv1Z@饮歌记：昨晚的盛乐×中乐团红馆演唱会，张敬轩在安可部分演绎了这首歌迷心中的...

www.bilibili.com/video/BV1gp...
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“我的天”用英语怎么说?有几种英语表达方式! - 知乎

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+ 2021年3月24日 在英语中,很多人在表示惊讶的时候,都会用“Oh, my God”。其实在英语口语会话中,还有其他几种表示“我的天”的感叹方式,今天小编带大家学习一下其他说法! 1、OMG 是“Oh, my God”...
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+ + + + + + + + + + + + + From e6a0d1fc7db60392e79227cae70dc8aefd452c80 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Wed, 9 Nov 2022 22:28:56 +0800 Subject: [PATCH 09/12] 20221109 --- Part.1.E.1.entrance.ipynb | 11 +- Part.1.E.3.controlflow.ipynb | 76 +++- Part.1.E.4.functions.ipynb | 7 +- Part.1.E.5.strings.ipynb | 8 +- Part.1.E.6.containers.ipynb | 7 + Part.1.E.7.files.ipynb | 93 ++++ Part.1.E.8.json.ipynb | 148 ++++++ Part.2.D.3-lambda.ipynb | 41 ++ Part.2.D.9-internet.ipynb | 44 ++ Part.2.E.10.email.ipynb | 134 ++++++ Part.3.B.1.classes-1.ipynb | 4 +- Part5.B.downloadcsv.ipynb | 475 ++++++++++++++++++++ Part5.C.NLP.ipynb | 294 ++++++++++++ in.txt | 10 + lhy_comments.txt | 274 +++++++++++ out.txt | 127 ++++++ params.json | 1 + "\346\235\216\347\204\225\350\213\2611.png" | Bin 0 -> 299564 bytes "\346\235\216\347\204\225\350\213\2612.png" | Bin 0 -> 188029 bytes 19 files changed, 1740 insertions(+), 14 deletions(-) create mode 100644 Part.1.E.8.json.ipynb create mode 100644 Part.2.D.9-internet.ipynb create mode 100644 Part.2.E.10.email.ipynb create mode 100644 Part5.C.NLP.ipynb create mode 100644 in.txt create mode 100644 lhy_comments.txt create mode 100644 out.txt create mode 100644 params.json create mode 100644 "\346\235\216\347\204\225\350\213\2611.png" create mode 100644 "\346\235\216\347\204\225\350\213\2612.png" diff --git a/Part.1.E.1.entrance.ipynb b/Part.1.E.1.entrance.ipynb index af6778243..88035be6a 100644 --- a/Part.1.E.1.entrance.ipynb +++ b/Part.1.E.1.entrance.ipynb @@ -280,14 +280,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "693 is odd.\n" + "534 is even.\n" ] } ], @@ -858,8 +858,11 @@ } ], "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.0 64-bit", "language": "python", "name": "python3" }, @@ -873,7 +876,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.10.0" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false diff --git a/Part.1.E.3.controlflow.ipynb b/Part.1.E.3.controlflow.ipynb index ef1fe1d20..e40e897bd 100644 --- a/Part.1.E.3.controlflow.ipynb +++ b/Part.1.E.3.controlflow.ipynb @@ -1784,6 +1784,75 @@ "> * 函数从控制流角度去看其实就是子程序" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考题\n", + "最后给你留一个思考题。\n", + "给定下面两个列表 attributes 和 values，要求针对 values 中每一组子列表 value，输出其和 attributes 中的键对应后的字典，最后返回字典组成的列表。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'name': 'jason', 'dob': '2000-01-01', 'gender': 'male'},\n", + " {'name': 'mike', 'dob': '1999-01-01', 'gender': 'male'},\n", + " {'name': 'nancy', 'dob': '2001-02-01', 'gender': 'female'}]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "attributes = ['name', 'dob', 'gender']\n", + "values = [['jason', '2000-01-01', 'male'], ['mike', '1999-01-01', 'male'],['nancy', '2001-02-01', 'female']]\n", + "# expected output:\n", + "result=[{'name': 'jason', 'dob': '2000-01-01', 'gender': 'male'}, {'name': 'mike', 'dob': '1999-01-01', 'gender': 'male'}, {'name': 'nancy', 'dob': '2001-02-01', 'gender': 'female'}]\n", + "#你能分别用一行和多行条件循环语句，来实现这个功能吗？\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'name': 'jason', 'dob': '2000-01-01', 'gender': 'male'},\n", + " {'name': 'mike', 'dob': '1999-01-01', 'gender': 'male'},\n", + " {'name': 'nancy', 'dob': '2001-02-01', 'gender': 'female'}]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "attributes = ['name', 'dob', 'gender']\n", + "values = [['jason', '2000-01-01', 'male'], ['mike', '1999-01-01', 'male'],['nancy', '2001-02-01', 'female']]\n", + "list_a=[]\n", + "for i in range(len(values)):\n", + " dict_a={}\n", + " for j in range(len(attributes)):\n", + " dict_a[attributes[j]]=values[i][j]\n", + " # dict_a[attributes[1]]=values[0][1]\n", + " # dict_a[attributes[2]]=values[0][2]\n", + " list_a.append(dict_a)\n", + "# list_a.insert(1, attributes[0]+':'+values[0][0]) # 在索引 1 的位置插入 'example'\n", + "list_a" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1793,8 +1862,11 @@ } ], "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.10.0 64-bit", "language": "python", "name": "python3" }, @@ -1808,7 +1880,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/Part.1.E.4.functions.ipynb b/Part.1.E.4.functions.ipynb index 2e69d1e08..ef68f620a 100644 --- a/Part.1.E.4.functions.ipynb +++ b/Part.1.E.4.functions.ipynb @@ -690,8 +690,11 @@ } ], "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -705,7 +708,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" }, "toc-autonumbering": true }, diff --git a/Part.1.E.5.strings.ipynb b/Part.1.E.5.strings.ipynb index 5ec5bfc2a..ff4c79fe3 100644 --- a/Part.1.E.5.strings.ipynb +++ b/Part.1.E.5.strings.ipynb @@ -1595,7 +1595,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -1605,7 +1605,7 @@ "s.lower().replace('mp', '[ ]', 2):\n", "\n", "si[ ]le is better than co[ ]lex.\n", - "complex is better than complicated.\n" + "co[ ]lex is better than co[ ]licated.\n" ] } ], @@ -2423,7 +2423,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -2432,7 +2432,7 @@ "'John is 25 years old.'" ] }, - "execution_count": 48, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } diff --git a/Part.1.E.6.containers.ipynb b/Part.1.E.6.containers.ipynb index 40f695fb5..552686ad4 100644 --- a/Part.1.E.6.containers.ipynb +++ b/Part.1.E.6.containers.ipynb @@ -424,6 +424,13 @@ "del s[2] # 这一句会报错" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.1.E.7.files.ipynb b/Part.1.E.7.files.ipynb index b07e6a430..f32f88ab2 100644 --- a/Part.1.E.7.files.ipynb +++ b/Part.1.E.7.files.ipynb @@ -9679,6 +9679,99 @@ "> * 可以用 `with` 把相关操作都放入同一个语句块……" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考\n", + "### NLP分词\n", + "首先，我们要清楚 NLP 任务的基本步骤，也就是下面的四步：\n", + "> 1. 读取文件；\n", + "> 2. 去除所有标点符号和换行符，并把所有大写变成小写；\n", + "> 3. 合并相同的词，统计每个词出现的频率，并按照词频从大到小排序；\n", + "> 4. 将结果按行输出到文件 out.txt。\n", + "有一个in.txt文件，我们需要将这个文件中的语句内容进行分词，并统计词频。\n", + "如以下例子：" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character. I have a dream today.\n", + "\n", + "\n", + "\n", + "I have a dream that one day down in Alabama, with its vicious racists, . . . one day right there in Alabama little black boys and black girls will be able to join hands with little white boys and white girls as sisters and brothers. I have a dream today.\n", + "\n", + "\n", + "\n", + "I have a dream that one day every valley shall be exalted, every hill and mountain shall be made low, the rough places will be made plain, and the crooked places will be made straight, and the glory of the Lord shall be revealed, and all flesh shall see it together.\n", + "\n", + "\n", + "\n", + "This is our hope. . . With this faith we will be able to hew out of the mountain of despair a stone of hope. With this faith we will be able to transform the jangling discords of our nation into a beautiful symphony of brotherhood. With this faith we will be able to work together, to pray together, to struggle together, to go to jail together, to stand up for freedom together, knowing that we will be free one day. . . .\n", + "\n", + "\n", + "\n", + "And when this happens, and when we allow freedom ring, when we let it ring from every village and every hamlet, from every state and every city, we will be able to speed up that day when all of God's children, black men and white men, Jews and Gentiles, Protestants and Catholics, will be able to join hands and sing in the words of the old Negro spiritual: \"Free at last! Free at last! Thank God Almighty, we are free at last!\"\n", + "[('and', 15), ('be', 13), ('will', 11), ('to', 11), ('the', 10), ('of', 10), ('a', 8), ('we', 8), ('day', 6), ('able', 6), ('every', 6), ('together', 6), ('i', 5), ('have', 5), ('dream', 5), ('that', 5), ('one', 5), ('with', 5), ('this', 5), ('in', 4), ('shall', 4), ('free', 4), ('when', 4), ('little', 3), ('black', 3), ('white', 3), ('made', 3), ('faith', 3), ('at', 3), ('last', 3), ('children', 2), ('nation', 2), ('by', 2), ('their', 2), ('today', 2), ('alabama', 2), ('boys', 2), ('girls', 2), ('join', 2), ('hands', 2), ('mountain', 2), ('places', 2), ('all', 2), ('it', 2), ('our', 2), ('hope', 2), ('up', 2), ('freedom', 2), ('ring', 2), ('from', 2), ('god', 2), ('men', 2), ('my', 1), ('four', 1), ('live', 1), ('where', 1), ('they', 1), ('not', 1), ('judged', 1), ('color', 1), ('skin', 1), ('but', 1), ('content', 1), ('character', 1), ('down', 1), ('its', 1), ('vicious', 1), ('racists', 1), ('right', 1), ('there', 1), ('as', 1), ('sisters', 1), ('brothers', 1), ('valley', 1), ('exalted', 1), ('hill', 1), ('low', 1), ('rough', 1), ('plain', 1), ('crooked', 1), ('straight', 1), ('glory', 1), ('lord', 1), ('revealed', 1), ('flesh', 1), ('see', 1), ('is', 1), ('hew', 1), ('out', 1), ('despair', 1), ('stone', 1), ('transform', 1), ('jangling', 1), ('discords', 1), ('into', 1), ('beautiful', 1), ('symphony', 1), ('brotherhood', 1), ('work', 1), ('pray', 1), ('struggle', 1), ('go', 1), ('jail', 1), ('stand', 1), ('for', 1), ('knowing', 1), ('happens', 1), ('allow', 1), ('let', 1), ('village', 1), ('hamlet', 1), ('state', 1), ('city', 1), ('speed', 1), ('s', 1), ('jews', 1), ('gentiles', 1), ('protestants', 1), ('catholics', 1), ('sing', 1), ('words', 1), ('old', 1), ('negro', 1), ('spiritual', 1), ('thank', 1), ('almighty', 1), ('are', 1)]\n" + ] + } + ], + "source": [ + "\n", + "import re\n", + "\n", + "# 你不用太关心这个函数\n", + "# 生成单词和词频的字典\n", + "word_cnt = {}#考虑到要读取多行，如果这个在函数代码块中就无法生效，所以当成全局变量，这样就会因为执行多次函数而发生变化\n", + "\n", + "\n", + "def parse(text):\n", + " # 使用正则表达式去除标点符号和换行符\n", + " text = re.sub(r'[^\\w ]', ' ', text)\n", + "\n", + " # 转为小写\n", + " text = text.lower()\n", + " \n", + " # 生成所有单词的列表\n", + " word_list = text.split(' ')\n", + " \n", + " # 去除空白单词\n", + " word_list = filter(None, word_list)\n", + " \n", + " \n", + " \n", + " for word in word_list:\n", + " if word not in word_cnt:\n", + " word_cnt[word] = 0\n", + " word_cnt[word] += 1\n", + " # 按照词频排序\n", + " sorted_word_cnt = sorted(word_cnt.items(), key=lambda kv: kv[1], reverse=True) \n", + " return sorted_word_cnt\n", + "\n", + "with open('in.txt', 'r') as fin:\n", + " for line in fin.readlines():\n", + " print(line)\n", + " # print(word_cnt)\n", + " word_and_freq = parse(line)\n", + " # print(word_cnt)\n", + "print(word_and_freq)\n", + "\n", + "\n", + "with open('out.txt', 'w') as fout:\n", + " for word, freq in word_and_freq:\n", + " fout.write('{} {}\\n'.format(word, freq))\n" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.1.E.8.json.ipynb b/Part.1.E.8.json.ipynb new file mode 100644 index 000000000..449202c48 --- /dev/null +++ b/Part.1.E.8.json.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# JSON 序列化与实战\n", + "最后，我来讲一个和实际应用很贴近的知识点。\n", + "\n", + "JSON（JavaScript Object Notation）是一种轻量级的数据交换格式，它的设计意图是把所有事情都用设计的字符串来表示，这样既方便在互联网上传递信息，也方便人进行阅读（相比一些 binary 的协议）。\n", + "\n", + "JSON 在当今互联网中应用非常广泛，也是每一个用 Python 程序员应当熟练掌握的技能点。\n", + "\n", + "设想一个情景，你要向交易所购买一定数额的股票。\n", + "\n", + "那么，你需要提交股票代码、方向（买入 / 卖出）、订单类型（市价 / 限价）、价格（如果是限价单）、数量等一系列参数，而这些数据里，有字符串，有整数，有浮点数，甚至还有布尔型变量，全部混在一起并不方便交易所解包。\n", + "\n", + "那该怎么办呢？\n", + "\n", + "其实，我们要讲的 JSON ，正能解决这个场景。\n", + "\n", + "你可以把它简单地理解为两种黑箱：\n", + "> * 第一种，输入这些杂七杂八的信息，比如 Python 字典，输出一个字符串；\n", + "> * 第二种，输入这个字符串，可以输出包含原始信息的 Python 字典。\n", + "\n", + "具体代码如下：" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "after json serialization\n", + "type of params_str = , params_str = {'symbol': '123456', 'type': 'limit', 'price': 123.4, 'amount': 23}\n", + "after json deserialization\n", + "type of original_params = , original_params = {'symbol': '123456', 'type': 'limit', 'price': 123.4, 'amount': 23}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "params = {\n", + " 'symbol': '123456',\n", + " 'type': 'limit',\n", + " 'price': 123.4,\n", + " 'amount': 23\n", + "}\n", + "\n", + "params_str = json.dumps(params)\n", + "\n", + "print('after json serialization')\n", + "print('type of params_str = {}, params_str = {}'.format(type(params_str), params))\n", + "\n", + "original_params = json.loads(params_str)\n", + "\n", + "print('after json deserialization')\n", + "print('type of original_params = {}, original_params = {}'.format(type(original_params), original_params))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "其中，\n", + "\n", + "json.dumps() 这个函数，接受 Python 的基本数据类型，然后将其序列化为 string；\n", + "\n", + "而 json.loads() 这个函数，接受一个合法字符串，然后将其反序列化为 Python 的基本数据类型。\n", + "\n", + "是不是很简单呢？\n", + "\n", + "不过还是那句话，请记得加上错误处理。\n", + "\n", + "不然，哪怕只是给 json.loads() 发送了一个非法字符串，而你没有 catch 到，程序就会崩溃了。\n", + "\n", + "到这一步，你可能会想，如果我要输出字符串到文件，或者从文件中读取 JSON 字符串，又该怎么办呢？\n", + "\n", + "是的，你仍然可以使用上面提到的 open() 和 read()/write() ，先将字符串读取 / 输出到内存，再进行 JSON 编码 / 解码，当然这有点麻烦。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "after json deserialization\n", + "type of original_params = , original_params = {'symbol': '123456', 'type': 'limit', 'price': 123.4, 'amount': 23}\n" + ] + } + ], + "source": [ + "\n", + "import json\n", + "\n", + "params = {\n", + " 'symbol': '123456',\n", + " 'type': 'limit',\n", + " 'price': 123.4,\n", + " 'amount': 23\n", + "}\n", + "\n", + "with open('params.json', 'w') as fout:\n", + " params_str = json.dump(params, fout)\n", + "\n", + "with open('params.json', 'r') as fin:\n", + " original_params = json.load(fin)\n", + "\n", + "print('after json deserialization')\n", + "print('type of original_params = {}, original_params = {}'.format(type(original_params), original_params))" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, + "kernelspec": { + "display_name": "Python 3.10.0 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part.2.D.3-lambda.ipynb b/Part.2.D.3-lambda.ipynb index 8a734e951..1f8df951f 100644 --- a/Part.2.D.3-lambda.ipynb +++ b/Part.2.D.3-lambda.ipynb @@ -768,6 +768,47 @@ "list(map(lambda x:x,phonebook1))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 思考题\n", + "最后，我想给你留下两道思考题。\n", + "\n", + "第一问：如果让你对一个字典，根据值进行由高到底的排序，该怎么做呢？以下面这段代码为例，你可以思考一下。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = {'mike': 10, 'lucy': 2, 'ben': 30}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ben': 30, 'mike': 10, 'lucy': 2}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = {'mike': 10, 'lucy': 2, 'ben': 30}\n", + "d = dict(sorted(d.items(), key=lambda kv: kv[1], reverse=True))\n", + "d" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Part.2.D.9-internet.ipynb b/Part.2.D.9-internet.ipynb new file mode 100644 index 000000000..261e2b47b --- /dev/null +++ b/Part.2.D.9-internet.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# 导入socket库:\n", + "import socket\n", + "\n", + "# 创建一个socket:\n", + "s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", + "# 建立连接:\n", + "s.connect(('www.sina.com.cn', 80))" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, + "kernelspec": { + "display_name": "Python 3.10.0 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part.2.E.10.email.ipynb b/Part.2.E.10.email.ipynb new file mode 100644 index 000000000..c81a8484b --- /dev/null +++ b/Part.2.E.10.email.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 发送email与接收email\n", + "## 发送email\n", + "SMTP是发送邮件的协议，Python内置对SMTP的支持，可以发送纯文本邮件、HTML邮件以及带附件的邮件。\n", + "Python对SMTP支持有smtplib和email两个模块，email负责构造邮件，smtplib负责发送邮件。\n", + "\n", + "首先，我们来构造一个最简单的纯文本邮件：" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from email.mime.text import MIMEText\n", + "msg = MIMEText('hello, send by Python...', 'plain', 'utf-8')\n", + "msg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "注意到构造MIMEText对象时，第一个参数就是邮件正文，第二个参数是MIME的subtype，传入'plain'表示纯文本，最终的MIME就是'text/plain'，最后一定要用utf-8编码保证多语言兼容性。\n", + "\n", + "然后，通过SMTP发出去：" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "send: 'ehlo chenqiangdeMacBook-Pro.local\\r\\n'\n", + "reply: b'250-newxmesmtplogicsvrsza2-0.qq.com\\r\\n'\n", + "reply: b'250-PIPELINING\\r\\n'\n", + "reply: b'250-SIZE 73400320\\r\\n'\n", + "reply: b'250-STARTTLS\\r\\n'\n", + "reply: b'250-AUTH LOGIN PLAIN XOAUTH XOAUTH2\\r\\n'\n", + "reply: b'250-AUTH=LOGIN\\r\\n'\n", + "reply: b'250-MAILCOMPRESS\\r\\n'\n", + "reply: b'250 8BITMIME\\r\\n'\n", + "reply: retcode (250); Msg: b'newxmesmtplogicsvrsza2-0.qq.com\\nPIPELINING\\nSIZE 73400320\\nSTARTTLS\\nAUTH LOGIN PLAIN XOAUTH XOAUTH2\\nAUTH=LOGIN\\nMAILCOMPRESS\\n8BITMIME'\n", + "send: 'AUTH PLAIN ADQ2OTE2NTE3MUBxcS5jb20AQzFoNUU4bjU=\\r\\n'\n", + "reply: b'535 Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256\\r\\n'\n", + "reply: retcode (535); Msg: b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256'\n", + "send: 'AUTH LOGIN NDY5MTY1MTcxQHFxLmNvbQ==\\r\\n'\n", + "reply: b'334 UGFzc3dvcmQ6\\r\\n'\n", + "reply: retcode (334); Msg: b'UGFzc3dvcmQ6'\n", + "send: 'QzFoNUU4bjU=\\r\\n'\n", + "reply: b'535 Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256\\r\\n'\n", + "reply: retcode (535); Msg: b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256'\n" + ] + }, + { + "ename": "SMTPAuthenticationError", + "evalue": "(535, b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256')", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mSMTPAuthenticationError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/chenqiang/Documents/the-craft-of-selfteaching/Part.2.E.10.email.ipynb Cell 4'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m server \u001b[39m=\u001b[39m smtplib\u001b[39m.\u001b[39mSMTP(smtp_server, \u001b[39m25\u001b[39m) \u001b[39m# SMTP协议默认端口是25\u001b[39;00m\n\u001b[1;32m 11\u001b[0m server\u001b[39m.\u001b[39mset_debuglevel(\u001b[39m1\u001b[39m)\n\u001b[0;32m---> 12\u001b[0m server\u001b[39m.\u001b[39;49mlogin(from_addr, password)\n\u001b[1;32m 13\u001b[0m server\u001b[39m.\u001b[39msendmail(from_addr, [to_addr], msg\u001b[39m.\u001b[39mas_string())\n\u001b[1;32m 14\u001b[0m server\u001b[39m.\u001b[39mquit()\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/smtplib.py:750\u001b[0m, in \u001b[0;36mSMTP.login\u001b[0;34m(self, user, password, initial_response_ok)\u001b[0m\n\u001b[1;32m 747\u001b[0m last_exception \u001b[39m=\u001b[39m e\n\u001b[1;32m 749\u001b[0m \u001b[39m# We could not login successfully. Return result of last attempt.\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m \u001b[39mraise\u001b[39;00m last_exception\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/smtplib.py:739\u001b[0m, in \u001b[0;36mSMTP.login\u001b[0;34m(self, user, password, initial_response_ok)\u001b[0m\n\u001b[1;32m 737\u001b[0m method_name \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mauth_\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m authmethod\u001b[39m.\u001b[39mlower()\u001b[39m.\u001b[39mreplace(\u001b[39m'\u001b[39m\u001b[39m-\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m_\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 738\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 739\u001b[0m (code, resp) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mauth(\n\u001b[1;32m 740\u001b[0m authmethod, \u001b[39mgetattr\u001b[39;49m(\u001b[39mself\u001b[39;49m, method_name),\n\u001b[1;32m 741\u001b[0m initial_response_ok\u001b[39m=\u001b[39;49minitial_response_ok)\n\u001b[1;32m 742\u001b[0m \u001b[39m# 235 == 'Authentication successful'\u001b[39;00m\n\u001b[1;32m 743\u001b[0m \u001b[39m# 503 == 'Error: already authenticated'\u001b[39;00m\n\u001b[1;32m 744\u001b[0m \u001b[39mif\u001b[39;00m code \u001b[39min\u001b[39;00m (\u001b[39m235\u001b[39m, \u001b[39m503\u001b[39m):\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/smtplib.py:662\u001b[0m, in \u001b[0;36mSMTP.auth\u001b[0;34m(self, mechanism, authobject, initial_response_ok)\u001b[0m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m code \u001b[39min\u001b[39;00m (\u001b[39m235\u001b[39m, \u001b[39m503\u001b[39m):\n\u001b[1;32m 661\u001b[0m \u001b[39mreturn\u001b[39;00m (code, resp)\n\u001b[0;32m--> 662\u001b[0m \u001b[39mraise\u001b[39;00m SMTPAuthenticationError(code, resp)\n", + "\u001b[0;31mSMTPAuthenticationError\u001b[0m: (535, b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256')" + ] + } + ], + "source": [ + "# 输入Email地址和口令:\n", + "from_addr = '469165171@qq.com'#input('From: ')\n", + "password = 'C1h5E8n5'#input('Password: ')\n", + "# 输入收件人地址:\n", + "to_addr = '469165171@qq.com'#input('To: ')\n", + "# 输入SMTP服务器地址:\n", + "smtp_server = 'smtp.qq.com'#input('SMTP server: ')\n", + "\n", + "import smtplib\n", + "server = smtplib.SMTP(smtp_server, 25) # SMTP协议默认端口是25\n", + "server.set_debuglevel(1)\n", + "server.login(from_addr, password)\n", + "server.sendmail(from_addr, [to_addr], msg.as_string())\n", + "server.quit()" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad" + }, + "kernelspec": { + "display_name": "Python 3.10.0 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part.3.B.1.classes-1.ipynb b/Part.3.B.1.classes-1.ipynb index aae544aa4..6718542bb 100644 --- a/Part.3.B.1.classes-1.ipynb +++ b/Part.3.B.1.classes-1.ipynb @@ -153,10 +153,10 @@ "> * 界面，属性，方法\n", "> * 继承，类，子类，实例\n", "- 对象其实就是object，比如我们说的动物就是对象。\n", - "- 对象和类之间的区别是什么，其实对象就是类和子类的统称\n", + "- 对象和类之间的区别是什么，其实对象就是实例，类是抽象归纳的类别，比如我们类里有狗类，子类的话就有狼狗类，但是我们有个符合狼狗类的狗名叫“二狗”。\n", "- 子类是继承了类的所有属性和方法，但也可以去重写属性和方法。\n", "- 我们创建实例就相当于是创建某个类的实例，比如我们要创建狗，狗属于哺乳类这样\n", - "- 那抽象是有些类，我们不需要写明他具体是哪个类，我们在实例化时可以具体声明\n", + "- 那抽象是有些类，我们不需要写明他具体是哪个类，我们在实例化时可以具体声明，抽象类的作用是什么呢，举个场景，有一样东西是现实中不可能出现的东西或者难以描述的东西，当子类继承后并且能够对其进行二次描绘“重写”才能生成实例，而这样的子类很多，有些属性和方法我们可以进行抽象，因此抽象类就由此而来。\n", "- 封装指的是我们要把不需要给使用者看的东西封装好，不被他们看到，给他们看到就是个开关\n", "- 界面就是由属性和方法组成，就是英文里头的动词和名词" ] diff --git a/Part5.B.downloadcsv.ipynb b/Part5.B.downloadcsv.ipynb index a06fc59d1..e4efc4397 100644 --- a/Part5.B.downloadcsv.ipynb +++ b/Part5.B.downloadcsv.ipynb @@ -982,6 +982,481 @@ " # 单行写入\n", " writer.writerow({'first_name': 'Wonderful', 'last_name': 'Spam'})" ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['https://movie.douban.com/subject/34841067/comments?start=0&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=20&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=40&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=60&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=80&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=100&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=120&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=140&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=160&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=180&limit=20&status=P&sort=new_score']\n" + ] + } + ], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import time\n", + "import random\n", + "urls=['https://movie.douban.com/subject/34841067/comments?start={}&limit=20&status=P&sort=new_score'.format(str(i)) for i in range(0, 200, 20)] #通过观察的url翻页的规律，使用for循环得到10个链接，保存到urls列表中\n", + "print(urls)\n", + "dic_h = {\n", + " \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\"}\n", + "comments_list = [] #初始化用于保存短评的列表\n", + "\n", + "for url in urls: #使用for循环分别获取每个页面的数据，保存到comments_list列表\n", + " r = requests.get(url=url,headers = dic_h).text\n", + "\n", + " soup = BeautifulSoup(r, 'lxml')\n", + " ul = soup.find('div',id=\"comments\")\n", + " lis= ul.find_all('p')\n", + "\n", + " list2 =[]\n", + " for li in lis:\n", + " list2.append(li.find('span').string)\n", + " # print(list2)\n", + " comments_list.extend(list2)\n", + " time.sleep(random.randint(0,3)) # 暂停0~3秒\n", + " \n", + "with open('lhy_comments.txt', 'w', encoding='utf-8') as f: #使用with open()新建对象f\n", + " # 将列表中的数据循环写入到文本文件中\n", + " for i in comments_list:\n", + " f.write(i+\"\\n\") #写入数据" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['https://movie.douban.com/subject/34841067/comments?start=0&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=20&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=40&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=60&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=80&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=100&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=120&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=140&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=160&limit=20&status=P&sort=new_score', 'https://movie.douban.com/subject/34841067/comments?start=180&limit=20&status=P&sort=new_score']\n" + ] + } + ], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import time\n", + "import random\n", + "urls=['https://movie.douban.com/subject/34841067/comments?start={}&limit=20&status=P&sort=new_score'.format(str(i)) for i in range(0, 200, 20)] #通过观察的url翻页的规律，使用for循环得到10个链接，保存到urls列表中\n", + "print(urls)\n", + "dic_h = {\n", + " \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\"}\n", + "comments_list = [] #初始化用于保存短评的列表\n", + "\n", + "for url in urls: #使用for循环分别获取每个页面的数据，保存到comments_list列表\n", + " r = requests.get(url=url,headers = dic_h).text\n", + "\n", + " soup = BeautifulSoup(r, 'lxml')\n", + " ul = soup.find('div',id=\"comments\")\n", + " lis= ul.find_all('p')\n", + "\n", + " list2 =[]\n", + " for li in lis:\n", + " list2.append(li.find('span').string)\n", + " # print(list2)\n", + " comments_list.extend(list2)\n", + " time.sleep(random.randint(0,3)) # 暂停0~3秒\n", + " \n", + "with open('lhy_comments.txt', 'w', encoding='utf-8') as f: #使用with open()新建对象f\n", + " # 将列表中的数据循环写入到文本文件中\n", + " for i in comments_list:\n", + " f.write(i+\"\\n\") #写入数据" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "把二十多分钟的小品拉长到两个小时，没有好导演和好剧本还是省省吧。\n", + "有笑有泪的，不明白陈赫这个人物有什么作用，完全多余\n", + "跟唐人街探案三的分数相比，很明显观众们评判电影的标准不仅在于你拍的好坏，而更在于你拍的态度是否真诚\n", + "这次感受注定是感性压倒理性的，所以就不打分了。\n", + "贾玲还是适合做小品，她的表演方式、她的叙事能力都更适合能和观众互动的、短小精炼的舞台而不是要被观众一帧一帧检视的电影。更何况她非科班出身，参与电影项目也不多，要执导筒讲故事就更是自曝其短。\n", + "这是一部非常开心麻花式的片子，如果你喜欢《夏洛特烦恼》那么可以去看看，如果是抱着宁浩电影的期待那就赶紧睡个回笼觉，或者喝杯咖啡把瞌睡醒醒。\n", + "但说一千道一万，《你好李焕英》只能是贾玲来演、贾玲来导，它是贾玲自己的情感寄托和内心牵挂。哪怕在我觉得很尬的场景里，她对母亲的爱意和思念都饱满到要冲出屏幕来逼我落泪。我也确实掉了眼泪，在大年初一这天。\n", + "这种情绪不是浓汤宝能兑出来的煽情，哪怕我和她的人生轨迹并不相同，但我也在电影院里体会了一把贾玲的过往青春。\n", + "我以为是我在为你圆梦，其实还是你陪着我做了一场好梦。\n", + "贾玲水平有限，奈何感情无比真挚。虽然结尾让我哭的稀里哗啦，但也没能改变前半段就是个低配版夏洛特烦恼的状况。\n", + "这完全是贾玲拍给自己的作品，不是拍给观众的，充满了个人的执念。\n", + "你以为你已经很爱很爱妈妈了，但妈妈远比你想象中更爱更爱更爱你\n", + "贾玲哭的时候很明显没有在演戏，这是最让人伤心的。\n", + "前一个小时强行塞包袱，后面干嚎占二十分钟。。。\n", + "我不喜欢，我不觉得好笑，也不觉得感动\n", + "以为穿越是为了改变她的人生轨迹，竟然只是了却自己的遗憾。原来从天而降的大胖妞无论有多重，她都会毫不犹豫接住她的宝儿。裤子无论多破，她都会缝成小狗。今生母女一场，只能化作目送。以为她希望我有出息，原来她只需要我平安。以为她想换个女儿，原来她从没后悔。以为多了解她，原来还是误会了她。\n", + "7分。豆瓣8.2分过分了，就电影论电影的话，贾玲这部作品最大的问题在于它不太像电影（但也确实比小品形态提升了不少）。如果无视这一点，主要凭是否感动或者哭了多少眼泪来评价它的优劣，那就好比让情色片与AV同台竞技，主要凭观众生理反应的指标来评价优劣。 \n", + "同样是女性导演的亲情题材，许鞍华《桃姐》8.3分，张艾嘉《相爱相亲》8.4分，就是把贾玲纪念母亲的真诚用心全算上，它跟蒋雯丽《我们天上见》——同样演员出身的导演处女作，同样纪念自己至亲——也差了几条街，后者的确是一部电影，蒋雯丽之后没再做导演让我觉得可惜，而贾玲如果之后再做导演我只会觉得她是飘了。\n", + "如果你只是把它当成一件商品，那么眼泪的容量可以跟商品的质量成正比。如果你还把它当成一件艺术品，那么真诚只是优秀艺术品的要素之一，技巧和手法同等重要。【仙桃金逸】\n", + "8.3分绝对是太不正常了。《李焕英》你不能说它差，但也绝对称不上好，只是前些年综艺大电影版的变形罢了。主题选择了“子欲养而亲不在”，表达上却仅限于一味的情感宣泄，里面其实你很难找出一个完整鲜活的角色，母、女也只是传统文化几千年来堆砌出来的固定形象，除了伟大和孝顺找不到更多的形容词。\n", + "贾晓玲撮合李焕英和沈光林这段有很多笑点，但内核是很扎心的。\n", + "因为它体现了贾晓玲的自卑和丧，她觉得自己活着什么用都没有，她凭借自己无法让母亲过上更好的生活，所以她才想出这个办法。\n", + "很多人看了《李焕英》后都说：如果母亲生的不是我，她是不是会过得更幸福。\n", + "但这并不是电影想要表达的主题，电影想表达的是，你的母亲觉得有你这样的儿女，就已经很幸福了。\n", + "贾晓玲说她会看相，她看到李焕英未来的女儿会出国留学，而且收入很高。\n", + "而李焕英说，我的女儿，我就让她健康快乐就行了。\n", + "其实天下母亲都是如此。\n", + "如果我们能功成名就当然更好，但如果我们没有功成名就，难道她们就会觉得我们不配做她们的儿女吗？\n", + "事实是无论我们贫穷还是富贵，成功还是平凡，她们都会以我们为傲。\n", + "因为重要的不是我们做出多大的成就，重要的是“我们”。\n", + "贾玲：我给你们讲个笑话，你们别哭。\n", + "我要强烈批评影片最后刻画母亲买票又退票在大雪天中一个人走回去的镜头，我不接受，母爱不是因为牺牲才伟大，爱与关怀本身就是伟大。\n", + "其实还是小品段子电影，最大的梗电影看得稍多些的观众一早就能猜到，但胜在有真感情，没有绝大部分国产喜剧那股鸡贼味。这片或许确实只能由贾玲自己来拍，换别的导演，就算拍得更成熟、更电影，恐怕也达不到现在的效果，或者说，就会是另一个故事了。\n", + "这个底太好了，比起以往穿越片的个人救赎迈的更深了一步。本以为影片会如同以往的俗套一般会终结在「希望孩子健康快乐」上，没想到结尾以针线为引，将「无论你在哪里我都会找到你并且保护你」的母爱穿进了观众的心里，以个人故事为本，动了真情的贾玲担得起导演的称呼。在喜剧元素上，影片也做的十分自然，不刻意不尴尬，更是在演员的选角上玩出了花，乔杉和贾玲的父女堪比去年黄渤和彭昱畅。\n", + "看到8.3分才决定去看的，看完了，就这？是我要求太高了吗？很多桥段感觉很刻意和做作，张小菲的假笑只有我感受到了吗……最后的反转的确有感动到，但也不至于如热搜所说湿了两个口罩吧，我还特意有所准备，我的感受是这部剧营销过头了吧\n", + "贾玲昨晚的喜剧已经尴尬到我了… 这个导演的电影真的更加尴尬… 还是好好当个综艺咖吧！电影就算了…别伤害影迷的眼睛了…\n", + "“我宝”那句出来的时候真的直接泪奔\n", + "我以为只有我回到了1981,我以为我可以牺牲我自己改变你往后的命运，我以为你可以更幸福的，我没想到你愿意过的就是摊上我的这一生😭\n", + "国产+喜剧+催泪＝烂片，这是豆瓣一贯的风气，因此我知道自己的评分一定不会公允，这里有掺杂太多我的主观感受：有生以来第一次在电影院哭得泣不成声。\n", + "昨晚是除夕，我和妈妈大吵了一架。她气得直骂我没良心、白眼狼，我爸也心寒地把我妈牵到楼下，说不指望以后让我给他们养老。\n", + "凌晨的时候，我给我妈朋友圈的拜年视频点了个赞，并发给她一个拜年红包，和她说“今晚我脾气不好，你别往心里去”我妈收下红包，给我发来好多幸福快乐的祝福。\n", + "今天看到这电影，许多情节点都让我泪眼婆娑，直到女儿明白真相后，一路奔跑的那一段蒙太奇，我极力掩着眼泪，把哭声压到最小。\n", + "观影是一个创作者通过作品感染观众的过程，无论它是否合乎艺术规律，只要能让观众感动，它就值得那个观众为它叫好，不必因为它是国产片就舍不得掌声。\n", + "感谢编剧没有让李焕英以少胜多赢下排球赛。这个剧其实好就好在它的质朴情感上。没有什么输赢胜负，也没有什么人生逆袭改变命运。穿越回来只为了让你开心。不完美的小人物在闪闪发光。\n", + "2.5 穿越梗是人家三十多年前玩剩下的套路，拍得也很糟糕（导演问题），全程味同嚼蜡，直到最后那个反转。这是皮克斯式的情感/视角反转（参考瓦力、玩具3和Coco），学到了精髓而奏效，可惜又处理得过于繁重拖沓。当然电影之外的真情实感的确很动人。\n", + "真诚打动内心，没办法客观评价\n", + "我最忘情的哭声有两次，一次，在我生命的开始；一次，在你生命的告终。第一次，我不会记得，是听你说的；第二次，你不会晓得，我说也没有用。但两次哭声的中间啊，有无穷无尽的笑声。一遍一遍又一遍，回荡了整整三十年。你都晓得，我都记得。——余光中《今生今世》\n", + "8分？是因为我没看过开心麻花，对这个体系的喜剧存在一些误会吗？我以为自己对贺岁片已经够宽容低期待了，唐探，飞驰人生，流浪地球，都是手松一松能给4星的贺岁片，但李焕英是真的不行，毫无新意，所有的场景台词都是能找到一百八十个前辈的陈词滥调，听听你妈年轻时候的故事都比买票跟着别人哭强啊，脱离不了屎尿屁和融梗的国产喜剧，太廉价。尊重贾玲的心意，但不认同这是一部值得被夸赞至此的电影。\n", + "在电影院哭得稀里哗啦。隔壁座的小姐姐也在哭。她问我：想妈了？我说：嗯！小姐姐：你也春节没回家？我指了指前面：我妈坐第三排，我俩没买着连号的座。这下小姐姐哭得更凶了。\n", + "有一种“上当”了的感觉，第一次觉得豆瓣评分不可信\n", + "贾玲你这样算不算恰烂钱？我就想问这个电影要干嘛？你要煽情没问题要找人客串没问题，要搞笑都没问题！可你什么都来一点。大杂糅无非就是要保证票房而已！\n", + "我讨厌煽情喜剧！即使再感人 流泪会有种被“打亲情牌”而被占便宜的感觉\n", + "最戳我的就是在知道了48岁的李焕英是穿越回去的后，想起那句“我这一辈子过得挺幸福的，怎么就没人相信呢。”\n", + "意识到妈妈什么都知道的时候，我哭到抽搐。贾玲太有才了。在读你和我时就在想，逝去的父母是靠儿女的记忆继续活着，而如果你可以写作，或者像贾玲一样用一部电影来纪念，那他们就一直活着。他们的精彩也都没有被忘却。这是贾玲给自己的交代。我不停的想，到底我不能接受的是什么，如果她的一生在自己活着时就去全都熠熠闪光，那死去时也不能称之为遗憾，只是命运。\n", + "贾玲说她把心掏出来给观众看了，我想说，我看到了。\n", + "打着亲情牌圈钱 说难听就叫消费亲妈\n", + "张小斐还行，整体观感就像看现如今强迫你笑你哭的春晚小品，对不起我真的入不了戏。\n", + "这是今年春节档，我最最想看的一部电影。《你好李焕英》的小品，是千禧年之后我心目中最佳小品前三，即便珠玉在前，当得知要影视化的时候，依然期待爆棚。作为贾玲的导演处女座，这部作品是值得肯定的，笑点泪点恰到好处，没有硬搞笑也没有刻意煽情。子欲养而亲不待，她在讲一个珍惜亲情的故事，也在讲当代女性意识的觉醒，不鼓吹生育，不鼓吹婚姻，而是让我们看到，任何一个女性在成为妈妈之前，第一个身份首先应该是她自己。祝天下所有“李焕英”永远快乐。\n", + "不想在大过年的接受情感强奸。\n", + "基本上和夏洛特烦恼一个套路，穿越过去弥补现实生活的遗憾。但剧本比不上夏洛打磨的千锤百炼。能看出贾玲对母亲的真挚感情，但不足以撑起一部电影的篇幅。\n", + "逻辑很差让人无法共情，前面笑料勉强可以，后面哭得可尴尬了，作为导演，她想导观众的是，看我都哭成这样了，你们快给我哭！\n", + "放在大过年，大年初一给老子哭？？？？为什么喜剧一定要哭！不哭不行嘛？？\n", + "为什么表妹撕了你刚拿到的结婚证你都不生气。因为那是你女儿啊。\n", + "最让人泪崩的是，我知晓未来所发生的一切，依然选择你这件小棉袄来陪我走过一生。\n", + "不是我拉男女对立啊。当屏幕上出现 “贾玲 执导”字样的时候，就比起那些拍了几个直男癌片子就写“××× 导演作品”的，要真诚多了。\n", + "“作品”这个词是自己叫的吗。。\n", + "说实话吧，这要都算电影，电影的门槛是太低了点。\n", + "散场以后我挎着妈妈散步回家，我想着她，她应该想着她妈妈吧。\n", + "电影有一种聪明的真诚，可是这种真诚并不新鲜，每隔一长段时间，从让人疲惫的视效大片出来，你都能找到类似的感动，在国内电影市场上成为黑马。我们被这样的片子感动过太多次了。创作者不能老是这么拍电影，观众亦不能老是做煽情大保健。\n", + "\n", + "它起了一个坏示范，无法从沉湎于悲伤中，走向理智，走向探索生活本质，因而它用冗长的煽情，绕开了现实的复杂。在某种程度上，它绑架了大多数母亲，建立一种刻板的母亲形象，抹掉了母亲形象本身的丰富，也绑架了身为子女的观众。\n", + "\n", + "从技巧上，即使对比《相爱相亲》情节剧，它也缺乏力量，就更难对比类似《痛苦与荣耀》里母亲和子女作为独立个体相互平视的亲密关系。真的，我们不需要黏糊糊的中国式不分你我的亲情，我们不需要下跪父母，我们更需要在这种亲情中，走向人格的独立、人的平等和思想的升华。\n", + "实际上三星都很勉强的……豆瓣分太高了8.2，好吧2星……没有任何创新，剧情拖沓，强行煽情……\n", + "仅强于《囧妈》，结尾不错改分了\n", + "如果你要陪长辈看电影的话，那就看这部吧。基本上就是贾玲版的《夏洛特烦恼》，完成度很好，笑点和泪点都到位，感觉会是春节档期大众口碑最坚挺的一部电影。一开始你以为这是一个熟悉的穿越设定，最后一反转，你会发现这不仅仅是一次穿越，而穿越的背后正应了母女情的主题。完了结尾的字幕又补了一刀，原来这是贾玲自己的经历，用这样一部电影去追忆母亲年轻时的样子。穿越回八十年代，从工厂记忆，女排比赛，黑白电视到祖父悖论，都是能引发几代人共鸣的设计，以前都是看贾玲笑，这次是看贾玲哭，很多人却是跟着她笑着哭。\n", + "笑的不尴尬，哭的脑壳疼。\n", + "看完真是气死了，求求中国的导演了，拍喜剧就纯粹点吧，咱别煽情别拔高别搞低级催泪行吗？我花了比平时贵的票价是来寻开心的不是来哭的啊喂。\n", + "本来前半部分还算是精致轻松的风格，后半部分就又往煽情的低级趣味上去了。对，亲情确实很容易感人很容易把观众弄哭，咱们能不能选个难点的level,把它拍成云淡风轻，春风化雨好不好？\n", + "真TM妥妥春晚小品风。躲过了春晚，却没躲过这货。\n", + "实话实说我哭抽抽了 看见张小斐想喊声妈的程度\n", + "笼统的看下豆瓣评分就去看了，真的很失望，感觉评分是刷的，不值票价和时间，贾玲值得尊敬，母爱更值得赞扬，但这种形式，就像小学的感恩大会，有一个人拼命的在台上讲，拿出各种故事拼命的渲染，好像不感动不哭就是不合适宜。\n", + "感谢贾导。感谢李阿姨，是您的女儿在开局落后15亿的情况下，阻止了《唐3》这种烂片成为2021年春节档票房冠军，她永远是您的骄傲。\n", + "我未来的女儿，我就让她健康快乐就行了。\n", + "\n", + "\n", + "\n", + "大家来看这一部吧，别看唐3了\n", + "我求求这群演员了，真不是是个人都能当导演\n", + "现在的喜剧已经不是喜剧，都是让你一会笑一会哭的，pua套路\n", + "80年代性转夏洛一点不烦恼，从头到尾的无聊和尴尬，贾玲说说是讲述自己和妈妈的故事，其实真的和妈妈一点关系都没有，尤其是李焕应这个角色，就是一个工具人而已。一个小品撑长到电影长度已经很勉强了，结果还在小品里面套小品和段子就更让人难以忍受了。整个故事真的可以说一马平川，没有真正的人物矛盾，没有真正的情节波折，甚至，连整个故事的情感支点都是虚的，剧本的堆砌感实在是太强了，结构散得一塌糊涂，观众是真的好骗呀！\n", + "值得一看，但没有你想象中那么好看，尤其是当你带着豆瓣8点多高分期待去看！都说哭的稀里哗啦，但我确实没有哭……\n", + "到底是一种怎样的执念，让焕英这老母亲，在人生最后的回光返照里，在明知晓玲的破洞牛仔裤是流行的情况下，都要把那洞给缝上？\n", + "看过这个小品，人都没换，变成电影了。。。作为骗钱贺岁档一点诚意都没有，史上最i差，看不下了。\n", + "如果你看过贾玲的小品，看过这个的话。我不知道你什么心态。看什么不好看这个。实力劝退。\n", + "嘴一句，是挺感人，但是我都说了。\n", + "这并不是一个穿越的故事，而是是一个母亲在弥留之际创造出来安慰女儿的梦，真的特别特别特别好，贾玲值得。9/10 \n", + "让她再选一次，她还是义无反顾的选择了有你的人生\n", + "咋说呢，贾玲太牛了，不玩梗的那种搞笑，一点都不低俗，但是笑点通俗易懂，演员也都特别好，而且有一个点很戳我，就是后面暗示了王琴的发达不是因为靠男人，中国就是应该多有这样的思想纠正啊！！催泪的地方也容易产生共情，后半段我一边哭一边骂：这特么咋没完没了了……然后继续哭....好了好了，我爱我妈妈，虽然我们也会吵架，但是我很爱她，一直爱她\n", + "自我安慰式小品\n", + "豆瓣也开始水了\n", + "“我的女儿，只要健康快乐就行了”\n", + "神不能一直在，所以创造了妈妈\n", + "陈赫的戏份太恶心了，和贾玲一起躲雨，陈赫和贾玲肥胖身躯挤来挤去恶心死我了，尴尬到我脚趾母都扣起了。\n", + "贾玲好棒 抢结婚证那里 结婚证被撕坏了 小斐眼里含泪的说 我这一生真的很幸福 你为什么就不相信呢 当时看的时候觉得很奇怪 后来全明白了 剧本好棒 拍的也很好 不过李焕英不管票房多少 她应该只想让贾玲健健康康 开开心心吧\n", + "感谢贾玲，我们太需要女性视角的影视作品了，在女性眼里，女人不和性挂钩，不被凝视，所以不需要少女，不需要磨皮，滤镜，让人看的非常舒服，今年春节最佳了\n", + "那句\"我宝\"真是让我眼泪都要下来了\n", + "吹的太过了，6.5分顶天了，整个就一个小品，这样的画质和声效离影院标准差太远了，网播就行了。据说投资3.8个亿，少两个0都不影响效果。要不是我的免费票没别的看了，真不会看。\n", + "一开始觉得冷特的角色多余，晓玲总要回来的，这样留下一段感情对冷特来说很残忍，后来反转了才明白根本不是穿越，是妈妈为晓玲创造的梦境，在妈妈创造的梦境里，为了抚平晓玲的自卑而创造了冷特的角色，妈妈想告诉女儿 她也值得被爱。\n", + "挺细腻的一部剧吧。\n", + "以为是帮妈妈圆梦，其实是妈妈陪女儿回去帮她圆梦。\n", + "我这一辈子过得挺幸福，你咋就是不信呢？\n", + "不知道有多尴尬 全程尬笑\n", + "作为一部电影来说，是不及格的，严重过誉。剧情结构太弱，逻辑基础混乱，表演舞台化严重。强行抖机灵的台词和用力过重的表演，都让人尴尬得出戏，反而掩盖和混淆了剧作本身表达的内容。前半个小时，我深刻自我怀疑：就这？中间一个小时我满心期待会有更好的编排，却越来越失望。最后半个小时，我感觉被捏住脖子摁在地上强行质问：你哭不哭？体验极为不好😞\n", + "从妈妈张开手喊着“我宝”去接人那瞬间开始，哭到停不下来。你以为自己已经很爱很爱妈妈了，想尽办法让她开心，其实她更爱更爱你，她包容你、守护你，甚至下辈子也舍不得让你当妈妈。笑点有，泪点足，反转到位，其实剧情的反转就是感情的反转，因此带来的触动是极大的。除了陈赫这个角色以及这条感情线略鸡肋，其他我觉得都很好。真诚永远是最打动人的，这部电影真的很用心、很真诚。【8.2】\n", + "观感有些复杂 哭了\n", + "但你又知道这个哭是生理性的，被煽动的\n", + "你不太确定那是不是感动，你为这样的自己感到厌恶\n", + "贾玲第一次当导演就能拍出这水准的片子，可见用心和不用心做事的区别到底在哪里，我觉得做春节烂片的导演们真的该好好反省反省。\n", + "故事虽然有点像某个电视剧的剧情，但也不全是一样，加上看得出来贾玲确实放了真情实感，后面的回忆杀真的让人泪目。\n", + "前半段的笑点也很多，划船那段真的笑到打滚！沈腾贾玲真的是喜剧质量的保证。而且演员们的演技还是都不错的。值得去电影院支持。\n", + "看完之后感觉可能是春节档最好看的吧……\n", + "有泪有笑，这不是一个人的感觉，是整个影厅的反馈\n", + "````最讨厌这种电影！！\n", + "女性导演还是会更细腻一些，结局并没有刻意要所谓的“圆满”，贾玲第一次导戏非常成功啊👍\n", + "看哪，贾晓玲考上了艺校，打铁娘子队赢得了新的搪瓷杯，王琴靠自己的努力过上了更好的日子，而李焕英，她得到了一次重返青春的机会，真好。这些女孩都有光明的未来，而这也就是中国电影需要更多女性视角的原因。（理性角度来看，这部电影自带的一些争议以及很多稚嫩的处理手法，确实不应该拿到五星。但是揉揉自己哭肿了的眼睛，我确实没办法公正的打分。一颗心都捧出来给观众看了，这不叫技巧性煽情，而叫真诚。）每位演员的表现都让这个剧本变得更加熠熠生辉（包括热评里觉得多余的一些角色的演员），而这部电影也证明了，不用一些带有性别倾向的梗（无贬低之意），也能让人发自内心地笑出来。最后表白结尾的文字：每个母亲，都曾经是少女。而在母亲这个身份之前，请不要忘记她是一个独立的人。\n", + "“你以为自己已经很爱很爱妈妈了，妈妈却远比想象中更爱更爱你。你以为自己是在为妈妈圆梦，没成想只是她陪你做了一场好梦。”\n", + "好无聊好无聊，旁边的小朋友被无聊哭了，真的难看。不知道要讲什么，笑点还都很尬，贾玲演的这个人真的好奇怪，还一直愁眉苦脸的，她煎熬，我看她也煎熬。看在沈腾的份上两星吧，张小斐也还行\n", + "看评论时有人说了这样一个观点，我特别特别赞同：“看这个片会哭地很惨的人都是特别幸福的人，因为能和贾玲产生共鸣说明有一个爱你的好母亲，一个只想你幸福快乐无私付出的母亲，但现实中也有一些人不那么走运，ta们的母亲可能自私，也会让孩子寒心…因为不是全天下的母亲都是合格的母亲，所以看哭了其实真的是一件特别幸福的事，这足以说明你幸运地拥有一个好妈妈。”\n", + "“我的女儿，只要她健康快乐就行了。”本来以为只是贾晓玲穿越回了1981年，没想到原来2001年的李焕英也穿越回了20年前，这里真的设计得太巧妙了。重来一回，李焕英还是会选择当玲儿的妈妈啊。开头李焕英看见从天而降的玲儿，下意识地就要去接住玲儿，这就是母爱吧。这种感情可能真的只有母女会懂吧。\n", + "前半部分贾玲沈腾真的好好笑哈哈哈哈哈哈哈。\n", + "贾玲在影片里用了好几次华仔的歌，不愧是华仔的真爱粉。\n", + "啊对，通过黑白照片染色这一方式呈现画面也蛮有创意。\n", + "哭笑交叉的一部电影。\n", + "前面一个半小时三星，后面半小时五星，眼泪水真是摥勿牢\n", + "1.5。喜剧元素出众，笑点属上乘。靠细节抓住时代感，通过时代落差反过来促进笑点\n", + "\"自我记事以来，母亲就一直是一个中年妇女，但是我们却容易忘记她曾经也是一个花季少女\"。说得很好，可惜没有任何意义，在鸡汤里都算下流水平\n", + "电影很好地完成了风格的转变，至少效果是达到了的，电影院里前半段笑声不断，后半段鸦雀无声，真正地做到了\"有笑有泪，喜剧的内核是悲剧\"的大众低俗标准，可惜转场的生硬导致全片漏洞百出，然而这些都在观众的哽咽中抛在脑后\n", + "贾玲的确用心拍摄了这部电影，真情实感在影像的流动间溢出屏幕，对母爱的赞美让我们感同身受，很好地做到了共情，可惜这不是电影的评判标准\n", + "\"献给所有伟大的母亲\"，主题一下子升华，电影结束后观众的赞美声再次证明了煽情永远是电影最低级最取巧的表现手法。片中的母爱远没有达到伟大的地步\n", + "新年第一部觉得最值得五星的片子，很好笑也很好哭。阳光下张小斐穿百褶裙真的漂亮，打排球真的飒。\n", + "另，沈腾叔叔的表演让我相信雪姨阿姨是靠自己让她女儿去米国念书的。\n", + "\n", + "微博上发不出负面评论，怒打低分。浓缩成一出小品 或者 拍一个短片，也许会不错。作为一部电影，一塌糊涂。难过。\n", + "贾玲和他爸的乔杉真像啊！\n", + "是不是被隔壁唐3给惯的了，明明也就一般的片子，怎么一下就彪到30亿了，贾玲真应该感谢一下陈思诚\n", + "催泪神器，给四颗星五颗星都可以理解，毕竟大家都太想“让妈妈高兴”了。\n", + "“你以为你已经很爱很爱妈妈了，其实妈妈比你想象中更爱更爱你。”\n", + "歌颂母德，令人不适。如果一个创作者的私人情绪泛滥成灾，那么最终成品也只能是一个灾难。\n", + "春节档电影票价很贵，选来选去还是看了这部，倒不是喜欢沈腾和贾玲，而是抱着宣传的噱头:喜剧片才带着爸爸妈妈去看，看完之后只有一个感觉，喜剧片不是各位的噱头，特别是春节元旦这样的档期，还有希望各位对喜剧演员的包容度稍微减一减，沈腾贾玲尴尬至极，还有一个要命的陈赫。。。。\n", + "“可是我妈还不会缝啊” 自此开始眼泪止不住。\n", + "以为穿越是为了改变她的人生轨迹，竟然只是了却自己的遗憾。原来从天而降的大胖妞无论有多重，她都会毫不犹豫接住她的宝儿。裤子无论多破，她都会缝成小熊。今生母女一场，只能化作目送。以为她希望我有出息，原来她只需要我平安。以为她想换个女儿，原来她从没后悔。以为多了解她，原来还是误会了她。\n", + "豆瓣也开始水了\n", + "有时候不需要太深的立意，故事流畅，人物真诚就很足够了\n", + "催泪神器，给四颗星五颗星都可以理解，毕竟大家都太想“让妈妈高兴”了。\n", + "承认是贾玲的真实感情，但仅限于此。\n", + "什么电影手法，在真挚感人面前不重要\n", + "所有不是沈腾主演却写他主演，预告片就减他出场的部分骗我去看的片子全部最低分\n", + "如此成功地调动了观众的情绪和心理，诠释了电影作为一个现实生活的宣泄口和安慰剂，在意识形态上功能可以多么强大。越是回到过去，我们离现实越远。实际上，你和你妈的矛盾，根本不是靠两个人重新选择可以解决的，而是靠你妈接受了你的平庸，达不到社会成功人士的标准。可是，有出息的标准是注定不会被质疑的，只恨发财买车没有早一点，让妈赶上享福。这难道不是一个成功人士对“失败者”的冷酷规训吗？最后，观众在愧疚、自责和遗憾中流下感动的泪水。可是，女性之不幸，原来终究是子女一代的错吗？社会变迁、家庭结构，隐没在母爱的伟大里，逃避了追问。我为这样地利用一个母亲而感到悲哀，因为她的不幸仍要在这个大地延续下去，在循环往复的过新年里被虚假地应付，实际上这不幸是永远过不去的。\n", + "完虐隔壁姓唐的好么？\n", + "被捧得很高的贺岁档 感觉剧情有点俗套 全片唯一的亮点大概是她妈也穿越了吧\n", + "30/100 俗套至极的剧作本就奠定了不佳的观感，而后大篇幅的自我感动更叫人尴尬得无地自容。也许贾玲真的把电影当作了小品，如乱炖的类型语气割裂了所有闪光点，却依然妄图用“真实事件”来掩盖除了俗套的情感升华什么也不会的事实，并且前者早已在矫揉的煽情桥段里被消弭得一干二净。在影院看纯粹浪费票钱。\n", + "你好李焕英或是春节档最佳\n", + "陈赫的戏份太恶心了，和贾玲一起躲雨，陈赫和贾玲肥胖身躯挤来挤去恶心死我了，尴尬到我脚趾母都扣起了。\n", + "提前看影评说会哭的稀里哗啦 所以看的时候我一直在憋着等最感人的时候再哭 结果憋着憋着就结束了……\n", + "真情动人\n", + "多一星给最后的反转\n", + "电影一旦入了真情 讨论技法之类的似乎就落了下乘 \n", + "好无聊好无聊，旁边的小朋友被无聊哭了，真的难看。不知道要讲什么，笑点还都很尬，贾玲演的这个人真的好奇怪，还一直愁眉苦脸的，她煎熬，我看她也煎熬。看在沈腾的份上两星吧，张小斐也还行\n", + "mv级别的电影\n", + "贾玲现在不仅健康快乐，还出息了，焕英阿姨你看到了吗\n", + "得知两个人一起穿越，很催泪，想要用手接住从天而降的女儿那个画面也是泪点\n", + "所有人都把它吹上了天，但这就是一部笑点低级强行煽情的小品式电影\n", + "带着口碑去的，有点强行感人的味道，评分高可能都是同行衬托得好\n", + "剧情感情不落俗套，但煽情手段太落俗套，为什么国内电影都是这种【外放】的形式来煽情升华啊！！在影院里一边心里吐槽一边忍不住跟着哭的心情真的好崩溃\n", + "题材很容易共鸣，不代表就拍的很好，就很中规中矩，甚至不如唐人街\n", + "建议以后的导演处女作学习一下，老老实实讲一个发自内心的故事。\n", + "太刻意了，结构方面甚至可以说还是个小品\n", + "这么说吧，带了很多纸巾，没用上。\n", + "千万别和你妈一起看这片，特别是知道你妈当年有的选并且选错了而且你现在不太行的情况下，看完我只敢聊去哪吃饭\n", + "如果没有最后二十分钟，这部电影也就是一般只能打五分的程度。贾晓玲吃着包子忽然意识到什么开始哭，然后前几十分钟铺垫的情感一泻千里，哭得我撕心裂肺。结构能让内容翻倍地表现出来。另外，女性视角的女性真的太美好了，不只是张小斐，中年妈妈和阿姨也很可爱\n", + "其实影片前面部分很一般，最多3星，但确实最后情真意切，加一星\n", + "从前有个女儿，为了让妈妈长脸，宁可让别人家的孩子代替自己的位置，结果呢，妈妈从一开始就知道她的小心思了，默默地陪她玩这个游戏。因为，妈妈只想要女儿健康快乐。\n", + "单纯对穿越梗感到反感与厌恶。\n", + "终于有一个女主角，或者说是妈妈形象不再是人忍气吞声，忍辱负重的了。就喜欢焕英的洒脱和大气。\n", + "诚然这部电影有很多不足之处，但它是唯一一部后劲大到让我看完还哭了俩小时的片，我中途还纳闷过为什么李焕英脾气那么好，晓玲把她的结婚证撕了她都不生气，后来揭晓原因时我已经泣不成声。对于电影，技巧只能加分，真情才最动人。\n", + "从片子本身来说，算不上优秀大作。许多人抹着眼泪给出了春节档高分，一方面是其他对手太不争气，另一方面，最重要的原因，大家把高分投给了全天下所有人的妈妈。\n", + "玲啊，真好，真好啊。\n", + "\n", + "妈妈飞扑过去接住女儿时喊出地那句：“我宝！”直接让我在电影院把眼泪流进了衬衣。\n", + "真诚的编导才能换取观众的真心，打动观众的故事才能称得上好故事。中国喜剧能拥有这样一群创作者实在是非常幸运。\n", + "大过年的，一家人看一部有笑声、有泪点的电影，出了电影院还能一起开心地聊聊，还有比这更适合过年的吗？\n", + "这是这几年看到的国产影片中，从头到尾让人感觉最舒服的，这应该和导演及编剧是以贾玲为中心的女性团体有关系，在脱离了男性凝视，在不用女性当工具人的电影中，女演员的魅力被无限放大，不知道男观众如何想，但是作为女观众完全感受到了，那种仿佛闪着光的被超放大的魅力，把李焕英不只是作为母亲，更多作为一个飒利的女性的角色呈现在观众面前。\n", + "过誉了真的。。。大概就是贾玲小品集锦+王牌对王牌煽情套路。\n", + "镜头像微电影的质量，几个穿越情节设计得毫无新意，甚至俗，立意容易打动观众，但没有任何艺术感。评分这么高票房这么高，说明中国观众的艺术审美落后。\n", + "女导演太棒了，喜剧并不是一定要黄段子！！前面合家欢喜剧尬点很少，后面猝不及防的高潮呜呜呜呜呜呜，世上只有妈妈好。\n", + "前面笑到人仰马翻，结尾哭到无法呼吸。贾玲对妈妈的爱在电影里淋漓尽致的展现，忽然觉得拍好一部电影或许真的不是那么难的事情。你看贾玲就做得挺好\n", + "谁不想去父母年轻的那个时候看看呢？！老生常谈的故事，司空见惯的段落，有些笑点，有些泪点，但大部分是挺无趣的。放在春节档里，倒是有挺好的宣传口（带着父母等等）来吸引观众入场。\n", + "当你以为自己和电影中的贾玲一样拥有一个全知视角时，可能正是你要被这部电影深深催泪的时刻。电影最后，当另一个我压根就没有预料到的视角被打开的那个瞬间，我的眼泪就止不住了，真的太感人了。\n", + "要说贾玲才华横溢那肯定是没有的，这部电影也确实像一个大型小品。但是以近年的喜剧片横向比较，至少这部基本没有那些低俗笑话。虽然离高级幽默还有距离，但足以为同行表率。在悲剧内核里嵌入喜剧是本片成功的重要前提，而这悲剧不仅在于对主角的同情而更多是共情，就再给影片增加了后劲。结尾显得长而直白的煽情是减分项，我想说导演不要太低估观众，最好的表达方式不一定要说出来，让悲剧在喜剧性之下涌动可能更好。\n", + "太过个人化了，贾玲哭了太多，纪念自己的母亲没问题，但是拍成电影给观众看，能不能考虑下观众的感受？一味宣泄导演的个人情感，拍成家庭录像自己纪念不是更好？\n", + "2.2 真的太无趣了，根本不配称之为电影，贾玲的导演成分只有小品式的搬弄翻版，表演只会大哭和傻笑，每当镜头从近景zoom in至脸部特写时，失效的脸部浮夸表情与镜头的运动衔接此刻极其地不匹配，还在拿舞台的思维进行电影表演呢， 而且这个镜头在全片还有滥用之嫌，一股狠不成熟的视听手段暴露显现，可见当下院线电影的导演门槛有多低。如此老套的穿越梗情节，黑白到彩色普普通通低级的设计真的不是大一编导学生想出来的吗，尤其那几段剪辑太差了，几乎每一段情节都是如此尴尬拖沓，况且连处于时空错位之下都不会有效设计笑点，《乘风破浪》处理得都比这好。非要说真诚，全片唯一真诚的时刻只在片尾对于电影之外真实人物的致敬。\n", + "女性主义的电影，描绘的每一个女性角色都很可爱，我喜欢王琴阿姨，看到电影最后她是靠自己的努力工作抓住机遇才得到后面富裕的生活，就很庆幸贾玲没有让这个角色流于俗套。整部电影最巧妙的地方就是在于最后一部分——双穿越。确实催人泪下。但是父母真的对孩子没有要求吗？我想并非天下父母都是如此。但是真挚的感情可以打动人。\n", + "没有想象中的好看，中规中矩，评分虚高。\n", + "额… 电影不差 ，但也没有那么好吧… 8.1过誉了 ，6.8分差不多了\n", + "前面100分钟笑点密集，笑得人仰马翻，后面从贾玲奔跑那里没忍住看哭了，全片没有拉垮的演员，满分好评。\n", + "既没让我笑也没让我哭，每个“爆笑”段落都尬出天际，最后又来一个大型煽情。片尾曲第二首是啥玩意？第一次遇到这么赶客的片尾曲。还有那段女儿撮合妈妈相亲的戏，跟这个年代催婚的母亲们有啥区别，约等于把“时代倒退”几个字打在了大银幕上。唯一让我共情的点在于即使是有李焕英这么好的妈妈，做女儿的也一直一直在努力想让妈妈高兴。这说明大多数母亲都让孩子感到亏欠，而孩子却在无条件地爱她们——宁愿不出生，也希望妈妈能过得更幸福。\n", + "真的很好哭。\n", + "脱离了男性凝视，镜头聚焦的胸和屁股。我只记得李焕英的每一个漂亮裙摆，被风吹起来露出一截细长的小腿，原来每一个妈妈都曾是美丽的少女。\n", + "“观众已经笑得不行了”\n", + "结尾给整部电影涨了分，前半部分的笑料设计其实很一般，处于也可笑但又确实又有点勉强的线上。\n", + "就这？就这？8.3分？我居然花了60块在大年初三去看了这么一部电影？？大家对中国电影要求这么低了吗？我承认有些许的小亮点，题材也不错，所以在我心里它可以打到及格线6分，但是这完全没有到优秀吧？剧情老套，转折生硬，最后一大段贾玲的哭戏哭了我一地尴尬的鸡皮疙瘩…哪怕换个有演技些的演员我都能再多打一星，一整部电影就像是强行煽情的哄闹小品，居然能上大银幕还有8分的高分？我服\n", + "“人在委屈的时候就会想妈妈”\n", + "故事说不上新颖，可是母亲这个点总是能打动人，希望每个做母亲的女生记得对自己好点，不要牺牲不要伟大，让自己开心快乐很重要。\n", + "3.5，之前从来没觉得乔杉和贾玲长得这么像。\n", + "前面这些天听够了大家的笑死哭死等夸张评论，眼瞅着春节档这唯一的8分电影满怀期待，看完后内心一片狐疑“就这？？？”不管是造梦还是穿越都特别偷懒，甚至编剧都没想怎么去说服观众，只想我逗你们笑让你们哭就行了，然而这电影既不好笑也不好哭。一切都特别矫揉造作，甚至还不如当初的小品好笑，也不如贾玲在综艺节目里回忆自己妈妈煽情。沈腾的出现就好像贾玲需要一个拉票房的演员，然后靠友情求过来硬塞进来。你以为这就够工具人了？那么陈赫的出现让你无比困惑，他存在的意义是什么？\n", + "哭瞎了，啊，女孩子真好啊，又甜又暖\n", + "好真诚 诚恳得不像一部电影 像是一首散文诗 \n", + "在贾玲说出对啊，我妈怎么会缝的那一瞬间整个电影在这里开始反转，从这里开始显得不一样，我爱这一瞬间！！！！\n", + "有一个影评人说过，其实好作品没有什么标准，只要她打动了你，就是好作品。张小斐那句“我宝”出来我都哭抽抽了，无论在哪里，妈妈的本能就是保护孩子。BTW我真的无法正视沈腾了，他不说话站在那我就很想笑\n", + "“打我有记忆起妈妈就是个中年妇女的模样，所以我总忘记妈妈曾经也是个花季少女。” \n", + "熟练地搞笑，稚嫩地煽情\n", + "想让隔壁唐探3编剧瞧瞧 \n", + "什 么 T M D 叫 反 转\n", + "贾玲最后干吼的那一段太尬 ，有时候悲伤真的不是靠哭来让别人有共鸣\n", + "让这部电影挂在中国影视的前列，告诉所有电影人，电影是抒发自己情感引起全国共鸣的好办法，质朴的手法、真挚的感情就能获得观众的理解。\n", + "戴口罩看这种电影太反人类了，哭得口罩都湿了，糊在脸上好难受\n", + "诚意之作，希望往后能多一些这样的电影，少一些圈钱工业垃圾，没错，说的就是陈思诚。\n", + "最美好的一幕是贾玲在山坡下，看着她爸骑车奔向她妈。我就想着，我也愿意去我妈的年轻世界里走一走。\n", + "好好笑啊！\n", + "而且没有令人不适的颜色笑话，女性视角就很棒👍🏻\n", + "虽然有点煽情但是不妨碍好笑\n", + "这破烂电影能成为中国电影史票房第二，是中国电影人，乃至世界电影人的耻辱。煽情、刻意、莫名其妙，什么感动，老子只想笑。\n", + "看电影最厌恶的就是电影里的人强行煽情哭了但是看的人很尴尬。喜剧部分也做得不怎么样，沈腾说是男一的戏份但是存在感比陈赫还弱，更看得人难受了。\n", + "这片子好时机，能这么高分，只能说明人民群众的情感匮乏到什么程度了\n", + "上映前评价不好，我就觉得就当爆米花搞笑剧图一乐吧。看到一半觉得这绝对算搞笑剧里的一流作品。看到最后，我错了我全错了，这一流喜剧啊，好笑但又戳泪点，而且这泪点真的不是一哭即完。还有片尾那些话让我回顾起剧情更难受了，说贾玲靠妈赚钱的能消停会儿不？\n", + "李焕英阿姨 您女儿可太棒了 她真的好爱您 也真的很遗憾妹让您坐上敞篷车 看着她现在事业有成 我也好爱我的妈妈 希望您保佑所有妈妈都健康平安 长命百岁\n", + "没有想到这么好。一开始笑死，后来又哭死。结尾尤为动人。\n", + "不太是电影的视听语言，像是抻长了的好几个小品，但沈腾贡献了很好笑的表演，以及妈妈对女儿的爱永远比女儿对妈妈的爱更多这个题眼还是很动人的，是强烈的抱憾的女儿视角。在穿越回去的那场母女对谈中，双方都心知肚明，但都打着哑谜。女儿希望母亲幸福，即便这样的幸福图景中没有自己。母亲希望女儿健康，即便时光倒流也从未对当时的选择感到后悔，从未想过没有这个女儿的人生何以可能。希望这样题材的电影越来越多。母女之间的共情和联结，是一个眼神，些许沉默，无数眼泪和一声叹息。\n", + "1.道路千万条，安全第一条，各位上路一定要看路啊！2.感觉像是夏洛特烦恼的翻版，夏洛特毕竟第一次，还有点经验，这个，，，毕竟这年头穿越剧好多好多3.母爱太过沉重，看不下去，反正母爱伟大完事4.作为男的，看这片怎么老想着名词“丧偶式育儿”。。。\n", + "电影的所有呈现，打四分其实就可以了，但是多出来一星要给到女导演视角下的女性主题，完全没有让人有任何不适（拉踩隔壁某探3）。没有刻意制造的笑点更没有刻意制造的哭点，一切都是很自然的发生了然后触动观众，这很难得。好喜欢贾玲，有谁会不喜欢贾玲呢。\n", + "得比唐3低两分\n", + "就这破电影，一堆人哭的稀里哗啦，还觉得自己倍有欣赏能力，还觉得自己共情。打个三分不吹不黑，但硬要把他推上45分我就不敢苟同了。\n", + "摊手。打个两星凭着我的权重把分拉下来。\n", + "\n", + "我又回来了，改一星了。什么鬼剧情什么烂演技，这玩意有脸刷8分？？？？？恰烂钱？？？\n", + "\n", + "来杠我！三无小号尽管来杠！\n", + "\n", + "\n", + "……3月16日更新\n", + "听说这玩意还能出口国外，真把自己当啥了\n", + "“可是我妈现在还不会”这句话出现后的转折是我怎么也没想到的 一贯的思路都以为只有晓玲自己穿越回去希望能让年轻的妈妈更加高兴 谁可知母女两人一起穿越回去了 这是这部电影最大的转折 也是全片泪点开始 让老套的穿越剧更加新颖 挺中规中矩的一部片却也很特别 母爱一个讲到烂的题材 可是这部电影还是刷新了大家的印象 妈妈怎么会不爱自己的孩子呢 \n", + "生女儿的妈看不了的电影\n", + "眼泪哗哗的\n", + "但是还是推荐阖家观看\n", + "贾玲说了这片“我把心都掏给你们看了……”\n", + "任何时候真心都是最打动人的。\n", + "有的人见妈妈坐飞机，有的人见妈妈要坐时光机。\n", + "其实如果从电影的角度来说，五星是肯定不到的，四星顶多吧。但从整部电影的情感来说的话，真的太真挚了。我相信贾玲一定是把那个充满了遗憾的自己刀剐了千百遍，抚慰了千万遍，把见母亲最后一面的一幕想象了无数遍，才能产出如此真诚的一部电影\n", + "\n", + "当然，导演水平挺普通的，艺术性也基本没有，但真的就胜在了真诚，嗯。\n", + "\n", + "确实很好哭，也挺好笑的（但有些笑点还是比较冗杂）\n", + "这应该是贾玲作为一个女儿最极致的浪漫了。千千万万走到电影院的人，去了解李焕英、纪念李焕英的同时，想起总是被我们忽略的母亲。\n", + "\"我宝\"那一句真的泪奔了，五星好评，前面笑点不尬，后面反转真的流泪了…\n", + "缺点：1、诉求和故事主线不统一不连贯，主角不够聚焦，故事成了拼盘一会儿是张小斐的戏一会又成了沈腾的戏(在这点上同类型的影片《夏洛特烦恼》就要比这部强，主角欲望强烈，诉求明确，主题突出，当然夏也有很多问题)；2、情绪缺乏克制，没有留白，这是无法成为经典影片的最大障碍。优点：1、女性导演女性视角，难能可贵(当然并没有突破男性视角)；2、结尾处理很好，催泪全在这段，准确说是104分钟到110分钟；3、笑点设置比原版小品好，尤其沈腾在舞台上演出那段设置特别巧妙，打破了荧幕，直接和影院观众互动那种。影响：好的影响是未来大概会出现一批女艺人来做导演拍自身故事，这部片子8.3的评分说明我们影视欣赏水平稳步降低，良好的市场表现会引发大量模仿，咱国影视质量又将大幅下降。\n", + "大型欢乐喜剧人小品一则，胜在感情真挚。张小斐可以的。我和我妈全程无表情看完，无情母女。\n", + "这分数真是绝了，太虚高了。没啥笑点也没啥哭点，唯一感觉演员演的都不错，尤其是张小斐。全国人民给贾玲的情怀买单……还把全国人民都感动了……好无语……\n", + "不至于不至于，逻辑都没有，从电影层面来说问题太多\n", + "6 看简介以为是女性版《乘风破浪》，其实是80年代版《夏洛特烦恼》。细节工夫到位，OST有流行音乐，也有真正属于那代人的《年轻的朋友来相会》！情绪处理得挺好，也善于营造气氛，很让人感动。尤其处于疫情背景之下，“亲情”与“告别”很能令人共情，更何况是“妈妈曾经是少女”引发的女性意识呢？到了最后20分钟左右，很多人都在抹眼泪😢 影片不乏许多问题（尤其是剧作结构），但作为贺岁片，绝对及格了。PS：场下UCLA毕业生一脸辛酸笑。\n", + "张小斐喊出哪句“我宝”的时候\n", + "宛如某晚小品，前面不管咋地最后反正都必须给你一个煽情。感觉前面拍那么多有的没的都是为了那个煽情结尾，可是你起码也把前面故事编圆了好吧？真要图个感动就拍最后十分钟足够了\n", + "贺岁档基本都看过了，怎么说呢，这部片子的口碑真得感谢同行，不差，但也没有特别好。\n", + "从另一个方面来说，真正用心用感情去拍一部电影，真的是可以弥补专业水平的不足，甚至可以打动观众的。相比之下《唐人街探案3》就输在这里，陈导很傲慢，砸钱就完了；贾导很真诚，只想记录一段平凡的母爱和想念，人民群众真正喜闻乐见的是什么，答案已经有了。电影不过是影人和观众的交流，真诚的情感可能不会被所有人接受，但空洞的生意不会被任何人共情。\n", + "我也曾想过，如果母亲不用为我牺牲，会不会成为更伟大的人；或许会吧，但她就不会成为一个伟大的母亲了，我又何必帮她做出选择，我能选择的只有如何回报她\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import jieba\n", + "import wordcloud\n", + "# 读取文本\n", + "with open(\"lhy_comments.txt\",encoding=\"utf-8\") as f:\n", + " s = f.read()\n", + "print(s)\n", + "ls = jieba.lcut(s) # 生成分词列表\n", + "text = ' '.join(ls) # 连接成字符串\n", + "\n", + "\n", + "stopwords = [\"的\",\"是\",\"了\"] # 去掉不需要显示的词\n", + "\n", + "wc = wordcloud.WordCloud(font_path=\"/Library/Fonts/Songti.ttc\",\n", + " width = 1000,\n", + " height = 700,\n", + " background_color='white',\n", + " max_words=100,stopwords=s)\n", + "# msyh.ttc电脑本地字体，写可以写成绝对路径\n", + "wc.generate(text) # 加载词云文本\n", + "wc.to_file(\"李焕英1.png\") # 保存词云文件" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 示例代码\n", + "from wordcloud import WordCloud\n", + "from PIL import Image\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import jieba\n", + "\n", + "# 打开文本\n", + "with open(\"lhy_comments.txt\",encoding=\"utf-8\") as f:\n", + " s = f.read()\n", + "\n", + "# 中文分词\n", + "text = ' '.join(jieba.cut(s))\n", + "\n", + "# 生成对象\n", + "img = Image.open(\"/Users/chenqiang/Documents/the-craft-of-selfteaching/images/code-review.png\") # 打开遮罩图片\n", + "mask = np.array(img) #将图片转换为数组\n", + "\n", + "stopwords = [\"我\",\"你\",\"她\",\"的\",\"是\",\"了\",\"在\",\"也\",\"和\",\"就\",\"都\",\"这\"]\n", + "wc = WordCloud(font_path=\"/Library/Fonts/Songti.ttc\",\n", + " mask=mask,\n", + " width = 1000,\n", + " height = 700,\n", + " background_color='white',\n", + " max_words=200,\n", + " stopwords=stopwords).generate(text)\n", + "\n", + "# 显示词云\n", + "plt.imshow(wc, interpolation='bilinear')# 用plt显示图片\n", + "plt.axis(\"off\") # 不显示坐标轴\n", + "plt.show() # 显示图片\n", + "\n", + "# 保存到文件\n", + "wc.to_file(\"李焕英2.png\")" + ] } ], "metadata": { diff --git a/Part5.C.NLP.ipynb b/Part5.C.NLP.ipynb new file mode 100644 index 000000000..97e3d8faf --- /dev/null +++ b/Part5.C.NLP.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character. I have a dream today.\n", + "\n", + "\n", + "\n", + "I have a dream that one day down in Alabama, with its vicious racists, . . . one day right there in Alabama little black boys and black girls will be able to join hands with little white boys and white girls as sisters and brothers. I have a dream today.\n", + "\n", + "\n", + "\n", + "I have a dream that one day every valley shall be exalted, every hill and mountain shall be made low, the rough places will be made plain, and the crooked places will be made straight, and the glory of the Lord shall be revealed, and all flesh shall see it together.\n", + "\n", + "\n", + "\n", + "This is our hope. . . With this faith we will be able to hew out of the mountain of despair a stone of hope. With this faith we will be able to transform the jangling discords of our nation into a beautiful symphony of brotherhood. With this faith we will be able to work together, to pray together, to struggle together, to go to jail together, to stand up for freedom together, knowing that we will be free one day. . . .\n", + "\n", + "\n", + "\n", + "And when this happens, and when we allow freedom ring, when we let it ring from every village and every hamlet, from every state and every city, we will be able to speed up that day when all of God's children, black men and white men, Jews and Gentiles, Protestants and Catholics, will be able to join hands and sing in the words of the old Negro spiritual: \"Free at last! Free at last! Thank God Almighty, we are free at last!\"\n", + "[('be', 11), ('will', 9), ('to', 9), ('a', 8), ('the', 8), ('of', 8), ('and', 7), ('together', 6), ('i', 5), ('have', 5), ('dream', 5), ('one', 5), ('day', 5), ('with', 5), ('that', 4), ('able', 4), ('shall', 4), ('this', 4), ('we', 4), ('little', 3), ('in', 3), ('made', 3), ('faith', 3), ('nation', 2), ('by', 2), ('their', 2), ('today', 2), ('alabama', 2), ('black', 2), ('boys', 2), ('girls', 2), ('white', 2), ('every', 2), ('mountain', 2), ('places', 2), ('our', 2), ('hope', 2), ('my', 1), ('four', 1), ('children', 1), ('live', 1), ('where', 1), ('they', 1), ('not', 1), ('judged', 1), ('color', 1), ('skin', 1), ('but', 1), ('content', 1), ('character', 1), ('down', 1), ('its', 1), ('vicious', 1), ('racists', 1), ('right', 1), ('there', 1), ('join', 1), ('hands', 1), ('as', 1), ('sisters', 1), ('brothers', 1), ('valley', 1), ('exalted', 1), ('hill', 1), ('low', 1), ('rough', 1), ('plain', 1), ('crooked', 1), ('straight', 1), ('glory', 1), ('lord', 1), ('revealed', 1), ('all', 1), ('flesh', 1), ('see', 1), ('it', 1), ('is', 1), ('hew', 1), ('out', 1), ('despair', 1), ('stone', 1), ('transform', 1), ('jangling', 1), ('discords', 1), ('into', 1), ('beautiful', 1), ('symphony', 1), ('brotherhood', 1), ('work', 1), ('pray', 1), ('struggle', 1), ('go', 1), ('jail', 1), ('stand', 1), ('up', 1), ('for', 1), ('freedom', 1), ('knowing', 1), ('free', 1)]\n" + ] + } + ], + "source": [ + "\n", + "import re\n", + "\n", + "# 你不用太关心这个函数\n", + "# 生成单词和词频的字典\n", + "word_cnt = {}\n", + "# 按照词频排序\n", + "sorted_word_cnt = sorted(word_cnt.items(), key=lambda kv: kv[1], reverse=True)\n", + "def parse(text):\n", + " # 使用正则表达式去除标点符号和换行符\n", + " text = re.sub(r'[^\\w ]', ' ', text)\n", + "\n", + " # 转为小写\n", + " text = text.lower()\n", + " \n", + " # 生成所有单词的列表\n", + " word_list = text.split(' ')\n", + " \n", + " # 去除空白单词\n", + " word_list = filter(None, word_list)\n", + " \n", + " \n", + " \n", + " for word in word_list:\n", + " if word not in word_cnt:\n", + " word_cnt[word] = 0\n", + " word_cnt[word] += 1\n", + " \n", + "\n", + " \n", + " return sorted_word_cnt\n", + "\n", + "with open('in.txt', 'r') as fin:\n", + " for line in fin.readlines():\n", + " print(line)\n", + " # print(word_cnt)\n", + " word_and_freq = parse(line)\n", + " # print(word_cnt)\n", + " sorted_word_cnt = sorted(word_cnt.items(), key=lambda kv: kv[1], reverse=True)\n", + "print(word_and_freq)\n", + "\n", + "\n", + "with open('out.txt', 'w') as fout:\n", + " for word, freq in word_and_freq:\n", + " fout.write('{} {}\\n'.format(word, freq))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (3.6.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (9.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (3.0.9)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: packaging>=20.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (21.3)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (1.0.5)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (4.37.4)\n", + "Requirement already satisfied: numpy>=1.19 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib) (1.22.3)\n", + "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: You are using pip version 21.2.4; however, version 22.2.2 is available.\n", + "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.9/bin/python3.9 -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "%%bash\n", + "pip3 install matplotlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " CloseTime OpenPrice HighPrice LowPrice ClosePrice Volume \\\n", + "0 1661929200 20358.06 20484.98 20211.13 20250.07 68.357146 \n", + "1 1661932800 20257.32 20302.44 20194.65 20273.36 8.685938 \n", + "2 1661936400 20250.32 20325.13 20126.42 20229.54 41.947429 \n", + "3 1661940000 20227.91 20239.09 20167.63 20176.93 16.156967 \n", + "4 1661943600 20176.93 20400.00 20167.63 20336.43 19.578004 \n", + "\n", + " NA \n", + "0 1.392026e+06 \n", + "1 1.759082e+05 \n", + "2 8.489017e+05 \n", + "3 3.264651e+05 \n", + "4 3.976858e+05 \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import requests\n", + "\n", + "# 选择要获取的数据时间段\n", + "periods = '3600'\n", + "\n", + "# 通过Http抓取btc历史价格数据\n", + "resp = requests.get('https://api.cryptowat.ch/markets/gemini/btcusd/ohlc', \n", + " params={\n", + " 'periods': periods\n", + " })\n", + "data = resp.json()\n", + "\n", + "# 转换成pandas data frame\n", + "df = pd.DataFrame(\n", + " data['result'][periods], \n", + " columns=[\n", + " 'CloseTime',\n", + " 'OpenPrice',\n", + " 'HighPrice',\n", + " 'LowPrice',\n", + " 'ClosePrice',\n", + " 'Volume',\n", + " 'NA'])\n", + "\n", + "# 输出DataFrame的头部几行\n", + "print(df.head())\n", + "\n", + "# 绘制btc价格曲线\n", + "df['ClosePrice'].plot(figsize=(14, 7))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "crawling url_1\n", + "OK url_1\n", + "crawling url_2\n", + "OK url_2\n", + "crawling url_3\n", + "OK url_3\n", + "crawling url_4\n", + "OK url_4\n", + "CPU times: user 6.29 ms, sys: 2.81 ms, total: 9.1 ms\n", + "Wall time: 10 s\n" + ] + } + ], + "source": [ + "\n", + "import time\n", + "\n", + "def crawl_page(url):\n", + " print('crawling {}'.format(url))\n", + " sleep_time = int(url.split('_')[-1])\n", + " time.sleep(sleep_time)\n", + " print('OK {}'.format(url))\n", + "\n", + "def main(urls):\n", + " for url in urls:\n", + " crawl_page(url)\n", + "\n", + "%time main(['url_1', 'url_2', 'url_3', 'url_4'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "'await' outside function (, line 1)", + "output_type": "error", + "traceback": [ + "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", + " File \u001b[1;32m~/opt/anaconda3/envs/myenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3369\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", + " Input \u001b[1;32mIn [2]\u001b[0m in \u001b[1;35m\u001b[0m\n get_ipython().run_line_magic('time', \"await(main(['url_1', 'url_2', 'url_3', 'url_4']))\")\n", + " File \u001b[1;32m~/opt/anaconda3/envs/myenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2294\u001b[0m in \u001b[1;35mrun_line_magic\u001b[0m\n result = fn(*args, **kwargs)\n", + " File \u001b[1;32m~/opt/anaconda3/envs/myenv/lib/python3.9/site-packages/IPython/core/magics/execution.py:1297\u001b[0m in \u001b[1;35mtime\u001b[0m\n code = self.shell.compile(expr_ast, source, mode)\n", + "\u001b[0;36m File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/codeop.py:143\u001b[0;36m in \u001b[0;35m__call__\u001b[0;36m\u001b[0m\n\u001b[0;31m codeob = compile(source, filename, symbol, self.flags, True)\u001b[0m\n", + "\u001b[0;36m File \u001b[0;32m:1\u001b[0;36m\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m 'await' outside function\n" + ] + } + ], + "source": [ + "\n", + "import asyncio\n", + "\n", + "async def crawl_page(url):\n", + " print('crawling {}'.format(url))\n", + " sleep_time = int(url.split('_')[-1])\n", + " await asyncio.sleep(sleep_time)\n", + " print('OK {}'.format(url))\n", + "\n", + "async def main(urls):\n", + " for url in urls:\n", + " await crawl_page(url)\n", + "\n", + "%time await(main(['url_1', 'url_2', 'url_3', 'url_4']))" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "034c70736e9d25f3f57bc70ec9a2164e1c27bdcdecfc656ea6098a86262350a2" + }, + "kernelspec": { + "display_name": "Python 3.9.12 ('myenv')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/in.txt b/in.txt new file mode 100644 index 000000000..1288c03c5 --- /dev/null +++ b/in.txt @@ -0,0 +1,10 @@ + +I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character. I have a dream today. + +I have a dream that one day down in Alabama, with its vicious racists, . . . one day right there in Alabama little black boys and black girls will be able to join hands with little white boys and white girls as sisters and brothers. I have a dream today. + +I have a dream that one day every valley shall be exalted, every hill and mountain shall be made low, the rough places will be made plain, and the crooked places will be made straight, and the glory of the Lord shall be revealed, and all flesh shall see it together. + +This is our hope. . . With this faith we will be able to hew out of the mountain of despair a stone of hope. With this faith we will be able to transform the jangling discords of our nation into a beautiful symphony of brotherhood. With this faith we will be able to work together, to pray together, to struggle together, to go to jail together, to stand up for freedom together, knowing that we will be free one day. . . . + +And when this happens, and when we allow freedom ring, when we let it ring from every village and every hamlet, from every state and every city, we will be able to speed up that day when all of God's children, black men and white men, Jews and Gentiles, Protestants and Catholics, will be able to join hands and sing in the words of the old Negro spiritual: "Free at last! Free at last! Thank God Almighty, we are free at last!" \ No newline at end of file diff --git a/lhy_comments.txt b/lhy_comments.txt new file mode 100644 index 000000000..07de17b34 --- /dev/null +++ b/lhy_comments.txt @@ -0,0 +1,274 @@ +把二十多分钟的小品拉长到两个小时，没有好导演和好剧本还是省省吧。 +有笑有泪的，不明白陈赫这个人物有什么作用，完全多余 +跟唐人街探案三的分数相比，很明显观众们评判电影的标准不仅在于你拍的好坏，而更在于你拍的态度是否真诚 +这次感受注定是感性压倒理性的，所以就不打分了。 +贾玲还是适合做小品，她的表演方式、她的叙事能力都更适合能和观众互动的、短小精炼的舞台而不是要被观众一帧一帧检视的电影。更何况她非科班出身，参与电影项目也不多，要执导筒讲故事就更是自曝其短。 +这是一部非常开心麻花式的片子，如果你喜欢《夏洛特烦恼》那么可以去看看，如果是抱着宁浩电影的期待那就赶紧睡个回笼觉，或者喝杯咖啡把瞌睡醒醒。 +但说一千道一万，《你好李焕英》只能是贾玲来演、贾玲来导，它是贾玲自己的情感寄托和内心牵挂。哪怕在我觉得很尬的场景里，她对母亲的爱意和思念都饱满到要冲出屏幕来逼我落泪。我也确实掉了眼泪，在大年初一这天。 +这种情绪不是浓汤宝能兑出来的煽情，哪怕我和她的人生轨迹并不相同，但我也在电影院里体会了一把贾玲的过往青春。 +我以为是我在为你圆梦，其实还是你陪着我做了一场好梦。 +贾玲水平有限，奈何感情无比真挚。虽然结尾让我哭的稀里哗啦，但也没能改变前半段就是个低配版夏洛特烦恼的状况。 +这完全是贾玲拍给自己的作品，不是拍给观众的，充满了个人的执念。 +你以为你已经很爱很爱妈妈了，但妈妈远比你想象中更爱更爱更爱你 +贾玲哭的时候很明显没有在演戏，这是最让人伤心的。 +前一个小时强行塞包袱，后面干嚎占二十分钟。。。 +我不喜欢，我不觉得好笑，也不觉得感动 +以为穿越是为了改变她的人生轨迹，竟然只是了却自己的遗憾。原来从天而降的大胖妞无论有多重，她都会毫不犹豫接住她的宝儿。裤子无论多破，她都会缝成小狗。今生母女一场，只能化作目送。以为她希望我有出息，原来她只需要我平安。以为她想换个女儿，原来她从没后悔。以为多了解她，原来还是误会了她。 +7分。豆瓣8.2分过分了，就电影论电影的话，贾玲这部作品最大的问题在于它不太像电影（但也确实比小品形态提升了不少）。如果无视这一点，主要凭是否感动或者哭了多少眼泪来评价它的优劣，那就好比让情色片与AV同台竞技，主要凭观众生理反应的指标来评价优劣。 +同样是女性导演的亲情题材，许鞍华《桃姐》8.3分，张艾嘉《相爱相亲》8.4分，就是把贾玲纪念母亲的真诚用心全算上，它跟蒋雯丽《我们天上见》——同样演员出身的导演处女作，同样纪念自己至亲——也差了几条街，后者的确是一部电影，蒋雯丽之后没再做导演让我觉得可惜，而贾玲如果之后再做导演我只会觉得她是飘了。 +如果你只是把它当成一件商品，那么眼泪的容量可以跟商品的质量成正比。如果你还把它当成一件艺术品，那么真诚只是优秀艺术品的要素之一，技巧和手法同等重要。【仙桃金逸】 +8.3分绝对是太不正常了。《李焕英》你不能说它差，但也绝对称不上好，只是前些年综艺大电影版的变形罢了。主题选择了“子欲养而亲不在”，表达上却仅限于一味的情感宣泄，里面其实你很难找出一个完整鲜活的角色，母、女也只是传统文化几千年来堆砌出来的固定形象，除了伟大和孝顺找不到更多的形容词。 +贾晓玲撮合李焕英和沈光林这段有很多笑点，但内核是很扎心的。 +因为它体现了贾晓玲的自卑和丧，她觉得自己活着什么用都没有，她凭借自己无法让母亲过上更好的生活，所以她才想出这个办法。 +很多人看了《李焕英》后都说：如果母亲生的不是我，她是不是会过得更幸福。 +但这并不是电影想要表达的主题，电影想表达的是，你的母亲觉得有你这样的儿女，就已经很幸福了。 +贾晓玲说她会看相，她看到李焕英未来的女儿会出国留学，而且收入很高。 +而李焕英说，我的女儿，我就让她健康快乐就行了。 +其实天下母亲都是如此。 +如果我们能功成名就当然更好，但如果我们没有功成名就，难道她们就会觉得我们不配做她们的儿女吗？ +事实是无论我们贫穷还是富贵，成功还是平凡，她们都会以我们为傲。 +因为重要的不是我们做出多大的成就，重要的是“我们”。 +贾玲：我给你们讲个笑话，你们别哭。 +我要强烈批评影片最后刻画母亲买票又退票在大雪天中一个人走回去的镜头，我不接受，母爱不是因为牺牲才伟大，爱与关怀本身就是伟大。 +其实还是小品段子电影，最大的梗电影看得稍多些的观众一早就能猜到，但胜在有真感情，没有绝大部分国产喜剧那股鸡贼味。这片或许确实只能由贾玲自己来拍，换别的导演，就算拍得更成熟、更电影，恐怕也达不到现在的效果，或者说，就会是另一个故事了。 +这个底太好了，比起以往穿越片的个人救赎迈的更深了一步。本以为影片会如同以往的俗套一般会终结在「希望孩子健康快乐」上，没想到结尾以针线为引，将「无论你在哪里我都会找到你并且保护你」的母爱穿进了观众的心里，以个人故事为本，动了真情的贾玲担得起导演的称呼。在喜剧元素上，影片也做的十分自然，不刻意不尴尬，更是在演员的选角上玩出了花，乔杉和贾玲的父女堪比去年黄渤和彭昱畅。 +看到8.3分才决定去看的，看完了，就这？是我要求太高了吗？很多桥段感觉很刻意和做作，张小菲的假笑只有我感受到了吗……最后的反转的确有感动到，但也不至于如热搜所说湿了两个口罩吧，我还特意有所准备，我的感受是这部剧营销过头了吧 +贾玲昨晚的喜剧已经尴尬到我了… 这个导演的电影真的更加尴尬… 还是好好当个综艺咖吧！电影就算了…别伤害影迷的眼睛了… +“我宝”那句出来的时候真的直接泪奔 +我以为只有我回到了1981,我以为我可以牺牲我自己改变你往后的命运，我以为你可以更幸福的，我没想到你愿意过的就是摊上我的这一生😭 +国产+喜剧+催泪＝烂片，这是豆瓣一贯的风气，因此我知道自己的评分一定不会公允，这里有掺杂太多我的主观感受：有生以来第一次在电影院哭得泣不成声。 +昨晚是除夕，我和妈妈大吵了一架。她气得直骂我没良心、白眼狼，我爸也心寒地把我妈牵到楼下，说不指望以后让我给他们养老。 +凌晨的时候，我给我妈朋友圈的拜年视频点了个赞，并发给她一个拜年红包，和她说“今晚我脾气不好，你别往心里去”我妈收下红包，给我发来好多幸福快乐的祝福。 +今天看到这电影，许多情节点都让我泪眼婆娑，直到女儿明白真相后，一路奔跑的那一段蒙太奇，我极力掩着眼泪，把哭声压到最小。 +观影是一个创作者通过作品感染观众的过程，无论它是否合乎艺术规律，只要能让观众感动，它就值得那个观众为它叫好，不必因为它是国产片就舍不得掌声。 +感谢编剧没有让李焕英以少胜多赢下排球赛。这个剧其实好就好在它的质朴情感上。没有什么输赢胜负，也没有什么人生逆袭改变命运。穿越回来只为了让你开心。不完美的小人物在闪闪发光。 +2.5 穿越梗是人家三十多年前玩剩下的套路，拍得也很糟糕（导演问题），全程味同嚼蜡，直到最后那个反转。这是皮克斯式的情感/视角反转（参考瓦力、玩具3和Coco），学到了精髓而奏效，可惜又处理得过于繁重拖沓。当然电影之外的真情实感的确很动人。 +真诚打动内心，没办法客观评价 +我最忘情的哭声有两次，一次，在我生命的开始；一次，在你生命的告终。第一次，我不会记得，是听你说的；第二次，你不会晓得，我说也没有用。但两次哭声的中间啊，有无穷无尽的笑声。一遍一遍又一遍，回荡了整整三十年。你都晓得，我都记得。——余光中《今生今世》 +8分？是因为我没看过开心麻花，对这个体系的喜剧存在一些误会吗？我以为自己对贺岁片已经够宽容低期待了，唐探，飞驰人生，流浪地球，都是手松一松能给4星的贺岁片，但李焕英是真的不行，毫无新意，所有的场景台词都是能找到一百八十个前辈的陈词滥调，听听你妈年轻时候的故事都比买票跟着别人哭强啊，脱离不了屎尿屁和融梗的国产喜剧，太廉价。尊重贾玲的心意，但不认同这是一部值得被夸赞至此的电影。 +在电影院哭得稀里哗啦。隔壁座的小姐姐也在哭。她问我：想妈了？我说：嗯！小姐姐：你也春节没回家？我指了指前面：我妈坐第三排，我俩没买着连号的座。这下小姐姐哭得更凶了。 +有一种“上当”了的感觉，第一次觉得豆瓣评分不可信 +贾玲你这样算不算恰烂钱？我就想问这个电影要干嘛？你要煽情没问题要找人客串没问题，要搞笑都没问题！可你什么都来一点。大杂糅无非就是要保证票房而已！ +我讨厌煽情喜剧！即使再感人 流泪会有种被“打亲情牌”而被占便宜的感觉 +最戳我的就是在知道了48岁的李焕英是穿越回去的后，想起那句“我这一辈子过得挺幸福的，怎么就没人相信呢。” +意识到妈妈什么都知道的时候，我哭到抽搐。贾玲太有才了。在读你和我时就在想，逝去的父母是靠儿女的记忆继续活着，而如果你可以写作，或者像贾玲一样用一部电影来纪念，那他们就一直活着。他们的精彩也都没有被忘却。这是贾玲给自己的交代。我不停的想，到底我不能接受的是什么，如果她的一生在自己活着时就去全都熠熠闪光，那死去时也不能称之为遗憾，只是命运。 +贾玲说她把心掏出来给观众看了，我想说，我看到了。 +打着亲情牌圈钱 说难听就叫消费亲妈 +张小斐还行，整体观感就像看现如今强迫你笑你哭的春晚小品，对不起我真的入不了戏。 +这是今年春节档，我最最想看的一部电影。《你好李焕英》的小品，是千禧年之后我心目中最佳小品前三，即便珠玉在前，当得知要影视化的时候，依然期待爆棚。作为贾玲的导演处女座，这部作品是值得肯定的，笑点泪点恰到好处，没有硬搞笑也没有刻意煽情。子欲养而亲不待，她在讲一个珍惜亲情的故事，也在讲当代女性意识的觉醒，不鼓吹生育，不鼓吹婚姻，而是让我们看到，任何一个女性在成为妈妈之前，第一个身份首先应该是她自己。祝天下所有“李焕英”永远快乐。 +不想在大过年的接受情感强奸。 +基本上和夏洛特烦恼一个套路，穿越过去弥补现实生活的遗憾。但剧本比不上夏洛打磨的千锤百炼。能看出贾玲对母亲的真挚感情，但不足以撑起一部电影的篇幅。 +逻辑很差让人无法共情，前面笑料勉强可以，后面哭得可尴尬了，作为导演，她想导观众的是，看我都哭成这样了，你们快给我哭！ +放在大过年，大年初一给老子哭？？？？为什么喜剧一定要哭！不哭不行嘛？？ +为什么表妹撕了你刚拿到的结婚证你都不生气。因为那是你女儿啊。 +最让人泪崩的是，我知晓未来所发生的一切，依然选择你这件小棉袄来陪我走过一生。 +不是我拉男女对立啊。当屏幕上出现 “贾玲 执导”字样的时候，就比起那些拍了几个直男癌片子就写“××× 导演作品”的，要真诚多了。 +“作品”这个词是自己叫的吗。。 +说实话吧，这要都算电影，电影的门槛是太低了点。 +散场以后我挎着妈妈散步回家，我想着她，她应该想着她妈妈吧。 +电影有一种聪明的真诚，可是这种真诚并不新鲜，每隔一长段时间，从让人疲惫的视效大片出来，你都能找到类似的感动，在国内电影市场上成为黑马。我们被这样的片子感动过太多次了。创作者不能老是这么拍电影，观众亦不能老是做煽情大保健。 + +它起了一个坏示范，无法从沉湎于悲伤中，走向理智，走向探索生活本质，因而它用冗长的煽情，绕开了现实的复杂。在某种程度上，它绑架了大多数母亲，建立一种刻板的母亲形象，抹掉了母亲形象本身的丰富，也绑架了身为子女的观众。 + +从技巧上，即使对比《相爱相亲》情节剧，它也缺乏力量，就更难对比类似《痛苦与荣耀》里母亲和子女作为独立个体相互平视的亲密关系。真的，我们不需要黏糊糊的中国式不分你我的亲情，我们不需要下跪父母，我们更需要在这种亲情中，走向人格的独立、人的平等和思想的升华。 +实际上三星都很勉强的……豆瓣分太高了8.2，好吧2星……没有任何创新，剧情拖沓，强行煽情…… +仅强于《囧妈》，结尾不错改分了 +如果你要陪长辈看电影的话，那就看这部吧。基本上就是贾玲版的《夏洛特烦恼》，完成度很好，笑点和泪点都到位，感觉会是春节档期大众口碑最坚挺的一部电影。一开始你以为这是一个熟悉的穿越设定，最后一反转，你会发现这不仅仅是一次穿越，而穿越的背后正应了母女情的主题。完了结尾的字幕又补了一刀，原来这是贾玲自己的经历，用这样一部电影去追忆母亲年轻时的样子。穿越回八十年代，从工厂记忆，女排比赛，黑白电视到祖父悖论，都是能引发几代人共鸣的设计，以前都是看贾玲笑，这次是看贾玲哭，很多人却是跟着她笑着哭。 +笑的不尴尬，哭的脑壳疼。 +看完真是气死了，求求中国的导演了，拍喜剧就纯粹点吧，咱别煽情别拔高别搞低级催泪行吗？我花了比平时贵的票价是来寻开心的不是来哭的啊喂。 +本来前半部分还算是精致轻松的风格，后半部分就又往煽情的低级趣味上去了。对，亲情确实很容易感人很容易把观众弄哭，咱们能不能选个难点的level,把它拍成云淡风轻，春风化雨好不好？ +真TM妥妥春晚小品风。躲过了春晚，却没躲过这货。 +实话实说我哭抽抽了 看见张小斐想喊声妈的程度 +笼统的看下豆瓣评分就去看了，真的很失望，感觉评分是刷的，不值票价和时间，贾玲值得尊敬，母爱更值得赞扬，但这种形式，就像小学的感恩大会，有一个人拼命的在台上讲，拿出各种故事拼命的渲染，好像不感动不哭就是不合适宜。 +感谢贾导。感谢李阿姨，是您的女儿在开局落后15亿的情况下，阻止了《唐3》这种烂片成为2021年春节档票房冠军，她永远是您的骄傲。 +我未来的女儿，我就让她健康快乐就行了。 + + + +大家来看这一部吧，别看唐3了 +我求求这群演员了，真不是是个人都能当导演 +现在的喜剧已经不是喜剧，都是让你一会笑一会哭的，pua套路 +80年代性转夏洛一点不烦恼，从头到尾的无聊和尴尬，贾玲说说是讲述自己和妈妈的故事，其实真的和妈妈一点关系都没有，尤其是李焕应这个角色，就是一个工具人而已。一个小品撑长到电影长度已经很勉强了，结果还在小品里面套小品和段子就更让人难以忍受了。整个故事真的可以说一马平川，没有真正的人物矛盾，没有真正的情节波折，甚至，连整个故事的情感支点都是虚的，剧本的堆砌感实在是太强了，结构散得一塌糊涂，观众是真的好骗呀！ +值得一看，但没有你想象中那么好看，尤其是当你带着豆瓣8点多高分期待去看！都说哭的稀里哗啦，但我确实没有哭…… +到底是一种怎样的执念，让焕英这老母亲，在人生最后的回光返照里，在明知晓玲的破洞牛仔裤是流行的情况下，都要把那洞给缝上？ +看过这个小品，人都没换，变成电影了。。。作为骗钱贺岁档一点诚意都没有，史上最i差，看不下了。 +如果你看过贾玲的小品，看过这个的话。我不知道你什么心态。看什么不好看这个。实力劝退。 +嘴一句，是挺感人，但是我都说了。 +这并不是一个穿越的故事，而是是一个母亲在弥留之际创造出来安慰女儿的梦，真的特别特别特别好，贾玲值得。9/10 +让她再选一次，她还是义无反顾的选择了有你的人生 +咋说呢，贾玲太牛了，不玩梗的那种搞笑，一点都不低俗，但是笑点通俗易懂，演员也都特别好，而且有一个点很戳我，就是后面暗示了王琴的发达不是因为靠男人，中国就是应该多有这样的思想纠正啊！！催泪的地方也容易产生共情，后半段我一边哭一边骂：这特么咋没完没了了……然后继续哭....好了好了，我爱我妈妈，虽然我们也会吵架，但是我很爱她，一直爱她 +自我安慰式小品 +豆瓣也开始水了 +“我的女儿，只要健康快乐就行了” +神不能一直在，所以创造了妈妈 +陈赫的戏份太恶心了，和贾玲一起躲雨，陈赫和贾玲肥胖身躯挤来挤去恶心死我了，尴尬到我脚趾母都扣起了。 +贾玲好棒 抢结婚证那里 结婚证被撕坏了 小斐眼里含泪的说 我这一生真的很幸福 你为什么就不相信呢 当时看的时候觉得很奇怪 后来全明白了 剧本好棒 拍的也很好 不过李焕英不管票房多少 她应该只想让贾玲健健康康 开开心心吧 +感谢贾玲，我们太需要女性视角的影视作品了，在女性眼里，女人不和性挂钩，不被凝视，所以不需要少女，不需要磨皮，滤镜，让人看的非常舒服，今年春节最佳了 +那句"我宝"真是让我眼泪都要下来了 +吹的太过了，6.5分顶天了，整个就一个小品，这样的画质和声效离影院标准差太远了，网播就行了。据说投资3.8个亿，少两个0都不影响效果。要不是我的免费票没别的看了，真不会看。 +一开始觉得冷特的角色多余，晓玲总要回来的，这样留下一段感情对冷特来说很残忍，后来反转了才明白根本不是穿越，是妈妈为晓玲创造的梦境，在妈妈创造的梦境里，为了抚平晓玲的自卑而创造了冷特的角色，妈妈想告诉女儿 她也值得被爱。 +挺细腻的一部剧吧。 +以为是帮妈妈圆梦，其实是妈妈陪女儿回去帮她圆梦。 +我这一辈子过得挺幸福，你咋就是不信呢？ +不知道有多尴尬 全程尬笑 +作为一部电影来说，是不及格的，严重过誉。剧情结构太弱，逻辑基础混乱，表演舞台化严重。强行抖机灵的台词和用力过重的表演，都让人尴尬得出戏，反而掩盖和混淆了剧作本身表达的内容。前半个小时，我深刻自我怀疑：就这？中间一个小时我满心期待会有更好的编排，却越来越失望。最后半个小时，我感觉被捏住脖子摁在地上强行质问：你哭不哭？体验极为不好😞 +从妈妈张开手喊着“我宝”去接人那瞬间开始，哭到停不下来。你以为自己已经很爱很爱妈妈了，想尽办法让她开心，其实她更爱更爱你，她包容你、守护你，甚至下辈子也舍不得让你当妈妈。笑点有，泪点足，反转到位，其实剧情的反转就是感情的反转，因此带来的触动是极大的。除了陈赫这个角色以及这条感情线略鸡肋，其他我觉得都很好。真诚永远是最打动人的，这部电影真的很用心、很真诚。【8.2】 +观感有些复杂 哭了 +但你又知道这个哭是生理性的，被煽动的 +你不太确定那是不是感动，你为这样的自己感到厌恶 +贾玲第一次当导演就能拍出这水准的片子，可见用心和不用心做事的区别到底在哪里，我觉得做春节烂片的导演们真的该好好反省反省。 故事虽然有点像某个电视剧的剧情，但也不全是一样，加上看得出来贾玲确实放了真情实感，后面的回忆杀真的让人泪目。 前半段的笑点也很多，划船那段真的笑到打滚！沈腾贾玲真的是喜剧质量的保证。而且演员们的演技还是都不错的。值得去电影院支持。 +看完之后感觉可能是春节档最好看的吧…… +有泪有笑，这不是一个人的感觉，是整个影厅的反馈 +````最讨厌这种电影！！ +女性导演还是会更细腻一些，结局并没有刻意要所谓的“圆满”，贾玲第一次导戏非常成功啊👍 +看哪，贾晓玲考上了艺校，打铁娘子队赢得了新的搪瓷杯，王琴靠自己的努力过上了更好的日子，而李焕英，她得到了一次重返青春的机会，真好。这些女孩都有光明的未来，而这也就是中国电影需要更多女性视角的原因。（理性角度来看，这部电影自带的一些争议以及很多稚嫩的处理手法，确实不应该拿到五星。但是揉揉自己哭肿了的眼睛，我确实没办法公正的打分。一颗心都捧出来给观众看了，这不叫技巧性煽情，而叫真诚。）每位演员的表现都让这个剧本变得更加熠熠生辉（包括热评里觉得多余的一些角色的演员），而这部电影也证明了，不用一些带有性别倾向的梗（无贬低之意），也能让人发自内心地笑出来。最后表白结尾的文字：每个母亲，都曾经是少女。而在母亲这个身份之前，请不要忘记她是一个独立的人。 +“你以为自己已经很爱很爱妈妈了，妈妈却远比想象中更爱更爱你。你以为自己是在为妈妈圆梦，没成想只是她陪你做了一场好梦。” +好无聊好无聊，旁边的小朋友被无聊哭了，真的难看。不知道要讲什么，笑点还都很尬，贾玲演的这个人真的好奇怪，还一直愁眉苦脸的，她煎熬，我看她也煎熬。看在沈腾的份上两星吧，张小斐也还行 +看评论时有人说了这样一个观点，我特别特别赞同：“看这个片会哭地很惨的人都是特别幸福的人，因为能和贾玲产生共鸣说明有一个爱你的好母亲，一个只想你幸福快乐无私付出的母亲，但现实中也有一些人不那么走运，ta们的母亲可能自私，也会让孩子寒心…因为不是全天下的母亲都是合格的母亲，所以看哭了其实真的是一件特别幸福的事，这足以说明你幸运地拥有一个好妈妈。” +“我的女儿，只要她健康快乐就行了。”本来以为只是贾晓玲穿越回了1981年，没想到原来2001年的李焕英也穿越回了20年前，这里真的设计得太巧妙了。重来一回，李焕英还是会选择当玲儿的妈妈啊。开头李焕英看见从天而降的玲儿，下意识地就要去接住玲儿，这就是母爱吧。这种感情可能真的只有母女会懂吧。 +前半部分贾玲沈腾真的好好笑哈哈哈哈哈哈哈。 +贾玲在影片里用了好几次华仔的歌，不愧是华仔的真爱粉。 +啊对，通过黑白照片染色这一方式呈现画面也蛮有创意。 +哭笑交叉的一部电影。 +前面一个半小时三星，后面半小时五星，眼泪水真是摥勿牢 +1.5。喜剧元素出众，笑点属上乘。靠细节抓住时代感，通过时代落差反过来促进笑点 +"自我记事以来，母亲就一直是一个中年妇女，但是我们却容易忘记她曾经也是一个花季少女"。说得很好，可惜没有任何意义，在鸡汤里都算下流水平 +电影很好地完成了风格的转变，至少效果是达到了的，电影院里前半段笑声不断，后半段鸦雀无声，真正地做到了"有笑有泪，喜剧的内核是悲剧"的大众低俗标准，可惜转场的生硬导致全片漏洞百出，然而这些都在观众的哽咽中抛在脑后 +贾玲的确用心拍摄了这部电影，真情实感在影像的流动间溢出屏幕，对母爱的赞美让我们感同身受，很好地做到了共情，可惜这不是电影的评判标准 +"献给所有伟大的母亲"，主题一下子升华，电影结束后观众的赞美声再次证明了煽情永远是电影最低级最取巧的表现手法。片中的母爱远没有达到伟大的地步 +新年第一部觉得最值得五星的片子，很好笑也很好哭。阳光下张小斐穿百褶裙真的漂亮，打排球真的飒。 +另，沈腾叔叔的表演让我相信雪姨阿姨是靠自己让她女儿去米国念书的。 + +微博上发不出负面评论，怒打低分。浓缩成一出小品 或者 拍一个短片，也许会不错。作为一部电影，一塌糊涂。难过。 +贾玲和他爸的乔杉真像啊！ +是不是被隔壁唐3给惯的了，明明也就一般的片子，怎么一下就彪到30亿了，贾玲真应该感谢一下陈思诚 +催泪神器，给四颗星五颗星都可以理解，毕竟大家都太想“让妈妈高兴”了。 +“你以为你已经很爱很爱妈妈了，其实妈妈比你想象中更爱更爱你。” +歌颂母德，令人不适。如果一个创作者的私人情绪泛滥成灾，那么最终成品也只能是一个灾难。 +春节档电影票价很贵，选来选去还是看了这部，倒不是喜欢沈腾和贾玲，而是抱着宣传的噱头:喜剧片才带着爸爸妈妈去看，看完之后只有一个感觉，喜剧片不是各位的噱头，特别是春节元旦这样的档期，还有希望各位对喜剧演员的包容度稍微减一减，沈腾贾玲尴尬至极，还有一个要命的陈赫。。。。 +“可是我妈还不会缝啊” 自此开始眼泪止不住。 +以为穿越是为了改变她的人生轨迹，竟然只是了却自己的遗憾。原来从天而降的大胖妞无论有多重，她都会毫不犹豫接住她的宝儿。裤子无论多破，她都会缝成小熊。今生母女一场，只能化作目送。以为她希望我有出息，原来她只需要我平安。以为她想换个女儿，原来她从没后悔。以为多了解她，原来还是误会了她。 +豆瓣也开始水了 +有时候不需要太深的立意，故事流畅，人物真诚就很足够了 +催泪神器，给四颗星五颗星都可以理解，毕竟大家都太想“让妈妈高兴”了。 +承认是贾玲的真实感情，但仅限于此。 +什么电影手法，在真挚感人面前不重要 +所有不是沈腾主演却写他主演，预告片就减他出场的部分骗我去看的片子全部最低分 +如此成功地调动了观众的情绪和心理，诠释了电影作为一个现实生活的宣泄口和安慰剂，在意识形态上功能可以多么强大。越是回到过去，我们离现实越远。实际上，你和你妈的矛盾，根本不是靠两个人重新选择可以解决的，而是靠你妈接受了你的平庸，达不到社会成功人士的标准。可是，有出息的标准是注定不会被质疑的，只恨发财买车没有早一点，让妈赶上享福。这难道不是一个成功人士对“失败者”的冷酷规训吗？最后，观众在愧疚、自责和遗憾中流下感动的泪水。可是，女性之不幸，原来终究是子女一代的错吗？社会变迁、家庭结构，隐没在母爱的伟大里，逃避了追问。我为这样地利用一个母亲而感到悲哀，因为她的不幸仍要在这个大地延续下去，在循环往复的过新年里被虚假地应付，实际上这不幸是永远过不去的。 +完虐隔壁姓唐的好么？ +被捧得很高的贺岁档 感觉剧情有点俗套 全片唯一的亮点大概是她妈也穿越了吧 +30/100 俗套至极的剧作本就奠定了不佳的观感，而后大篇幅的自我感动更叫人尴尬得无地自容。也许贾玲真的把电影当作了小品，如乱炖的类型语气割裂了所有闪光点，却依然妄图用“真实事件”来掩盖除了俗套的情感升华什么也不会的事实，并且前者早已在矫揉的煽情桥段里被消弭得一干二净。在影院看纯粹浪费票钱。 +你好李焕英或是春节档最佳 +陈赫的戏份太恶心了，和贾玲一起躲雨，陈赫和贾玲肥胖身躯挤来挤去恶心死我了，尴尬到我脚趾母都扣起了。 +提前看影评说会哭的稀里哗啦 所以看的时候我一直在憋着等最感人的时候再哭 结果憋着憋着就结束了…… +真情动人 +多一星给最后的反转 +电影一旦入了真情 讨论技法之类的似乎就落了下乘 +好无聊好无聊，旁边的小朋友被无聊哭了，真的难看。不知道要讲什么，笑点还都很尬，贾玲演的这个人真的好奇怪，还一直愁眉苦脸的，她煎熬，我看她也煎熬。看在沈腾的份上两星吧，张小斐也还行 +mv级别的电影 +贾玲现在不仅健康快乐，还出息了，焕英阿姨你看到了吗 +得知两个人一起穿越，很催泪，想要用手接住从天而降的女儿那个画面也是泪点 +所有人都把它吹上了天，但这就是一部笑点低级强行煽情的小品式电影 +带着口碑去的，有点强行感人的味道，评分高可能都是同行衬托得好 +剧情感情不落俗套，但煽情手段太落俗套，为什么国内电影都是这种【外放】的形式来煽情升华啊！！在影院里一边心里吐槽一边忍不住跟着哭的心情真的好崩溃 +题材很容易共鸣，不代表就拍的很好，就很中规中矩，甚至不如唐人街 +建议以后的导演处女作学习一下，老老实实讲一个发自内心的故事。 +太刻意了，结构方面甚至可以说还是个小品 +这么说吧，带了很多纸巾，没用上。 +千万别和你妈一起看这片，特别是知道你妈当年有的选并且选错了而且你现在不太行的情况下，看完我只敢聊去哪吃饭 +如果没有最后二十分钟，这部电影也就是一般只能打五分的程度。贾晓玲吃着包子忽然意识到什么开始哭，然后前几十分钟铺垫的情感一泻千里，哭得我撕心裂肺。结构能让内容翻倍地表现出来。另外，女性视角的女性真的太美好了，不只是张小斐，中年妈妈和阿姨也很可爱 +其实影片前面部分很一般，最多3星，但确实最后情真意切，加一星 +从前有个女儿，为了让妈妈长脸，宁可让别人家的孩子代替自己的位置，结果呢，妈妈从一开始就知道她的小心思了，默默地陪她玩这个游戏。因为，妈妈只想要女儿健康快乐。 +单纯对穿越梗感到反感与厌恶。 +终于有一个女主角，或者说是妈妈形象不再是人忍气吞声，忍辱负重的了。就喜欢焕英的洒脱和大气。 +诚然这部电影有很多不足之处，但它是唯一一部后劲大到让我看完还哭了俩小时的片，我中途还纳闷过为什么李焕英脾气那么好，晓玲把她的结婚证撕了她都不生气，后来揭晓原因时我已经泣不成声。对于电影，技巧只能加分，真情才最动人。 +从片子本身来说，算不上优秀大作。许多人抹着眼泪给出了春节档高分，一方面是其他对手太不争气，另一方面，最重要的原因，大家把高分投给了全天下所有人的妈妈。 +玲啊，真好，真好啊。 + +妈妈飞扑过去接住女儿时喊出地那句：“我宝！”直接让我在电影院把眼泪流进了衬衣。 +真诚的编导才能换取观众的真心，打动观众的故事才能称得上好故事。中国喜剧能拥有这样一群创作者实在是非常幸运。 +大过年的，一家人看一部有笑声、有泪点的电影，出了电影院还能一起开心地聊聊，还有比这更适合过年的吗？ +这是这几年看到的国产影片中，从头到尾让人感觉最舒服的，这应该和导演及编剧是以贾玲为中心的女性团体有关系，在脱离了男性凝视，在不用女性当工具人的电影中，女演员的魅力被无限放大，不知道男观众如何想，但是作为女观众完全感受到了，那种仿佛闪着光的被超放大的魅力，把李焕英不只是作为母亲，更多作为一个飒利的女性的角色呈现在观众面前。 +过誉了真的。。。大概就是贾玲小品集锦+王牌对王牌煽情套路。 +镜头像微电影的质量，几个穿越情节设计得毫无新意，甚至俗，立意容易打动观众，但没有任何艺术感。评分这么高票房这么高，说明中国观众的艺术审美落后。 +女导演太棒了，喜剧并不是一定要黄段子！！前面合家欢喜剧尬点很少，后面猝不及防的高潮呜呜呜呜呜呜，世上只有妈妈好。 +前面笑到人仰马翻，结尾哭到无法呼吸。贾玲对妈妈的爱在电影里淋漓尽致的展现，忽然觉得拍好一部电影或许真的不是那么难的事情。你看贾玲就做得挺好 +谁不想去父母年轻的那个时候看看呢？！老生常谈的故事，司空见惯的段落，有些笑点，有些泪点，但大部分是挺无趣的。放在春节档里，倒是有挺好的宣传口（带着父母等等）来吸引观众入场。 +当你以为自己和电影中的贾玲一样拥有一个全知视角时，可能正是你要被这部电影深深催泪的时刻。电影最后，当另一个我压根就没有预料到的视角被打开的那个瞬间，我的眼泪就止不住了，真的太感人了。 +要说贾玲才华横溢那肯定是没有的，这部电影也确实像一个大型小品。但是以近年的喜剧片横向比较，至少这部基本没有那些低俗笑话。虽然离高级幽默还有距离，但足以为同行表率。在悲剧内核里嵌入喜剧是本片成功的重要前提，而这悲剧不仅在于对主角的同情而更多是共情，就再给影片增加了后劲。结尾显得长而直白的煽情是减分项，我想说导演不要太低估观众，最好的表达方式不一定要说出来，让悲剧在喜剧性之下涌动可能更好。 +太过个人化了，贾玲哭了太多，纪念自己的母亲没问题，但是拍成电影给观众看，能不能考虑下观众的感受？一味宣泄导演的个人情感，拍成家庭录像自己纪念不是更好？ +2.2 真的太无趣了，根本不配称之为电影，贾玲的导演成分只有小品式的搬弄翻版，表演只会大哭和傻笑，每当镜头从近景zoom in至脸部特写时，失效的脸部浮夸表情与镜头的运动衔接此刻极其地不匹配，还在拿舞台的思维进行电影表演呢， 而且这个镜头在全片还有滥用之嫌，一股狠不成熟的视听手段暴露显现，可见当下院线电影的导演门槛有多低。如此老套的穿越梗情节，黑白到彩色普普通通低级的设计真的不是大一编导学生想出来的吗，尤其那几段剪辑太差了，几乎每一段情节都是如此尴尬拖沓，况且连处于时空错位之下都不会有效设计笑点，《乘风破浪》处理得都比这好。非要说真诚，全片唯一真诚的时刻只在片尾对于电影之外真实人物的致敬。 +女性主义的电影，描绘的每一个女性角色都很可爱，我喜欢王琴阿姨，看到电影最后她是靠自己的努力工作抓住机遇才得到后面富裕的生活，就很庆幸贾玲没有让这个角色流于俗套。整部电影最巧妙的地方就是在于最后一部分——双穿越。确实催人泪下。但是父母真的对孩子没有要求吗？我想并非天下父母都是如此。但是真挚的感情可以打动人。 +没有想象中的好看，中规中矩，评分虚高。 +额… 电影不差 ，但也没有那么好吧… 8.1过誉了 ，6.8分差不多了 +前面100分钟笑点密集，笑得人仰马翻，后面从贾玲奔跑那里没忍住看哭了，全片没有拉垮的演员，满分好评。 +既没让我笑也没让我哭，每个“爆笑”段落都尬出天际，最后又来一个大型煽情。片尾曲第二首是啥玩意？第一次遇到这么赶客的片尾曲。还有那段女儿撮合妈妈相亲的戏，跟这个年代催婚的母亲们有啥区别，约等于把“时代倒退”几个字打在了大银幕上。唯一让我共情的点在于即使是有李焕英这么好的妈妈，做女儿的也一直一直在努力想让妈妈高兴。这说明大多数母亲都让孩子感到亏欠，而孩子却在无条件地爱她们——宁愿不出生，也希望妈妈能过得更幸福。 +真的很好哭。 +脱离了男性凝视，镜头聚焦的胸和屁股。我只记得李焕英的每一个漂亮裙摆，被风吹起来露出一截细长的小腿，原来每一个妈妈都曾是美丽的少女。 +“观众已经笑得不行了” +结尾给整部电影涨了分，前半部分的笑料设计其实很一般，处于也可笑但又确实又有点勉强的线上。 +就这？就这？8.3分？我居然花了60块在大年初三去看了这么一部电影？？大家对中国电影要求这么低了吗？我承认有些许的小亮点，题材也不错，所以在我心里它可以打到及格线6分，但是这完全没有到优秀吧？剧情老套，转折生硬，最后一大段贾玲的哭戏哭了我一地尴尬的鸡皮疙瘩…哪怕换个有演技些的演员我都能再多打一星，一整部电影就像是强行煽情的哄闹小品，居然能上大银幕还有8分的高分？我服 +“人在委屈的时候就会想妈妈” +故事说不上新颖，可是母亲这个点总是能打动人，希望每个做母亲的女生记得对自己好点，不要牺牲不要伟大，让自己开心快乐很重要。 +3.5，之前从来没觉得乔杉和贾玲长得这么像。 +前面这些天听够了大家的笑死哭死等夸张评论，眼瞅着春节档这唯一的8分电影满怀期待，看完后内心一片狐疑“就这？？？”不管是造梦还是穿越都特别偷懒，甚至编剧都没想怎么去说服观众，只想我逗你们笑让你们哭就行了，然而这电影既不好笑也不好哭。一切都特别矫揉造作，甚至还不如当初的小品好笑，也不如贾玲在综艺节目里回忆自己妈妈煽情。沈腾的出现就好像贾玲需要一个拉票房的演员，然后靠友情求过来硬塞进来。你以为这就够工具人了？那么陈赫的出现让你无比困惑，他存在的意义是什么？ +哭瞎了，啊，女孩子真好啊，又甜又暖 +好真诚 诚恳得不像一部电影 像是一首散文诗 +在贾玲说出对啊，我妈怎么会缝的那一瞬间整个电影在这里开始反转，从这里开始显得不一样，我爱这一瞬间！！！！ +有一个影评人说过，其实好作品没有什么标准，只要她打动了你，就是好作品。张小斐那句“我宝”出来我都哭抽抽了，无论在哪里，妈妈的本能就是保护孩子。BTW我真的无法正视沈腾了，他不说话站在那我就很想笑 +“打我有记忆起妈妈就是个中年妇女的模样，所以我总忘记妈妈曾经也是个花季少女。” +熟练地搞笑，稚嫩地煽情 +想让隔壁唐探3编剧瞧瞧 +什 么 T M D 叫 反 转 +贾玲最后干吼的那一段太尬 ，有时候悲伤真的不是靠哭来让别人有共鸣 +让这部电影挂在中国影视的前列，告诉所有电影人，电影是抒发自己情感引起全国共鸣的好办法，质朴的手法、真挚的感情就能获得观众的理解。 +戴口罩看这种电影太反人类了，哭得口罩都湿了，糊在脸上好难受 +诚意之作，希望往后能多一些这样的电影，少一些圈钱工业垃圾，没错，说的就是陈思诚。 +最美好的一幕是贾玲在山坡下，看着她爸骑车奔向她妈。我就想着，我也愿意去我妈的年轻世界里走一走。 +好好笑啊！ +而且没有令人不适的颜色笑话，女性视角就很棒👍🏻 +虽然有点煽情但是不妨碍好笑 +这破烂电影能成为中国电影史票房第二，是中国电影人，乃至世界电影人的耻辱。煽情、刻意、莫名其妙，什么感动，老子只想笑。 +看电影最厌恶的就是电影里的人强行煽情哭了但是看的人很尴尬。喜剧部分也做得不怎么样，沈腾说是男一的戏份但是存在感比陈赫还弱，更看得人难受了。 +这片子好时机，能这么高分，只能说明人民群众的情感匮乏到什么程度了 +上映前评价不好，我就觉得就当爆米花搞笑剧图一乐吧。看到一半觉得这绝对算搞笑剧里的一流作品。看到最后，我错了我全错了，这一流喜剧啊，好笑但又戳泪点，而且这泪点真的不是一哭即完。还有片尾那些话让我回顾起剧情更难受了，说贾玲靠妈赚钱的能消停会儿不？ +李焕英阿姨 您女儿可太棒了 她真的好爱您 也真的很遗憾妹让您坐上敞篷车 看着她现在事业有成 我也好爱我的妈妈 希望您保佑所有妈妈都健康平安 长命百岁 +没有想到这么好。一开始笑死，后来又哭死。结尾尤为动人。 +不太是电影的视听语言，像是抻长了的好几个小品，但沈腾贡献了很好笑的表演，以及妈妈对女儿的爱永远比女儿对妈妈的爱更多这个题眼还是很动人的，是强烈的抱憾的女儿视角。在穿越回去的那场母女对谈中，双方都心知肚明，但都打着哑谜。女儿希望母亲幸福，即便这样的幸福图景中没有自己。母亲希望女儿健康，即便时光倒流也从未对当时的选择感到后悔，从未想过没有这个女儿的人生何以可能。希望这样题材的电影越来越多。母女之间的共情和联结，是一个眼神，些许沉默，无数眼泪和一声叹息。 +1.道路千万条，安全第一条，各位上路一定要看路啊！2.感觉像是夏洛特烦恼的翻版，夏洛特毕竟第一次，还有点经验，这个，，，毕竟这年头穿越剧好多好多3.母爱太过沉重，看不下去，反正母爱伟大完事4.作为男的，看这片怎么老想着名词“丧偶式育儿”。。。 +电影的所有呈现，打四分其实就可以了，但是多出来一星要给到女导演视角下的女性主题，完全没有让人有任何不适（拉踩隔壁某探3）。没有刻意制造的笑点更没有刻意制造的哭点，一切都是很自然的发生了然后触动观众，这很难得。好喜欢贾玲，有谁会不喜欢贾玲呢。 +得比唐3低两分 +就这破电影，一堆人哭的稀里哗啦，还觉得自己倍有欣赏能力，还觉得自己共情。打个三分不吹不黑，但硬要把他推上45分我就不敢苟同了。 +摊手。打个两星凭着我的权重把分拉下来。 + +我又回来了，改一星了。什么鬼剧情什么烂演技，这玩意有脸刷8分？？？？？恰烂钱？？？ + +来杠我！三无小号尽管来杠！ + + +……3月16日更新 +听说这玩意还能出口国外，真把自己当啥了 +“可是我妈现在还不会”这句话出现后的转折是我怎么也没想到的 一贯的思路都以为只有晓玲自己穿越回去希望能让年轻的妈妈更加高兴 谁可知母女两人一起穿越回去了 这是这部电影最大的转折 也是全片泪点开始 让老套的穿越剧更加新颖 挺中规中矩的一部片却也很特别 母爱一个讲到烂的题材 可是这部电影还是刷新了大家的印象 妈妈怎么会不爱自己的孩子呢 +生女儿的妈看不了的电影 +眼泪哗哗的 +但是还是推荐阖家观看 +贾玲说了这片“我把心都掏给你们看了……” +任何时候真心都是最打动人的。 +有的人见妈妈坐飞机，有的人见妈妈要坐时光机。 +其实如果从电影的角度来说，五星是肯定不到的，四星顶多吧。但从整部电影的情感来说的话，真的太真挚了。我相信贾玲一定是把那个充满了遗憾的自己刀剐了千百遍，抚慰了千万遍，把见母亲最后一面的一幕想象了无数遍，才能产出如此真诚的一部电影 + +当然，导演水平挺普通的，艺术性也基本没有，但真的就胜在了真诚，嗯。 + +确实很好哭，也挺好笑的（但有些笑点还是比较冗杂） +这应该是贾玲作为一个女儿最极致的浪漫了。千千万万走到电影院的人，去了解李焕英、纪念李焕英的同时，想起总是被我们忽略的母亲。 +"我宝"那一句真的泪奔了，五星好评，前面笑点不尬，后面反转真的流泪了… +缺点：1、诉求和故事主线不统一不连贯，主角不够聚焦，故事成了拼盘一会儿是张小斐的戏一会又成了沈腾的戏(在这点上同类型的影片《夏洛特烦恼》就要比这部强，主角欲望强烈，诉求明确，主题突出，当然夏也有很多问题)；2、情绪缺乏克制，没有留白，这是无法成为经典影片的最大障碍。优点：1、女性导演女性视角，难能可贵(当然并没有突破男性视角)；2、结尾处理很好，催泪全在这段，准确说是104分钟到110分钟；3、笑点设置比原版小品好，尤其沈腾在舞台上演出那段设置特别巧妙，打破了荧幕，直接和影院观众互动那种。影响：好的影响是未来大概会出现一批女艺人来做导演拍自身故事，这部片子8.3的评分说明我们影视欣赏水平稳步降低，良好的市场表现会引发大量模仿，咱国影视质量又将大幅下降。 +大型欢乐喜剧人小品一则，胜在感情真挚。张小斐可以的。我和我妈全程无表情看完，无情母女。 +这分数真是绝了，太虚高了。没啥笑点也没啥哭点，唯一感觉演员演的都不错，尤其是张小斐。全国人民给贾玲的情怀买单……还把全国人民都感动了……好无语…… +不至于不至于，逻辑都没有，从电影层面来说问题太多 +6 看简介以为是女性版《乘风破浪》，其实是80年代版《夏洛特烦恼》。细节工夫到位，OST有流行音乐，也有真正属于那代人的《年轻的朋友来相会》！情绪处理得挺好，也善于营造气氛，很让人感动。尤其处于疫情背景之下，“亲情”与“告别”很能令人共情，更何况是“妈妈曾经是少女”引发的女性意识呢？到了最后20分钟左右，很多人都在抹眼泪😢 影片不乏许多问题（尤其是剧作结构），但作为贺岁片，绝对及格了。PS：场下UCLA毕业生一脸辛酸笑。 +张小斐喊出哪句“我宝”的时候 +宛如某晚小品，前面不管咋地最后反正都必须给你一个煽情。感觉前面拍那么多有的没的都是为了那个煽情结尾，可是你起码也把前面故事编圆了好吧？真要图个感动就拍最后十分钟足够了 +贺岁档基本都看过了，怎么说呢，这部片子的口碑真得感谢同行，不差，但也没有特别好。 +从另一个方面来说，真正用心用感情去拍一部电影，真的是可以弥补专业水平的不足，甚至可以打动观众的。相比之下《唐人街探案3》就输在这里，陈导很傲慢，砸钱就完了；贾导很真诚，只想记录一段平凡的母爱和想念，人民群众真正喜闻乐见的是什么，答案已经有了。电影不过是影人和观众的交流，真诚的情感可能不会被所有人接受，但空洞的生意不会被任何人共情。 +我也曾想过，如果母亲不用为我牺牲，会不会成为更伟大的人；或许会吧，但她就不会成为一个伟大的母亲了，我又何必帮她做出选择，我能选择的只有如何回报她 diff --git a/out.txt b/out.txt new file mode 100644 index 000000000..65925f14e --- /dev/null +++ b/out.txt @@ -0,0 +1,127 @@ +and 15 +be 13 +will 11 +to 11 +the 10 +of 10 +a 8 +we 8 +day 6 +able 6 +every 6 +together 6 +i 5 +have 5 +dream 5 +that 5 +one 5 +with 5 +this 5 +in 4 +shall 4 +free 4 +when 4 +little 3 +black 3 +white 3 +made 3 +faith 3 +at 3 +last 3 +children 2 +nation 2 +by 2 +their 2 +today 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\n", + "亲爱的用戶：\n", + "

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\n", + " LongBridge App V4.19.0 版本现已全新升级上线，欢迎更新体验！ \n", + "

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\n", + " \n", + " Longbridge 长桥\n", + "

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\n", + " 本电子邮件为系统邮件，请勿回复。
\n", + " 本邮件的内容（含附件）均为普通信息，不包含也不应被理解为投资建议或者鼓动购买所提及证券或金融工具的提议。此邮件内容并未参考您任何持有的投资标的、财务状况和投资需求。因此，对任何由此邮件直接或间接造成的损失，长桥均不作任何保证，也不承担任何责任。投资有⻛险，⼊市须谨慎。 \n", + "

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "...\n", + "*cmd* 'QUIT'\n" + ] + }, + { + "data": { + "text/plain": [ + "b'+OK core mail'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import poplib\n", + "from email.parser import Parser\n", + "from email.header import decode_header\n", + "from email.utils import parseaddr\n", + "\n", + "# 输入邮件地址, 口令和POP3服务器地址:\n", + "email = 'hoolich@163.com'\n", + "password = 'FYIRMDFDJGHFIOIA'\n", + "pop3_server = 'pop3.163.com'\n", + "\n", + "def guess_charset(msg):\n", + " charset = msg.get_charset()\n", + " if charset is None:\n", + " content_type = msg.get('Content-Type', '').lower()\n", + " pos = content_type.find('charset=')\n", + " if pos >= 0:\n", + " charset = content_type[pos + 8:].strip()\n", + " return charset\n", + "\n", + "def decode_str(s):\n", + " value, charset = decode_header(s)[0]\n", + " if charset:\n", + " value = value.decode(charset)\n", + " return value\n", + "\n", + "def print_info(msg, indent=0):\n", + " if indent == 0:\n", + " for header in ['From', 'To', 'Subject']:\n", + " value = msg.get(header, '')\n", + " if value:\n", + " if header=='Subject':\n", + " value = decode_str(value)\n", + " else:\n", + " hdr, addr = parseaddr(value)\n", + " name = decode_str(hdr)\n", + " value = u'%s <%s>' % (name, addr)\n", + " print('%s%s: %s' % (' ' * indent, header, value))\n", + " if (msg.is_multipart()):\n", + " parts = msg.get_payload()\n", + " for n, part in enumerate(parts):\n", + " print('%spart %s' % (' ' * indent, n))\n", + " print('%s--------------------' % (' ' * indent))\n", + " print_info(part, indent + 1)\n", + " else:\n", + " content_type = msg.get_content_type()\n", + " if content_type=='text/plain' or content_type=='text/html':\n", + " content = msg.get_payload(decode=True)\n", + " charset = guess_charset(msg)\n", + " if charset:\n", + " content = content.decode(charset)\n", + " print('%sText: %s' % (' ' * indent, content + '...'))\n", + " else:\n", + " print('%sAttachment: %s' % (' ' * indent, content_type))\n", + "\n", + "# 连接到POP3服务器:\n", + "server = poplib.POP3(pop3_server)\n", + "# 可以打开或关闭调试信息:\n", + "server.set_debuglevel(1)\n", + "# 可选:打印POP3服务器的欢迎文字:\n", + "print(server.getwelcome().decode('utf-8'))\n", + "# 身份认证:\n", + "server.user(email)\n", + "server.pass_(password)\n", + "# stat()返回邮件数量和占用空间:\n", + "print('Messages: %s. Size: %s' % server.stat())\n", + "# list()返回所有邮件的编号:\n", + "resp, mails, octets = server.list()\n", + "# 可以查看返回的列表类似[b'1 82923', b'2 2184', ...]\n", + "print(mails)\n", + "# 获取最新一封邮件, 注意索引号从1开始:\n", + "index = len(mails)\n", + "resp, lines, octets = server.retr(index)\n", + "# lines存储了邮件的原始文本的每一行,\n", + "# 可以获得整个邮件的原始文本:\n", + "msg_content = b'\\r\\n'.join(lines).decode('utf-8')\n", + "# 稍后解析出邮件:\n", + "msg = Parser().parsestr(msg_content)\n", + "print_info(msg)\n", + "# 可以根据邮件索引号直接从服务器删除邮件:\n", + "# server.dele(index)\n", + "# 关闭连接:\n", + "server.quit()" + ] } ], "metadata": { diff --git a/Part.5.B.combine-csv.ipynb b/Part.5.B.combine-csv.ipynb new file mode 100644 index 000000000..a4837f92e --- /dev/null +++ b/Part.5.B.combine-csv.ipynb @@ -0,0 +1,111 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 合并excel" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.0, '张三', 300.0, 30.0, 100.0]\n", + "[2.0, '李四', 200.0, 20.0, 100.0]\n", + "[1.0, '王五', 200.0, 20.0, 100.0]\n", + "[2.0, '陈器', 200.0, 20.0, 100.0]\n", + "[1.0, '陈王', 200.0, 20.0, 100.0]\n", + "[2.0, '陈器', 200.0, 20.0, 100.0]\n" + ] + } + ], + "source": [ + "\n", + "import xlrd\n", + "import xlwt\n", + "from pathlib import Path, PurePath\n", + "\n", + "# 指定要合并excel的路径\n", + "src_path = '/Users/chenqiang/Desktop/cq'\n", + "# 目标文件地址\n", + "dst_file='/Users/chenqiang/Desktop/dst.xls'\n", + "# 取得该目录下所有的xlsx格式文件\n", + "p = Path(src_path)\n", + "# 把匹配文件放入到files的数组中\n", + "files = [x for x in p.iterdir() if PurePath(x).match('*.xls')]\n", + "# 准备一个列表存放读取结果\n", + "new_content = []\n", + "# 对每一个文件进行重复处理\n", + "for file in files: \n", + " # 用文件名作为每个文件编号的标识 \n", + " number = file.stem \n", + " # 打开文件\n", + " data = xlrd.open_workbook(file)\n", + " # 将第一个表写入到table中 \n", + " table = data.sheets()[0]\n", + " # 取得每个excel的总行数\n", + " employee_number = table.nrows\n", + " for line in range(1,employee_number): \n", + " content = table.row_values(rowx=line, start_colx=0, end_colx=None)\n", + " new_content.append(content)\n", + " print(content)\n", + "# 准备写入文件的表头\n", + "table_header = ['员工编号', '姓名', '工资','出勤','奖金']\n", + "workbook = xlwt.Workbook(encoding='utf-8')\n", + "xlsheet = workbook.add_sheet(\"统计结果\")\n", + "# 写入表头\n", + "row = 0\n", + "col = 0\n", + "for cell_header in table_header: \n", + " xlsheet.write(row, col, cell_header) \n", + " col += 1\n", + "# 向下移动一行\n", + "row += 1\n", + "# 取出每一行内容\n", + "for line in new_content: \n", + " col = 0 \n", + " # 取出每个单元格内容 \n", + " for cell in line: \n", + " # 写入内容 \n", + " xlsheet.write(row, col, cell) \n", + " # 向右移动一个单元格 \n", + " col += 1 \n", + " # 向下移动一行 \n", + " row += 1\n", + "# 保存最终结果\n", + "workbook.save(dst_file)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part.5.combine-doc.ipynb b/Part.5.combine-doc.ipynb new file mode 100644 index 000000000..f2748eac5 --- /dev/null +++ b/Part.5.combine-doc.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'docx'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/0n/6mryfm8x06gfzddk4dympfg80000gn/T/ipykernel_10167/192274820.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdocx\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDocument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpathlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPurePath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'docx'" + ] + } + ], + "source": [ + "import xlrd\n", + "from docx import Document\n", + "from pathlib import Path, PurePath\n", + "import datetime\n", + "\n", + "today=datetime.date.today().strftime('%Y-%m-%d')\n", + "\n", + "# 客户信息文件\n", + "customer = '/Users/edz/Desktop/效率专栏/文章2/邀请函样例文件/客户信息.xlsx'\n", + "\n", + "# 邀请函模版\n", + "invitation = '/Users/edz/Desktop/效率专栏/文章2/邀请函样例文件/邀请函模版.docx'\n", + "\n", + "# 邀请函路径\n", + "invitation_path = '/Users/edz/Desktop/效率专栏/文章2/邀请函样例文件/'\n", + "\n", + "# 替换内容\n", + "replace_content = {\n", + " '<姓名>' : 'no_name',\n", + " '<性别>' : 'm_f',\n", + " '<今天日期>' : today\n", + "}\n", + "\n", + "def generat_invitation():\n", + " '''\n", + " 生成邀请函文件\n", + " '''\n", + " doc = Document(invitation)\n", + " # 取出每一段\n", + " for para in doc.paragraphs:\n", + " for key, value in replace_content.items():\n", + " if key in para.text:\n", + " # 逐个关键字进行替换\n", + " para.text = para.text.replace(key, value)\n", + "\n", + " file_name = PurePath(invitation_path).with_name(replace_content['<姓名>']).with_suffix('.docx')\n", + "\n", + " doc.save(file_name)\n", + "\n", + "\n", + "def get_customer(customer_file: Path):\n", + " '''\n", + " 获取邀请函信息\n", + " '''\n", + " # 从第一个sheet中取出客户信息\n", + " data = xlrd.open_workbook(customer_file)\n", + " table = data.sheets()[0]\n", + "\n", + " # 取得客户数量\n", + " customer_number = table.nrows\n", + "\n", + " for line in range(1, customer_number):\n", + " content = table.row_values(rowx=line, start_colx=0, end_colx=None)\n", + " replace_content['<姓名>'] = content[0]\n", + " replace_content['<性别>'] = content[1]\n", + " # print(replace_content)\n", + " # {'<姓名>': '韩梅梅', '<性别>': '女士', '<今天日期>': '2021-01-01'}\n", + " # {'<姓名>': '李雷', '<性别>': '先生', '<今天日期>': '2021-01-01'}\n", + " generat_invitation()\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " get_customer(customer)\n", + "\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part.5.split-csv.ipynb b/Part.5.split-csv.ipynb new file mode 100644 index 000000000..3b3e36ff8 --- /dev/null +++ b/Part.5.split-csv.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 拆分excel" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import xlrd\n", + "import xlwt\n", + "from pathlib import Path, PurePath\n", + "\n", + "# 工资单文件\n", + "src_file = '/Users/chenqiang/Desktop/cq-dst/dst.xls'\n", + "# 拆分文件保存路径\n", + "dst_path = '/Users/chenqiang/Desktop/cq-dst'\n", + "\n", + "\n", + "data = xlrd.open_workbook(src_file)\n", + "table = data.sheets()[0]\n", + "# 取得表头\n", + "salary_header = table.row_values(rowx=0, start_colx=0, end_colx=None) \n", + "\n", + "# 定义写入文件的函数\n", + "def write_to_file(filename, cnt):\n", + " ''' \n", + " filename : 写入的文件名\n", + " cnt : 写入的内容\n", + " '''\n", + " workbook = xlwt.Workbook(encoding='utf-8')\n", + " xlsheet = workbook.add_sheet(\"本月工资\")\n", + "\n", + " row = 0\n", + " for line in cnt:\n", + " col = 0\n", + " for cell in line:\n", + " xlsheet.write(row, col, cell)\n", + " col += 1\n", + " row += 1\n", + "\n", + " workbook.save(PurePath(src_file).with_name(filename).with_suffix('.xls'))\n", + "\n", + "\n", + "\n", + "# 取得员工数量\n", + "employee_number = table.nrows\n", + "# 取得每一行,并用第二个单元格作为新的文件名\n", + "for line in range(1,employee_number):\n", + " content = table.row_values(rowx=line, start_colx=0, end_colx=None)\n", + " # 将表头和员工数量重新组成一个新的文件 \n", + " new_content = []\n", + " # 增加表头到要写入的内容中\n", + " new_content.append(salary_header)\n", + " # 增加员工工资到要写入的内容中\n", + " new_content.append(content)\n", + " # 调用自定义函数write_to_file()写入新的文件\n", + " write_to_file(filename = content[1], cnt = new_content)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + }, + "kernelspec": { + "display_name": "Python 3.9.7 ('base')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Part5.B.downloadcsv.ipynb b/Part5.B.downloadcsv.ipynb index e4efc4397..eab8c85c2 100644 --- a/Part5.B.downloadcsv.ipynb +++ b/Part5.B.downloadcsv.ipynb @@ -73,566 +73,36 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n", - "\n", - "\n", - " 开放式基金排行 _ 天天基金网\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "
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\n", - "郑重声明：天天基金网发布此信息目的在于传播更多信息，与本网站立场无关。天天基金网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资决策建议，据此操作，风险自担。数据来源：东方财富Choice数据。\n", - "
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encode_chunked\u001b[39m=\u001b[39;49mreq\u001b[39m.\u001b[39;49mhas_header(\u001b[39m'\u001b[39;49m\u001b[39mTransfer-encoding\u001b[39;49m\u001b[39m'\u001b[39;49m))\n\u001b[1;32m 1348\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m err: \u001b[39m# timeout error\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1285\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[39m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1285\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_request(method, url, body, headers, encode_chunked)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1331\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1330\u001b[0m body \u001b[39m=\u001b[39m _encode(body, \u001b[39m'\u001b[39m\u001b[39mbody\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 1331\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mendheaders(body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1280\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1279\u001b[0m \u001b[39mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1280\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_output(message_body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1040\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1039\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1040\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(msg)\n\u001b[1;32m 1042\u001b[0m \u001b[39mif\u001b[39;00m message_body \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1043\u001b[0m \n\u001b[1;32m 1044\u001b[0m \u001b[39m# create a consistent interface to message_body\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:980\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_open:\n\u001b[0;32m--> 980\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n\u001b[1;32m 981\u001b[0m \u001b[39melse\u001b[39;00m:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:946\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[39m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 946\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection(\n\u001b[1;32m 947\u001b[0m (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhost,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mport), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msource_address)\n\u001b[1;32m 948\u001b[0m \u001b[39m# Might fail in OSs that don't implement TCP_NODELAY\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/socket.py:844\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 844\u001b[0m \u001b[39mraise\u001b[39;00m err\n\u001b[1;32m 845\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/socket.py:832\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 831\u001b[0m sock\u001b[39m.\u001b[39mbind(source_address)\n\u001b[0;32m--> 832\u001b[0m sock\u001b[39m.\u001b[39;49mconnect(sa)\n\u001b[1;32m 833\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n", + "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 61] Connection refused", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/chenqiang/Documents/the-craft-of-selfteaching/Part5.B.downloadcsv.ipynb Cell 5'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m headers \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mUser-Agent\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39mMozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Mobile Safari/537.36\u001b[39m\u001b[39m'\u001b[39m}\n\u001b[1;32m 7\u001b[0m req \u001b[39m=\u001b[39m urllib\u001b[39m.\u001b[39mrequest\u001b[39m.\u001b[39mRequest(url\u001b[39m=\u001b[39murl,headers\u001b[39m=\u001b[39mheaders)\n\u001b[0;32m----> 8\u001b[0m res \u001b[39m=\u001b[39m urllib\u001b[39m.\u001b[39;49mrequest\u001b[39m.\u001b[39;49murlopen(req)\n\u001b[1;32m 9\u001b[0m \u001b[39m# res=urllib.request.urlopen(req)#先由 urllib 模块的 request 方法打开 URL 得到网页 HTML 对象。\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[39m# print(res)#打印返回结果\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[39m#提取响应内容\u001b[39;00m\n\u001b[1;32m 12\u001b[0m html \u001b[39m=\u001b[39m res\u001b[39m.\u001b[39mread()\u001b[39m.\u001b[39mdecode(\u001b[39m'\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m'\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:214\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m opener \u001b[39m=\u001b[39m _opener\n\u001b[0;32m--> 214\u001b[0m \u001b[39mreturn\u001b[39;00m opener\u001b[39m.\u001b[39;49mopen(url, data, timeout)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:517\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 514\u001b[0m req \u001b[39m=\u001b[39m meth(req)\n\u001b[1;32m 516\u001b[0m sys\u001b[39m.\u001b[39maudit(\u001b[39m'\u001b[39m\u001b[39murllib.Request\u001b[39m\u001b[39m'\u001b[39m, req\u001b[39m.\u001b[39mfull_url, req\u001b[39m.\u001b[39mdata, req\u001b[39m.\u001b[39mheaders, req\u001b[39m.\u001b[39mget_method())\n\u001b[0;32m--> 517\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_open(req, data)\n\u001b[1;32m 519\u001b[0m \u001b[39m# post-process response\u001b[39;00m\n\u001b[1;32m 520\u001b[0m meth_name \u001b[39m=\u001b[39m protocol\u001b[39m+\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_response\u001b[39m\u001b[39m\"\u001b[39m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:534\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[39mreturn\u001b[39;00m result\n\u001b[1;32m 533\u001b[0m protocol \u001b[39m=\u001b[39m req\u001b[39m.\u001b[39mtype\n\u001b[0;32m--> 534\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_chain(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhandle_open, protocol, protocol \u001b[39m+\u001b[39;49m\n\u001b[1;32m 535\u001b[0m \u001b[39m'\u001b[39;49m\u001b[39m_open\u001b[39;49m\u001b[39m'\u001b[39;49m, req)\n\u001b[1;32m 536\u001b[0m \u001b[39mif\u001b[39;00m result:\n\u001b[1;32m 537\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:494\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[39mfor\u001b[39;00m handler \u001b[39min\u001b[39;00m handlers:\n\u001b[1;32m 493\u001b[0m func \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 494\u001b[0m result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49margs)\n\u001b[1;32m 495\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 496\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:1375\u001b[0m, in \u001b[0;36mHTTPHandler.http_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhttp_open\u001b[39m(\u001b[39mself\u001b[39m, req):\n\u001b[0;32m-> 1375\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo_open(http\u001b[39m.\u001b[39;49mclient\u001b[39m.\u001b[39;49mHTTPConnection, req)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:1349\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1346\u001b[0m h\u001b[39m.\u001b[39mrequest(req\u001b[39m.\u001b[39mget_method(), req\u001b[39m.\u001b[39mselector, req\u001b[39m.\u001b[39mdata, headers,\n\u001b[1;32m 1347\u001b[0m encode_chunked\u001b[39m=\u001b[39mreq\u001b[39m.\u001b[39mhas_header(\u001b[39m'\u001b[39m\u001b[39mTransfer-encoding\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m 1348\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m err: \u001b[39m# timeout error\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m \u001b[39mraise\u001b[39;00m URLError(err)\n\u001b[1;32m 1350\u001b[0m r \u001b[39m=\u001b[39m h\u001b[39m.\u001b[39mgetresponse()\n\u001b[1;32m 1351\u001b[0m \u001b[39mexcept\u001b[39;00m:\n", + "\u001b[0;31mURLError\u001b[0m: " ] } ], @@ -1461,10 +931,10 @@ ], "metadata": { "interpreter": { - "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" + "hash": "034c70736e9d25f3f57bc70ec9a2164e1c27bdcdecfc656ea6098a86262350a2" }, "kernelspec": { - "display_name": "Python 3.9.7 ('base')", + "display_name": "Python 3.9.12 ('myenv')", "language": "python", "name": "python3" }, @@ -1478,7 +948,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.12" }, "orig_nbformat": 4 }, diff --git a/awesome-python3-webapp/LICENSE b/awesome-python3-webapp/LICENSE new file mode 100644 index 000000000..e69de29bb diff --git a/awesome-python3-webapp/app.py b/awesome-python3-webapp/app.py new file mode 100644 index 000000000..f5dfb0ec2 --- /dev/null +++ b/awesome-python3-webapp/app.py @@ -0,0 +1,21 @@ +import logging; logging.basicConfig(level=logging.INFO) + +import asyncio, os, json, time +from datetime import datetime + +from aiohttp import web + +def index(request): + return web.Response(body=b'

Awesome

') + +@asyncio.coroutine +def init(loop): + app = web.Application(loop=loop) + app.router.add_route('GET', '/', index) + srv = yield from loop.create_server(app.make_handler(), '127.0.0.1', 9000) + logging.info('server started at http://127.0.0.1:9000...') + return srv + +loop = asyncio.get_event_loop() +loop.run_until_complete(init(loop)) +loop.run_forever() \ No newline at end of file From d033033760dfe2011e724a7eac81f936f34f6cce Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Tue, 25 Apr 2023 11:37:13 +0800 Subject: [PATCH 11/12] 20230425 --- Part5.B.downloadcsv.ipynb | 698 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 669 insertions(+), 29 deletions(-) diff --git a/Part5.B.downloadcsv.ipynb b/Part5.B.downloadcsv.ipynb index eab8c85c2..ada8b4447 100644 --- a/Part5.B.downloadcsv.ipynb +++ b/Part5.B.downloadcsv.ipynb @@ -77,32 +77,574 @@ "metadata": {}, "outputs": [ { - "ename": "URLError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:1346\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1345\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1346\u001b[0m h\u001b[39m.\u001b[39;49mrequest(req\u001b[39m.\u001b[39;49mget_method(), req\u001b[39m.\u001b[39;49mselector, req\u001b[39m.\u001b[39;49mdata, headers,\n\u001b[1;32m 1347\u001b[0m encode_chunked\u001b[39m=\u001b[39;49mreq\u001b[39m.\u001b[39;49mhas_header(\u001b[39m'\u001b[39;49m\u001b[39mTransfer-encoding\u001b[39;49m\u001b[39m'\u001b[39;49m))\n\u001b[1;32m 1348\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m err: \u001b[39m# timeout error\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1285\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[39m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1285\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_request(method, url, body, headers, encode_chunked)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1331\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1330\u001b[0m body \u001b[39m=\u001b[39m _encode(body, \u001b[39m'\u001b[39m\u001b[39mbody\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 1331\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mendheaders(body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1280\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1279\u001b[0m \u001b[39mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1280\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_output(message_body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:1040\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1039\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1040\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(msg)\n\u001b[1;32m 1042\u001b[0m \u001b[39mif\u001b[39;00m message_body \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1043\u001b[0m \n\u001b[1;32m 1044\u001b[0m \u001b[39m# create a consistent interface to message_body\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:980\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_open:\n\u001b[0;32m--> 980\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n\u001b[1;32m 981\u001b[0m \u001b[39melse\u001b[39;00m:\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/http/client.py:946\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[39m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 946\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection(\n\u001b[1;32m 947\u001b[0m (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhost,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mport), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msource_address)\n\u001b[1;32m 948\u001b[0m \u001b[39m# Might fail in OSs that don't implement TCP_NODELAY\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/socket.py:844\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 844\u001b[0m \u001b[39mraise\u001b[39;00m err\n\u001b[1;32m 845\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/socket.py:832\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 831\u001b[0m sock\u001b[39m.\u001b[39mbind(source_address)\n\u001b[0;32m--> 832\u001b[0m sock\u001b[39m.\u001b[39;49mconnect(sa)\n\u001b[1;32m 833\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n", - "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 61] Connection refused", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/chenqiang/Documents/the-craft-of-selfteaching/Part5.B.downloadcsv.ipynb Cell 5'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m headers \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mUser-Agent\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39mMozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Mobile Safari/537.36\u001b[39m\u001b[39m'\u001b[39m}\n\u001b[1;32m 7\u001b[0m req \u001b[39m=\u001b[39m urllib\u001b[39m.\u001b[39mrequest\u001b[39m.\u001b[39mRequest(url\u001b[39m=\u001b[39murl,headers\u001b[39m=\u001b[39mheaders)\n\u001b[0;32m----> 8\u001b[0m res \u001b[39m=\u001b[39m urllib\u001b[39m.\u001b[39;49mrequest\u001b[39m.\u001b[39;49murlopen(req)\n\u001b[1;32m 9\u001b[0m \u001b[39m# res=urllib.request.urlopen(req)#先由 urllib 模块的 request 方法打开 URL 得到网页 HTML 对象。\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[39m# print(res)#打印返回结果\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[39m#提取响应内容\u001b[39;00m\n\u001b[1;32m 12\u001b[0m html \u001b[39m=\u001b[39m res\u001b[39m.\u001b[39mread()\u001b[39m.\u001b[39mdecode(\u001b[39m'\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m'\u001b[39m)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:214\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m opener \u001b[39m=\u001b[39m _opener\n\u001b[0;32m--> 214\u001b[0m \u001b[39mreturn\u001b[39;00m opener\u001b[39m.\u001b[39;49mopen(url, data, timeout)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:517\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 514\u001b[0m req \u001b[39m=\u001b[39m meth(req)\n\u001b[1;32m 516\u001b[0m sys\u001b[39m.\u001b[39maudit(\u001b[39m'\u001b[39m\u001b[39murllib.Request\u001b[39m\u001b[39m'\u001b[39m, req\u001b[39m.\u001b[39mfull_url, req\u001b[39m.\u001b[39mdata, req\u001b[39m.\u001b[39mheaders, req\u001b[39m.\u001b[39mget_method())\n\u001b[0;32m--> 517\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_open(req, data)\n\u001b[1;32m 519\u001b[0m \u001b[39m# post-process response\u001b[39;00m\n\u001b[1;32m 520\u001b[0m meth_name \u001b[39m=\u001b[39m protocol\u001b[39m+\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_response\u001b[39m\u001b[39m\"\u001b[39m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:534\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[39mreturn\u001b[39;00m result\n\u001b[1;32m 533\u001b[0m protocol \u001b[39m=\u001b[39m req\u001b[39m.\u001b[39mtype\n\u001b[0;32m--> 534\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_chain(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhandle_open, protocol, protocol \u001b[39m+\u001b[39;49m\n\u001b[1;32m 535\u001b[0m \u001b[39m'\u001b[39;49m\u001b[39m_open\u001b[39;49m\u001b[39m'\u001b[39;49m, req)\n\u001b[1;32m 536\u001b[0m \u001b[39mif\u001b[39;00m result:\n\u001b[1;32m 537\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:494\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[39mfor\u001b[39;00m handler \u001b[39min\u001b[39;00m handlers:\n\u001b[1;32m 493\u001b[0m func \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 494\u001b[0m result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49margs)\n\u001b[1;32m 495\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 496\u001b[0m \u001b[39mreturn\u001b[39;00m result\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:1375\u001b[0m, in \u001b[0;36mHTTPHandler.http_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhttp_open\u001b[39m(\u001b[39mself\u001b[39m, req):\n\u001b[0;32m-> 1375\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo_open(http\u001b[39m.\u001b[39;49mclient\u001b[39m.\u001b[39;49mHTTPConnection, req)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/myenv/lib/python3.9/urllib/request.py:1349\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1346\u001b[0m h\u001b[39m.\u001b[39mrequest(req\u001b[39m.\u001b[39mget_method(), req\u001b[39m.\u001b[39mselector, req\u001b[39m.\u001b[39mdata, headers,\n\u001b[1;32m 1347\u001b[0m encode_chunked\u001b[39m=\u001b[39mreq\u001b[39m.\u001b[39mhas_header(\u001b[39m'\u001b[39m\u001b[39mTransfer-encoding\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m 1348\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m err: \u001b[39m# timeout error\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m \u001b[39mraise\u001b[39;00m URLError(err)\n\u001b[1;32m 1350\u001b[0m r \u001b[39m=\u001b[39m h\u001b[39m.\u001b[39mgetresponse()\n\u001b[1;32m 1351\u001b[0m \u001b[39mexcept\u001b[39;00m:\n", - "\u001b[0;31mURLError\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + " 开放式基金排行 _ 天天基金网\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "
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基金排行

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基 金

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  • 基 金
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定投排行
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[意见反馈]
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\n", + "\n", + " 展开更多筛选 \n", + " 数据来源：东方财富Choice数据\n", + " \n", + " \n", + " \n", + "
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\n", + " 按基金公司筛选:\n", + " \n", + " \n", + " \n", + "\n", + "
\n", + " 页面放大\n", + "
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选择筛选条件，页面直接显示自定义的筛选结果
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  • 近1周涨幅排名：不限前10名前20名前50名前100名
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  • 近1月涨幅排名：不限前10名前20名前50名前100名
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  • 近3月涨幅排名：不限前10名前20名前50名前100名
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  • 近6月涨幅排名：不限前10名前20名前50名前100名
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  • 近1年涨幅排名：不限前10名前20名前50名前100名
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  • 近2年涨幅排名：不限前10名前20名前50名前100名
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  • 近3年涨幅排名：不限前10名前20名前50名前100名
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  • 今年涨幅排名：不限前10名前20名前50名前100名
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  • 全部(0)
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\n", + " QDII基金分类筛选：\n", + "

\n", + " 全部\n", + " 全球股票\n", + " 亚太股票\n", + " 大中华区股票\n", + " 新兴市场股票\n", + " 金砖国家股票\n", + " 成熟市场股票\n", + " 美国股票\n", + " 全球指数\n", + " QDII-ETF联接
\n", + " 股债混合\n", + " 债券\n", + " 商品\n", + "

\n", + "
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\n", + " 分类：\n", + "

\n", + " 全部\n", + " 长期纯债\n", + " 短期纯债\n", + " 混合债基\n", + " 定期开放债券\n", + " 可转债\n", + " 定开债开放日一览\n", + "

\n", + "
\n", + "
\n", + " 杠杆比例：\n", + "

\n", + " 全部\n", + " 0-100%\n", + " 100%-150%\n", + " 150%-200%\n", + " 200%以上\n", + "

\n", + "
\n", + "
\n", + " 跟踪标的：\n", + "

\n", + " 全部\n", + " 沪深指数\n", + " 行业主题\n", + " 大盘指数\n", + " 中小盘指数\n", + " 股票指数\n", + " 债券指数\n", + "

\n", + "
\n", + "
\n", + " 跟踪方式：\n", + "

\n", + " 全部\n", + " 被动指数型\n", + " 增强指数型\n", + "

\n", + "
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\n", + "

\n", + " \n", + " 2022-04-24至2023-04-24\n", + " \n", + "

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比较序号基金
\n", + " 代码		基金简称日期单位净值累计净值日增长率近1周近1月近3月近6月近1年近2年近3年今年来\n", + " 成立来\n", + " \n", + "
\n", + " 自定义\n", + " \n", + "
\n", + " 		手续费\n", + "
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\n", + "郑重声明：天天基金网发布此信息目的在于传播更多信息，与本网站立场无关。天天基金网不保证该信息（包括但不限于文字、视频、音频、数据及图表）全部或者部分内容的准确性、真实性、完整性、有效性、及时性、原创性等。相关信息并未经过本网站证实，不对您构成任何投资决策建议，据此操作，风险自担。数据来源：东方财富Choice数据。\n", + "
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\n", + "

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "

\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " \n", + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "[]\n" ] } ], @@ -927,14 +1469,112 @@ "# 保存到文件\n", "wc.to_file(\"李焕英2.png\")" ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "俄罗斯方块 8.1\n", + "饥渴游戏 5.6\n", + "杀死福顺 6.7\n", + "下一个素熙 8.2\n", + "雷霆沙赞！众神之怒 5.6\n", + "疾速追杀4 8.0\n", + "蚁人与黄蜂女：量子狂潮 6.0\n", + "网络谜踪2 8.0\n", + "鲸 8.0\n", + "第三次世界大战 7.9\n" + ] + } + ], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "\n", + "url = 'https://movie.douban.com/chart'\n", + "headers = {\n", + " 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36',\n", + " 'Cookie': 'bid=M4XudwoRjwk; douban-fav-remind=1; viewed=\"35862895\"; gr_user_id=21f9bda2-4808-4273-96c3-dc221917ecd9; ll=\"118172\"; __utmz=30149280.1678846289.3.3.utmcsr=baidu|utmccn=(organic)|utmcmd=organic; apiKey=; __utmc=30149280; __utma=30149280.194980502.1676723652.1682087186.1682142104.5; __utmt=1; __utmb=30149280.6.9.1682142158817'\n", + "}\n", + "response = requests.get(url, headers=headers)\n", + "soup = BeautifulSoup(response.content, 'html.parser')\n", + "\n", + "items = soup.select('.item')\n", + "for item in items:\n", + " title_elem = item.select_one('.pl2 > a').contents[0]\n", + " score_elem = item.select_one('.rating_nums')\n", + " if title_elem and score_elem:\n", + " title = title_elem.string.strip().replace('/', '').replace('\\n','').rstrip()\n", + " score = score_elem.string.strip()\n", + " print(f'{title} {score}')\n", + " else:\n", + " print('title or score not found:', item)\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Request failed: HTTPConnectionPool(host='fund.eastmoney.com', port=80): Read timed out. (read timeout=10)\n", + "Request failed: HTTPConnectionPool(host='fund.eastmoney.com', port=80): Read timed out. (read timeout=10)\n", + "Request failed: HTTPConnectionPool(host='fund.eastmoney.com', port=80): Read timed out. (read timeout=10)\n" + ] + } + ], + "source": [ + "import requests # 导入 requests 库\n", + "from bs4 import BeautifulSoup # 导入 BeautifulSoup 库\n", + "\n", + "# 定义 URL 模板和 User-Agent\n", + "url_template = 'http://fund.eastmoney.com/data/fundranking.html#tgp;c0;r;s1nzf;pn{page};ddesc;qsd20220422;qed20230422;qdii;zq;gg;gzbd;gzfs;bbzt;sfbb'\n", + "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36'}\n", + "\n", + "page = 1 # 初始化页码为 1\n", + "while True:\n", + " url = url_template.format(page=page) # 构造新的 URL\n", + " try:\n", + " response = requests.get(url, headers=headers, timeout=10) # 发送 HTTP 请求，并设置超时时间为 10 秒\n", + " soup = BeautifulSoup(response.content, 'lxml') # 使用 lxml 解析器解析响应内容\n", + " except Exception as e: # 如果请求失败，则输出错误信息并继续下一次循环\n", + " print(f'Request failed: {e}')\n", + " continue\n", + "\n", + " table = soup.select_one('#dbtable') # 选中基金排行列表\n", + " if table is not None: # 判断基金排行列表是否存在\n", + " rows = table.select('tbody tr') # 选中所有基金排行行\n", + " for row in rows: # 遍历每一行基金排行\n", + " cols = row.select('td') # 选中每一行基金排行中展示的基金信息\n", + " code = cols[0].string # 提取基金代码\n", + " name = cols[1].select_one('a').string # 提取基金名称\n", + " net_value = cols[2].string # 提取净值\n", + " change_percent = cols[3].string # 提取涨跌幅\n", + " print(f'{code} {name} {net_value} {change_percent}') # 输出基金信息\n", + " else: # 如果基金排行列表不存在，则退出循环\n", + " break\n", + "\n", + " page += 1 # 将页码加 1，准备爬取下一页数据\n" + ] } ], "metadata": { "interpreter": { - "hash": "034c70736e9d25f3f57bc70ec9a2164e1c27bdcdecfc656ea6098a86262350a2" + "hash": "21ee8af2579a8192c5b3880b850b5f93eed7daf2eb79d75b82c39de695db0d6e" }, "kernelspec": { - "display_name": "Python 3.9.12 ('myenv')", + "display_name": "Python 3.9.7 ('base')", "language": "python", "name": "python3" }, @@ -948,7 +1588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.7" }, "orig_nbformat": 4 }, From 57beb80cb49c7951446d72d3e425acacbcd9dc41 Mon Sep 17 00:00:00 2001 From: chenqiang3 Date: Sat, 7 Dec 2024 15:54:26 +0800 Subject: [PATCH 12/12] 20241207 --- .vscode/settings.json | 6 ++++++ Part.1.E.1.entrance.ipynb | 4 ++-- Part.1.E.2.values-and-their-operators.ipynb | 2 +- Part.1.E.4.functions.ipynb | 2 +- Part.1.E.5.strings.ipynb | 2 +- Part.2.C.why-start-from-writing-functions.ipynb | 2 +- Part.2.D.4-recursion.ipynb | 2 +- Part.3.B.4.regex.ipynb | 2 +- Part.3.C.breaking-good-and-bad.ipynb | 2 +- Part.3.D.indispensable-illusion.ipynb | 2 +- Part4.A.2data_summury_and_group.ipynb | 2 +- Part5.A.fund-choice.ipynb | 2 +- Part5.B.downloadcsv.ipynb | 2 +- 13 files changed, 19 insertions(+), 13 deletions(-) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..ac056b1cf --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,6 @@ +{ + "editor.detectIndentation": false, + "editor.tabSize": 4, + "editor.formatOnPaste": true, + "editor.formatOnSave": true +} \ No newline at end of file diff --git a/Part.1.E.1.entrance.ipynb b/Part.1.E.1.entrance.ipynb index 88035be6a..214a4a84b 100644 --- a/Part.1.E.1.entrance.ipynb +++ b/Part.1.E.1.entrance.ipynb @@ -280,14 +280,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "534 is even.\n" + "375 is odd.\n" ] } ], diff --git a/Part.1.E.2.values-and-their-operators.ipynb b/Part.1.E.2.values-and-their-operators.ipynb index 02abe9e2d..d1c61bf6e 100644 --- a/Part.1.E.2.values-and-their-operators.ipynb +++ b/Part.1.E.2.values-and-their-operators.ipynb @@ -1138,7 +1138,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/Part.1.E.4.functions.ipynb b/Part.1.E.4.functions.ipynb index ef68f620a..a896c41df 100644 --- a/Part.1.E.4.functions.ipynb +++ b/Part.1.E.4.functions.ipynb @@ -708,7 +708,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" }, "toc-autonumbering": true }, diff --git a/Part.1.E.5.strings.ipynb b/Part.1.E.5.strings.ipynb index ff4c79fe3..534eef55a 100644 --- a/Part.1.E.5.strings.ipynb +++ b/Part.1.E.5.strings.ipynb @@ -2643,7 +2643,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" }, "toc-autonumbering": true }, diff --git a/Part.2.C.why-start-from-writing-functions.ipynb b/Part.2.C.why-start-from-writing-functions.ipynb index f71b04a0a..5f52f6ef3 100644 --- a/Part.2.C.why-start-from-writing-functions.ipynb +++ b/Part.2.C.why-start-from-writing-functions.ipynb @@ -127,7 +127,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/Part.2.D.4-recursion.ipynb b/Part.2.D.4-recursion.ipynb index 6d4663254..dc63528e0 100644 --- a/Part.2.D.4-recursion.ipynb +++ b/Part.2.D.4-recursion.ipynb @@ -741,7 +741,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/Part.3.B.4.regex.ipynb b/Part.3.B.4.regex.ipynb index bed2243ec..399c7da98 100644 --- a/Part.3.B.4.regex.ipynb +++ b/Part.3.B.4.regex.ipynb @@ -1694,7 +1694,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/Part.3.C.breaking-good-and-bad.ipynb b/Part.3.C.breaking-good-and-bad.ipynb index df2dd11b2..f21af82c0 100644 --- a/Part.3.C.breaking-good-and-bad.ipynb +++ b/Part.3.C.breaking-good-and-bad.ipynb @@ -286,7 +286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/Part.3.D.indispensable-illusion.ipynb b/Part.3.D.indispensable-illusion.ipynb index 6671bb2a8..09cd344d5 100644 --- a/Part.3.D.indispensable-illusion.ipynb +++ b/Part.3.D.indispensable-illusion.ipynb @@ -336,7 +336,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/Part4.A.2data_summury_and_group.ipynb b/Part4.A.2data_summury_and_group.ipynb index 53abb4b25..56e7940cc 100644 --- a/Part4.A.2data_summury_and_group.ipynb +++ b/Part4.A.2data_summury_and_group.ipynb @@ -203,7 +203,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" }, "orig_nbformat": 4 }, diff --git a/Part5.A.fund-choice.ipynb b/Part5.A.fund-choice.ipynb index 6be0726c4..712817744 100644 --- a/Part5.A.fund-choice.ipynb +++ b/Part5.A.fund-choice.ipynb @@ -84,7 +84,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" }, "orig_nbformat": 4 }, diff --git a/Part5.B.downloadcsv.ipynb b/Part5.B.downloadcsv.ipynb index ada8b4447..489ff5d7f 100644 --- a/Part5.B.downloadcsv.ipynb +++ b/Part5.B.downloadcsv.ipynb @@ -1588,7 +1588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.16" }, "orig_nbformat": 4 },

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zQ5vOEYZ_zW)0_2T(Z;}j1za`>iydw6rV|chO3G>kf{|`6s!v}Q6djIePl;fdbQQO?DX>9{oT-L)U|Wb5f4%z2!svs3hwI? z^*qzJmz}4tH2|QYj=y@*>79pXnOQKuJcuv$FGbdym#>YV8c|%ZJ0|Mcw7IH{X>~PT zMkf6)oVqn(I)gy?&;6+XU0D5MU+jy0u`l+;y4_G;ZowD(VqffweX%e01y;Y<7yDvg s?2CP|FR=Q>zStN0Vqffw{r|Q91F&uBJ>4TpQ~&?~07*qoM6N<$f-wo$d;kCd literal 0 HcmV?d00001 From beeb6ac2ff263726f9285284a9582a646f8b2cf4 Mon Sep 17 00:00:00 2001 From: hoolich <469165171@qq.com> Date: Mon, 27 Mar 2023 18:46:55 +0800 Subject: [PATCH 10/12] 20230327 --- Part.2.E.10.email.ipynb | 625 +++++++++++++++++++++++++++++++-- Part.5.B.combine-csv.ipynb | 111 ++++++ Part.5.combine-doc.ipynb | 113 ++++++ Part.5.split-csv.ipynb | 93 +++++ Part5.B.downloadcsv.ipynb | 590 ++----------------------------- awesome-python3-webapp/LICENSE | 0 awesome-python3-webapp/app.py | 21 ++ 7 files changed, 958 insertions(+), 595 deletions(-) create mode 100644 Part.5.B.combine-csv.ipynb create mode 100644 Part.5.combine-doc.ipynb create mode 100644 Part.5.split-csv.ipynb create mode 100644 awesome-python3-webapp/LICENSE create mode 100644 awesome-python3-webapp/app.py diff --git a/Part.2.E.10.email.ipynb b/Part.2.E.10.email.ipynb index c81a8484b..023fb1a8d 100644 --- a/Part.2.E.10.email.ipynb +++ b/Part.2.E.10.email.ipynb @@ -14,16 +14,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -53,49 +53,56 @@ "output_type": "stream", "text": [ "send: 'ehlo chenqiangdeMacBook-Pro.local\\r\\n'\n", - "reply: b'250-newxmesmtplogicsvrsza2-0.qq.com\\r\\n'\n", + "reply: b'250-mail\\r\\n'\n", "reply: b'250-PIPELINING\\r\\n'\n", - "reply: b'250-SIZE 73400320\\r\\n'\n", + "reply: b'250-AUTH LOGIN PLAIN XOAUTH2\\r\\n'\n", + "reply: b'250-AUTH=LOGIN PLAIN XOAUTH2\\r\\n'\n", + "reply: b'250-coremail 1Uxr2xKj7kG0xkI17xGrU7I0s8FY2U3Uj8Cz28x1UUUUU7Ic2I0Y2Urk-CIYUCa0xDrUUUUj\\r\\n'\n", "reply: b'250-STARTTLS\\r\\n'\n", - "reply: b'250-AUTH LOGIN PLAIN XOAUTH XOAUTH2\\r\\n'\n", - "reply: b'250-AUTH=LOGIN\\r\\n'\n", - "reply: b'250-MAILCOMPRESS\\r\\n'\n", + "reply: b'250-ID\\r\\n'\n", "reply: b'250 8BITMIME\\r\\n'\n", - "reply: retcode (250); Msg: b'newxmesmtplogicsvrsza2-0.qq.com\\nPIPELINING\\nSIZE 73400320\\nSTARTTLS\\nAUTH LOGIN PLAIN XOAUTH XOAUTH2\\nAUTH=LOGIN\\nMAILCOMPRESS\\n8BITMIME'\n", - "send: 'AUTH PLAIN ADQ2OTE2NTE3MUBxcS5jb20AQzFoNUU4bjU=\\r\\n'\n", - "reply: b'535 Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256\\r\\n'\n", - "reply: retcode (535); Msg: b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256'\n", - "send: 'AUTH LOGIN NDY5MTY1MTcxQHFxLmNvbQ==\\r\\n'\n", - "reply: b'334 UGFzc3dvcmQ6\\r\\n'\n", - "reply: retcode (334); Msg: b'UGFzc3dvcmQ6'\n", - "send: 'QzFoNUU4bjU=\\r\\n'\n", - "reply: b'535 Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256\\r\\n'\n", - "reply: retcode (535); Msg: b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256'\n" + "reply: retcode (250); Msg: b'mail\\nPIPELINING\\nAUTH LOGIN PLAIN XOAUTH2\\nAUTH=LOGIN PLAIN XOAUTH2\\ncoremail 1Uxr2xKj7kG0xkI17xGrU7I0s8FY2U3Uj8Cz28x1UUUUU7Ic2I0Y2Urk-CIYUCa0xDrUUUUj\\nSTARTTLS\\nID\\n8BITMIME'\n", + "send: 'AUTH PLAIN AGhvb2xpY2hAMTYzLmNvbQBGWUlSTURGREpHSEZJT0lB\\r\\n'\n", + "reply: b'235 Authentication successful\\r\\n'\n", + "reply: retcode (235); Msg: b'Authentication successful'\n", + "send: 'mail FROM:\\r\\n'\n", + "reply: b'250 Mail OK\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK'\n", + "send: 'rcpt TO:\\r\\n'\n", + "reply: b'250 Mail OK\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK'\n", + "send: 'data\\r\\n'\n", + "reply: b'354 End data with .\\r\\n'\n", + "reply: retcode (354); Msg: b'End data with .'\n", + "data: (354, b'End data with .')\n", + "send: b'Content-Type: text/plain; charset=\"utf-8\"\\r\\nMIME-Version: 1.0\\r\\nContent-Transfer-Encoding: base64\\r\\n\\r\\naGVsbG8sIHNlbmQgYnkgUHl0aG9uLi4u\\r\\n.\\r\\n'\n", + "reply: b'250 Mail OK queued as smtp10,DsCowAA38jCToW9jIKNdMQ--.25745S2 1668260248\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK queued as smtp10,DsCowAA38jCToW9jIKNdMQ--.25745S2 1668260248'\n", + "data: (250, b'Mail OK queued as smtp10,DsCowAA38jCToW9jIKNdMQ--.25745S2 1668260248')\n", + "send: 'quit\\r\\n'\n", + "reply: b'221 Bye\\r\\n'\n", + "reply: retcode (221); Msg: b'Bye'\n" ] }, { - "ename": "SMTPAuthenticationError", - "evalue": "(535, b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256')", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSMTPAuthenticationError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/chenqiang/Documents/the-craft-of-selfteaching/Part.2.E.10.email.ipynb Cell 4'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m server \u001b[39m=\u001b[39m smtplib\u001b[39m.\u001b[39mSMTP(smtp_server, \u001b[39m25\u001b[39m) \u001b[39m# SMTP协议默认端口是25\u001b[39;00m\n\u001b[1;32m 11\u001b[0m server\u001b[39m.\u001b[39mset_debuglevel(\u001b[39m1\u001b[39m)\n\u001b[0;32m---> 12\u001b[0m server\u001b[39m.\u001b[39;49mlogin(from_addr, password)\n\u001b[1;32m 13\u001b[0m server\u001b[39m.\u001b[39msendmail(from_addr, [to_addr], msg\u001b[39m.\u001b[39mas_string())\n\u001b[1;32m 14\u001b[0m server\u001b[39m.\u001b[39mquit()\n", - "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/smtplib.py:750\u001b[0m, in \u001b[0;36mSMTP.login\u001b[0;34m(self, user, password, initial_response_ok)\u001b[0m\n\u001b[1;32m 747\u001b[0m last_exception \u001b[39m=\u001b[39m e\n\u001b[1;32m 749\u001b[0m \u001b[39m# We could not login successfully. Return result of last attempt.\u001b[39;00m\n\u001b[0;32m--> 750\u001b[0m \u001b[39mraise\u001b[39;00m last_exception\n", - "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/smtplib.py:739\u001b[0m, in \u001b[0;36mSMTP.login\u001b[0;34m(self, user, password, initial_response_ok)\u001b[0m\n\u001b[1;32m 737\u001b[0m method_name \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mauth_\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m authmethod\u001b[39m.\u001b[39mlower()\u001b[39m.\u001b[39mreplace(\u001b[39m'\u001b[39m\u001b[39m-\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m_\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 738\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 739\u001b[0m (code, resp) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mauth(\n\u001b[1;32m 740\u001b[0m authmethod, \u001b[39mgetattr\u001b[39;49m(\u001b[39mself\u001b[39;49m, method_name),\n\u001b[1;32m 741\u001b[0m initial_response_ok\u001b[39m=\u001b[39;49minitial_response_ok)\n\u001b[1;32m 742\u001b[0m \u001b[39m# 235 == 'Authentication successful'\u001b[39;00m\n\u001b[1;32m 743\u001b[0m \u001b[39m# 503 == 'Error: already authenticated'\u001b[39;00m\n\u001b[1;32m 744\u001b[0m \u001b[39mif\u001b[39;00m code \u001b[39min\u001b[39;00m (\u001b[39m235\u001b[39m, \u001b[39m503\u001b[39m):\n", - "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/smtplib.py:662\u001b[0m, in \u001b[0;36mSMTP.auth\u001b[0;34m(self, mechanism, authobject, initial_response_ok)\u001b[0m\n\u001b[1;32m 660\u001b[0m \u001b[39mif\u001b[39;00m code \u001b[39min\u001b[39;00m (\u001b[39m235\u001b[39m, \u001b[39m503\u001b[39m):\n\u001b[1;32m 661\u001b[0m \u001b[39mreturn\u001b[39;00m (code, resp)\n\u001b[0;32m--> 662\u001b[0m \u001b[39mraise\u001b[39;00m SMTPAuthenticationError(code, resp)\n", - "\u001b[0;31mSMTPAuthenticationError\u001b[0m: (535, b'Login Fail. Please enter your authorization code to login. More information in http://service.mail.qq.com/cgi-bin/help?subtype=1&&id=28&&no=1001256')" - ] + "data": { + "text/plain": [ + "(221, b'Bye')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# 输入Email地址和口令:\n", - "from_addr = '469165171@qq.com'#input('From: ')\n", - "password = 'C1h5E8n5'#input('Password: ')\n", + "from_addr = 'hoolich@163.com'#input('From: ')\n", + "password = 'FYIRMDFDJGHFIOIA'#input('Password: ')授权码不是邮箱密码\n", "# 输入收件人地址:\n", - "to_addr = '469165171@qq.com'#input('To: ')\n", + "to_addr = 'hoolich@163.com'#input('To: ')\n", "# 输入SMTP服务器地址:\n", - "smtp_server = 'smtp.qq.com'#input('SMTP server: ')\n", + "smtp_server = 'smtp.163.com'#input('SMTP server: ')\n", "\n", "import smtplib\n", "server = smtplib.SMTP(smtp_server, 25) # SMTP协议默认端口是25\n", @@ -104,6 +111,554 @@ "server.sendmail(from_addr, [to_addr], msg.as_string())\n", "server.quit()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "仔细观察，发现如下问题：\n", + "\n", + "邮件没有主题；\n", + "收件人的名字没有显示为友好的名字，比如Mr Green ；\n", + "明明收到了邮件，却提示不在收件人中。\n", + "这是因为邮件主题、如何显示发件人、收件人等信息并不是通过SMTP协议发给MTA，而是包含在发给MTA的文本中的，所以，我们必须把From、To和Subject添加到MIMEText中，才是一封完整的邮件：" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "send: 'ehlo chenqiangdeMacBook-Pro.local\\r\\n'\n", + "reply: b'250-mail\\r\\n'\n", + "reply: b'250-PIPELINING\\r\\n'\n", + "reply: b'250-AUTH LOGIN PLAIN XOAUTH2\\r\\n'\n", + "reply: b'250-AUTH=LOGIN PLAIN XOAUTH2\\r\\n'\n", + "reply: b'250-coremail 1Uxr2xKj7kG0xkI17xGrU7I0s8FY2U3Uj8Cz28x1UUUUU7Ic2I0Y2UFeB1lBUCa0xDrUUUUj\\r\\n'\n", + "reply: b'250-STARTTLS\\r\\n'\n", + "reply: b'250-ID\\r\\n'\n", + "reply: b'250 8BITMIME\\r\\n'\n", + "reply: retcode (250); Msg: b'mail\\nPIPELINING\\nAUTH LOGIN PLAIN XOAUTH2\\nAUTH=LOGIN PLAIN XOAUTH2\\ncoremail 1Uxr2xKj7kG0xkI17xGrU7I0s8FY2U3Uj8Cz28x1UUUUU7Ic2I0Y2UFeB1lBUCa0xDrUUUUj\\nSTARTTLS\\nID\\n8BITMIME'\n", + "send: 'AUTH PLAIN AGhvb2xpY2hAMTYzLmNvbQBGWUlSTURGREpHSEZJT0lB\\r\\n'\n", + "reply: b'235 Authentication successful\\r\\n'\n", + "reply: retcode (235); Msg: b'Authentication successful'\n", + "send: 'mail FROM:\\r\\n'\n", + "reply: b'250 Mail OK\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK'\n", + "send: 'rcpt TO:\\r\\n'\n", + "reply: b'250 Mail OK\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK'\n", + "send: 'data\\r\\n'\n", + "reply: b'354 End data with .\\r\\n'\n", + "reply: retcode (354); Msg: b'End data with .'\n", + "data: (354, b'End data with .')\n", + "send: b'Content-Type: text/plain; charset=\"utf-8\"\\r\\nMIME-Version: 1.0\\r\\nContent-Transfer-Encoding: base64\\r\\nFrom: =?utf-8?b?UHl0aG9u54ix5aW96ICF?= \\r\\nTo: =?utf-8?b?566h55CG5ZGY?= \\r\\nSubject: =?utf-8?b?5p2l6IeqU01UUOeahOmXruWAmeKApuKApg==?=\\r\\n\\r\\naGVsbG8sIHNlbmQgYnkgUHl0aG9uLi4u\\r\\n.\\r\\n'\n", + "reply: b'250 Mail OK queued as smtp14,EsCowABXTwCsom9jPemjLw--.37105S2 1668260530\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK queued as smtp14,EsCowABXTwCsom9jPemjLw--.37105S2 1668260530'\n", + "data: (250, b'Mail OK queued as smtp14,EsCowABXTwCsom9jPemjLw--.37105S2 1668260530')\n", + "send: 'quit\\r\\n'\n", + "reply: b'221 Bye\\r\\n'\n", + "reply: retcode (221); Msg: b'Bye'\n" + ] + }, + { + "data": { + "text/plain": [ + "(221, b'Bye')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from email import encoders\n", + "from email.header import Header\n", + "from email.mime.text import MIMEText\n", + "from email.utils import parseaddr, formataddr\n", + "\n", + "import smtplib\n", + " \n", + "def _format_addr(s):\n", + " name, addr = parseaddr(s)\n", + " return formataddr((Header(name, 'utf-8').encode(), addr))\n", + "\n", + "# 输入Email地址和口令:\n", + "from_addr = 'hoolich@163.com'#input('From: ')\n", + "password = 'FYIRMDFDJGHFIOIA'#input('Password: ')授权码不是邮箱密码\n", + "# 输入收件人地址:\n", + "to_addr = 'hoolich@163.com'#input('To: ')\n", + "# 输入SMTP服务器地址:\n", + "smtp_server = 'smtp.163.com'#input('SMTP server: ')\n", + "\n", + "msg = MIMEText('hello, send by Python...', 'plain', 'utf-8')#内容\n", + "msg['From'] = _format_addr('Python爱好者 <%s>' % from_addr)#发件人\n", + "msg['To'] = _format_addr('管理员 <%s>' % to_addr)#收件人\n", + "msg['Subject'] = Header('来自SMTP的问候……', 'utf-8').encode()#邮件标题\n", + " \n", + "server = smtplib.SMTP(smtp_server, 25)\n", + "server.set_debuglevel(1)\n", + "server.login(from_addr, password)\n", + "server.sendmail(from_addr, [to_addr], msg.as_string())\n", + "server.quit()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 发送附件\n", + "如果Email中要加上附件怎么办？带附件的邮件可以看做包含若干部分的邮件：文本和各个附件本身，所以，可以构造一个MIMEMultipart对象代表邮件本身，然后往里面加上一个MIMEText作为邮件正文，再继续往里面加上表示附件的MIMEBase对象即可：" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "send: 'ehlo chenqiangdeMacBook-Pro.local\\r\\n'\n", + "reply: b'250-mail\\r\\n'\n", + "reply: b'250-PIPELINING\\r\\n'\n", + "reply: b'250-AUTH LOGIN PLAIN XOAUTH2\\r\\n'\n", + "reply: b'250-AUTH=LOGIN PLAIN XOAUTH2\\r\\n'\n", + "reply: b'250-coremail 1Uxr2xKj7kG0xkI17xGrU7I0s8FY2U3Uj8Cz28x1UUUUU7Ic2I0Y2UFSlqqpUCa0xDrUUUUj\\r\\n'\n", + "reply: b'250-STARTTLS\\r\\n'\n", + "reply: b'250-ID\\r\\n'\n", + "reply: b'250 8BITMIME\\r\\n'\n", + "reply: retcode (250); Msg: b'mail\\nPIPELINING\\nAUTH LOGIN PLAIN XOAUTH2\\nAUTH=LOGIN PLAIN XOAUTH2\\ncoremail 1Uxr2xKj7kG0xkI17xGrU7I0s8FY2U3Uj8Cz28x1UUUUU7Ic2I0Y2UFSlqqpUCa0xDrUUUUj\\nSTARTTLS\\nID\\n8BITMIME'\n", + "send: 'AUTH PLAIN AGhvb2xpY2hAMTYzLmNvbQBGWUlSTURGREpHSEZJT0lB\\r\\n'\n", + "reply: b'235 Authentication successful\\r\\n'\n", + "reply: retcode (235); Msg: b'Authentication successful'\n", + "send: 'mail FROM:\\r\\n'\n", + "reply: b'250 Mail OK\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK'\n", + "send: 'rcpt TO:\\r\\n'\n", + "reply: b'250 Mail OK\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK'\n", + "send: 'data\\r\\n'\n", + "reply: b'354 End data with .\\r\\n'\n", + "reply: retcode (354); Msg: b'End data with .'\n", + "data: (354, b'End data with .')\n", + "send: b'Content-Type: multipart/mixed; boundary=\"===============7298002500338024684==\"\\r\\nMIME-Version: 1.0\\r\\nFrom: =?utf-8?b?UHl0aG9u54ix5aW96ICF?= \\r\\nTo: =?utf-8?b?566h55CG5ZGY?= \\r\\nSubject: =?utf-8?b?5p2l6IeqU01UUOeahOmXruWAmeKApuKApg==?=\\r\\n\\r\\n--===============7298002500338024684==\\r\\nContent-Type: text/plain; charset=\"utf-8\"\\r\\nMIME-Version: 1.0\\r\\nContent-Transfer-Encoding: base64\\r\\n\\r\\nc2VuZCB3aXRoIGZpbGUuLi4=\\r\\n\\r\\n--===============7298002500338024684==\\r\\nContent-Type: image/png; filename=\"archimedes-eureka.png\"\\r\\nMIME-Version: 1.0\\r\\nContent-Disposition: attachment; filename=\"archimedes-eureka.png\"\\r\\nContent-ID: <0>\\r\\nX-Attachment-Id: 0\\r\\nContent-Transfer-Encoding: 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8NPukIY5G7dBWc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\\r\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r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\\r\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r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\\r\\naAQtekwAAAAAAElFTkSuQmCC\\r\\n\\r\\n--===============7298002500338024684==--\\r\\n.\\r\\n'\n", + "reply: b'250 Mail OK queued as smtp2,GtxpCgCXW+5p1XxjPhgNuA--.30750S2 1669125487\\r\\n'\n", + "reply: retcode (250); Msg: b'Mail OK queued as smtp2,GtxpCgCXW+5p1XxjPhgNuA--.30750S2 1669125487'\n", + "data: (250, b'Mail OK queued as smtp2,GtxpCgCXW+5p1XxjPhgNuA--.30750S2 1669125487')\n", + "send: 'quit\\r\\n'\n", + "reply: b'221 Bye\\r\\n'\n", + "reply: retcode (221); Msg: b'Bye'\n" + ] + }, + { + "data": { + "text/plain": [ + "(221, b'Bye')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from email import encoders\n", + "from email.header import Header\n", + "from email.mime.text import MIMEText\n", + "from email.utils import parseaddr, formataddr\n", + "from email.mime.multipart import MIMEMultipart\n", + "from email.mime.base import MIMEBase\n", + "\n", + "import smtplib\n", + " \n", + "def _format_addr(s):\n", + " name, addr = parseaddr(s)\n", + " return formataddr((Header(name, 'utf-8').encode(), addr))\n", + "\n", + "# 输入Email地址和口令:\n", + "from_addr = 'hoolich@163.com'#input('From: ')\n", + "password = 'FYIRMDFDJGHFIOIA'#input('Password: ')授权码不是邮箱密码\n", + "# 输入收件人地址:\n", + "to_addr = 'hoolich@163.com'#input('To: ')\n", + "# 输入SMTP服务器地址:\n", + "smtp_server = 'smtp.163.com'#input('SMTP server: ')\n", + "msg = MIMEMultipart()#创建一个可以带附件的正文\n", + "# msg = MIMEText('hello, send by Python...', 'plain', 'utf-8')#内容\n", + "msg['From'] = _format_addr('Python爱好者 <%s>' % from_addr)#发件人\n", + "msg['To'] = _format_addr('管理员 <%s>' % to_addr)#收件人\n", + "msg['Subject'] = Header('来自SMTP的问候……', 'utf-8').encode()#邮件标题\n", + "\n", + "# 邮件正文是MIMEText:\n", + "msg.attach(MIMEText('send with file...', 'plain', 'utf-8'))\n", + "\n", + "# 添加附件就是加上一个MIMEBase，从本地读取一个图片:\n", + "with open('/Users/chenqiang/Documents/the-craft-of-selfteaching/images/archimedes-eureka.png', 'rb') as f:\n", + " # 设置附件的MIME和文件名，这里是png类型:\n", + " mime = MIMEBase('image', 'png', filename='archimedes-eureka.png')\n", + " # 加上必要的头信息:\n", + " mime.add_header('Content-Disposition', 'attachment', filename='archimedes-eureka.png')\n", + " mime.add_header('Content-ID', '<0>')\n", + " mime.add_header('X-Attachment-Id', '0')\n", + " # 把附件的内容读进来:\n", + " mime.set_payload(f.read())\n", + " # 用Base64编码:\n", + " encoders.encode_base64(mime)\n", + " # 添加到MIMEMultipart:\n", + " msg.attach(mime)\n", + " \n", + "server = smtplib.SMTP(smtp_server, 25)\n", + "server.set_debuglevel(1)\n", + "server.login(from_addr, password)\n", + "server.sendmail(from_addr, [to_addr], msg.as_string())\n", + "server.quit()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 收取邮件\n", + "SMTP用于发送邮件，如果要收取邮件呢？\n", + "\n", + "收取邮件就是编写一个MUA作为客户端，从MDA把邮件获取到用户的电脑或者手机上。收取邮件最常用的协议是POP协议，目前版本号是3，俗称POP3。\n", + "\n", + "Python内置一个poplib模块，实现了POP3协议，可以直接用来收邮件。\n", + "\n", + "注意到POP3协议收取的不是一个已经可以阅读的邮件本身，而是邮件的原始文本，这和SMTP协议很像，SMTP发送的也是经过编码后的一大段文本。\n", + "\n", + "要把POP3收取的文本变成可以阅读的邮件，还需要用email模块提供的各种类来解析原始文本，变成可阅读的邮件对象。\n", + "\n", + "所以，收取邮件分两步：\n", + "\n", + ">第一步：用poplib把邮件的原始文本下载到本地；\n", + "\n", + ">第二部：用email解析原始文本，还原为邮件对象。\n", + "\n", + "通过POP3下载邮件\n", + "POP3协议本身很简单，以下面的代码为例，我们来获取最新的一封邮件内容：" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+OK Welcome to coremail Mail Pop3 Server (163coms[10774b260cc7a37d26d71b52404dcf5cs])\n", + "*cmd* 'USER hoolich@163.com'\n", + "*cmd* 'PASS FYIRMDFDJGHFIOIA'\n", + "*cmd* 'STAT'\n", + "*stat* [b'+OK', b'11', b'447684']\n", + "Messages: 11. Size: 447684\n", + "*cmd* 'LIST'\n", + "[b'1 15154', b'2 709', b'3 851', b'4 22603', b'5 14917', b'6 867', b'7 865', b'8 860', b'9 860', b'10 375022', b'11 14976']\n", + "*cmd* 'RETR 11'\n", + "From: Longbridge 长桥 \n", + "To: \n", + "Subject: LongBridge V4.19.0 更新来袭，抢先了解功能\n", + "Text: \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + "
\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "

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