ch10_时间序列.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path =str(os.getcwd()).replace('\\\\','/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 6, 14, 20, 51, 0, 947750)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = datetime.now()  # datetime是以毫秒存储日期和时间\n",
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 6 14\n"
     ]
    }
   ],
   "source": [
    "print(now.year,now.month,now.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(926, 56700)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime.timedelta表示两个datetime对象之间的时间差\n",
    "delta = datetime(2011,1,7)-datetime(2008,6,24,8,15)\n",
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "926 56700\n"
     ]
    }
   ],
   "source": [
    "print(delta.days,delta.seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 给datetime对象加上（或减去）一个或多个timedelta，这样会产生一个新对象\n",
    "from datetime import timedelta\n",
    "start = datetime(2011,1,7)\n",
    "start+timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 14, 0, 0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start-2*timedelta(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 字符串和datetime的相互转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用str或strftime方法（传入一个格式化字符串），datetime对象和pandas的Timestamp对象可以被格式化为字符串\n",
    "stamp = datetime(2011,1,3)\n",
    "str(stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime.strptime可以用这些格式化编码将字符串转换为日期，是通过已知格式进行日期解析的\n",
    "value = '2011-01-03'\n",
    "datetime.strptime(value,'%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datestrs = ['7/6/2011','8/6/2011']\n",
    "[datetime.strptime(x,'%m/%d/%Y') for x in datestrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dateutil第三方包中的parse.parse方法可以对场景的日期格式进行转化\n",
    "from dateutil.parser import parse\n",
    "parse('2011-01-03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1997, 1, 31, 22, 45)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dateutil可以解析几乎所有人类能够理解的日期表示形式\n",
    "# 注意：中文不行\n",
    "parse('Jan 31, 1997 10:45 PM') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 12, 6, 0, 0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在国际通用的格式中，日期通常出现在月的前面，传入dayfirst=True即可解决该问题\n",
    "parse('6/12/2011',dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['7/6/2011', '8/6/2011']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas可以用来处理成组日期，to_datetime方法可以解析多种不同的日期表示形式\n",
    "datestrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06', '2011-08-06'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(datestrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06', '2011-08-06', 'NaT'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to_datetime 也可以处理缺失值（None、空字符串等）\n",
    "# NaT (Not a Time)是pandas中时间戳数据的NA值\n",
    "idx = pd.to_datetime(datestrs+[None])\n",
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaT"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[2]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时间序列基础"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.026906\n",
       "2011-01-05   -1.358466\n",
       "2011-01-07   -0.449260\n",
       "2011-01-08   -0.026582\n",
       "2011-01-10    1.038115\n",
       "2011-01-12    0.931483\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = [datetime(2011,1,2),datetime(2011,1,5),datetime(2011,1,7),datetime(2011,1,8),datetime(2011,1,10),datetime(2011,1,12)]\n",
    "ts = pd.Series(np.random.randn(6),index=dates)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-07', '2011-01-08',\n",
       "               '2011-01-10', '2011-01-12'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.053812\n",
       "2011-01-05         NaN\n",
       "2011-01-07   -0.898520\n",
       "2011-01-08         NaN\n",
       "2011-01-10    2.076229\n",
       "2011-01-12         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不同索引的时间序列之间的算术运算会自动按日期对齐\n",
    "ts+ts[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = ts.index[0]\n",
    "stamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引、选取、子集构造"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.44926008318350497"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp = ts.index[2]\n",
    "ts[stamp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.038114705134627"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个可被解释为日期的字符串\n",
    "ts['1/10/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.038114705134627"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['20110110']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.030768\n",
       "2000-01-02   -0.038533\n",
       "2000-01-03    0.107162\n",
       "2000-01-04    0.336421\n",
       "2000-01-05    1.294330\n",
       "2000-01-06   -0.880786\n",
       "2000-01-07   -1.302284\n",
       "2000-01-08    0.816062\n",
       "2000-01-09   -0.515752\n",
       "2000-01-10   -1.943249\n",
       "2000-01-11    1.020287\n",
       "2000-01-12   -1.070639\n",
       "2000-01-13    0.597492\n",
       "2000-01-14   -1.646672\n",
       "2000-01-15    1.629929\n",
       "2000-01-16   -1.227930\n",
       "2000-01-17   -0.424373\n",
       "2000-01-18    0.073577\n",
       "2000-01-19   -1.602295\n",
       "2000-01-20    0.367656\n",
       "2000-01-21   -0.739535\n",
       "2000-01-22    2.174251\n",
       "2000-01-23    0.578211\n",
       "2000-01-24    0.528446\n",
       "2000-01-25   -0.624373\n",
       "2000-01-26   -0.156435\n",
       "2000-01-27    2.011863\n",
       "2000-01-28    0.039942\n",
       "2000-01-29   -3.093844\n",
       "2000-01-30   -1.334414\n",
       "                ...   \n",
       "2002-08-28   -0.364265\n",
       "2002-08-29   -1.916881\n",
       "2002-08-30   -1.098753\n",
       "2002-08-31   -1.669456\n",
       "2002-09-01    1.152999\n",
       "2002-09-02    1.368506\n",
       "2002-09-03   -0.893254\n",
       "2002-09-04    1.175309\n",
       "2002-09-05   -0.608498\n",
       "2002-09-06   -0.132670\n",
       "2002-09-07   -0.234972\n",
       "2002-09-08    1.170640\n",
       "2002-09-09    0.567738\n",
       "2002-09-10    0.251831\n",
       "2002-09-11    1.405904\n",
       "2002-09-12   -0.255637\n",
       "2002-09-13   -1.798172\n",
       "2002-09-14    1.594437\n",
       "2002-09-15    0.576304\n",
       "2002-09-16    0.688684\n",
       "2002-09-17   -0.047675\n",
       "2002-09-18    1.856160\n",
       "2002-09-19   -0.609079\n",
       "2002-09-20    0.734797\n",
       "2002-09-21    0.927182\n",
       "2002-09-22   -0.513093\n",
       "2002-09-23   -1.043452\n",
       "2002-09-24   -0.729928\n",
       "2002-09-25   -0.622364\n",
       "2002-09-26    1.466905\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于较长的时间徐磊，只需输入“年 ”或“年月”即可轻松选取数据的切片\n",
    "long_ts = pd.Series(np.random.randn(1000),\n",
    "                    index = pd.date_range('1/1/2000',periods=1000))\n",
    "long_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01   -0.908426\n",
       "2001-01-02   -0.496983\n",
       "2001-01-03   -1.096447\n",
       "2001-01-04   -1.318806\n",
       "2001-01-05    1.328236\n",
       "2001-01-06    1.140316\n",
       "2001-01-07   -0.353494\n",
       "2001-01-08    1.150397\n",
       "2001-01-09    0.522295\n",
       "2001-01-10   -0.071957\n",
       "2001-01-11    0.756678\n",
       "2001-01-12    0.685718\n",
       "2001-01-13    1.658870\n",
       "2001-01-14   -0.538116\n",
       "2001-01-15   -1.622445\n",
       "2001-01-16   -0.057839\n",
       "2001-01-17   -0.011965\n",
       "2001-01-18   -0.715185\n",
       "2001-01-19    0.862869\n",
       "2001-01-20    0.554317\n",
       "2001-01-21    0.066371\n",
       "2001-01-22    1.293521\n",
       "2001-01-23   -1.298680\n",
       "2001-01-24   -0.650091\n",
       "2001-01-25   -0.225448\n",
       "2001-01-26    0.501261\n",
       "2001-01-27   -1.454987\n",
       "2001-01-28   -0.907980\n",
       "2001-01-29   -0.674249\n",
       "2001-01-30    0.620768\n",
       "                ...   \n",
       "2001-12-02    0.805523\n",
       "2001-12-03    1.491471\n",
       "2001-12-04   -1.217426\n",
       "2001-12-05   -1.908339\n",
       "2001-12-06   -0.328365\n",
       "2001-12-07   -0.631049\n",
       "2001-12-08   -1.492346\n",
       "2001-12-09   -1.046873\n",
       "2001-12-10    0.237576\n",
       "2001-12-11   -0.403313\n",
       "2001-12-12    0.179311\n",
       "2001-12-13    0.249125\n",
       "2001-12-14   -0.121794\n",
       "2001-12-15    0.798181\n",
       "2001-12-16   -1.241020\n",
       "2001-12-17    0.672874\n",
       "2001-12-18   -0.559124\n",
       "2001-12-19   -1.187067\n",
       "2001-12-20    0.952212\n",
       "2001-12-21   -1.279551\n",
       "2001-12-22   -0.218395\n",
       "2001-12-23   -0.030462\n",
       "2001-12-24   -0.632494\n",
       "2001-12-25    0.048291\n",
       "2001-12-26    0.118704\n",
       "2001-12-27   -0.425599\n",
       "2001-12-28   -0.267815\n",
       "2001-12-29   -0.499474\n",
       "2001-12-30   -0.445743\n",
       "2001-12-31   -0.054211\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_ts['2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01   -1.459391\n",
       "2001-05-02   -0.712495\n",
       "2001-05-03    1.475672\n",
       "2001-05-04    0.235330\n",
       "2001-05-05   -0.000753\n",
       "2001-05-06    0.108864\n",
       "2001-05-07    0.302042\n",
       "2001-05-08    1.607126\n",
       "2001-05-09    0.162081\n",
       "2001-05-10    0.836966\n",
       "2001-05-11   -0.379072\n",
       "2001-05-12    0.077336\n",
       "2001-05-13    0.380187\n",
       "2001-05-14    0.925741\n",
       "2001-05-15    2.235668\n",
       "2001-05-16    2.409883\n",
       "2001-05-17    1.025795\n",
       "2001-05-18   -0.043660\n",
       "2001-05-19   -0.847388\n",
       "2001-05-20    0.286845\n",
       "2001-05-21   -1.237259\n",
       "2001-05-22    0.928409\n",
       "2001-05-23   -0.785265\n",
       "2001-05-24    1.818357\n",
       "2001-05-25   -1.417515\n",
       "2001-05-26   -0.228482\n",
       "2001-05-27   -1.004333\n",
       "2001-05-28   -1.068280\n",
       "2001-05-29   -1.231307\n",
       "2001-05-30    0.125205\n",
       "2001-05-31    0.731629\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_ts['2001-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.44926008318350497"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过日期进行切片的方式只对规则series有效\n",
    "ts[datetime(2011,1,7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.026906\n",
       "2011-01-05   -1.358466\n",
       "2011-01-07   -0.449260\n",
       "2011-01-08   -0.026582\n",
       "2011-01-10    1.038115\n",
       "2011-01-12    0.931483\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07   -0.449260\n",
       "2011-01-08   -0.026582\n",
       "2011-01-10    1.038115\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 大部分时间序列数据是按时间先后排序，因此可以用不存在于该时间序列中的时间戳对其进行切片（即范围查询）\n",
    "ts['1/6/2011':'1/11/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.026906\n",
       "2011-01-05   -1.358466\n",
       "2011-01-07   -0.449260\n",
       "2011-01-08   -0.026582\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用truncate方法也可以截取两个日期之间的TimeSeries\n",
    "ts.truncate(after='1/9/2011')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-02</th>\n",
       "      <td>0.483747</td>\n",
       "      <td>0.954314</td>\n",
       "      <td>-0.178777</td>\n",
       "      <td>-0.099333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>0.965785</td>\n",
       "      <td>0.733886</td>\n",
       "      <td>0.393827</td>\n",
       "      <td>-1.717427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>-1.071945</td>\n",
       "      <td>-1.325653</td>\n",
       "      <td>-0.282055</td>\n",
       "      <td>0.374826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>-0.066809</td>\n",
       "      <td>2.725121</td>\n",
       "      <td>-0.539026</td>\n",
       "      <td>-0.165097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>-1.094361</td>\n",
       "      <td>-0.809514</td>\n",
       "      <td>1.168028</td>\n",
       "      <td>-1.309420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2001-05-02  0.483747  0.954314 -0.178777 -0.099333\n",
       "2001-05-09  0.965785  0.733886  0.393827 -1.717427\n",
       "2001-05-16 -1.071945 -1.325653 -0.282055  0.374826\n",
       "2001-05-23 -0.066809  2.725121 -0.539026 -0.165097\n",
       "2001-05-30 -1.094361 -0.809514  1.168028 -1.309420"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 上面的操作对DataFrame同样有效\n",
    "dates = pd.date_range('1/1/2000',periods=100,freq='W-WED')\n",
    "long_df = pd.DataFrame(np.random.randn(100,4),\n",
    "                       index=dates,\n",
    "                      columns=['Colorado','Texas','New York','Ohio'])\n",
    "long_df.loc['5-2001']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 带有重复索引的时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "2000-01-03    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可能会存在多个观测数据落在同一个时间点上的情况\n",
    "dates = pd.DatetimeIndex(['1/1/2000','1/2/2000','1/2/2000','1/2/2000','1/3/2000'])\n",
    "dup_ts = pd.Series(np.arange(5),index=dates)\n",
    "dup_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过检查索引的is_unique属性来判断它是否唯一\n",
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据所选时间点是否重复，会获取标量值或切片\n",
    "dup_ts['1/3/2000']  # 索引不重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts['1/2/2000']  # 索引重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    2\n",
       "2000-01-03    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 假设想要对具有非唯一时间戳的数据进行聚类，用groupby，并传入level=0（索引的唯一一层）\n",
    "grouped = dup_ts.groupby(level=0)\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 日期的范围、频率以及移动"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.026906\n",
       "2011-01-05   -1.358466\n",
       "2011-01-07   -0.449260\n",
       "2011-01-08   -0.026582\n",
       "2011-01-10    1.038115\n",
       "2011-01-12    0.931483\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将之前那个时间序列转换为一个具有固定频率（每日）的时间序列，只需调用resample即可\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02   -0.026906\n",
       "2011-01-03         NaN\n",
       "2011-01-04         NaN\n",
       "2011-01-05   -1.358466\n",
       "2011-01-06         NaN\n",
       "2011-01-07   -0.449260\n",
       "2011-01-08   -0.026582\n",
       "2011-01-09         NaN\n",
       "2011-01-10    1.038115\n",
       "2011-01-11         NaN\n",
       "2011-01-12    0.931483\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('D').asfreq()  # D 表示按天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20',\n",
       "               '2012-04-21', '2012-04-22', '2012-04-23', '2012-04-24',\n",
       "               '2012-04-25', '2012-04-26', '2012-04-27', '2012-04-28',\n",
       "               '2012-04-29', '2012-04-30', '2012-05-01', '2012-05-02',\n",
       "               '2012-05-03', '2012-05-04', '2012-05-05', '2012-05-06',\n",
       "               '2012-05-07', '2012-05-08', '2012-05-09', '2012-05-10',\n",
       "               '2012-05-11', '2012-05-12', '2012-05-13', '2012-05-14',\n",
       "               '2012-05-15', '2012-05-16', '2012-05-17', '2012-05-18',\n",
       "               '2012-05-19', '2012-05-20', '2012-05-21', '2012-05-22',\n",
       "               '2012-05-23', '2012-05-24', '2012-05-25', '2012-05-26',\n",
       "               '2012-05-27', '2012-05-28', '2012-05-29', '2012-05-30',\n",
       "               '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成日期范围 pandas.date_range()用于生成指定长度的DatetimeIndex\n",
    "index = pd.date_range('4/1/2012','6/1/2012')\n",
    "index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认情况下，date_range会产生 按天计算的时间点。\n",
    "# 如果只传入起始或结束日期，那还得传入一个表示一段时间的数字\n",
    "pd.date_range(start='4/1/2012',periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-13', '2012-05-14', '2012-05-15', '2012-05-16',\n",
       "               '2012-05-17', '2012-05-18', '2012-05-19', '2012-05-20',\n",
       "               '2012-05-21', '2012-05-22', '2012-05-23', '2012-05-24',\n",
       "               '2012-05-25', '2012-05-26', '2012-05-27', '2012-05-28',\n",
       "               '2012-05-29', '2012-05-30', '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end='6/1/2012',periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-28',\n",
       "               '2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',\n",
       "               '2000-09-29', '2000-10-31', '2000-11-30'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 起始和结束日期定义了日期索引的严格边界\n",
    "# 如果想要生成一个由每月最后一个工作日组成的日期索引，需要传入‘BM’频率（表示 business end of month）,这样只会包含时间间隔内（或刚好在边界上）符合频率要求的日期\n",
    "pd.date_range('1/1/2000','12/1/2000',freq='BM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02 12:56:31', '2012-05-03 12:56:31',\n",
       "               '2012-05-04 12:56:31', '2012-05-05 12:56:31',\n",
       "               '2012-05-06 12:56:31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# date_range默认会保留起始和结束时间戳的时间信息（如果有的话）\n",
    "pd.date_range('5/2/2012 12:56:31',periods=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02', '2012-05-03', '2012-05-04', '2012-05-05',\n",
       "               '2012-05-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize选项可以将数据规范化到午夜的时间戳\n",
    "pd.date_range('5/2/2012 12:56:31',periods=5,normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 频率和日期偏移量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hour>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas中的频率是由一个基础频率和一个乘数组成的\n",
    "# 基础频率通常是以一个字符串别名表示，比如“M”表示每月，“H”表示没小时\n",
    "# 对于基础频率都有一个被称为日期偏移量（date offset）的对象与之对应，比如，按小时计算的频率可以用Hour类表示\n",
    "from pandas.tseries.offsets import Hour,Minute\n",
    "hour =Hour()\n",
    "hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4 * Hours>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个整数即可定义偏移量的倍数\n",
    "four_hours = Hour(4)\n",
    "four_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 04:00:00',\n",
       "               '2000-01-01 08:00:00', '2000-01-01 12:00:00',\n",
       "               '2000-01-01 16:00:00', '2000-01-01 20:00:00',\n",
       "               '2000-01-02 00:00:00', '2000-01-02 04:00:00',\n",
       "               '2000-01-02 08:00:00', '2000-01-02 12:00:00',\n",
       "               '2000-01-02 16:00:00', '2000-01-02 20:00:00',\n",
       "               '2000-01-03 00:00:00', '2000-01-03 04:00:00',\n",
       "               '2000-01-03 08:00:00', '2000-01-03 12:00:00',\n",
       "               '2000-01-03 16:00:00', '2000-01-03 20:00:00'],\n",
       "              dtype='datetime64[ns]', freq='4H')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一般情况只需使用诸如‘H’、‘4H’这样的字符串别名即可。在基础频率前面放一个整数即可创建倍数\n",
    "pd.date_range('1/1/2000','1/3/2000 23:59',freq='4h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<150 * Minutes>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 大部分偏移量对象都可通过加法进行连接\n",
    "Hour(2)+Minute(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:30:00',\n",
       "               '2000-01-01 03:00:00', '2000-01-01 04:30:00',\n",
       "               '2000-01-01 06:00:00', '2000-01-01 07:30:00',\n",
       "               '2000-01-01 09:00:00', '2000-01-01 10:30:00',\n",
       "               '2000-01-01 12:00:00', '2000-01-01 13:30:00'],\n",
       "              dtype='datetime64[ns]', freq='90T')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也亏传入频率字符串（如“2h30min”）\n",
    "pd.date_range('1/1/2000',periods=10,freq='1h30min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-20', '2012-02-17', '2012-03-16', '2012-04-20',\n",
       "               '2012-05-18', '2012-06-15', '2012-07-20', '2012-08-17'],\n",
       "              dtype='datetime64[ns]', freq='WOM-3FRI')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有些频率所描述的时间点并不是均匀分隔的，\n",
    "# 如，“M”（日历月末）和“BM”（每月最后一个工作日）就取决于每月的天数，对于后者还要考虑是不是周末\n",
    "\n",
    "rng=pd.date_range('1/1/2012','9/1/2012',freq='WOM-3FRI')  #'WOM-3FRI':每月第3个星期五\n",
    "rng"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 移动（超前和滞后）数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    1.177342\n",
       "2000-02-29   -0.016540\n",
       "2000-03-31   -0.427302\n",
       "2000-04-30   -0.663773\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 移动（shifting）指的是沿着时间轴将数据前移或后移\n",
    "ts = pd.Series(np.random.randn(4),\n",
    "               index = pd.date_range('1/1/2000',periods=4,freq='M'))\n",
    "ts              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31    1.177342\n",
       "2000-04-30   -0.016540\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2)  # 后移"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.427302\n",
       "2000-02-29   -0.663773\n",
       "2000-03-31         NaN\n",
       "2000-04-30         NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(-2) # 前移"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31          NaN\n",
       "2000-02-29    -1.014049\n",
       "2000-03-31    24.834006\n",
       "2000-04-30     0.553406\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shift通常用于计算一个时间序列或多个时间序列中的百分比变化\n",
    "ts/ts.shift(1)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31    1.177342\n",
       "2000-04-30   -0.016540\n",
       "2000-05-31   -0.427302\n",
       "2000-06-30   -0.663773\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果频率已知，可以将其传入给shift实现对时间戳进行位移而不是对数据进行简单位移\n",
    "ts.shift(2,freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-03    1.177342\n",
       "2000-03-03   -0.016540\n",
       "2000-04-03   -0.427302\n",
       "2000-05-03   -0.663773\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 还可以使用其他频率\n",
    "ts.shift(3,freq=\"D\")  # 按天后移"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-03    1.177342\n",
       "2000-03-03   -0.016540\n",
       "2000-04-03   -0.427302\n",
       "2000-05-03   -0.663773\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(1,freq='3D')  # 后移3天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31 01:30:00    1.177342\n",
       "2000-02-29 01:30:00   -0.016540\n",
       "2000-03-31 01:30:00   -0.427302\n",
       "2000-04-30 01:30:00   -0.663773\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(1,freq='90T')  # 后移90分钟"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过偏移量对日期进行位移"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-20 00:00:00')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas的日期偏移量HIA可以用子啊datetime或Timestamp对象上\n",
    "from pandas.tseries.offsets import Day,MonthEnd\n",
    "now = datetime(2011,11,17)\n",
    "now+3*Day()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果加的是锚点偏移量（如MonthEnd）,第一次增量会将原日期向前滚动到符合频率规则的下一个日期\n",
    "now+MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-31 00:00:00')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now+MonthEnd(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过锚点偏移量的rollforward和rollback可将日期向前或向后“滚动”\n",
    "offset = MonthEnd()\n",
    "offset.rollforward(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-10-31 00:00:00')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollback(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-15    0.320161\n",
       "2000-01-19   -0.011558\n",
       "2000-01-23   -0.521236\n",
       "2000-01-27    0.443262\n",
       "2000-01-31   -0.319174\n",
       "2000-02-04    0.775321\n",
       "2000-02-08    1.953400\n",
       "2000-02-12   -0.210154\n",
       "2000-02-16   -1.115276\n",
       "2000-02-20   -0.460008\n",
       "2000-02-24   -0.302293\n",
       "2000-02-28    0.919462\n",
       "2000-03-03    1.014179\n",
       "2000-03-07   -0.510196\n",
       "2000-03-11    0.759294\n",
       "2000-03-15    0.979393\n",
       "2000-03-19    1.446787\n",
       "2000-03-23   -0.140305\n",
       "2000-03-27    1.484436\n",
       "2000-03-31   -1.220484\n",
       "Freq: 4D, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以结合groupby使用这两个“滚动”\n",
    "ts=pd.Series(np.random.randn(20),\n",
    "             index = pd.date_range('1/15/2000',periods=20,freq='4d'))\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.017709\n",
       "2000-02-29    0.222922\n",
       "2000-03-31    0.476638\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(offset.rollforward).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.017709\n",
       "2000-02-29    0.222922\n",
       "2000-03-31    0.476638\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以用resample\n",
    "ts.resample(\"M\",how='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时区处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'US/Eastern' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从pytz中获取时区对象，使用pytz.timezone即可\n",
    "tz = pytz.timezone('US/Eastern')\n",
    "tz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 本地化和转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00    1.511205\n",
       "2012-03-10 09:30:00   -0.819952\n",
       "2012-03-11 09:30:00    0.896729\n",
       "2012-03-12 09:30:00    1.881004\n",
       "2012-03-13 09:30:00   -0.001824\n",
       "2012-03-14 09:30:00    0.711314\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认情况下，pandas中的时间序列是单纯的（naive）时区\n",
    "rng = pd.date_range('3/9/2012 9:30',periods=6,freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)),index = rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "# 其索引tz字段为None\n",
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00', '2012-03-16 09:30:00+00:00',\n",
       "               '2012-03-17 09:30:00+00:00', '2012-03-18 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在生成日期范围的时候还可以加上一个时区集\n",
    "pd.date_range('3/9/2012 9:30',periods=10,freq='D',tz='UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00    1.511205\n",
       "2012-03-10 09:30:00+00:00   -0.819952\n",
       "2012-03-11 09:30:00+00:00    0.896729\n",
       "2012-03-12 09:30:00+00:00    1.881004\n",
       "2012-03-13 09:30:00+00:00   -0.001824\n",
       "2012-03-14 09:30:00+00:00    0.711314\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从单纯到本地化的转换是通过tz_localize方法来处理的\n",
    "ts_utc = ts.tz_localize(\"UTC\")\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 04:30:00-05:00    1.511205\n",
       "2012-03-10 04:30:00-05:00   -0.819952\n",
       "2012-03-11 05:30:00-04:00    0.896729\n",
       "2012-03-12 05:30:00-04:00    1.881004\n",
       "2012-03-13 05:30:00-04:00   -0.001824\n",
       "2012-03-14 05:30:00-04:00    0.711314\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一旦时间序列被本地化到某个特定时区，就可以用tz_convert将其转换到别的时区\n",
    "ts_utc.tz_convert('US/Eastern')  # 跨越了美国东部时区的夏令营时转变期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 14:30:00+00:00    1.511205\n",
       "2012-03-10 14:30:00+00:00   -0.819952\n",
       "2012-03-11 13:30:00+00:00    0.896729\n",
       "2012-03-12 13:30:00+00:00    1.881004\n",
       "2012-03-13 13:30:00+00:00   -0.001824\n",
       "2012-03-14 13:30:00+00:00    0.711314\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以将其本地化到EST，然后转换为UTC或柏林时间\n",
    "ts_eastern = ts.tz_localize('US/Eastern')\n",
    "ts_eastern.tz_convert('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 15:30:00+01:00    1.511205\n",
       "2012-03-10 15:30:00+01:00   -0.819952\n",
       "2012-03-11 14:30:00+01:00    0.896729\n",
       "2012-03-12 14:30:00+01:00    1.881004\n",
       "2012-03-13 14:30:00+01:00   -0.001824\n",
       "2012-03-14 14:30:00+01:00    0.711314\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert('Europe/Berlin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+08:00', '2012-03-10 09:30:00+08:00',\n",
       "               '2012-03-11 09:30:00+08:00', '2012-03-12 09:30:00+08:00',\n",
       "               '2012-03-13 09:30:00+08:00', '2012-03-14 09:30:00+08:00'],\n",
       "              dtype='datetime64[ns, Asia/Shanghai]', freq='D')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tz_localize和tz_convert也是DatetimeIndex的实例方法\n",
    "ts.index.tz_localize('Asia/Shanghai')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 操作时区意识型Timestamp对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-11 23:00:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Timestamp对象也能被从单纯型（naive）本地化为时区意识型（time zone-aware），并从一个时区转换到另一个时区\n",
    "stamp = pd.Timestamp('2011-03-12 04:00')\n",
    "stamp_utc = stamp.tz_localize('UTC')\n",
    "stamp_utc.tz_convert(\"US/Eastern\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在创建Timestamp时，还可以传入一个时区信息\n",
    "stamp_moscow = pd.Timestamp('2011-03-12 04:00',tz='Europe/Moscow')\n",
    "stamp_moscow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时区意识型Timestamp对象在内部保存了一个UTC时间戳值，这个UTC值在时区转换过程中是不会发生改变的\n",
    "stamp_utc.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('US/Eastern').value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 01:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当使用pandas的DateOffset对象执行时间算术运算时，运算过程会自动关注是否存在夏令时转变期\n",
    "# 夏令时期间，纽约比UTC慢4个小时，而在全年其他时间则比UTC慢5个小时\n",
    "# 夏令时转变前30分钟\n",
    "from pandas.tseries.offsets import Hour\n",
    "stamp = pd.Timestamp('2012-03-12 01:30',tz='US/Eastern')\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-12 02:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp+Hour()  # 加一个小时"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 夏令时转变前90分钟\n",
    "stamp = pd.Timestamp('2012-11-4 00:30',tz='US/Eastern')\n",
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp+2*Hour()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同时区之间的运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00    1.377834\n",
       "2012-03-08 09:30:00    1.293104\n",
       "2012-03-09 09:30:00    0.544066\n",
       "2012-03-12 09:30:00   -1.765744\n",
       "2012-03-13 09:30:00   -0.931764\n",
       "2012-03-14 09:30:00    0.208003\n",
       "2012-03-15 09:30:00   -0.297206\n",
       "2012-03-16 09:30:00   -1.090454\n",
       "2012-03-19 09:30:00    0.464914\n",
       "2012-03-20 09:30:00    0.021064\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果两个时间序列的时区不同，将它们合并在一起时，最终结果就会是UTC\n",
    "rng = pd.date_range('3/7/2012 9:30',periods=10,freq='B')\n",
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00+00:00    1.377834\n",
       "2012-03-08 09:30:00+00:00    1.293104\n",
       "2012-03-09 09:30:00+00:00    0.544066\n",
       "2012-03-12 09:30:00+00:00   -1.765744\n",
       "2012-03-13 09:30:00+00:00   -0.931764\n",
       "2012-03-14 09:30:00+00:00    0.208003\n",
       "2012-03-15 09:30:00+00:00   -0.297206\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1 = ts[:7].tz_localize('Europe/London')\n",
    "ts1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 13:30:00+04:00    0.544066\n",
       "2012-03-12 13:30:00+04:00   -1.765744\n",
       "2012-03-13 13:30:00+04:00   -0.931764\n",
       "2012-03-14 13:30:00+04:00    0.208003\n",
       "2012-03-15 13:30:00+04:00   -0.297206\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2 = ts1[2:].tz_convert('Europe/Moscow')\n",
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00+00:00         NaN\n",
       "2012-03-08 09:30:00+00:00         NaN\n",
       "2012-03-09 09:30:00+00:00    1.088133\n",
       "2012-03-12 09:30:00+00:00   -3.531489\n",
       "2012-03-13 09:30:00+00:00   -1.863528\n",
       "2012-03-14 09:30:00+00:00    0.416006\n",
       "2012-03-15 09:30:00+00:00   -0.594411\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = ts1 + ts2\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-07 09:30:00+00:00', '2012-03-08 09:30:00+00:00',\n",
       "               '2012-03-09 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='B')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时期及其算术运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时期（period）表示的是时间区间，比如数日、数月、数季、数年等,其构造需要用到一个字符串或整数\n",
    "p = pd.Period(2007,freq='A-DEC')  # 表示从2007年1月1日到2007年12月31日之间的整段时间\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-DEC')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# period对象加上或减去一个整数即可达到根据其频率进行位移的效果\n",
    "p+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2005', 'A-DEC')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果两个Period对象拥有相同的频率，则它们的差就是它们之间的单位数量\n",
    "pd.Period('2014',freq='A-DEC')-p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# period_range则可用于创建规则的时期范围\n",
    "rng = pd.period_range('1/1/2000','6/30/2000',freq='M')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.124111\n",
       "2000-02   -0.168415\n",
       "2000-03   -1.944805\n",
       "2000-04   -0.654792\n",
       "2000-05    1.298723\n",
       "2000-06   -0.485451\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PeriodIndex类保存了一组Period，它可以在任何pandas数据结构中被用作轴索引\n",
    "pd.Series(np.random.randn(6),index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PeriodIndex类还允许直接使用一组字符串\n",
    "values = ['2001Q3','2002Q2','2003Q1']\n",
    "index = pd.PeriodIndex(values,freq='Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时期的频率转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007', 'A-DEC')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Period和PeriodIndex对象都可以通过其asfreq方法被转换成别的频率\n",
    "# 假设有一个年度时期，将其转换为当年年初或年末的一个月度时期\n",
    "p= pd.Period('2007',freq='A-DEC')  # 一个被划分为多个月度时期的时间段中的游标\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-01', 'M')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2007-12', 'M')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M',how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2008', 'A-JUN')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在将高频率转换为低频率时，超时期是由子时期所属的位置决定的\n",
    "# 例如，在A-JUN频率中，月份“2007年8月”实际上是属于周期“2008年”的\n",
    "p = pd.Period('2007-08','M')\n",
    "p.asfreq('A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    0.385979\n",
       "2007    0.042141\n",
       "2008    1.242181\n",
       "2009    0.105930\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PeriodIndex和TimeSeries的频率转换方式也是如此\n",
    "rng = pd.period_range('2006','2009',freq='A-DEC')\n",
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01    0.385979\n",
       "2007-01    0.042141\n",
       "2008-01    1.242181\n",
       "2009-01    0.105930\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('M',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-12-29    0.385979\n",
       "2007-12-31    0.042141\n",
       "2008-12-31    1.242181\n",
       "2009-12-31    0.105930\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq('B',how='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 按季度计算的时期频率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 季度型数据在会计、金融等领域场景，通常是一年12个月中某月的最后一个日历日或工作日\n",
    "# 时期“2012Q4”根据财年末的不同会有不同的含义\n",
    "p= pd.Period('2012Q4',freq='Q-JAN') # 以1月结束的财年中，2012Q4是从11月到1月\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-11-01', 'D')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D','start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-31', 'D')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D','end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-30 16:00', 'T')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Period之间的算术运算非常简单，如要获取该季度倒数第二个工作日下午4点的时间戳\n",
    "p4pm = (p.asfreq('B','e')-1).asfreq('T','s')+16*60\n",
    "p4pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011Q3    0\n",
       "2011Q4    1\n",
       "2012Q1    2\n",
       "2012Q2    3\n",
       "2012Q3    4\n",
       "2012Q4    5\n",
       "Freq: Q-JAN, dtype: int32"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# period_range还可以用于生成季度型范围\n",
    "rng = pd.period_range('2011Q3','2012Q4',freq='Q-JAN')\n",
    "ts = pd.Series(np.arange(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2010-10-28 16:00', '2011-01-28 16:00', '2011-04-28 16:00',\n",
       "             '2011-07-28 16:00', '2011-10-28 16:00', '2012-01-30 16:00'],\n",
       "            dtype='period[T]', freq='T')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rng = (rng.asfreq('B','e')-1).asfreq('T','s')+16*60\n",
    "new_rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010-10-28 16:00:00    0\n",
       "2011-01-28 16:00:00    1\n",
       "2011-04-28 16:00:00    2\n",
       "2011-07-28 16:00:00    3\n",
       "2011-10-28 16:00:00    4\n",
       "2012-01-30 16:00:00    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index = new_rng.to_timestamp()\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将Timestamp转化为Period(及其反向过程)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    2.065636\n",
       "2000-02-29   -1.564795\n",
       "2000-03-31   -0.730515\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过to_period将由时间戳索引的Series和DataFrame对象转换为以时期索引\n",
    "rng = pd.date_range('1/1/2000',periods=3,freq='M')\n",
    "ts = pd.Series(np.random.randn(3),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    2.065636\n",
       "2000-02   -1.564795\n",
       "2000-03   -0.730515\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = ts.to_period()\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.708399\n",
       "2000-01   -0.371816\n",
       "2000-01    0.327681\n",
       "2000-02    0.971143\n",
       "2000-02    0.972533\n",
       "2000-02   -0.763592\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于时期指的是非重叠时间区间，因此对于给定的频率，一个时间戳只属于一个时期\n",
    "# 新的PeriodIndex的频率默认是从时间戳推断出来的，你也可以指定任何别的频率，结果中允许存在重复时期\n",
    "rng = pd.date_range('1/29/2000',periods=6,freq='D')\n",
    "ts2 = pd.Series(np.random.randn(6),index=rng)\n",
    "ts2.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    2.065636\n",
       "2000-02   -1.564795\n",
       "2000-03   -0.730515\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要转换为时间戳，用to_timestamp()即可\n",
    "pts = ts.to_period()\n",
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    2.065636\n",
       "2000-02-29   -1.564795\n",
       "2000-03-31   -0.730515\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts.to_timestamp(how='end')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  通过数组创建PeriodIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1959.0\n",
       "1      1959.0\n",
       "2      1959.0\n",
       "3      1959.0\n",
       "4      1960.0\n",
       "5      1960.0\n",
       "6      1960.0\n",
       "7      1960.0\n",
       "8      1961.0\n",
       "9      1961.0\n",
       "10     1961.0\n",
       "11     1961.0\n",
       "12     1962.0\n",
       "13     1962.0\n",
       "14     1962.0\n",
       "15     1962.0\n",
       "16     1963.0\n",
       "17     1963.0\n",
       "18     1963.0\n",
       "19     1963.0\n",
       "20     1964.0\n",
       "21     1964.0\n",
       "22     1964.0\n",
       "23     1964.0\n",
       "24     1965.0\n",
       "25     1965.0\n",
       "26     1965.0\n",
       "27     1965.0\n",
       "28     1966.0\n",
       "29     1966.0\n",
       "        ...  \n",
       "173    2002.0\n",
       "174    2002.0\n",
       "175    2002.0\n",
       "176    2003.0\n",
       "177    2003.0\n",
       "178    2003.0\n",
       "179    2003.0\n",
       "180    2004.0\n",
       "181    2004.0\n",
       "182    2004.0\n",
       "183    2004.0\n",
       "184    2005.0\n",
       "185    2005.0\n",
       "186    2005.0\n",
       "187    2005.0\n",
       "188    2006.0\n",
       "189    2006.0\n",
       "190    2006.0\n",
       "191    2006.0\n",
       "192    2007.0\n",
       "193    2007.0\n",
       "194    2007.0\n",
       "195    2007.0\n",
       "196    2008.0\n",
       "197    2008.0\n",
       "198    2008.0\n",
       "199    2008.0\n",
       "200    2009.0\n",
       "201    2009.0\n",
       "202    2009.0\n",
       "Name: year, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 固定频率的数据集通常会将时间信息分开存放在多个列中，比如下面的年度和季度分别存在不同的列中\n",
    "data = pd.read_csv(path +'/datasets/ch08_data/macrodata.csv',engine = 'python')\n",
    "data.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      4.0\n",
       "4      1.0\n",
       "5      2.0\n",
       "6      3.0\n",
       "7      4.0\n",
       "8      1.0\n",
       "9      2.0\n",
       "10     3.0\n",
       "11     4.0\n",
       "12     1.0\n",
       "13     2.0\n",
       "14     3.0\n",
       "15     4.0\n",
       "16     1.0\n",
       "17     2.0\n",
       "18     3.0\n",
       "19     4.0\n",
       "20     1.0\n",
       "21     2.0\n",
       "22     3.0\n",
       "23     4.0\n",
       "24     1.0\n",
       "25     2.0\n",
       "26     3.0\n",
       "27     4.0\n",
       "28     1.0\n",
       "29     2.0\n",
       "      ... \n",
       "173    2.0\n",
       "174    3.0\n",
       "175    4.0\n",
       "176    1.0\n",
       "177    2.0\n",
       "178    3.0\n",
       "179    4.0\n",
       "180    1.0\n",
       "181    2.0\n",
       "182    3.0\n",
       "183    4.0\n",
       "184    1.0\n",
       "185    2.0\n",
       "186    3.0\n",
       "187    4.0\n",
       "188    1.0\n",
       "189    2.0\n",
       "190    3.0\n",
       "191    4.0\n",
       "192    1.0\n",
       "193    2.0\n",
       "194    3.0\n",
       "195    4.0\n",
       "196    1.0\n",
       "197    2.0\n",
       "198    3.0\n",
       "199    4.0\n",
       "200    1.0\n",
       "201    2.0\n",
       "202    3.0\n",
       "Name: quarter, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
       "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
       "             ...\n",
       "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
       "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
       "            dtype='period[Q-DEC]', length=203, freq='Q-DEC')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将这两个数组以及一个频率传入到PeriodIndex，就可以将它们合并成DataFrame的一个索引\n",
    "index = pd.PeriodIndex(year = data.year,quarter = data.quarter,freq='Q-DEC')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959Q1    0.00\n",
       "1959Q2    2.34\n",
       "1959Q3    2.74\n",
       "1959Q4    0.27\n",
       "1960Q1    2.31\n",
       "1960Q2    0.14\n",
       "1960Q3    2.70\n",
       "1960Q4    1.21\n",
       "1961Q1   -0.40\n",
       "1961Q2    1.47\n",
       "1961Q3    0.80\n",
       "1961Q4    0.80\n",
       "1962Q1    2.26\n",
       "1962Q2    0.13\n",
       "1962Q3    2.11\n",
       "1962Q4    0.79\n",
       "1963Q1    0.53\n",
       "1963Q2    2.75\n",
       "1963Q3    0.78\n",
       "1963Q4    2.46\n",
       "1964Q1    0.13\n",
       "1964Q2    0.90\n",
       "1964Q3    1.29\n",
       "1964Q4    2.05\n",
       "1965Q1    1.28\n",
       "1965Q2    2.54\n",
       "1965Q3    0.89\n",
       "1965Q4    2.90\n",
       "1966Q1    4.99\n",
       "1966Q2    2.10\n",
       "          ... \n",
       "2002Q2    1.56\n",
       "2002Q3    2.66\n",
       "2002Q4    3.08\n",
       "2003Q1    1.31\n",
       "2003Q2    1.09\n",
       "2003Q3    2.60\n",
       "2003Q4    3.02\n",
       "2004Q1    2.35\n",
       "2004Q2    3.61\n",
       "2004Q3    3.58\n",
       "2004Q4    2.09\n",
       "2005Q1    4.15\n",
       "2005Q2    1.85\n",
       "2005Q3    9.14\n",
       "2005Q4    0.40\n",
       "2006Q1    2.60\n",
       "2006Q2    3.97\n",
       "2006Q3   -1.58\n",
       "2006Q4    3.30\n",
       "2007Q1    4.58\n",
       "2007Q2    2.75\n",
       "2007Q3    3.45\n",
       "2007Q4    6.38\n",
       "2008Q1    2.82\n",
       "2008Q2    8.53\n",
       "2008Q3   -3.16\n",
       "2008Q4   -8.79\n",
       "2009Q1    0.94\n",
       "2009Q2    3.37\n",
       "2009Q3    3.56\n",
       "Freq: Q-DEC, Name: infl, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = index\n",
    "data.infl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 重采样及频率转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.509905\n",
       "2000-01-02   -1.396983\n",
       "2000-01-03    0.414493\n",
       "2000-01-04   -0.265373\n",
       "2000-01-05   -0.391967\n",
       "2000-01-06   -0.080902\n",
       "2000-01-07   -1.913365\n",
       "2000-01-08    1.266454\n",
       "2000-01-09   -1.126395\n",
       "2000-01-10    0.073585\n",
       "2000-01-11    0.344678\n",
       "2000-01-12   -0.807830\n",
       "2000-01-13    1.148428\n",
       "2000-01-14   -0.156064\n",
       "2000-01-15    2.273244\n",
       "2000-01-16    0.467836\n",
       "2000-01-17   -2.341019\n",
       "2000-01-18    0.313348\n",
       "2000-01-19   -1.009888\n",
       "2000-01-20   -1.673228\n",
       "2000-01-21   -0.575995\n",
       "2000-01-22    0.009822\n",
       "2000-01-23   -0.158432\n",
       "2000-01-24   -0.268800\n",
       "2000-01-25   -0.976183\n",
       "2000-01-26   -1.435232\n",
       "2000-01-27    0.070184\n",
       "2000-01-28    0.326715\n",
       "2000-01-29   -0.241339\n",
       "2000-01-30    1.090580\n",
       "                ...   \n",
       "2000-03-11   -0.647185\n",
       "2000-03-12   -0.074215\n",
       "2000-03-13    0.230533\n",
       "2000-03-14   -0.454096\n",
       "2000-03-15   -0.636438\n",
       "2000-03-16   -0.449287\n",
       "2000-03-17   -0.946176\n",
       "2000-03-18    1.050849\n",
       "2000-03-19   -0.341870\n",
       "2000-03-20   -0.721648\n",
       "2000-03-21   -0.106334\n",
       "2000-03-22    1.172381\n",
       "2000-03-23   -0.579987\n",
       "2000-03-24    0.508178\n",
       "2000-03-25    0.080022\n",
       "2000-03-26   -0.781116\n",
       "2000-03-27    0.340035\n",
       "2000-03-28    0.143292\n",
       "2000-03-29   -0.495817\n",
       "2000-03-30   -2.469445\n",
       "2000-03-31    1.022683\n",
       "2000-04-01   -0.053479\n",
       "2000-04-02   -0.448239\n",
       "2000-04-03   -0.605415\n",
       "2000-04-04   -0.102150\n",
       "2000-04-05    0.619815\n",
       "2000-04-06   -2.159264\n",
       "2000-04-07   -0.459879\n",
       "2000-04-08    0.382935\n",
       "2000-04-09    0.915980\n",
       "Freq: D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重采样指的是将时间序列从一个频率转换到另一个频率的处理过程\n",
    "# 高频率数据聚合到低频率称为降采样，低频率数据转换到高频率则称为升采样\n",
    "# 并不是所有的重采样都能被划分到这两大类中，如将W-WED（每周三）转换为W-FRI即不是降采样也不是升采样\n",
    "# pandas自带的resample,可以实现各种频率转换的工作\n",
    "rng = pd.date_range('1/1/2000',periods=100,freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.159628\n",
       "2000-02-29    0.014878\n",
       "2000-03-31   -0.138197\n",
       "2000-04-30   -0.212188\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M',how='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01   -0.159628\n",
       "2000-02    0.014878\n",
       "2000-03   -0.138197\n",
       "2000-04   -0.212188\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M',how='mean',kind='period')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 降采样\n",
    " 将数据集合到规整的低频\n",
    " ### 在resample对数据进行降采样时，需要考虑两点：\n",
    "-   1.各区间哪边是闭合 \n",
    "-   2.如何标记各个聚合面元，用区间的开头还是末尾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:01:00     1\n",
       "2000-01-01 00:02:00     2\n",
       "2000-01-01 00:03:00     3\n",
       "2000-01-01 00:04:00     4\n",
       "2000-01-01 00:05:00     5\n",
       "2000-01-01 00:06:00     6\n",
       "2000-01-01 00:07:00     7\n",
       "2000-01-01 00:08:00     8\n",
       "2000-01-01 00:09:00     9\n",
       "2000-01-01 00:10:00    10\n",
       "2000-01-01 00:11:00    11\n",
       "Freq: T, dtype: int32"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# “1分钟”数据\n",
    "rng = pd.date_range('1/1/2000',periods=12,freq='T')\n",
    "ts = pd.Series(np.arange(12),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-01 00:00:00    10\n",
      "2000-01-01 00:05:00    35\n",
      "2000-01-01 00:10:00    21\n",
      "Freq: 5T, dtype: int32\n",
      "\n",
      "\n",
      "2000-01-01 00:00:00    10\n",
      "2000-01-01 00:05:00    35\n",
      "2000-01-01 00:10:00    21\n",
      "Freq: 5T, dtype: int32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).sum()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).sum()\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# 将数据聚合到“5分钟”块中，传入的频率将会以“5分钟”的增量定义面元边界。\n",
    "# 默认情况下，面元的左边界是包含的，因此00:00到00:05的区间中是包含00:00的，不包含00:05的\n",
    "print(ts.resample('5min',how='sum'))  # 默认 label='left',closed='left'\n",
    "# 与上式等价\n",
    "print(\"\\n\")\n",
    "print(ts.resample('5min',how='sum',label='left',closed='left') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).sum()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:05:00    10\n",
       "2000-01-01 00:10:00    35\n",
       "2000-01-01 00:15:00    21\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入label='right’会让区间以右边界对其进行标记\n",
    "ts.resample('5min',how='sum',label='right')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).sum()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:05:00    15\n",
       "2000-01-01 00:10:00    40\n",
       "2000-01-01 00:15:00    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入closed='right’会让区间以右边界闭合\n",
    "ts.resample('5min',how='sum',closed='right',label='right') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).sum()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:59:59     0\n",
       "2000-01-01 00:04:59    15\n",
       "2000-01-01 00:09:59    40\n",
       "2000-01-01 00:14:59    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对结果索引做位移，如从右边界减去一秒一遍更容易明白该时间戳到底表示的是哪个区间，只需要通过loffset设置一个字符串或日期偏移量即可\n",
    "ts.resample('5min',how='sum',loffset='-1s',label='right',closed='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OHLC采样\n",
    "金融领域的时间序列聚合方式，计算各面元的四个值：开盘值(open)、最大高值(high)、最低值(low)、收盘值（close）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).ohlc()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:05:00</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:10:00</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:15:00</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     open  high  low  close\n",
       "2000-01-01 00:00:00     0     0    0      0\n",
       "2000-01-01 00:05:00     1     5    1      5\n",
       "2000-01-01 00:10:00     6    10    6     10\n",
       "2000-01-01 00:15:00    11    11   11     11"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入how=‘ohlc’即可得到含有这四种聚合值的DataFrame,整个过程很高效，只需一次扫描即可计算结果\n",
    "ts.resample('5min',how='ohlc',label='right',closed='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过groupby进行重采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01     0\n",
       "2000-01-02     1\n",
       "2000-01-03     2\n",
       "2000-01-04     3\n",
       "2000-01-05     4\n",
       "2000-01-06     5\n",
       "2000-01-07     6\n",
       "2000-01-08     7\n",
       "2000-01-09     8\n",
       "2000-01-10     9\n",
       "2000-01-11    10\n",
       "2000-01-12    11\n",
       "2000-01-13    12\n",
       "2000-01-14    13\n",
       "2000-01-15    14\n",
       "2000-01-16    15\n",
       "2000-01-17    16\n",
       "2000-01-18    17\n",
       "2000-01-19    18\n",
       "2000-01-20    19\n",
       "2000-01-21    20\n",
       "2000-01-22    21\n",
       "2000-01-23    22\n",
       "2000-01-24    23\n",
       "2000-01-25    24\n",
       "2000-01-26    25\n",
       "2000-01-27    26\n",
       "2000-01-28    27\n",
       "2000-01-29    28\n",
       "2000-01-30    29\n",
       "              ..\n",
       "2000-03-11    70\n",
       "2000-03-12    71\n",
       "2000-03-13    72\n",
       "2000-03-14    73\n",
       "2000-03-15    74\n",
       "2000-03-16    75\n",
       "2000-03-17    76\n",
       "2000-03-18    77\n",
       "2000-03-19    78\n",
       "2000-03-20    79\n",
       "2000-03-21    80\n",
       "2000-03-22    81\n",
       "2000-03-23    82\n",
       "2000-03-24    83\n",
       "2000-03-25    84\n",
       "2000-03-26    85\n",
       "2000-03-27    86\n",
       "2000-03-28    87\n",
       "2000-03-29    88\n",
       "2000-03-30    89\n",
       "2000-03-31    90\n",
       "2000-04-01    91\n",
       "2000-04-02    92\n",
       "2000-04-03    93\n",
       "2000-04-04    94\n",
       "2000-04-05    95\n",
       "2000-04-06    96\n",
       "2000-04-07    97\n",
       "2000-04-08    98\n",
       "2000-04-09    99\n",
       "Freq: D, Length: 100, dtype: int32"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据月份或星期几进行分组，只需传入一个能访问时间序列的索引上的这些字段的函数即可\n",
    "rng = pd.date_range('1/1/2000',periods=100,freq='D')\n",
    "ts = pd.Series(np.arange(100),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    15\n",
       "2    45\n",
       "3    75\n",
       "4    95\n",
       "dtype: int32"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(lambda x: x.month).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    47.5\n",
       "1    48.5\n",
       "2    49.5\n",
       "3    50.5\n",
       "4    51.5\n",
       "5    49.0\n",
       "6    50.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(lambda x: x.weekday).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 升采样和插值\n",
    "将数据从低频转换到高频时，就不需要聚合了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-2.030251</td>\n",
       "      <td>-0.015468</td>\n",
       "      <td>-1.096736</td>\n",
       "      <td>-2.415709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-12 -2.030251 -0.015468 -1.096736 -2.415709"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.random.randn(2,4),\n",
    "                     index = pd.date_range('1/1/2000',periods=2,freq='W-WED'),\n",
    "                     columns=['Colorado','Texas','New York','Ohio'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-2.030251</td>\n",
       "      <td>-0.015468</td>\n",
       "      <td>-1.096736</td>\n",
       "      <td>-2.415709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-06       NaN       NaN       NaN       NaN\n",
       "2000-01-07       NaN       NaN       NaN       NaN\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -2.030251 -0.015468 -1.096736 -2.415709"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将其重采样到日频率，默认会引入缺失值\n",
    "df_daily = frame.resample('D').asfreq()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.243595</td>\n",
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       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-2.030251</td>\n",
       "      <td>-0.015468</td>\n",
       "      <td>-1.096736</td>\n",
       "      <td>-2.415709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-06 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-07 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-08 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-09 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-10 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-11 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-12 -2.030251 -0.015468 -1.096736 -2.415709"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 想要用前面的周型值填充“非星期三”。resampling的填充和插值方式跟fillna和reindex一样\n",
    "frame.resample('D',fill_method='ffill') # ffill用缺失值前面的值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill(limit=2)\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-2.030251</td>\n",
       "      <td>-0.015468</td>\n",
       "      <td>-1.096736</td>\n",
       "      <td>-2.415709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-06 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-07 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -2.030251 -0.015468 -1.096736 -2.415709"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以只填充指定的时期数（目的是限制前面的 观测值的持续使用距离）\n",
    "frame.resample('D',fill_method='ffill',limit=2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.243595</td>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.197429</td>\n",
       "      <td>0.040174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-2.030251</td>\n",
       "      <td>-0.015468</td>\n",
       "      <td>-1.096736</td>\n",
       "      <td>-2.415709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-06 -0.243595  0.588970  0.197429  0.040174\n",
       "2000-01-13 -2.030251 -0.015468 -1.096736 -2.415709"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 新的索引完全没必要跟旧的相交\n",
    "frame.resample('W-THU',fill_method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过日期进行重采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01</th>\n",
       "      <td>0.430718</td>\n",
       "      <td>1.511030</td>\n",
       "      <td>-0.615286</td>\n",
       "      <td>-1.090147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-02</th>\n",
       "      <td>-0.978052</td>\n",
       "      <td>0.499093</td>\n",
       "      <td>-1.416619</td>\n",
       "      <td>0.787168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03</th>\n",
       "      <td>0.915867</td>\n",
       "      <td>-1.536053</td>\n",
       "      <td>-1.861992</td>\n",
       "      <td>-0.750708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04</th>\n",
       "      <td>-0.106891</td>\n",
       "      <td>1.479839</td>\n",
       "      <td>0.931179</td>\n",
       "      <td>-0.921310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05</th>\n",
       "      <td>-0.451627</td>\n",
       "      <td>1.079680</td>\n",
       "      <td>0.500295</td>\n",
       "      <td>0.062862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Colorado     Texas  New York      Ohio\n",
       "2000-01  0.430718  1.511030 -0.615286 -1.090147\n",
       "2000-02 -0.978052  0.499093 -1.416619  0.787168\n",
       "2000-03  0.915867 -1.536053 -1.861992 -0.750708\n",
       "2000-04 -0.106891  1.479839  0.931179 -0.921310\n",
       "2000-05 -0.451627  1.079680  0.500295  0.062862"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.random.randn(24,4),\n",
    "                     index = pd.period_range('1-2000','12-2001',freq='M'),\n",
    "                    columns=['Colorado','Texas','New York','Ohio'])\n",
    "frame[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Colorado     Texas  New York      Ohio\n",
       "2000  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001 -0.301287  0.034021 -0.011056  0.081401"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame = frame.resample('A-DEC',how='mean')\n",
    "annual_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q1</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q1  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q2       NaN       NaN       NaN       NaN\n",
       "2000Q3       NaN       NaN       NaN       NaN\n",
       "2000Q4       NaN       NaN       NaN       NaN\n",
       "2001Q1 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q2       NaN       NaN       NaN       NaN\n",
       "2001Q3       NaN       NaN       NaN       NaN\n",
       "2001Q4       NaN       NaN       NaN       NaN"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q-DEC：季度型（每年以12月结束）\n",
    "annual_frame.resample('Q-DEC').asfreq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q1</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q2</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q3</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q1  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q2  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q3  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q4  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001Q1 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q2 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q3 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q4 -0.301287  0.034021 -0.011056  0.081401"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 升采样稍微麻烦点，因为要决定在新频率中各区间的哪端用于放置原来的值。convention参数默认为‘end’，可设置为‘start’\n",
    "annual_frame.resample('Q-DEC',fill_method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
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       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
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       "      <th>2000Q2</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
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       "      <th>2000Q4</th>\n",
       "      <td>0.320066</td>\n",
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       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
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       "    <tr>\n",
       "      <th>2001Q1</th>\n",
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       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
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       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q1  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q2  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q3  0.320066  0.255818 -0.267170 -0.108321\n",
       "2000Q4  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001Q1 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q2 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q3 -0.301287  0.034021 -0.011056  0.081401\n",
       "2001Q4 -0.301287  0.034021 -0.011056  0.081401"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample('Q-DEC',fill_method='ffill',convention='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000Q4</th>\n",
       "      <td>0.320066</td>\n",
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       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>0.320066</td>\n",
       "      <td>0.255818</td>\n",
       "      <td>-0.267170</td>\n",
       "      <td>-0.108321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.301287</td>\n",
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       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q1</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q2</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002Q3</th>\n",
       "      <td>-0.301287</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>-0.011056</td>\n",
       "      <td>0.081401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001Q1  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001Q2  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001Q3  0.320066  0.255818 -0.267170 -0.108321\n",
       "2001Q4 -0.301287  0.034021 -0.011056  0.081401\n",
       "2002Q1 -0.301287  0.034021 -0.011056  0.081401\n",
       "2002Q2 -0.301287  0.034021 -0.011056  0.081401\n",
       "2002Q3 -0.301287  0.034021 -0.011056  0.081401"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于时期指的是时间区间，所以升采样和降采样的股则比较严格\n",
    "## 在降采样中，目标频率必须是源频率的子时期\n",
    "## 在升采样中，目标频率必须是源频率的超时期\n",
    "# 如果不满足这些条件，则会引发异常。这主要影响的是按季、年、周计算的频率\n",
    "# 例如，由Q-MAR 定义的时间区间只能升采样为A-MAR/A-JAN/A-SEP/A-DEC等\n",
    "annual_frame.resample('Q-MAR',fill_method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时间序列绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>7.86</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-02</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-05</th>\n",
       "      <td>8.18</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-06</th>\n",
       "      <td>8.12</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-07</th>\n",
       "      <td>7.77</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-08</th>\n",
       "      <td>7.71</td>\n",
       "      <td>0.51</td>\n",
       "      <td>6.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-09</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-12</th>\n",
       "      <td>7.94</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-13</th>\n",
       "      <td>8.06</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-14</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-15</th>\n",
       "      <td>8.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-16</th>\n",
       "      <td>7.91</td>\n",
       "      <td>0.53</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-19</th>\n",
       "      <td>7.91</td>\n",
       "      <td>0.53</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-20</th>\n",
       "      <td>7.85</td>\n",
       "      <td>0.55</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-21</th>\n",
       "      <td>7.97</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-22</th>\n",
       "      <td>7.73</td>\n",
       "      <td>0.53</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-23</th>\n",
       "      <td>7.79</td>\n",
       "      <td>0.53</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-26</th>\n",
       "      <td>7.97</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-27</th>\n",
       "      <td>7.85</td>\n",
       "      <td>0.53</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-28</th>\n",
       "      <td>7.97</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>8.03</td>\n",
       "      <td>0.55</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-02</th>\n",
       "      <td>7.91</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-05</th>\n",
       "      <td>8.08</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-06</th>\n",
       "      <td>8.26</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-07</th>\n",
       "      <td>8.29</td>\n",
       "      <td>0.56</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-08</th>\n",
       "      <td>8.61</td>\n",
       "      <td>0.58</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-09</th>\n",
       "      <td>8.64</td>\n",
       "      <td>0.58</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-12</th>\n",
       "      <td>8.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-13</th>\n",
       "      <td>8.64</td>\n",
       "      <td>0.58</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-14</th>\n",
       "      <td>8.67</td>\n",
       "      <td>0.59</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-05</th>\n",
       "      <td>374.05</td>\n",
       "      <td>25.80</td>\n",
       "      <td>72.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-06</th>\n",
       "      <td>379.74</td>\n",
       "      <td>25.51</td>\n",
       "      <td>71.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-07</th>\n",
       "      <td>383.93</td>\n",
       "      <td>26.00</td>\n",
       "      <td>73.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-08</th>\n",
       "      <td>384.14</td>\n",
       "      <td>26.22</td>\n",
       "      <td>72.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-09</th>\n",
       "      <td>377.48</td>\n",
       "      <td>25.74</td>\n",
       "      <td>71.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-12</th>\n",
       "      <td>379.94</td>\n",
       "      <td>25.89</td>\n",
       "      <td>71.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-13</th>\n",
       "      <td>384.62</td>\n",
       "      <td>26.04</td>\n",
       "      <td>71.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-14</th>\n",
       "      <td>389.30</td>\n",
       "      <td>26.50</td>\n",
       "      <td>72.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-15</th>\n",
       "      <td>392.96</td>\n",
       "      <td>26.99</td>\n",
       "      <td>74.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-16</th>\n",
       "      <td>400.50</td>\n",
       "      <td>27.12</td>\n",
       "      <td>74.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-19</th>\n",
       "      <td>411.63</td>\n",
       "      <td>27.21</td>\n",
       "      <td>73.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-20</th>\n",
       "      <td>413.45</td>\n",
       "      <td>26.98</td>\n",
       "      <td>74.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-21</th>\n",
       "      <td>412.14</td>\n",
       "      <td>25.99</td>\n",
       "      <td>71.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-22</th>\n",
       "      <td>401.82</td>\n",
       "      <td>25.06</td>\n",
       "      <td>69.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-23</th>\n",
       "      <td>404.30</td>\n",
       "      <td>25.06</td>\n",
       "      <td>69.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-26</th>\n",
       "      <td>403.17</td>\n",
       "      <td>25.44</td>\n",
       "      <td>71.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-27</th>\n",
       "      <td>399.26</td>\n",
       "      <td>25.67</td>\n",
       "      <td>72.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-28</th>\n",
       "      <td>397.01</td>\n",
       "      <td>25.58</td>\n",
       "      <td>72.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-29</th>\n",
       "      <td>390.57</td>\n",
       "      <td>25.45</td>\n",
       "      <td>73.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>381.32</td>\n",
       "      <td>24.89</td>\n",
       "      <td>72.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>374.60</td>\n",
       "      <td>24.53</td>\n",
       "      <td>71.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>372.50</td>\n",
       "      <td>25.34</td>\n",
       "      <td>72.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>378.25</td>\n",
       "      <td>25.89</td>\n",
       "      <td>73.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>377.37</td>\n",
       "      <td>26.34</td>\n",
       "      <td>73.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>369.80</td>\n",
       "      <td>26.25</td>\n",
       "      <td>73.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>388.81</td>\n",
       "      <td>26.94</td>\n",
       "      <td>76.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>400.29</td>\n",
       "      <td>27.00</td>\n",
       "      <td>76.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>402.19</td>\n",
       "      <td>26.96</td>\n",
       "      <td>77.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>408.43</td>\n",
       "      <td>27.18</td>\n",
       "      <td>76.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>422.00</td>\n",
       "      <td>27.27</td>\n",
       "      <td>78.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5662 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL   MSFT    XOM\n",
       "1990-02-01    7.86   0.51   6.12\n",
       "1990-02-02    8.00   0.51   6.24\n",
       "1990-02-05    8.18   0.51   6.25\n",
       "1990-02-06    8.12   0.51   6.23\n",
       "1990-02-07    7.77   0.51   6.33\n",
       "1990-02-08    7.71   0.51   6.35\n",
       "1990-02-09    8.00   0.52   6.37\n",
       "1990-02-12    7.94   0.52   6.22\n",
       "1990-02-13    8.06   0.52   6.23\n",
       "1990-02-14    8.00   0.52   6.20\n",
       "1990-02-15    8.00   0.53   6.40\n",
       "1990-02-16    7.91   0.53   6.33\n",
       "1990-02-19    7.91   0.53   6.33\n",
       "1990-02-20    7.85   0.55   6.25\n",
       "1990-02-21    7.97   0.52   6.28\n",
       "1990-02-22    7.73   0.53   6.22\n",
       "1990-02-23    7.79   0.53   6.22\n",
       "1990-02-26    7.97   0.54   6.37\n",
       "1990-02-27    7.85   0.53   6.38\n",
       "1990-02-28    7.97   0.54   6.20\n",
       "1990-03-01    8.03   0.55   6.13\n",
       "1990-03-02    7.91   0.57   6.17\n",
       "1990-03-05    8.08   0.57   6.10\n",
       "1990-03-06    8.26   0.57   6.22\n",
       "1990-03-07    8.29   0.56   6.13\n",
       "1990-03-08    8.61   0.58   6.17\n",
       "1990-03-09    8.64   0.58   6.12\n",
       "1990-03-12    8.58   0.59   6.13\n",
       "1990-03-13    8.64   0.58   6.02\n",
       "1990-03-14    8.67   0.59   6.13\n",
       "...            ...    ...    ...\n",
       "2011-09-05  374.05  25.80  72.14\n",
       "2011-09-06  379.74  25.51  71.15\n",
       "2011-09-07  383.93  26.00  73.65\n",
       "2011-09-08  384.14  26.22  72.82\n",
       "2011-09-09  377.48  25.74  71.01\n",
       "2011-09-12  379.94  25.89  71.84\n",
       "2011-09-13  384.62  26.04  71.65\n",
       "2011-09-14  389.30  26.50  72.64\n",
       "2011-09-15  392.96  26.99  74.01\n",
       "2011-09-16  400.50  27.12  74.55\n",
       "2011-09-19  411.63  27.21  73.70\n",
       "2011-09-20  413.45  26.98  74.01\n",
       "2011-09-21  412.14  25.99  71.97\n",
       "2011-09-22  401.82  25.06  69.24\n",
       "2011-09-23  404.30  25.06  69.31\n",
       "2011-09-26  403.17  25.44  71.72\n",
       "2011-09-27  399.26  25.67  72.91\n",
       "2011-09-28  397.01  25.58  72.07\n",
       "2011-09-29  390.57  25.45  73.88\n",
       "2011-09-30  381.32  24.89  72.63\n",
       "2011-10-03  374.60  24.53  71.15\n",
       "2011-10-04  372.50  25.34  72.83\n",
       "2011-10-05  378.25  25.89  73.95\n",
       "2011-10-06  377.37  26.34  73.89\n",
       "2011-10-07  369.80  26.25  73.56\n",
       "2011-10-10  388.81  26.94  76.28\n",
       "2011-10-11  400.29  27.00  76.27\n",
       "2011-10-12  402.19  26.96  77.16\n",
       "2011-10-13  408.43  27.18  76.37\n",
       "2011-10-14  422.00  27.27  78.11\n",
       "\n",
       "[5662 rows x 3 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yahoo!Finance上下载的几只股票的一些价格数据\n",
    "close_px_all = pd.read_csv(path+'/datasets/ch09_data/stock_px.csv',parse_dates=True,index_col=0,engine = 'python')\n",
    "close_px = close_px_all[['AAPL','MSFT','XOM']]\n",
    "close_px = close_px.resample('B',fill_method='ffill')\n",
    "close_px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xce25f90>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce06bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% matplotlib  inline\n",
    "close_px['AAPL'].plot()  # AAPL每日价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcebff30>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcec6d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制2009年多个公司股票价格的数据\n",
    "close_px.ix['2009'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcdffbf0>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5rlpXyqjPPy05TUk+Iom6KQfuFxE7AdF+0BjzqIg8KyKlgACHgI9Y8+8A7hWRcWAYeI+1oasoC6J/ZIxD53q599o1c87Nyczgpc/euAirClj0o+N+ugZHKY3C973YjPn8tPWPTISELpTd1UU47Tb2nOrg2vX6qzyZiSTq5giwPcz4DTPM/zrw9YUvTVEms+9MNz6/mdgITBZC69Ins9C39o3gN5HH0M9FltPOpTWF7K1TP32yo5mxSsqwt7aDLIedHavciV7KJFKlLn3TRGhlbFw3AFetLeVE6wDtA6mxR7FUUaFXUoY9dZ1ctroobqGS8+WC0Ce32F1oOBIb1w0wEf304gKs+pExH6PjWjsnnqjQKylBc+8wZzoGuSrJ3DYAhdkOMjNsSW/RB9cXaZ2bSLioPJ+iHCd7Ts1P6H1+wx3ffolPPXgoZmtSphPJZqyiJJygH/iqOeLnE4GIUJnAEMtz3UNUuLOwzxEb39Q7TEluJi5H7H4R2WzClWtL2FvXiTFmxiS2mXjglXMcbepXiz7OqEWvpAQv1nVSkpvJhuV5iV5KWMrdroRkx3YMeLnhK8/z81ca55wbCK2MndsmyNVrS2gf8HKqLbqWgwMjY/zr0yeB1Kn+maqo0CtJj99veLGuk6vWFkdtMS4WFQWJyY59vaWfMZ/hpdNzp6o0RdhwJFqCv7L2RFkO4RvPnabTM8otF5czMDKOxzse87UpAVTolaTnROsAnZ5RrlqXvLHa5e4s2ge8i96Q42RroPvTwYaeWecZY2juHaaiIPZCX+HOYk1pDnuiKIfQ2DXED/ee5V07KnnT5kDBuVa16uOGCr2S9AQjOpJxIzZIpduFMYsvVidaB4CA62O2mvjdg6OMjPnjYtEDXL2ulH1nu/COR1YO4ctPHMduEz795o2U5QfcSSr08UOFXkl69tR1snZZLmUFsfcvx4qJuvSLLFYnWwcmKlEemMWqb4qyPHG0XLm2hJExP6+dn7uR+Mtnu3nstVb+7NrVlBW4Qp675I5aSmVU6JWkZmTMx8tnu5LamocLsemL6acf9/mpbffwtq0VZDvtHJil5syFGPr4CP3GssAmeV377Buyfr/hHx59nbJ8F/dcsxqAZfmBLyq16OOHCr2S1Bxs7GFkzD9nWeJEE7RKFzPypr5riNFxP5sr8tm2ws2Bxpkt+pNtA4jAiqLYZcWGUunOIjPDxpnOwVnn/eZwE6819fGZt2wg2xmI7nY57BTnOGnpV6GPFyr0SlLz+9Nd2G0yUXY4WcnJzKAgy7GoLQVPWBuxG8ry2LmqkOMtAwzOELny1LE2tq9wU5A1e9XP+WKzCTUlOZyew6J/4mgrK4qyuPWSyknjZQUutejjiAq9ktQcb+lnbWkuuZnJn9tXscgNSE62DmC3CWuX5bJzVSE+v+Hwud5p8851D/F6Sz83bymL63pWl+bMadHXtnnYXF4wrfFJuVXqOdkxxtAx4E30MqJGhV5JamrbPaydoTdsslFR4KJ5EcXqROsA1cXZuBx2tq8sRCT8huyTx1oBePPm+Ar9mtJcGruHZsxyHRnzUd81GLbXb8CiT/7N2H1nu7n0i7/jlwfOJ3opUaFCryQtw6M+GruHWLcsRYQ+ARb9xrJA96yCLAfrl+WxP4zQP3WsjY1leawqzonrelaX5uDzGxq7w1v1ZzoG8RtYFya7ubwgi56hMUbGkrtb1fGWgLvssw+/xqEwv56SFRV6JWk53eHBGFifpGUPplLudtE3PDajnzyWDHrHaeweYkPZhedmx6pCDjb2TOp01enx8kpDN2+KszUPsLok8IV8uiO80Ne2B2L+w72eqRJL39g9RJbDzrK8TD7yHwdSpjyzCr2StARD9VLGol/EePBTbQHRDBX6nasKGRgZpzZkQ/R3r7dhDLzZyj6NJ6tLA78Yzswk9G0e7DahumR65M+F5i3JLZyNXUOsKs7me3+8i77hMe796cFpSWKvNvZw2zdf5I5vvUSyNNdToVeSllNtA2TYJO4uh1ixmHXpT1oZsRtDhH7XqkJgsp/+yWOtVBVmcVGcG6QD5LkcLMvL5ExH+MibU22BPYVw/QSCyXCt/cntp2/oHmJlUTabyvP5lzsv4UBDD3//yDGMMbT3j/BXDx7mtm++xOvN/exv6OHVJHHvJH8og7JkqW33UFOSgzMjNeyRoFX6qQcPTcSIA2Q77fzwQ5fGNFnpROsA2U47KwovWMerirMpznGyv6Gb9122koGRMV6s6+IDV6xatGJwq0tzOD2D0Ne2eyZ9MYVSlgIWvd9vONc9xPUbAjWXbtlazusta/jGc6fxeH08e7yNMZ/h3uvW8CdvqObaf36eX+w/x46VhQleeQQWvYi4RORlETksIsdE5PPW+I9F5KyIHLIu26xxEZF/F5E6ETkiIjvi/SCU9KSu3cO6FIm4AagqzOLe69Zw9bpSdq4qZOeqQtYuy+VE6wBHm+YuDRANJ1r7Wb88b1KYooiwc1XhRIGz5092MOrzxz3aJpTVpblhQyxHxnw0dA2G3YgFyHYG8hCS2UffPuDFO+5nZcgvzL964wZu2LiM3x5u5vLVxTz5yWv4zM0bWZbv4pat5fz2cAtDo4mvyhmJRe8FbjDGeETEAewVkcetY39jjHloyvy3AOusy2XAt6y/ihIxQWF4+yUViV5KxIgIn7l546Sxc91DPHuind6hsZjdjzGGk60DYQV856pCnnq9jU6PlyePtVKc42TnqsWzKNeU5tI7NEb34ChFOc6J8dMdHvyGsKGVQZI9lr6hK/AFtjIku9hmE775/h2c7vCwuaJg0vx371rBQwfO8/hrrdy+s2pR1zqVOS16EyD4W8xhXWbbYbgV+Il1uz8AbhEpX/hSlaVEMBRvNmFIBQotsesZGo3ZOTsGvPQMjU3aiA2yqzog6r8/3cXzJzt440XL5+w8FUuCG7JT3TcXNtZnjqBK9uzYhu4hAFZNKSPhctiniTzApdWFVBdn8+D+c4uyvtmIyPkpInYROQS0A08bY/ZZh75ouWe+KiKZ1lglEPrIzltjU895j4jsF5H9HR3RNSxQ0p9gKN5swpAK5DjtOOxCTwwt+mBp4nBCv7miAKfdxjeeq8PjHV9Utw3AGivEcuqGbHBjvaZk5o31ZLfoz3UPYbdJxKWeRYQ7d61g39nuiV8DiSIioTfG+Iwx24AqYLeIbAE+C2wELgWKgM9Y08OZD9N+ARhjvmuM2WWM2VVamrwNJZTEMFsoXiohIriznfTG0KK/EHEzPZLG5bBzcVUBJ1oHyM3M4A1rF7dGUGVhFs4M27QQy1NtHqrn2Fgvy8+i0+NN2v6xDV1DVLhdOOyRBwe8a0clNoGHEpxJG1U4gzGmF3geuNkY02K5Z7zAj4Dd1rTzwIqQm1UBzTFYq7KEqG2fORQv1SjMdsTUdXOidYDSvMxJPvBQgj756zcuW/Tnz24TaoqnR97Utg3M6YYLRi21JWkVy2BoZTSUF2RxzfpSHjpwHp8/cTH1kUTdlIqI27qeBdwEnAj63SUQt/VO4Kh1k0eAP7aiby4H+owxLXFZvZK21LZ7Ut5tE8Sd7Yyp6+ZkW/+MYYpwIZ5+MZKkwrG6NGeSRT8y5qOhe2jO1/NCLH1yCv257iFWFkWf03HnzhW09I2wty7yVouxJhKLvhx4TkSOAK8Q8NE/CvxMRF4DXgNKgH+05j8GnAHqgO8BH435qpW0xjvuo6FrKKVCK2ejMNsRM9eNz2+obfOwYZayEDduWs63/2gnb92SmBiI1aU5NHYPTfTPjbSURTJnxw6MBCKJVhVH70q86aJluLMd/CKBm7JzhlcaY44A28OM3zDDfAP8+cKXpixVznYO4vObGWOuU43CbCcHh2KTIVnfNYh33B92IzaI3SZxL0k8G2tKcxn3Gxq7h1hTmkttmxVxM8cX94RFn4RVLBu6AhE30bpuADIz7LxzWyX/ua+R3qFR3NnhXW7xJDVSDpUlxYQwpEiNm7kIbsbGou7JiZaZN2KThdWlVnEzK6QyGHFTPUcpizyXg9zMjKS06M91z1/oIRBTP+rz8/Vn6zjW3IdnEQrfhaIlEBaRRw43T2QtBrGJ8ME3rEqZei6LQW3bADZh1lC8VKIw28GYzzA46ltwA5WTrf3YZG7rOJFMFDezMmRPtUVeyiJZY+mDMfQr5+G6AbioIp8rVhfz/b1n+f7eswAU5zhZUZTNey5dwXt2r4zZWsOhQr9IDI/6+MxDRzCYSZEQfcNjuBw2Pj0lo3IpU9vuobo4B5cj9SNuIOC6AegZHF2w0AeajST3c5PvclCSe6G4WW37AFvCJBSFI1lj6Ru6hijMdpDvmn8rxh//6aXUtnlo6BqisTt4GcS3CBUuVegXiRdqOxge8/HTuy/jqpBG11d++dmkfGMnktp2D2vTxG0D4M4OiEPv0BgrihZ2rpNtA2yuSF63TZA1pTmc7hicaB5z2/ZpOZNhKct3UduWuOiUmTjXPTSpxs18yMyws6WygC2VkX3pxRL10S8STx5rpSDLwWWrJ3/Sywtci9qVKNkZHfdT3zmY1K6JaAmWQeheYORNUDRToRHL6tJcznR4JiJuIg2VLS9w0T4wwrgvuZKmGroHp5U+SCVU6BeBMZ+fZ463c+PGZdOy6srdWWrRh1DfNci436SEmEVK4YRFvzChr2tPnY5ba0pz6Bka4+Wz3UDkNYvKCrLwG+jwJE8D7jGfn+bekXlvxCYDKvSLwL4z3fQNj/HmMCFvFdbmkz+BWXPJRLBzUnq5bi746BdC8LlJhUJvwQ3ZJ4614rAL1RFurCdjLH1z7zA+v5n3RmwyoEK/CDx5rBWXw8Y166bX9CkvcDHq89O1QBFIF2rbPNgkEIudLrizAhb9QrNjT7UP4LCnRset4Ov3Sn03NSU5EdeHuRBLnzxCH4yhV9eNMiN+v+HJY61ct34ZWc7pkRLl7sXrM5oK1LV7WFmUndRRJdGSYbeR78pYsOumts3D6pLcqIpqJYqqwmycdlvAPx+FqykZLfqJ8sQp8AU7E8n/jklxDp3vpX3Ay5u3hK87EmwovRh9RlOBU20DrE2TGjehFOYsvN7NqbYB1s+SEZtM2G0yUS5gfRSvZ0GWA5fDllTZsee6h3Bm2FiWlzn35CRFhT7OPHmslQybcMOG8EJf7g5YMBp5E9j0OptmETdBAoXN5m/RD3rHOd8zzPoU2rsI+umjeT1FhPKC5ApQaOgaZGVR9qS2jamGCn0cMcbw5NFWrlhTTEF2+ESL4hwnzgybum4IfKACETepI2aREihsNn+LvjbYoSkFIm6CBP300b6eZfnJlR3b0DWU0v55UKGPK6faPNR3Dc3a5UdEqChw0ZxEb+xEcapt7nZzqUrhAi36VIq4CfL2Syp432UrqSmJbs3JlB1rTKA42woVemUmnjzWigi86aLZ64KXF2TRoq4bXjrdSbbTnqaumwVa9G0DODNsKbUhuKk8ny/ddnHUPWvLCly09Y8ktFFHkK7BUYZGffMqT5xMqNDHkSeOtrJjZSHL8l2zzit3J48FkyiMMTx3ooMr15akRVepqRRmO/F4x+fdJu9Um4c1pbmL2ug7UZQXuBj3G7qSIGlqIrRShV4Jx7nuIV5v6Y+oy09FQRZt/cmX9r2Y1LV7aOod5voNyxK9lLgwkR07PD/3TW3bABvS8JdOOMoKgiHHiTd+GrsDFThTOSsWVOjjxpPHWgFm9c8HKXe78BtoH0i8BZMonj3RDsD1G9OzUXwwO3Y+7puBkTGa+0ZSaiN2ISRTLH1j1zAigbyAVEaFPg70DY/xoxfr2VKZH5FPtaJAk6aeO9nOxrI8yq3nIt0oXEAZhGDETSrUuIkFydRpqqF7kLJ8V8on8EXSHNwlIi+LyGEROSYin59y/D4R8YT8/yER6RCRQ9blw/FYeDLzd785Smv/CP9w65aI5l+IpU+8BZMI+kfG2F/fww0b09NtAxdKFc8naepUa+pF3CyEomwnTruNliRoEt7YlfoRNxBZPXovcIMxxiMiDmCviDxujPmDiOwC3GFu84Ax5mMxXWmK8JtDTfzmUDOfeuN6tq8sjOg25Uvcot9b28m433B9Ggt9sFTxfMognGrz4HLYWJFcg4aKAAAgAElEQVTi7oNIsdmE/CwH/cMLyySOBY3dQ1y7PvXdiXNa9CZA0GJ3WBcjInbgn4FPx3F9KcX5niH+98NH2bmqkI9etybi2+W7Mshx2tPGov/SY8f5ylMnI95cfu5EOwVZDravCGczpAfBzdj51KSvbR9g7bLclM7MjJZ8Vwb9I4vbV3Uqw6M+2ge8KR9xAxH66EXELiKHgHbgaWPMPuBjwCPGmJYwN7ldRI6IyEMismKGc94jIvtFZH9HR8e8H0Cy4PMbPvXgYQzwb3dtIyOKwlMiQoU7Ky3KIPj8hh+/WM99z9bxxz98me45fNJ+v+G5kx1cs740qucs1chy2MnMsM1rM/ZU20BU9WLSgTxXBgMJFvqGYMRNCuUuzEREnyxjjM8Ysw2oAnaLyDXAncB9Yab/Fqg2xmwFfgfcP8M5v2uM2WWM2VVamvo/jb7zwmlePtvN59+xeV4+vXRpQHK+Z4hRn5+bNi1nf0MPb79vL0eb+macf6y5n06Pl+s3pP57YDZEJJAdG+VmbN/wGG393pQpZhYr8lwOPCOJdd2cbg8I/ZrSJSL0QYwxvcDzwPXAWqBOROqBbBGps+Z0GWOCcYLfA3bGbLVJytGmPv71qVPcsrWcd+2IrDfmVCoKXGnhoz9tNYS+97rVPPSRKzDGcPu3XuLhV8+Hnf/siXZESAs/6Fy4sx1Rb8bWpmDpg1iQDBZ9sLl5TYRNU5KZSKJuSkXEbV3PAm4CDhhjyowx1caYamDIGLPWmlMecvN3AMdjv+zk4vt7zpDryuCL79yCyPz8qOUFWXR6RvGO+2K8usXlTEfAClpdksvWKjeP/MVVbFvh5pMPHOarT5/CTOl4/9zJdi6pclOcm7olYCOlMNsZ9WbsSUvo07H+z2zkZiZe6E93eKh0Z5HtjCRmJbmJxKIvB54TkSPAKwR89I/OMv/jVhjmYeDjwIcWvszkZXQ80A/2zReVTSTFzIdgiGUyVe2bD6c7PBTnOCeiTEpyM/nphy/jzp1VfO2ZWv41ROy7PF4On+9N67DKUApzHFEXNqtt85DttFPpTs/8gpnIczkYSLDr5kzn4ESp5VRnzq8qY8wRYPscc3JDrn8W+OzCl5YavHS6kwHv+IyNRSIltAHJYheu8vsNTx9v44o1xeS7wpdTjpTT7YPT2gA67Db+6fat2G3Cfc/W4fMb/ubNG/ifUx0YQ9qWPZiKO9sZ9WbsqbYB1i2xiBsIuG4GR334/CYh9X2MMZzpGOT2ebpik43U/02SYJ481kpuZgZvWFOyoPMELfpE+Omfer2Nj/z0ACuKsvi3u7azc1Vk8f/hON3h4U1h6vvYbMKXbrsYm0345vOn8RlDU88wpXmZbK7IX8jyU4bCbAe9w2MYYyJ28Z1q83Bdmm9UhyPPFZAmj3ecgqyFGR/zoX3Ai8c7zpoUavQyGyr0C8DnNzx1rI3rNy5bcIp0RQILOf3q4HmKcpwYA+/+zu/5xI3r+Oj1a6O2pHoGR+kaHJ2xsbfNJvzjrVuwCXznf84gAnfsqFoy1mphthOf39A/Epl49QyO0unxsmGJlD4IJSj0AyNjCRH6YFDB6ihr6Scr6Ru4vAjsr++ma3CUmyMoXDYXWU477mzHosfS9wyO8tzJdt61vZLH/vJqbrm4nK88fYr3fu8PUa/lTGfgwzGT0ENA7P/h1i188IpVGANvisFzlypcKGwWmZ8+2GwkHevzz0We5UJM1IbsaSuoYM2y9PDRq9AvgCeOteLMsMXsp3VFAnplPvpaC2M+w207Ksl3Ofjae7bxr+++hGNNfbztvr30R7EhdiHueHZhEhH+/h2befqT1/DGOZqypBOFUda7ObXEipmFcsGiT4zQn+kIbIKXzdFLIlVQoZ8nwX6w16wrJSczNh6wCrdr0S36hw+eZ8PyPC4qD/jJRYR37ajiK+/eRvfgKLVtnjnOcIHTHR6cGTYqC+eOEBGRJVN2N0jQoo808uZU6wB5mRkTZXuXEkGL3uNNTOTNmY5Bakpy5h0unWyo0M+T15r6aO4b4eYtsXM9lBcsbhmE+s5BDjb2ctuOymlv6GA24PmeoYjPd7rDQ01xzpLogjQfJpqPzCD0xhiOt/TzjefquPPbL/GzfQ1sKMtLG7GJhkRb9Kc7PHP+Mk0ldDN2njxxtBW7TbhpU+xCA8vdLvpHxhn0jsfsV8JsPPxqEyJw67aKaceCVvn5nsi/eM50DLKxfGlZ6dFwoSb9dCt1T20Hn37oyITrbktlPh+7fi3v2lG1qGtMFvKs938iCpuNjPlo6h3mjp3p89yr0M9C3/AYH/3ZAe65Zs2kFH1jDE8cbeXy1UULSpKaSmgDkrVxzoQ0xvDrQ028YU1x2GYf2c4MinOcEQv96Lifhu4hbtlaPvfkJUp+lgOR8Bb99/ecxec3/NPtF3PdhmUsTxPf8Hy5sBm7+K6b+q5BjJl7rymVUNfNLLx2vo8X67q4+8ev8JtDTRPjde0eznQOxiTaJpSgL3YxyhUfbOyhoWuI27bPbLVUFWZF7Lpp7B7E5zdp9eGINXab4M6aXu/G7ze82hhovHLXpSuXvMgDuBw2MmyCJwEWfTCoIF2yYkGFflbquwIv+KbyfP7y54f44d6zQMBtA7EPDaxwL14Dkl8dbMLlsM26x1BVmE1ThBZ9XYQRN0udwmzntM3YM52D9I+MsyPCRjVLARFJWGGzM2kWQw/qupmVhq5BXA4bv/jIFXzi54f4wqOv0+Hx8j8nO9ix0h1zy2t5vguR+Fv03nEfjx5p4c2by8idZS+gqjCLp4+34febOZOaJhJM0sgKigeBCpaThf5gYw8A21emb+OV+ZDrykiI6yZYzCzLmdp9YkNRi34W6ruGWFWUg8th5xvv38F7d6/kW8+f5vWW/phG2wRxZtgoyc2Mu0X/3IkO+obHuG377HU8qgqzGB330+nxzjoPAh+O8gLXomwipzKBmvSTxevVxh7yXRn6a2gKeZmOxFj0aVTMLIgK/Sw0dA1OtBGz24Qv3baFj9+4jqIcJ2+9OD6bjhWL0IDk4VfPU5KbyVVrZ6/PU2X1KD0XgfvmdMf0YmbKdNxhShUfbOhl28rCJVMKIlIS4boJFjNLt/eyCv0M+P2Ghq4hqkOaDogIn3rjevb/vzdNiGCsqSiIb9LUoHec50508PZLyuds3Vc1EWI5+4asMYYz7Z606MQTbwqnNB8ZGBnjVPsAO9RtM408l4MB7+IKfbCYmVr0S4S2gRG84/6wjYHjaXmVW2UQpjboiBWHz/cy6vNzTQQdnSKNpe8Y8DLgHWd1mllB8aAwx8nwmI+RsUCDmcPn+jAG3YgNQ36cfPTGGD7wg3385c9fnXYsuNekFv0Sob4zYMVWL3Jt+Aq3i6FRH/3D8bFkDjYENv52rJhbWC7E0s9u0del6YcjHrgnsmMDAhbciL1khVr0U8mNk+vmlfoe9tR28sjhZs52Dk46Fixmphb9EqHBCq0MZ9HHk2DyUlOc3DcHG3tZuyyXguzISr8GYulnX8uZNKv0F08Kp9S7OdjYw7pluQkpxZvs5Lky8HjHY/7r9lvP11GY7cBhs/GjF89OOpZuxcyCqNDPQH3XEE67LWzWaDypsBqQ1LYPxPzcfr/hYGMPO6NwE1QVZs8p9KfT9MMRD9wTFSxHrUSpXnXbzECey4HPbxgei10f5eMt/Tx3soO7r6rhHdsq+MX+85M2x890BCJu0q2+UCTNwV0i8rKIHLZ6wX5+yvH7RMQT8n+miDwgInUisk9EqmO/7PjT0DXIiqKsRS/QdXFlASuLsvnB3rMxt2TOdA7SOzQWVQepqsIsmnqG8ftnXksw4ibdPhzxoHCiJv0YZ7sG6RseY8cqdduEIx6Fzb79P6fJcdr5wOXV/OmVNQyP+fivl89NHD/d4UmrRKkgkVj0XuAGY8wlwDbgZhG5HEBEdgFT36V3Az3GmLXAV4F/iuF6F436rqFF988DZNht/Pn1azhyvo/nT3XE9NxBf3A0wlJVmMWoz0/HLLH0pzXiJmJCXTcT+yVq0YclmMwXqw3Zxq4hfnu4mfdfvoqCbAcXVeRz5dpi7n+pnjGff6KYWTruNc0p9CZA0GJ3WBcjInbgn4FPT7nJrcD91vWHgBslxUw9Y4wVQ58Y8bptexWV7iy+9rvamFr1Bxt6KMhyRGWxBMNIZ9qQHR5N3w9HPAjdjD3Y2EueJkrNSLBRfawqWH5vzxnsNuFPr6yZGLv7qhpa+0d47LUWznYGipml20YsROijFxG7iBwC2oGnjTH7gI8BjxhjWqZMrwTOARhjxoE+oDjMOe8Rkf0isr+jI7aW60Lp8HgZGvVRXbK4G7FBnBk2Pnr9Gg6d62VvXWfMznugoYcdK91RhYdWzRFiOdE+ME2aKMcbl8NOlsNOz+Aorzb2sG1FdK/HUmKiQXgMhL5jwMuD+8/xru1VlIU0crlu/TJWl+bwg71nJ4IKlqzQG2N8xphtQBWwW0SuAe4E7gszPdy7dppZaoz5rjFmlzFmV2lpcnW5b+gKWK+JsugB7thZRXmBK2ZWfd/wGLXtnqjdBHPF0k/01lSrNGKKrPLPJ9sG1G0zC7HsG/vjl84y6vNzz7WrJ43bLAv/yPk+fv5KI5BexcyCRBV1Y4zpBZ4HrgfWAnUiUg9ki0idNe08sAJARDKAAqA7RutdFOqt2NrqRQ6tDCUzw85Hrl3D/oYefn+ma8Hne9Xyz0ezEQtzx9Kfbvdgk8UPQ01l3NkOXqzrDCRKRfl6LCUubMYuzEc/MDLGT37fwM2by8IaJLfvqMKd7WBPbWfaFTMLEknUTamIuK3rWcBNwAFjTJkxptoYUw0MWZuvAI8AH7Su3wE8a+KV5hknGrqGyLAJle7FDa2cyl2XrmBZXib//kztgs91sKEHm8wvMWe2WPq6Dg9Vhdm4HOn34YgXhdnOidT+bZooNSO5MYq6+fnL5xgYGecj164JezzLaed9u1cC6em2gcgs+nLgORE5ArxCwEf/6CzzfwAUWxb+p4C/XfgyF5f6rkGqCrPmrAUTb1wOO3927Rr+cKabl88u7EfRgcYeNpblz6u65Eyx9MYYXjnbzdaqggWtbakR3JBdq4lSs5LrzEBk4Rb9wcYeVpfkzGrkfPAN1Tjswvo0bVgfSdTNEWPMdmPMVmPMFmPMF8LMyQ25PmKMudMYs9YYs9sYcybWi443DV1DCfXPh/K+3SspyXVy37Pzt+p9fsOhxt6o3TZBqorCx9KfaB2gfcAbUd0c5QLBEEstZDY7NpuQ68xYcGGz1v4Ryt2zJ/Mtz3fx8Eev5GPXr511XqqimbFTMMZQ3zWYUP98KFlOO3dftZo9tZ00dkXW1m8qJ1sHGBz1zV/oC7PDxtLvqQ1ES129bvZyx8pkCi2LXjdi5yYWpYrb+70sz5s7a3tLZQGFObHrAZ1MqNBPoWdojIGR8aSx6AF21wQEIVhZL1oONC4sMWemcsUvnOpk/fLcRS8TkeoUWWKiG7Fzk+dyLMh14/cb2vpHWF6wtMtzaDugKQT7xCYqhj4cwQzds52DXD+P27/a0ENJbiYriuYnyCtCQix3rgqMDY/6eLm+mz++fNW8zrmUuXVbJQXZDtZp7sGcLLSCZffQKON+w/K8zBiuKvVQi34KF6pWJo9FX5TjJM+VMfElFC0HGnvYuco971o0le5gduyFDdk/nO1idDyyuvbKZApznNy2vUprA0VAsILlfGnrD3RrK1viFr0K/RTqO4ewyQV3RTIgItSU5EyrnR0JnR4vDV1DC/IHZzntlOROjqV/4VQHmRk2dtcUzfu8ijIXAdfNwoV+2RKvrKpCP4WGrkEq3FlkZiRXXHh1cc68LPpg4az5bsQGqZwSYrmntpPdNUUaP6/ElbwFdplq6w8EECxXoVdCSVTVyrmoLsmhqWeY0XF/VLc70NiDwy5sqVxYrHto0lRz7zB17R6uVbeNEmfyMjMWVNSstc+y6NVHr4QSqFqZPBuxQWpKsvEbaOyOLsTyYEMPmysKFmx5h9alf8Eqn6z+eSXe5LkyGB334x2fX/OR9oERSnKdOBKc/Jholvajn0Lf0Bg9Q2PJadFba6qP0k9/tnOIjWULz/YLjaV/obaDsnyXRo0ocSdY2Gy+FSzb+r1L3m0DKvSTaOhOTJ/YSKgpsYQ+Cj+932/oGRqlOHfhSSDBzemGriH21nZy9boSjRpR4s5Cu0y19o2o0KNCP4l6K/O0uiT5LHp3thN3tiOqyJu+4TF8fkNRzsL9k8FY+sdea6F/ZFzdNsqisNBSxe0DKvSgQj+JBktEVxYln0UP0UfedA0Gmh6XxMCiD8bS//LgeUTgqrVa9kCJPwtpJzg67qfTM8ry/KW9EQsq9JOo7xqivMCVtCGDNSU51HdGvhnbbQl9UQzqdwRj6QdGxtmaxjVBlORiwnUzj6SpYG0mtehV6CeRrBE3QaqLc2juG2ZkLLIIhC7rjR4LoYdALD1otI2yeOQvwHUzkRWrQq9CH0qyxtAHqS7JxkQRYhl03RTHwEcPFzZkVeiVxWIhXabagjH06rpRoQ9ytnOQTo+XdUnceCAYeRPphmwsXTcAWyoKKMt3aVckZdFYSJcptegvoNUrLX554Dw2gbdtLU/0UmYkGA0UaSx99+Aoea4MnBmx+T6/55rVfOgN1Us++URZPBx2Gy6HbV6FzVr7vTjsMtHoZSmjn1gC8eYPv9rE1etKk3rjJt/loDjHGXHkTafHS3EMN03tNknLxslKcjPfmvTt/SMsy3Nhs2m+RyTNwV0i8rKIHBaRYyLyeWv8B9bYERF5SERyrfEPiUiHiByyLh+O94NYKH8400VT7zC376xK9FLmpDqKKpbdg6Mxc9soSqLIc82v3k3bwIiGVlpEYtF7gRuMMZcA24CbReRy4JPGmEuMMVuBRuBjIbd5wBizzbp8P/bLji0PHThPniuDN120PNFLmZNVxdkRh1h2D45SnKtvdCW1mW+pYs2KvUAkzcGNMSbYw85hXYwxph9AAnnwWYCZ4RRJjcc7zuNHW3nb1oqkjZ8PpaY4h9b+EYZH5w6x7BocjanrRlESQV7m/EoVt2udmwki8tGLiF1EDgHtwNPGmH3W+I+AVmAjcF/ITW4PcemsmOGc94jIfhHZ39HRsbBHsQAef62F4TEfd+ysTNgaoqE6wpo3fr+hR103ShqQ58qIuqjZoHecAe+4Cr1FREJvjPEZY7YBVcBuEdlijf8JUAEcB+6ypv8WqLZcOr8D7p/hnN81xuwyxuwqLU1cXPZDB85TU5KzoA5Mi0lNhJE3/SNjjPuNCr2S8uTN0Dd23OfniaMtGDPdmRAMrVQffYCoom6MMb3A88DNIWM+4AHgduv/LmOM1zr8PWBnTFYaB851D7HvbDe376hMmUqMQYv+7BwW/YU6N/pGV1KbmaJufne8jY/89CB7ajunHQt2ltIY+gCRRN2Uiojbup4F3AScFJG11pgAbwdOWP+HBqK/g4C1n5T86mATInDbjuSPtgmSm5lBSW7mnBZ9rJOlFCVR5GZmMDjqw+efbLkfOd8HwH6rXWYo2it2MpEkTJUD94uIncAXw4PAfwN7RCQfEOAwcK81/+Mi8g5gHOgGPhTrRccCYwy/PHieK1YXU+lOnkbgkVBTMnfkTZdHhV5JD4JlEDzecQqyHBPjx5r7AXi1cWahLytQoYcIhN4YcwTYHubQlTPM/yzw2QWuK6Z85amT/PeRFq7dUMobNy3n0poiXm3spbF7iE/ctC7Ry4ua6uIcnj81+wZ212Dgp2ssmo4oSiK5UNhsbELojTEcaw5Y9K829uLzG+whiVGt/SPkOO0TZY6XOmn/LIz5/Pzk9w24HDb+c18jP3qxnjxXBkU5TnKcdm7eUpboJUZNdUkOHQfO4/GOz/hG7laLXkkTwnWZah/w0ukZZWtVAUfO91HbPsDGsvwLxzW0chJpXwLh96e76Bse4x/feTGv/t0b+e4HdvKWLWUMj/p47+6VZDtT77suksibrsFR8jIzyMxI/twARZmNcF2mgtb8By5fBcCBKX76tn5Nlgol9VQuSh4/2kKO087V60pwOey8aXMZb9qcelZ8KBONwrsG2VJZEHZO9+AoReq2UdKA3Akf/YXIm2NNAf/8zVvK+PLjJzjY0Mv7L1s1cby1f4Rdq1IjZHoxSGuLftzn58ljbdy4aXlKZL1GSnVJoAHI7Ba9V902SloQznVzrLmf6uJs8lwOtq8snLQha4wJuG50I3aCtBb6l8920z04ylsvTm0LfirZzgyW52dydpbImy6Plj9Q0oOg0IcWNjva3Mdm69fsjlVuznQOToQU9wyNMerzszxPhT5IWgv9Y0dbyHLYuXb9skQvJebM1Si8e3A0Zp2lFCWRhEbdAPQNjXG+Z5jNFYHN151WVnvQqtfQyumkrdD7/IYnjrZxw8ZlaVlDPdAoPLzQG2PoGVIfvZIeZGbYyLDJhOvmWEtgI3ZzRcCi31rlJsMmHJwi9Fr+4AJpK/T767vp9Hh5S5q5bYLUlOTQNThK3/D01PD+kXHGfEZdN0paICKTCpu9biVKBS36LKediyryJyJvJrJi1XUzQdoK/eNHW8nMsHH9hvRz28Ds/WO7PIFkKd2MVdKF0Ho3R5v6WJ6fOamO046VhRw+18e4zz9R50abgl8gLYXe7zc8frSF6zaUkpOmmXGrS4NC75l2LLgppU1HlHQhtILlseZ+tlRMDivevtLN8JiPE60DtPaPUJTj1BySENJS6F8910Nbv5e3Xpy8jb4XysqiHGwCZzvCWPRBoVeLXkkTgkI/POrjdIdnwm0TZKcVM3+wsYd2TZaaRloK/WOvteK027hhY3q6bQCcGTZWFGVzJozrRitXKulGbqaDAe84J1r78Ru4aIpFX+nOYlleJgcbAkaebsROJu2E3hjD46+1cM36konU6XSlZoZG4eqjV9KNfFegneDRKRuxQUSEnasKOdDYQ2v/iMbQTyHthP7w+T6a+0Z4y5b0ddsECQr91A47XYOj5GZmpFU2sLK0CbpuXm/uoyDLQVXh9NLiO1YWcq57mI4BzYqdStoJ/QOvNOK027hp0/JELyXurC7NZWjUNxFlEKRbe8UqaUaey4HHO87Rpn42V+SH7Qi3Y5V74rq6biaTVkLf0DXIL/af5z27V1CQnd5uG4DVVojlmSmRNyr0SrqR68rA5ze83tI/zW0TZHNFAU57QNK0heBk0krov/ZMLXab8LHr1yZ6KYvCTLH0nVrnRkkzgvVufH4zkRE7FZfDzubKwJeARt1MJm2Evq59gF+/2sQH31C9ZPpEluW7cDls00Isu7VypZJmhAZWzGTRQ8BPD5osNZU5s4lExAW8AGRa8x8yxnxORH4A7CLQM/YU8CFjjEdEMoGfADuBLuAuY0x9nNY/wVefriXLYecj166J910lDTabUF2cMynE0hgTKGimyVJKGhG06F0OG6tLc2ec96E3VLM8P5NSff9PIhKL3gvcYIy5BNgG3CwilwOfNMZcYozZCjQCH7Pm3w30GGPWAl8F/ikO657E0aY+/vu1Fu6+qmbJWbJrSnMnuW4GvFrnRkk/8i2h31SeP6k37FRWFGVzzzVrwm7WLmXmFHoTILjb57AuxhjTDyCBZzQLCMb43Qrcb11/CLhR4vysf/XpU+S7Mrj76tXxvJukpKYkh8buIcZ8fkB7xSrpSW5mwHUzm9tGmZmIfPQiYheRQ0A78LQxZp81/iOgFdgI3GdNrwTOARhjxoE+oDjG657gYGMPz5xo58+uXTPRIX4pUVOSg89vONcdaELSNWglS2mJYiWNWJaXidNuY3dN3KQkrYlI6I0xPmPMNqAK2C0iW6zxPwEqgOPAXdb0cNa7mTogIveIyH4R2d/R0TGvxQN85amTlOQ6+dAbqud9jlSmxipudsbakO2yLPoSbTqipBGFOU72/u31vH1r+idCxoOoom6MMb3A88DNIWM+4AHgdmvoPLACQEQygAKgO8y5vmuM2WWM2VVaWjqvxb90upMX67q497q1aVulci5WTwmxnKhzoxa9kmYsy3Op732ezCn0IlIqIm7rehZwE3BSRNZaYwK8HThh3eQR4IPW9TuAZ83UHP0Y4c5y8o5LKnj/ZSvjcfqUwJ3tpCjHORF5o5UrFUWZSiRmcDlwv4jYCXwxPAj8N7BHRPIJuGoOA/da838A/IeI1BGw5N8T81VbXFSRz7+/d3u8Tp8yBGreBPbLuzyjZDvtWudGUZQJ5hR6Y8wRIJyaXjnD/BHgzgWuS4mCmpIc9tQG9jm6B70Uq9tGUZQQ0iYzdimzujSHtn4vHu84XYOjFOlGrKIoIajQpwHBDdn6zsFAVqz65xVFCUGFPg2oKQmkhJ/pHKTLo5UrFUWZjAp9GrCqOBsRONPhsercqNArinIBFfo0wOWwU1GQxdGmPkZ9fnXdKIoyCRX6NGF1aQ77G3oAdDNWUZRJqNCnCatLcugdGgM0WUpRlMmo0KcJwW5TgProFUWZhAp9mlAT0oxBo24URQlFhT5NWB1q0auPXlGUEJZmycc0pMKdhTPDhl2ELKfWuVEU5QIq9GmC3SZUF2cz6PUleimKoiQZKvRpxOWri2nv9yZ6GYqiJBkq9GnEF27dkuglKIqShOhmrKIoSpqjQq8oipLmqNAriqKkOSr0iqIoaU4kzcFdIvKyiBwWkWMi8nlr/GciclJEjorID0XEYY1fJyJ9InLIuvxdvB+EoiiKMjORRN14gRuMMR5LzPeKyOPAz4A/sub8J/Bh4FvW/3uMMW+L+WoVRVGUqImkObgBPNa/DutijDGPBeeIyMtAVVxWqCiKoiyIiHz0ImIXkUNAO/C0MWZfyDEH8AHgiZCbXGG5eh4Xkc0xXbGiKIoSFRElTBljfMA2EXEDD4vIFmPMUevwN4EXjDF7rP8PAqssVwVC7HcAAAXdSURBVM9bgV8D66aeU0TuAe6x/vWIyMl5PoYSoHOet42UAqBPzx/V+WP5uqT687MY9zH1/LH+XKT6a5Bq55/6+s30GVsV0dmMMVFdgM8Bfx1y/deAbZb59UBJtPcTxXr2x+vcIffxXT1/dOeP5euS6s9PIh5DrD8Xqf4apNr5p75+C/2MRRJ1U2pZ8ohIFnATcEJEPgy8GXivMcYfMr9MRMS6vpuAe6grom+d5OW3en49f5Lfh55fzz8jYn0zzDxBZCtwP2AnINoPGmO+ICLjQAMwYE39lTX+MeBeYBwYBj5ljHlpIYucY337jTG74nV+ZX7o65JY9PlPbSJ5/aJ5jSOJujkCbA8zHva2xpivA1+P5M5jxHcX8b6UyNHXJbHo85/aRPL6Rfwaz2nRK4qiKKmNlkBQFEVJc1ToFUVR0pyUEXoR8cw9S1ksRMQXUs/okIhUzzL3OhF5dPFWl/6IiBGR/wj5P0NEOvR5Th1E5DbrddwY7/tKGaFXko5hY8y2kEt9ohe0xBgEtlghzwBvBJqiOYGIaIe5xPJeYC/wnmhuJCL2aO8opYReRHJF5BkROSgir4nIrdZ4tYgcF5HvWRU2nwr5ACiLhFUq459F5BUROSIifxZyOF9EHhaR10Xk2yKSUu+9JOVx4Bbr+nuB/woeEJHdIvKSiLxq/d1gjX9IRH4hIr8Fnlr8JSsQ0DLgSuBuLKG3fvm+EO5zIiIeEfmCiOwDroj2/lLtwzYC3GaM2QFcD3wlmJxFoMzCN4wxm4Fe4PYErXGpkBXitnnYGrsb6DPGXApcCvw/IlJjHdsN/BVwMbAGeNeirzj9+DnwHhFxAVuBfSHHTgDXGGO2A38HfCnk2BXAB40xNyzaSpWpvBN4whhzCugWkR3W+EyfkxzgqDHmMmPM3mjvLNV+ugnwJRG5BvADlcBy69hZY8wh6/oBoHrxl7ekGDbGbJsy9iZgq4jcYf1fQOALeBR42RhzBkBE/gu4CnhosRabjhhjjlh7I+8FHptyuAC4X0TWAYZA1dkgTxtjuhdlkcpMvBf4N+v6z63//5uZPyc+4JfzvbNUE/r3A6XATmPMmIjUAy7rmDdkng9Q183iI8BfGGOenDQoch0BsQlFEzhiwyPAvwDXAcUh4/8APGeMuc36Mng+5NjgIq1NCYOIFAM3ENhjMQSqDhgCX9YzfU5GTKC45LxINddNAdBuifz1RFq5TVksngTuDek2tl5Ecqxju0WkxvI53kVgE0pZOD8EvmCMeW3KeAEXNmc/tKgrUubiDuAnxphVxphqY8wK4CwB6z0un5OUEHorOsBLoKvVLhHZT8C6P5HQhSlT+T7wOnBQRI4C3+HCr8bfA18GjhJ4Uz8c9gxKVBhjzhtjvhbm0P8P/B8ReZGAxagkD+9l+vv/l8D7iNPnJCVKIIjIJcD3jDG7E70WRVGUeGC5OP/axKENa9Jb9CLyEQJhY/870WtRFEVJRVLColcURVHmT9Jb9IqiKMrCSDqhF5EVIvKclel6TET+0hovEpGnRaTW+ltojW8Ukd+LiFdE/nrKuX4oIu3WxqCiKMqSJOmEnkBnqr8yxmwCLgf+XEQuAv4WeMYYsw54xvofoBv4OIFY4qn8GLg57itWFEVJYpJO6I0xLcaYg9b1AeA4gQzYWwm0NMT6+05rTrsx5hVgLMy5XiDwRaAoirJkSTqhD8XK6NtOoIbHcmNMCwS+DIBliVuZoihK6pC0Qm9Vd/sl8AljTH+i16MoipKqJKXQWyn0vwR+Zoz5lTXcJiLl1vFyoD1R61MURUklkk7orbLDPwCOG2P+NeTQI8AHresfBH6z2GtTFEVJRZIuYUpErgL2AK8RKEUM8L8I+OkfBFYCjcCdxphuESkD9gP51nwPcJExpt8q83kdUAK0AZ8zxvxgER+OoihKwkk6oVcURVFiS9K5bhRFUZTYokKvKIqS5qjQK4qipDkq9IqiKGmOCr2iKEqao0KvKIqS5qjQK4qipDn/F0n+covZRTMzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdf7bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 苹果公司在2011年1月到3月间的每日股价\n",
    "close_px['AAPL'].ix['01-2011':'03-2011'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: fill_method is deprecated to .resample()\n",
      "the new syntax is .resample(...).ffill()\n",
      "  \n",
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcdf7270>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdee150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 季度型频率的数据会用季度标记进行格式化\n",
    "appl_q = close_px['AAPL'].resample('Q-DEC',fill_method='ffill')\n",
    "appl_q.ix['2009':].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 移动窗口函数\n",
    "在移动窗口（可以带有指数衰减权数）上计算的各种统计函数也是一种常见于时间序列的数组变换，将它们称为移动窗口函数（moving window function）,其中，还包括那些窗口不定长的函数（如指数加权移动平均）。移动窗口函数也会自动排除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcf673d0>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ab9bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rolling_mean：接受一个TimeSeries或DataFrame以及一个window(表示期数)\n",
    "close_px.AAPL.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=250,center=False).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcfbe290>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd2a210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(close_px.AAPL,250).plot() # 默认情况下，需要指定一个窗口里必须有多少个非NA的观测值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=250,min_periods=10,center=False).std()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1990-02-01         NaN\n",
       "1990-02-02         NaN\n",
       "1990-02-05         NaN\n",
       "1990-02-06         NaN\n",
       "1990-02-07         NaN\n",
       "1990-02-08         NaN\n",
       "1990-02-09         NaN\n",
       "1990-02-12         NaN\n",
       "1990-02-13         NaN\n",
       "1990-02-14    0.148189\n",
       "1990-02-15    0.141003\n",
       "1990-02-16    0.135454\n",
       "Freq: B, Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appl_std250 = pd.rolling_std(close_px.AAPL,250,min_periods=10)\n",
    "appl_std250[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd069270>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb5e1750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appl_std250.plot()  # 苹果公司250日每日回报标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=60,center=False).mean()\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd111f30>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\matplotlib\\scale.py:111: RuntimeWarning: invalid value encountered in less_equal\n",
      "  out[a <= 0] = -1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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p5WSJu632YuY9HCy4lp5PQYmqrC0yOZfwpNy7ZhL2JrUWelmWjwB3FpDoD1yRZTlKluUSYBNwr6TwIbBLluVzlY0nSdJCSZICJUkKTE1NrayLVrYGa23cjfcsuLsJTcgmLDGHB7S8OrV7RwtkufyE7PbgRHQkmCiEvk44ArG3fY670fYMMBqYLknSU5WdKMvyGlmW/WRZ9rOzq1ga1MjIiPT09LtK+GRZJj09HSOjiulgAkFb5Y/AOAx0dZjaS7uTowPcbdDXlVh77BpqjYxaI/NXUDwD3Nthb353/Y01dDK2slkTWZblL4EvGzKwk5MTcXFxVOXtt1WMjIxwcmrbdTcEgpvkFJWyLSieMT3aY2VioNWxrUwMWHRPJ748cIWrKUcpUqmJSS/glfHdtHqd1kBDhT4OcL7tsxOQUEXfOqGvr4+7e9vc7UUguJuJSMolIauQtLxiVh64QnZhKYuGd2qUa70wtivJOcX8FqgEHrwdLRjfhssRV0VDhf4M4ClJkjsQD8wCHq7tyZIkTQGmdO7c9mtNCAQCOBiewvwfA1FrlJCsp70ZH0/vhbcWcuerYvk0b+7t7YClsT5OViZtuhxxVUi1jYFLkrQRGA7YAsnAW7Isr5UkaSLwOaALrJNl+f26GuHn5ycHBgbW9TSBQNCKiEjKZeaak3SwMOK50V3IKSxlcq+O1daaic2J5XDcYWJyYjDSM2KC+wS623RHkiTyS/M5Fn+MY/HHsDayZonvEgx0tRv+aSgl6hLWhqwlICEAX3tfhjsPx7udN/q6+loZX5Kks7Is+9XYryVMdgqhFwjaPrPXniIkPpttTw+psfRASkEKa4LX8Gfkn6g0Ksz1zSlUFaKSVdgY2TDJYxI7onaQUZSBiZ4JBaoCxrqO5aNhH6Gro9tEd1Q9pZpSnj3wLMfij9HJshMxOTFl9i/suZCHuz3c4MVhtRX6Zi1qJkI3AsHdwdmYDI5GpvHqhG7VivzemL1sidxCYFIgKo2K+zzvY77PfBxMHcgpyeHA9QPsiNrBT2E/4WjmyLpx6+ht35tfLv3CJ4Gf0PliZxb1WtSEd1Y160PWcyz+GG8MfIMZXWeQU5JDQEIAmy9v5oPTH5CYl8gLfi+gIzV+JRrh0QsEgnqj0ci1innPXnuKsIQcjr4yospQzY6oHbx69FWczJ0Y0HEA87zn4WzuXKGfLMsEpwXjYemBuYF5Wdtrx17jn6h/eGfwO9zveX/DbqyBXMm8wox/ZjDSZSSf3PNJuWMaWcOKUyvYFLGJmV1n8vqA1+vt2bcKj14gELRenv7lHDsuJjLOqz2rH+mLbhWCHxitePOvTexWpcjvjt7Na8deo1+HfqwatQojvarz3CVJopddrwpt7w5+l8yiTN49+S6m+qaMcxsHQFBKEGuC1zCg4wAe93q8nndbe1QaFW8cfwMzfTNeG/BaheM6kg6vDXgNIz0jNoRuoJNVJx7q1rh7VgihFwgEdSY6LZ8dFxPp2t6cPaHJfLk/kufHVNx3QZZl3t95CTtzQx4d6FrpWHG5cbx74l18bH1YOXJltSJfHfq6+nw6/FOe2vcUrxx5hdhcJaVyVdAqVBoVR+OPYmtsyySPSTWOVVBagIGuAXo6dZfIH0J/ICQ9hI/v+RgbI5tK+0iSxAt9X8DD0oMJ7hPqfI260qxliiVJmiJJ0prs7Oq3/BIIBC2LPaFJAHz/uB/Tejuy+tAVrqXlV+gXFJvF+etZPDuyMyYGeqg1ai6kXmBn1E4upF4gMCmQR3c+iozMiqErMNE3aZBdJvomrB61Gr/2fnxx7gu+OPcFzubOHJxxkD72fXjrxFsEpQRVeX6hqpAPTn/AwF8H0u/nfrx8+GWK1cW1vn5UdhSrg1Yz2mU041zHVdtXkiSmeU6r94OtLogYvUAgqDP3rz5OsUrDjmf9ScgqZOT/DlFUqmFoZ1s+mt6zrDLky39cYOfFRE69PhpDfZkn9z7JmaQz5cZyNndm5ciVdLLS3qIpWZYJSw/DRN8EJ3Mn9HX0ySzKZPau2WQVZ/HjhB85Hn+c4NRgrmZfxcbQBkmSCEoJokhdxH2d78NI14hNEZuY4DaBD4Z9gI6kU+1WomqNmsd2P0ZMTgzb7t2GrbGt1u6nKkSMXiAQNAopOUWcu57FCzdCNQ5Wxvwwtz+/Bcay62ISIz45xO9PDsLDzpR/ghO5r7cDZoZ6fHLmE84kneElv5fwd/TnZOJJCkoLeKT7Iw325O9EkiS8bL3KtVkbWfP1qK+ZtWMW9267FwBHM0fcLNxIK0xDLau53/N+xrmNo0/7PgC0N23PF+e+IDgtGH0dfZILknm0+6M87ft0hTTOny/9THBqMCv8VzSJyNcFIfQCgaBO7AlLBmC8961SAgM82jHAox1LR+Uz49uTvLb1IpN6dqSwVM0YHyM+PP0hP1/6mZldZ5ZNiHpYeTS57c4WzqwatYrfIn5jgvsEhjkNq7b/E95PYG1ozeG4wxSpinCzdOO7i99xJesKnw7/tCyGH50dzcrzKxnuNJxJ7jXPATQ1InQjEAjqxOy1p4jLLOTAi/dUGsbYEZzI07+eA9R06naQDN1DqDVqHuzyIMv6L9PaqtDm4qewn/jozEcs7LmQZ3o/Q0RGBEsOLKFQVciWqVuwN7FvMltaRehGLJgSCFoX2QWlnLyazhP+7lXGqif6dODtKT3Ynfgdofn7eKDTA8z3mY+Teduoyjq7x2wupV9iTfAaNoRsQC2raWfUju/Hft+kIl8XmlXoZVneDmz38/Nb0Jx2CASC2rE/PBmVRq60AmSxupiQtBDaGbXD1C6Q0CvbmdV1Fq8PfL0ZLG1c3hr8Fj52PiTmJaKro8vsHrOrTKVsCYgYvUAgqJGC0gJicmLYFZJFewtDejlZAUqmybH4Y2y7so3jCccpVBWWneNr58vL/V5uLpMbFUNdw0Zf5KRNhNALBIIywtLDOJt8lkkek7AxskGtUfNn5J+sClpFRlEGstoId/fBXM5yIjIzkq/Of0VCfgJ2xnZM7TSVwQ6DyS7OxtLQkqGOQ1tcNcm7FTEZKxAIAAhNC2XO7jkUqYuwN7ZnksckAhIDuJRxCb/2frSXhrIt4jBm1pco1hQB4NXOizlecxjlMqrVT7K2RsRkrEAgqDWlmlLeOPEGloaW/G/Q//j4zMesD12Pm4UbH/p/yHi38dz/9UkcSzuxeXovfrz0I55Wnox3H98k1RcFDUNMxgoEArZc3kJkZiSfD/+cYU7DGOY0DI2sKRPxk1fTCYrN4p2pXlgbW7O0z9JmtlhQF8SjWCC4yylRl/B9yPf0tu/NSJeRZe23e+qbz8ZhbqTHzH4VywYLWj5C6AWCu5iC0gLeOfkOSflJLPZdXGlufFGpmn9Dkxjn1QEj/Zaxe5OgboisG4HgLiSnJIfVQavZGrmVAlUBT/V6ioEdB1ba90B4CrnFKqb0cmhiKwXaQgi94O6gIAPOrge3YeDcr7mtaVaS8pNYtG8R0dnRjHcfz8yuM/G196WoVM2ukET6utjg0u5WkbHfA2PpaGnE0M4tq1CXoPYIoRe0fUoK4LdHIeY46OjB/H3g0Lu5rWoWjscf57Vjr1GiLuHrMV+X8+JXHohk1cGr2Jga8NfTQ3C2MeFMdAaHIlJ5brRnlTtICVo+YuMRQdvnwHKIOQETPgJTO9jyJKhKmtuqJiWlIIU3j7/Jon2LsDGy4eeJP5cT+dyiUn48EUPX9uYUlqiZs/40Men5vPVXKA6WRiwc1vSVJgXaQ6RXCto2+ekQuA58H4EBT4J5R/h9Nlw9AF3HN7d1jU5uSS7fX/yeXy79glpW81iPx3i699MY6xmX6/fn2Thyi1V8NL0nSTlFPP9bEPd8fAiAlQ/1rnKvV0HrQPz0BG0TVQmEbYNL20FVCIOfUdq7jAdDS6W9DQt9VlEWO6/t5OdLPxOXG8ckj0ks9l2Ms3nl6ZGbz8Xh42hJL2cregHfPebHnPWnGeZpx+SeHZvWeIHWEUIvaFvEn4NfZ0JhJmhKlbbuU8C+m/J/PQNF4CN2gPoL0K34J6CRNey8thOVRsU4t3EVvF+AiIwILmdext/RHysjq8a8ozpxJfMKG0I3sDt6N8XqYjytPVk/fj192/et+pyUPELic3hjco+ytiGdbTmxbBQ2pgZVliMWtB6E0AvaDpnR8Ptjyv/7PQEuA8GsAzj2Kd+v+xQI/k2ZnPW4p9yhIlURb554k13XdgGw8vxK3hr0VtlORLIsszt6N/859h9KNCVYGFjwzuB3GO06ukqzZFnmQOwB0gvTmeg+ETMDM63d8u0k5iWyYO8CMosymeQxice9HqeLdZcaz/v7QgKSBFPu8NztzA0bxU5B0yOEXtA6URVD0C+QHQ+lBWDtDvveVo7N2Q6OVXuwdBoF+iZK+OY2oVdpVDx36DlOxJ/gmd7P4Gvny4rTK3h6/9MMcRjC9C7T2Xx5M8cTjtPTridLfJew8vxKnj/0PC/5vcRjPR6r4P0WlBbwxvE3+DfmXwA2hm/kf8P/h4eldic3c0tyWbx/McWqYv6c+metN9qWZZl/LiQwyKMd9hZGWrVJ0HIQ1SsFrZN978CxTwFJSZnUlIJdN3jkD7ByqdA9KT8JtazG0cxRafhtNnLcGZIXHiRXlUehqpDNlzez9cpW3hz0Jg92eRBQygNsDN/ImuA15JTkYK5vzmLfxczsNhN9HX2K1cW8cuQV9l/fTwfTDkz2mMyMLjPoYNqBUk0p8/+dT1BKEEv7LKWLdRdeO/Yaxepifp/8O26Wbg36FsTnxVOqLkVXR5cXDr3AlcwrrB69mkEOg2o9xuXkXMZ+doTl93nz6EDXBtnTqBRkwIH3wO8J6ODd3Na0GGpbvVIIvaD1kRkNX/WHbhNhypegUZF14VcMuk7AwMoVjawpVwc9IiOCBf8uIKckh+ldpnN/55mkBexky/Uv2W9qUm7ouV5zecHvhQqXzCjK4ETCCQZ1HEQ743bljpWoS/jr6l8ciTvC4djDyMg4mztjbmBOWHoYHw37iAnuEwCIzY1l1j+zcDRzZMP4DZjom1S4VnUUqgqJyo5ib/ReNoQq29gBWBhYsMJ/RY2bXd/Jl/sj+WzfZU69Oqple/R/zIXQLWBsA4tPgnnFHa7uRoTQC9ouv82GK/vgmbPI5h35IfQHPj/3OWpZjY6kg0bWMKDDAJb2WYqBrgEL/l2AWlbja+/L8fjjijhq9DCQSpln3o3OfRdgrGeMq4UrrhYN82rDM8I5Fn+MM0lnSC1MZbrndB7u/nC5PkfijvDMgWfo36E/X436CkPd6mPheSV5rA9dz+5ru4nLi0MjawCY6D6RnnY9SSlIYWbXmTiY1b1EweSVRzHU0+XPRYPrfG6TEboV/pgDPjOUTCrv6TDt6+a2qkXQKoT+tnr0CyIjI5vNDkErIvoYbJgEI/4D97zMyvMrWRO8huHOw/Gx9aFIVYSMzJbILWQUZQDQ3qQ968atw8XChY/3nWHNuc0YmaTyemYED5rnofvMWaU8QvoVGP026FfMstE2f1/9m9ePvc44t3F8POxjJEkiNjeWs8lnsTGyob1Je6yNrNl/fT/fXPiGjKIM/B398bb1prNVZ7xtvesl7LeTlleM3/J9vDS2C0tGemrpzrRMcS582QcsHeGJfUr45vjnsOBA9fMw2iA5VHnIeIwAl0FQkgdxZ5RJfgPTxr12LWkVG4+IBVOCOhPwNZi1h8FLuJR+ibUX1zLFYwrvD32/3ETofJ/5/HLpFzKLMnmsx2N0NOtIqVrDn6dzGGB/P69O7MbPq95hlnotvHdbKMbCEYY8WztbZBnqmXo4tdNUUgpS+OLcF+yJ3kPf9n0JSw8rt+fqTfq278uqUavwttVubPr4lTQAhnraaXXcelNSoCxka9f5Vjrs8S8hPwUe2qSkwvq/qEzC738XHvur8WzJSYR146E4B458DCa2yqR/aYFSPuOxv8HIovGur2VE1o2gRZFbkou+jj5GerfixUn5Saw4tYIj8UcwUZXg49CRe+MO8c2Fb7AytOKV/q9UyHYx1TdlYc+F5dr2hCaRlFPE8vu88XKwJLr9GC7mnMLHrQNyn8dJ+PcLLI9/i9ngZ2oW8APL4cQY9CxkAAAgAElEQVRKcB8G930Dpu2q718Jc73mcjXrKpnFmcTnxjPSZSRzvOZQpCoipSCFzKJMPKw88Gvv1yi57CevpmNhpIePo6XWx66Sohw4+D7En1WynwYvAUNziNwHfz4BRVmABL0fUTzpk1+B1/3gdMN7N7KAIUvh3//Axc032qzAdTAY1G2+o1r2vwOqInjqGKSEQ+S/kJsInUbAgfdhx4vwwHeVn6vRwLkNcO4n5fdj5BuVrtdoSoTQC1oE2cXZfHTmI3ZE7cBQ15CRLiMZ7DCYs8ln2RG1A0mSuM9+IDqXd7Fb35TjR/4PPUmPVaNWYWlYs1Bl5JfwzvYwXGxMGNHNHoBRvbsyZcdr7B7tT0JWIbtS+/Cx/hrl9dy5f9WDJQTBkU+gvTdEHYbtz8KsX24d12jg3A9KCKjnzCofGro6uqzwX1Gn75M2CUnIppezVdMVK0u6CL/Ogpx45Xt3+EMI+hU8xyihM3svuHeVUpfo9Bo4/zMYWsCoN8uP028BXNikPBhu0q4zzNsDplqosBl3Fi5shCHPQQcf5avng7eOq0rg8AfQdw64DSl/rizD7lcU+y2dlTBT4gWYvg5MbG7106jh8h7Q0QWnfuWPNQJiMlbQ7KQVpvHwjodJLUhlVrdZFKoK2Ruzl5ySHPR19JnaaSpzvefiemodnPyKgudDiSnJVGLZpu1rdY3/bLvIzwHXWTO7L2O9lIyNlNwiRn1yGGtTA1Jyi9Atzee04WKM7DuhO+496FzFIqjfHoVrR+G5YDj1reKhLjwMDr7K8dPfwc6XlP97PwBTV7aYmO5NStUavN7cw9whbrw6sXvjXzDrOnw9VPk+zPhRKRV9/RT8tViZG/F+QMmgMryxmKwwSznHygWMK1l5nJukePQOvlCQDlsWgm0XmPQpOPnVO6SGLMPaMZAZA8+eU9427qSkAL7oCfY9lPDRzWvJsvK7cORjGLQExi5XHlY7XlBqLI3/QHkwhPwJZ9ZCcohy3n1fg+/DFa9TC1rFZOxNhNDf3bxw6AWOxB1h3bh19LTrCSiLl0LSQnAwc8DeRPHA+XoIGFvDnH/qNP6VlFxGf3qEuUPceGuKV7ljZ2MymP7NSWQZHuzrRM75ray0/g2D/ASY/Dn4zS0/WEo4rB4IQ5+H0W9BUTZ85q280s/4EUqL4EtfsHKFLmNh/3uKxzZnh1J+oYUQnpTD+M+P8sUsX+71daz7AKpiiNwLeoaKN5oSrsTVK5sglWX48V4lXPPUUbC5bbGYqkSJwVs61f9mQLHl98eUGLq5A4x4Ffo8Vvdxgn+HLQuUN4vej1bd7+Rq2POqEkYa8BSoS+HoJ3DuR+W6U7689QCIPQNbF0JG1K3z23sr59l6gk0nMKvfPEmrmIwVCA7HHmZvzF6e6f1MmcgD6Ono4Wvve6tjToLiAY1+p87X+OXUdQx0dVgyonOFY31dbTjy8gii0vJxtTFh+Nl+bB82mwcuLVXitD7Tb3l1sgx731S80kFLlDYjS+i/AI5+CmmRStpnbiLc/x24+yuC/+cTcOILGPZynW1vLELjcwCZPkTAgQ3KCmG3oZV31mgg9pSyKM11iHKP+9+D5Ivl++nowSOblYfe7QSug2uHYfJn5UUelIdfQ0UelPDP86EQ/g8c+hD+flbxoj3H1H6M/DTY+xZ09IVeNXjYA56ClFA4/oXyBYAE/i/BiNfLv1E494PFp5TvW9wZ6DxK+T42YQ0hIfSCRkOlUXEm6Qx7ovdwKPYQtsa2uFm6Mb3LdPp36E94RjhvnniTrtZdmes1t/rBIpUSAlWGU6pAlmV2hyQxvKsd7cwqz1d3tjHB2cYElVqDvq7ElcxS5YHy/UhFMB74HiQdOPM9RO6Bse+Xn3wdsEjx8H5/HLJjlQk4d3/lmM90uPQ3HP4IukxoMas6I+LTWGmwGudtx5WGIx8pk5+TPwMbd6VNo4bLuxVRT72ktBmYKWmGFk5K3NnYRplAtXaHrU/B1idhyRnlAQjK4rZ/3wCP4dC3hp9xQzGxUbxpnwdh9SBlHqW2Qh9/VlmUVZAGM38CnRq26tDRUbz+njMV8dY3hY69wLWKVcl6BsoCv24T63ZPWkIIvaBRCEoJ4uUjL5OUn4SxnjH+jv4UqAoITApkT/QeJCRkZCwMLPjA/wP0dfWrH/D8L0oMtr1X9f3u4HJyHonZRSwdVXOeuJ6uDi42JlxLzVeyPEa8rsRcLRyUOPGB5eA6FAYuKn+imR0MfQ4OrVC8yClflD8+8X8Qc1IJCSw4oL08/ZwExQvt4FOzd6gqufGwlMG8I/eGPI+XTjDcswz6L1SKvB1aoYTH/OZC2mUlhl6crYQW7vtGuUb0UWU7Ru/74c6f2X2r4buRSlbKxI+UN4G/ligPyalfNZ0Hq28M/ebDv69Dchi071F9/2tH4KdpStru3F1KjL+2uA9Tvlo4QugFWiezKJMXD72Ivq4+nw3/jCGOQ8pK/d4sF5Ccn0wH0w4Mdx6OrXElmRKqYiVbob2XEtuMOw3j/ltnsTgUkQLAPV1rFwN1tzUjKi1P+XDP/0FeipLiBzdWZH6rZErcyfBlSuijnSeY3zFBbGanTLj98gDsflXxmiu7j6Ic2L1M8X57zqjaSI0GTq5UPG1NKbj5Kw+lpIuKB95tEvjNu3WNy//CP88p2S436IIuvzm/wcwRNyaNBy1WqnrueFG5X2s38J4G7vco7TdFvdesqu1y7KNUDT29BnrNhOsByoNhypdgVXkd/Eaj50wlzHbxd2j/dtX98lJh8zzlYfbEHmUOqA0ihF6gVWRZ5q0Tb5FZnMmvk36lm023cscNdA3KCoZViVoF6ydCfKAinJZOyqtxr4fqbM+hiFS6dTCno2XtvGgPO1OORKai1shK2uG495XsD1NbmPhx9fnQVcW4ATxHKxN3x79QPPEpX4DFbWWBVcWw8SGIOaYsCLL1rHxf29JCJUQStg26TlK8z5OrYP2NTVRMbOHqfkgJU9ISw/6Gv5coGSKTPwdja9KTY5m6OYenvO6IpVs5wyO/K7boGtTPAx/1JoTvgB+mKuN0nVS/SdGGYmanxMKDNioPwareGA++r+xd8Pj2NivyIIReoGUOxh7kYOxBXuz7YgWRrzUhfyoi332KIhrpkUrJgzrmGmcXlnImOoP5/rUvCexha0qJSkNCViHONiZKVskjv9f1Dipn1NtKffwD78G6sUo45GYe9qEVishP/ASO/g+2LYYFB0HfSBH3lDAlJe/KPshLhjHvKbtmSdKt0IuNh+KB731D8crPrAVk6DQSZm1UxgLO5boQTyA9OlaxslOvAXXojSyVe9j6pLLBy9SVTTrpWI5+8+HXGUo5au/7Kx5Pv6pkyfR7AuybIMW0GWlWob+t1k1zmiHQEiXqEj4J/ITOVp15tEc1qWnVoVErecj2XvDgj5CboMR4Lepe1+XI5VRUGpnR3e1rfY67rZLvHpWWrwi9NtHRUUIkLgOVXbA2TIQZPynCevwL6D1byeCxdoNfpitpnNauEH1cCdHoGSlhmV4PK28INzE0U8TqJuPeVx6SVw/eOLagTOQBwhJykCTo1qGSHHFt0H0ydI+vuV9j03m0kvV05vvKhf7UN8rvlv+LTW9bEyNq3Qi0glqj5vVjrxObG8u3Y75FT6eev1qhWxUP/sEfFGFsQOrdwfAUrE306e1S+1dydztF6K+l5nFPl0aqAePYRym1+/MDykbloGRsjPuv8n/PMfDAWsXLjzqkhKxcBinCZVnLnHeXgcpXJYQlZuPezhRTwzb+Qq+jqzw4//2P8n30GH7rWGGWMsHvM/2uKHncxn/SgsamUFXIHxF/8FvEb1zPvc7zfZ9nsEM9S95q1Eoaol136D61QXapNTKHLqdyTxe7Oi3xtzMzxNxQj2tp+Q26fo2Y2sKsX2HNcMXrnr2tfJEsn+mK955xreaskTqg0cicjcliSOe61+ZplfSbr+Tx//0szN8HZjfe7s7/DKX5Sj78XYAQekG9KSgt4Ik9TxCSHoKvnS9Lei9hvNv4+g8YsgXSIpT87JrymGsgKDaLjPwSRnavXYmEm0iShLudKVGNLfSgeOeLA5Qc68qW2usba1XkAS7GZ5OWV8zwWmYhtXr0jWHcCtg4E74bpazMNTRXMoNcBt0qW9HGEUIvqBeyLPP2ibcJywjj8+GfM8p1VP0Hy0tRVqFe+E1ZGt7jvgbbdzA8BV0diXvqUYLXwdKYq6l5DbahVtSj6mVDOBiRgiTBPV1qP2/R6uk6Hh76TRH7nS+DuhiyYmDse81tWZMhhF5QL/64/Ae7onextM/Shom8LMMvD0LKJegzG4a/Vnmeeh05EJ5CXxdrLE1qWIhVCTZmBpyJLmmwDS0NjUZmR3AifVyssTFtOXV3moSu42Hws3DiS+Wz/0sNDg+2JoTQC+pMREYEH57+kCEOQ5jnPa9hg10PgMQgZRGRXwPHukFidiFhiTm8Mr5+6Z3tTA3ILChBo5HRaaoSvk3AZ/suE5mSxxez7o5wRQXGvKtMyBpZ1m31axtACH0dSM4pws7MsE398deVjKIMXjr8EpaGlrw/9H10pIbF0jnzPRhaKisZtcSaI1FIEoz3rl82hbWJARpZycO3biOe7+6QRFYeuMJMP2em9mrYFoStFklSFlHdhdyVQp+aW8wvp2J4eIAL9uZGNZ8AbDp9nWVbLuLvacu3s/tiYtD2v3URGRH8cfkPsoqz8LXzxdrImtVBq0kuSObr0V/TzriB8eW8FAj7S8mM0FK9drVGZvPZOO7zdSzLia8r7cwUcU/PL2kTQh+XWcDLm4PxdbbinXu9GmW3KkHLpu2rVSX83+YLHIxIJSg2i/Vz+tX4i59frGL5jkvo60ocu5LGhC+O8s2jfele1crCVk5SfhIfnfmIvTF7MdQ1xMbIhj3RewCwNrTm+7Hfly8hXF/OfK8sBOo3v+Fj3eBSYg65RaoGZZXcjF9n5Lf+OH12QSmLfzkHMqx8qDdG+g2f/xC0Pu46oc8rVnE0Mg1jfV0ORaSyNyy5bMehqvgrKIG8YhV/LhpEYYmGl/64wBMbzrD7+WFYGFU+2ReRlMszG89hZWLA7IGuTGnBr8vF6mJ2Ru0kND2UhLwEjiccR0fSYbHvYh7u9jCWhpYk5yeTWpiKm4UbZgZmDb9oQYay0Xf3KWCrvZXRAVHpAAxwr//bhrXJTaEv1opNzcnyHWGEJuTw9SN9tL/SV9BquOuE/sy1DFQamXVz+vLC70H8dSGhWqHXaGS+PxaFl4MFfVyskSSJrx/tw7TVJ/jpZAxPV7KZhSzLLN10nqTsIopVGp7ZeJ6colIeGeDamLdWL84ln+Plwy+TUpiCub45FoYWPOH9BNM6T8PZ4lbFwfam7Wu9bV85SvLh+kmwclMEXV2qFJja85pS13z4q9q7GZQNr13bmdDBsnYhucqwupGpk1Oo0pZZzcK+sGT+OBvHU/d0qtGZEbRt7jqhP34lDQM9Hfq72zDWqwPbzsdTVKqu8pV276VkolLzWflQ77IQT28Xa4Z2tuWHE9HMHeJWIV4fEJVBeFIuHz3Qkwf6OvHwdwF8tvcy03o7tqjY/qX0Szy17ynsTez53v97+nfor9347bUj8OcCyEtSPtt2Veqc23VTNrK4Z1md68tXR1GpmhNX03nQr2E7FlkY3xD6olJtmNUsRCbn8vSv5+jR0YIlI0UtqbudBqZMtD5OXE2nr4s1Rvq63NPFjoISNaEJ2ZX2lWWZbw5fxdnGmAl3ZHA8N9qTlNxi1h+PrnDed0ejsDE1YKqvA7o6Ev83vhtpeSVMW3WC+T8EtojYb2pBKs8ceAZLQ0s2jN/AgI4DtCvyGVHw6yxlWf9Dv8HIN5Tqkz7TFU+++xStb6138mo6haVqRnZr2GIgMwM9JAlyilqnR1+q1vD0r+cwM9Tjh3n9MWvrNW0ENXJXCX1GfglhiTlldT56uyi7y5+LySrrI8sygdEZhCZks+VcPOevZ7FwWCf0dMt/q/zcbLinix0/nIimRKUpa49IyuVAeApzBruVvSX0dbXmqXs6kZBVyL5Lyaw6eKWxb7VarmVfY9G+ReSU5LBy5MrKN/5oKIc+UNLZZm9VFqsMewnm7Va25Xs+BGb+XH1t93qw5Xw8pga6DPRoWDaQjo6EmaEeOYXa8+hLVBqupORSrFJrbcyq+PNsHJeT8/jv/T7YmTeg5LCgzXBXPepPXlUm6gZ1UoTN3twIZxtjzsZksgAlNe+jPeF8e/jWbu1eDhbM6lf57jiPD3Zl3oZADkakMO5GDPTbI1cx1tdl9sDy8fhlE7qxbEI3ntt0nk2nr/PsKE8sjeu+arM+XM68zKbwTYSmh1KqKSUyMxJTfVM+H/55/WvGV0duklK3pt8T2tn4uRZEpeaxIziBBcM8tJJZYmGkr5XQTXZhKS/+HsS+S8pOV/6etvw4T8shstvIK1bxv72X6e1ixdge9ZhTEbRJ7iqhD4hKx9RAl15OlmVt/VxtOBKZiizLrD54hW8PR/FAHyeGdbGluFTD6B7t0det/MVnmKcdtmaGbDkXxzivDiRkFfJ3UAKzB7lWmX8939+DbUEJ/HrqOouGd2qU+7yJWqPm48CP+eXSLxjqGtLLrhe6ki5T+05lksck7EwaqbBV4HrQqJQNMZqIDSei0dfVYf7Q2m8yUh0WxvpVTsaq1Bqi0/OJzShkRDVhooISFY+tPUVoQg7jvNpTWKrhyOVUAqIyGNSpcWrc/HoqhtTcYr55tK/IlxeUcVcJ/bnrmfi6WJULw/Rzt2HL+Xi2BcWz8sAVJvfsyP9m9KrVeHq6Otzr68C649cIiEpny7k4ZOCJoe5VnuPtaImfqzU7LyY2qtDLsszyU8vZfHkzD3d7mJzEERw+l09esYpF8/pjZ9JI26apiiFwLXiOhXaN+yC7SYlKw47gREZ3b6+1UIWFkV6lHv3paxk8seEMucXKQ2DdHD9GdqvoORer1Ny/+gThSbl8/UgfJvh0pKhUjd/yffwVFK8Vod8TmsR3R6KYPciVqb0cyClS8cOJGPq6WtPXte1uiyeoO1qP0UuS5CFJ0lpJkjZre+yGUFCiIjwplz53bEIx3qsDJga6PP/bBSyM9Xl7at2yQB70c0KWYdaaAH4PjGOGnzNO1tXnK/dxtSYiKZdStabafvVFlmU+O/cZmy9vZr7PfPqYzWVjQDputqbkFKkad44gdCvkp8LApqvzvSskkfT8EmZUEWKrDxbG+uTeMRkryzLvbA9FkuCxQa7o6kisORJV6fmbTscSnpTL6hsiD2Ckr8vIbvb8G5aMWiM3yL5TUeks+fUcgTGZLN0UxIxvT/LI9wEk5xTx6oRGCMcJWjW1EnpJktZJkpQiSVLIHe3jJUmKkCTpiiRJywBkWY6SZfmJykdqPoLjslFr5ApCb21qwMvjumJjasCXD/lia1Y3j7BbBwv+XDSID+734aMHevLWlJrrh3s5WFCi1nAlpepSuIWqQg7FHuJE/Ak0cu0fCNnF2Tx/6HnWh6xnRpcZPOP7DF/si8TDzpQf5/Xn2ZGd2R+eQnRj1FuXZWURlG1X8BhRc38t8UvAddxtTfHvrL1JZXOjipOxB8JTCE3I4c0pXrx7rzevjO9KQFQGgdEZ5fqVqDR8fyyKPi5WTPTpWO7YeO8OZOSXcOaOc+rC+euZzFl/BmcbE86/MYY3J/cgIauI6LQClt/njZ9b3fbWFbR9ahu62QB8Bfx4s0GSJF1gFTAGiAPOSJL0tyzLYdo2Uhucu54JgK+zVYVjc4e4M3dI1eGWmujrakNf15r/uOLz4jmXfI7wwlj0LDL4ISSOrukG5Jfmk1+aj76OPp7WniTkJfDzpZ/JKlaygTpZduJFvxfxd/KvcuyorCj+ifqHzRHbyCrJYH6PpTzr9wQno9IJS8zhwwd80NPV4dFBrnx9+Crrj1/jnXu9633PlRtxUKlEOenTJtsQOim7iNPRGbwwpotWi81ZGuuTfZvQl6o1vL09FBcbE+71VVY5PzLAlR9OxPDMxvPseNa/rHTC1vNxxGYU8k4lb4fDbux4dfxKWr2ygzQamZf+uICNqQG/LRyEtakB84a6M6+acKFAUCuhl2X5iCRJbnc09weuyLIcBSBJ0ibgXqBWQi9J0kJgIYCLi0stza2a3KJS3v47jPxiFS+N60pn+/LL9M/FZOFha9osRapK1aW8cvQV9sbsLWszdoQdCcoXgImeCaWaUko1irgMdhjM4z0eJ70onTXBa1i8fzEjnEfwf/3+DydzJZOlRF3C0fij7IvZzz9R/4AMqvxOFKc+yK/xbszxVrH+eDQ2pgbc66vsNWpvbsS03o5sPBPLvKHuuLbTTjExVCWw902wdIHe9dwYvBbIssypaxmYGerh7WjJjouJABU854ZiY2JAXrGKEpUGAz0d9l9KJjajkO8e8yubnDc11OObR/sy5atjbDx9nadHdEaWZX4KiKFre3NGdK04UWtmqIe3gwWnrtXPo98ZksjVGwv4ROqkoLY0ZDLWEYi97XMcMECSpHbA+0BvSZJelWV5RWUny7K8BlgD4OfnV++AZXZBKQ99F0BYYg4Ahno6HI1M5ccnBpRNSMmyTFBsZqPtqiPLMjE5MVzKuIRXOy9cLG49uJLyk1gesJzDcYdZ2HMhY13H4mDmwOTV/+BhY8tXDw3GWM8YHUmHEnUJcblxmBuYl8uIGec2jh/DfmRN8Bomb52Mm4Ub5gbmXMq4RLG6GEk2oCRjCI7SJPycnZk8xoHH15+m//v7KFZpeG60Z7mUw+dGd2HXxSQeWhPAyod71+ptpFrUKtj2FCRdVDb11tOeAJWoNOjqSOjqSGTml/DC70EcjEhFT0di+zND+SUghl7OVhUe7A3lpkOQVVCCvYURG0/H0sHCiBF3FEvzcbKkl7MV/4Ym8fSIzlyIyyYkPof3qqkS2d/dhh9OxlCsUmOoV/tU0KDYLF7ZHEy3DuZaf7AJ2jYNEfrKfotlWZbTgSabiftoTzhhiTlM6eXA7IGuuNiYMHnlMb7YH8mP8/oDEJ1eQFpeidYzEYpURawNWcvvEb+TUaR4aLqSLhPdJ+Jl60VKQQobwzdSoi7h9QGvM6vbrLJzu9h4EpOej6n+LY/aQNcAD6uK6YEGugbM95nPFI8p/HH5DyIzI8kqzmKy+/38edQUM7rzwSSfcn/8a2b78Z9tFxncybZCPR4HK2M2LhzI4+tOM+PbAN6/z5uZ/Zzrn463exmE/Kls7ODV8G0AQVnT8Pi60xy7koatmSGrHu7N8h2XiEjK5dmRndlwIpoJXxwFYPUjfbRyzdu5GYZJyS2mVCNzJDKVZ0Z0rrBwDmBsj/Z8vCeC5JwifgmIwcRAl/t6O1Y5dl9Xa747eo2whBx6u9T+d/KbQ1cx1Ndlw9z+ddrwXCBoiNDHAbenOTgBCQ0zp24ERmfwy6nrPDHUnTcm35oEfaCvI+uOXaOwRI2xgW7ZxFc/N+0JfURGBM8dfI64vDhGOo/E38mfzlad2XltJ9uvbmd71HYAxriO4YW+L5SFW27iYWvK4cupqDVyrf9o25u2Z0nvJWWfV+6PJDfrMltfGFzBox3Toz2ju9tXKd7ejpbsXOrPkz+dZdmWi+QWqVgwrB456LFn4Mx3MGARDFla9/OrYG9YEseupNHTyZKwhBxmrgkA4POZvtzX2xEHK2OWbblItw7mFcpTaINeN+Zyjl1JI+9G9s2DfpVn9Yy5IfSf74vkn+BE7vV1wLyKqqYAvs7K7+H561m1FvrCEjWHLqfwYF/nBhVsE9ydNETozwCekiS5A/HALODhugwgSdIUYErnzvUruhQYk4mTtTEvjOlSrn2gRzu+PRzFueuZDOlsS2B0BlYm+nSyq93rfXZxNgGJARjrGdPOuB3tjNqhp6NHO6N2SJLEtexrLPh3Afq6+qwdu5b+HfuXnetr78uy/svIKs5CpVFhb1J5uMjd1pQSlYaErMIqy8deScljR3AiM/o50dHSuNwxtUZm05lYhnRuV2XYoiYPvb2FEVsWDWbWdwH8fCqG+f7udffqj3wMJrYw8j91O68G1h2PxtnGmK2LhxCVmscbf4Wgr6tTtjvSzH7OGOjp0MvZqlEWBjlaGdPL2YoPdoUDMM6rfZU/J097M3o5WbLx9HXszA158p7q1w90sDSio6UR52Ozqu13O4cvp1JUqqn3rlmCu5taCb0kSRuB4YCtJElxwFuyLK+VJGkJsAfQBdbJshxal4vLsrwd2O7n57egbmYrPHVPJ2YPdMX0jqJN/dxs0LuR2aAIfSZ+rtaVZmUk5iWSXZJNXkkeV7OucjzhOAGJARSqCiv0dTF3oV+HfhyNO4okSawbtw5Xi4qlh3UkHWyMqo97d7ohzldS8yoVkNTcYqZ+dYyCEjVbz8ex/Zmh5bzEI5dTic8q5LWJ3au9Tk3o6EhM6+3Iq1suEpqQg7ejZc0n3SQzBiL3KKWGDbUXI49Oy+f0tQyWTeiGro6EZ3tzNi0chCzLZaIuSRL392nc8gorpvnwyb8RdLY34+nhVTsjkiTx25ODOH4lDR8ny1rtWtbbxYrA6Ixy91Qd/4YmYWmsT393kTopqDu1zbp5qIr2ncBOrVpUR+4UeVAyG/q4WHMkMpW5Q9yJSsuv8NodkRHBx2c+5lTSqXLtjmaOTPaYzJROU5CQyCjKILMokwJVAf9G/8vB2IN0NO3IO4PfqVTka0vnG28XV1PyKs3OWH/8GoWlat6c3IN3/wlj9aGr5Ta7/uXUdWzNDBijhXom47w68J9tIewKSayb0IdtU/7tNav6fnVkV4hS1vjOzVqaekl/DwcL1s3pV6u+Rvq6jOpe+5/F4E627LyYRFRafo1vmnnFKnaHJjG1l0OV5TgEgupo1hIIDQ3dVIe/py3/23uZrefjABh825LzlIIUntz7JE8FJTgAABY8SURBVABL+yzFzcINE30TnM2dcTJzqlJQZveYrTX7rE0NsDE14GpqxUVT4Uk5fH/sGpN8OjJvqDshCdmsPXqNuUPcsDc3IjG7kAPhyTx1TycM9Br+h29jasAgj3bsvJjES2O71l5QQ7aAQx+wdmuwDbdzIDwZb0cLHK2Ma+7cSvH3VBZ3/RWUUCH0eCdrDl+loETNQ/0bnoYsuDtpVvdAluXtsiwvtLSsgxdZSyb27IgkwX93huNiY1LmqZaqS3nh0AsUqApYO24t833mM9p1NIMdBuNs3oDMk3rQyc60wurYTaevM+Obk1gY6fPWFGXBzTMjPSlRa/j9jJLN+tuZWGTQ6h/+BJ8OXEvL51Jibu1OiD2jLI7qOVNrNgDEpOdz7noWwxspFbal4NrOlEk+Hfnm0FXOxmSwYuclxnx6mBGfHOLPs3Fl/XKKSll/PJqJPh3KJogFgrrSZt8DO9mZ8dqE7gz0sOG/03zQ1ZGQZZkPTn/AhdQLvDfkPTpZNU3RrarwcrDkYnx2WT376LR83vgrBHdbU357cmDZghh3W1OGdrbl11PXKSxR89uZWPw97bS6B+g4rw7o6khlb0A1cvQTMLLS+uKoL/ZFYqCrw+xBLW/bRW3z9lQvjA10eeDrk3x7JIqOVsaUqDS8tPkCSzed582/Qnjku1PkFqtYXM0cgUBQE226euWCYR5lKYPZxdm8efxNDsQeYK7XXMa5jWtm65SFMxtORBOSkI2vkxWv/BmMga4Oax7zo71F+Qm9Rwe68tTPZxn3+RESs4v4aHpPrdpia/b/7d15dFXVvcDx7y8JCUNCCBCSGEAIMgUBgSBYQailELGCFbVFW3FEa+tzeLbPPtu3fLVVWt9qK1gHtDi0SNVWKyCWIhJYyhgmgYAhJIphSMBgGDPv98c+kRAy33Nz7r35fda669677znn7vPj5Me5++yzdxTpQxJ5MzOfh749kA6RDdzIk58J2f+ys0a5dBHWGMNHOUf557YD3H553/P2PxTFx0Tx8m2j+SCrgGtHJDMgIYaS8koeW7yLZTsOUVpRRWlFFRMGxDfv2olStYRkG31ecR5/yfoLUeFRpCWmkVecx6LdiygqLeKnaT91ta3dF6Odwac25RVxuLiEDXlFzLluaJ1JbtLgHozp25UNeUV8f3Qvxvd3fyz5Wd/ow3s7DvH21vyGJzJf9QR06Apj7nbtu+9btJWlnxwiPiaKe/w8Tn8gGdk77pyB9tq3C2fOjGHMmTGMyirDhtwvGZgY42ENVSgQY3wbLtUNaWlpJjMz0+ftVJkq5m6Zy6tZr9IurB1llWVUGjt128geI/nZpT9jSDf3JqN2wzf/L4PeXTty8KszVFYZVjw0od4bqIwx5B87Q8+4Dn65lmCMYdozH3OqrIJl/zG+7pmaspfD6zfau2BdukHqcHEJY59cyYyRPXn06sFf35WqlGqYiGw2xqQ1tlzINN2UV5Xz+LrHeSfnHab1m8aDox6kvLKcwjOFJHVKqvfGJa+N7hPHm5m2XfzFW9IavEtWRFxtl69r+w9M6s+dr2VyzbyPuHtCP2aMTD77n0rxAXjnHki4GC5172x+RZbtTnnPhBRN8kr5QVAn+o2HNrK5cDPlleUszV3KoVOHuGf4Pdw7/N6vk1NSdGAP/pR+sW0Xv2lMbyYN9v4/o28NTuC5m0fy9MocHn5rOydLyrn18r5QVWUHLqsopXLGy+w8XEL/hAg6Rvp+CC3fVUBK906uD0ymlLKCOtFnFmTy3PbnABidOJpfjv1lg2O2B6IrByWw47HJDY6N0trSL05icmoiM19cz7MZ+5iZXEjU5pcgbw0Hx8/hR28Wsj1/L4MSY3j//vE+NSMVny5nfe6X3Dk+Rec4VcpPgvpi7L2X3MvsYbMpqyyjYzv/NWn4WyAl+WphYcJ/jaikaunPiXolGxAKLp7NpNV96BhZwug+cWz67Fjzh02oZeWeAiqqjI7hopQfBf0NUxFhEUGd5ANW6QlGrPsJ/SKO8IewWzj90D5mH55ObIdIlt43jud+MAoRWJFV0OimqpzeI3XNkfuvnYdJ7NyeYdp9UCm/CdkbppSPMuYgxz6jYMrzPH06nYnztrI9v5gHJvUnMbY93aOjGNU7jg92N57oF27cz/fmr+fVtZ+dU15aUcmavUeYPCTB1WkAlVLn0kSvzle4Bza8ACNuZtCYdO6ekMKZ8kouS+nG9aPODg43KTWBXQePc7i4pN5NGWN4YfU+wJ6917Sv8BQl5VU6mbVSfhbUF2OVH1RVwpL77R2vk/4XgJ9fNZifTRmEwDln3uMusgNzrcs9yndH1D1kcHbBSfKPnSElvhOZnx8j6+BxUi/o7Hxmx9UZpDcEKeVXekavzrXuGfhiPUx5Ejp1/7o4PEzOa15JTepMbId2rM35st7Nrdxjm3ae/8EoOrQLP6f5Zs/hE7QLF/q4NUG5UqpOniZ6EblGROYXFxd7WQ1V7djnsOpJGHBVk8aYDwsTxqZ0Ze2+L6nvDusVWQUMTY5lQEIM145I5p/bDnC6zE7Nl11wgpTu0a4MtayUql/Q97pRzbB/A8wdAc9eBqufgs8+OvtZZQW8958gYTD1KWhin/Zx/eM58NUZco+eOu+zguMlbN3/1deTo6RfnEhpRRWZnx0DIOvgcQYlabONUv6mbfRtRe5qWHgDhEdC2QkozLLlUbGQMATCIyBvDVz1FHSpexLsukwcYAdXW7WnkH7x0ZRWVGKMHZyr+uLr1KG2j/zoPnG0Cxc+3neUQYkxHD5ewrCeOsa6Uv6mib4tOF0Eb/wQuvWDWUvhTBEYA3mr4cBm+ORNMJUw+dcwZnazNt2ra0cu6hHNO1sPsD73Sz7cU0hEeBh/npXGB7sL6BffiYt62LP2jpERXNKrC+tzixh9oe1pM7yn/ppTyt800bcFm16C0mKY8T506mYfAPEDgLtgzD1wsgAGtGyM/okD4nnpozz2Fp7kritSeHfrQZ5a/in7Ck+eN4H32JRuzPswhx8t3ExMVISOs65UK9BEH+rKTsOG56H/FNtEU5cLLvHpK+4Y35cTJRXcNKY3w3t1IT46il+/txuwk6vUNHFgPPM+zKFrp0ieun543UMhK6VcFdRj3agm2PIqnP7StbHj65IU24Hf1pjxasbInizauJ+vTpd/3de+2qgLu7L0vnH0T4gmKkKTvFKtIaQmHlG1VFbA08Mh7kK4bVnrfnWVoayiquEpCZVSPmnqxCPagTmUZf8LjufbNvhWFh4mmuSVChCa6EPZphehczIMnOp1TZRSHtJEH6qO7oXcDBh1m+0jr5RqszTRh6otr0FYBIy8xeuaKKU8pok+FBUfgM2vwIB0iEnwujZKKY9pog8VleVwYAuUl8B7D0FVBUx+3OtaKaUCgDbehoLifHjrNsjfCBEdoOIMTHkCuqZ4XTOlVADQG6aC3dEcWDAFKkphwiO2O2VMkiddKpVSgcnTRG+MWQIsSUtLu8vLegSt00Xw+o2Agbs+dMauUUqpc2nTTbCqLIe3ZkHxFzBriSZ5pVS9NNEHo/ISePdeO378tc9D77Fe10gpFcA00QebY5/DGzfD4R0w6TG4ZKbXNVJKBThN9MEkbw38406oKIGZb8DAdK9rpJQKAprog0XBLjsVYJcL4YZXICHV6xoppYKEJvpgsfxRiIyGW9+D6Hiva6OUCiJ6Z6y/HPvMDixWW2WFfTTH4R2Quwou+7EmeaVUs+kZvdsqSmHpg7BtoX0/6la46ncQEQU5K+Htu+wyw2605eHtGt9mxhyI6gxpt/u16kqp0KSJ3m0bXrBJ/vL7bV/39c/CnvegXQc4fgi69YPEoZC5ALr0hnEPNry9/Rtgz1L45i+gQ5fW2QelVEjRRN9U5Wdg61+hzzjoMbjuZcpOw9q5kPJN+PavbFmf8bDrbftZnyvsQGMdu9qeMxlzYOiNEJvsrH8KThZC1j9t08+JAti3EqITYKwOaaCUahkd66aplj1sEz1A2h22vbxbv3OXyVwAp47AxEfOlg2aah+1Tf4NZC+HVU/AtLl2/PgV/wOlx+3n7WPtI+12GHM3RMX4Z7+UUiFPJwdviux/w+s3wCU/sO+3LwIMTJsHI5yykmKYN8qe7c9a0rTtLn8U1j1jR5ksyoXe34Dh34MLRkDScL/silIqdDR1cnBtumlMVZU90+7WH77ze3tR9cpf2CEIFt8Hpsr+B5AxB04dPdtk0xRXPAyfr7Vn8dcvgCHXgYj/9kUp1SZpom/M3uVwZDdc95JN8gCdk+B7C+0NTIvvs2fmpcdtk84FI5q+7Q5xMHuVf+qtlFIO7UffmG0LoVMPGPLdc8sjO8Ksxbb5pqLUXnSd8oQ3dVRKqQboGX1DSopt+/yoWyG8jlCFhdvJt1OnQ2QMhOn/m0qpwKOJviFZ70JlKQy9oeHl2se2Tn2UUqoF9BS0PpUVsPYZiB8MPRu9qK2UUgFLz+jrs+UVOPqpveiqPWGUUkFMz+jrUnwAVv4KLhwHg672ujZKKeUTTfR1Wf5z23Qzba6ezSulgp4m+tqO5kDWYhj7o/OHOFBKqSCkib62dfMgPNKOL6OUUiFAE31NJwth2yI74XZ0D69ro5RSrtBEX9OGF6CyDC67z+uaKKWUazTRVyvcbRP9oKuhexAMm6yUUk3UdvvRl56Ao9nwwWN29MmP/2hngUp/0uuaKaWUq9pmos98GZY+cPZ93hqQMJj5hp3eTymlQojriV5EOgHPAmVAhjFmodvf4RNj4OOnbUJPux0uvt6OCX/BCIgf4HXtlFLKdU1qoxeRBSJSKCI7a5Wni8inIpIjItXz510H/N0YcxcwzeX6+m7/OjiWBxP/207M3aWXndVJk7xSKkQ19WLsK0B6zQIRCQf+BFwFpAIzRSQV6Al84SxW6U41XbT1rxAZDYOv8bomSinVKpqU6I0xa4CiWsWXAjnGmFxjTBnwN2A6kI9N9k3efqspP2OHHh5yLURFe10bpZRqFb4k4mTOnrmDTfDJwNvADBF5Dqh3lmwRmS0imSKSeeTIER+q0Qx7/w1lJxsfX14ppUKILxdj6xrtyxhjTgG3NbayMWY+MB8gLS3N+FCPpstaDJ3i7bR/SinVRvhyRp8P9Krxvidw0Lfq+FFVFeRmQL8r7RSASinVRviS6DcB/UWkr4hEAt8HFjdnAyJyjYjMLy4u9qEaTVSYBaePQt8J/v8upZQKIE3tXrkIWAcMFJF8EbnDGFMB/ARYDuwG3jTG7GrOlxtjlhhjZsfGtsKcq7kZ9jlFE71Sqm1pUhu9MWZmPeXLgGWu1sgXp4ug7JTtG1/bnqV2/tfYnud/ppRSIczT7o+uNt2Un4EXroCnh0Hu6nM/+2q/vVFq6PW+f49SSgUZTxO9q003W/4CxV+AqYKMWgOT7fi7fdZEr5RqgwLrhqaWqqqEtfOg11hIn2PP3g9ssZ+VHIf1z9qJvuP6eFpNpZTyQmgk+pyVULzfzvN6yU0QGQPrnrGfffQHOHUEJj/ubR2VUsojodFGv+MtaN8FBk6F9rFw6Z2w823YuwI2vghDroPkke5UWimlgkzwt9GXn4FPl0HqNIiItGXjH4bOybDweig/BeMfcqfCSikVhIK/6SZ3tR2/JnX62bKoaJg+D7oPgMm/gcSh3tVPKaU8FvwzTGW/b4cdrj1+Tb8r4SebvKmTUkoFkOA+oy8vgd1L4aJvQUSU17VRSqmAFNwXYz95w45fk3aHuxVTSqkQEtwXYzt0gdRroe8V7lZMKaVCSHC30adOP/cirFJKqfMEdxu9UkqpRmmiV0qpEKeJXimlQlxw97pRSinVqODudaOUUqpR2nSjlFIhThO9UkqFOE30SikV4sQY43UdEJETwKdNXLw3sL8FXxMLtOSqb2uv58u6oR4bX2KqsalbS+Piy3cGy3rBEJuBxpiYRpcyxnj+ADKbseyRFn7H/GBYz8fvDOnY+BhTjY2LcQmyfQzZ2DQ1dwZj081XLVxvSZCs58u6oR4bX2KqsalbS+Piy3cGy3rBFJsGBUrTTaYxJs3tZdsajU39NDZ107jULxhi09Q6BsoZ/Xw/LdvWaGzqp7Gpm8alfsEQmybVMSDO6JVSSvlPoJzRK6WU8hPPE72ILBCRQhHZWaNsuIisE5EdIrJERDo75ZEi8rJTvl1EJtZYZ5RTniMic0VEPNgdV7kYmwwR+VREtjmPHh7sjqtEpJeIrBKR3SKyS0Tud8q7isgKEdnrPMc55eIcFzki8omIjKyxrVnO8ntFZJZX++QWl2NTWeO4WezVPrmhBXEZ5PytlYrIw7W2le78TeWIyCNe7E+ztLT7kFsP4ApgJLCzRtkmYILz+nbgcef1j4GXndc9gM1AmPN+I3AZIMD7wFVe71sAxSYDSPN6f1yOTRIw0nkdA2QDqcDvgEec8keA3zqvpzrHhQBjgQ1OeVcg13mOc17Heb1/gRAb57OTXu+Ph3HpAYwGfgM8XGM74cA+IAWIBLYDqV7vX0MPz8/ojTFrgKJaxQOBNc7rFcAM53UqsNJZrxDb/SlNRJKAzsaYdcb+S7wGXOvvuvubG7FphWp6whhzyBizxXl9AtgNJAPTgVedxV7l7HEwHXjNWOuBLs5xMwVYYYwpMsYcw8Y0vRV3xXUuxiakNDcuxphCY8wmoLzWpi4FcowxucaYMuBvzjYClueJvh47gWnO6xuAXs7r7cB0EYkQkb7AKOezZCC/xvr5Tlkoam5sqr3s/Pz+ZSg0a9UkIn2AEcAGIMEYcwjsHzb2rAzs8fBFjdWqj5H6ykOCj7EBaC8imSKyXkSC/uSpWhPjUp+gO2YCNdHfDvxYRDZjf2KVOeULsEHNBP4IrAUqsD85awvV7kTNjQ3AzcaYocB45/HDVq2xH4lINPAP4AFjzPGGFq2jzDRQHvRciA1Ab2P7ad8E/FFE+rlczVbXjLjUu4k6ygL6mAnIycGNMXuAyQAiMgC42imvAB6sXk5E1gJ7gWNAzxqb6AkcbK36tqYWxAZjzAHn+YSIvI796fla69bcfSLSDvsHu9AY87ZTXCAiScaYQ07zQ6FTns+5v3Cqj5F8YGKt8gx/1rs1uBQbjDHVz7kikoE9C97XCrvgF82MS33qjVegCsgz+upeISISBvwCeN5531FEOjmvvw1UGGOynJ9bJ0RkrNMscQvwrje196/mxsZpyunulLcDvoNt/glqzr/zn4Hdxpjf1/hoMVDdc2YWZ4+DxcAtTg+TsUCxc9wsByaLSJzT22KyUxa03IqNE5MoZ5vdgcuBrFbZCT9oQVzqswnoLyJ9RSQS+L6zjcDl9dVgYBFwCHvBIx+4A7gfe0U8G5jD2Ru7+mBHudwNfABcWGM7adgEtg94pnqdYH64ERugE7YHzifALuBpINzrfXMhNuOwP5c/AbY5j6lAN+xF6b3Oc1dneQH+5BwfO6jRCwnbHJbjPG7zet8CJTbAN5z3253nO7zet1aOS6Lzd3cc27khH9vpA2e9bCdmj3q9b4099M5YpZQKcQHZdKOUUso9muiVUirEaaJXSqkQp4leKaVCnCZ6pZQKcZrolVIqxGmiV0qpEKeJXimlQtz/A20yw1n+FvliAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd11f2f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 要计算扩展窗口平均（expanding window mean），可以将扩展窗口看做一个特殊的窗口，其长度与实践序列一样，但只需一期（或多期）即可计算一个值\n",
    "# 通过rolling_mean 定义扩展平均\n",
    "expanding_mean = lambda x: rolling_mean(x,len(x),min_periods=1)\n",
    "pd.rolling_mean(close_px,60).plot(logy=True)  # 各股价60日均线（对数Y轴）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 指数加权函数\n",
    "使用固定大小窗口及相等权数观测值的办法是，定义一个衰减因子（decay factor）常量，以便使近期的观测值拥有更大的权数\n",
    "- 衰减因子的定义方式很多，如使用时间间隔（span）,它可以使结果兼容于窗口大小等于时间间隔的简单移动窗口（simple moving window）函数\n",
    "- 由于指数加权统计会赋予近期的观测值更大的权数，因此相对于等权统计，它能“适应”更快的变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=60,min_periods=50,center=False).mean()\n",
      "  after removing the cwd from sys.path.\n",
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.ewm_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.ewm(span=60,min_periods=0,adjust=True,ignore_na=False).mean()\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Exponentially-weighted MA')"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd3744b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, axes = plt.subplots(nrows=2,ncols=1,sharex=True,sharey=True,figsize=(12,7))\n",
    "aapl_px = close_px.AAPL['2005':'2009']\n",
    "ma60 = pd.rolling_mean(aapl_px,60,min_periods=50)\n",
    "ewma60 = pd.ewma(aapl_px,span=60)\n",
    "\n",
    "aapl_px.plot(style='k-',ax=axes[0])\n",
    "ma60.plot(style='k--',ax=axes[0])\n",
    "aapl_px.plot(style='k-',ax=axes[1])\n",
    "ewma60.plot(style='k--',ax=axes[1])\n",
    "axes[0].set_title('Simple MA')\n",
    "axes[1].set_title('Exponentially-weighted MA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二元移动窗口函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_corr is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=125,min_periods=100).corr(other=<Series>)\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd3496d0>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iWB0O9ThZacQ40dVkslTNchncvZNjrPzey0agON+Pvl010bDpo3rhW5MHhW8S++u0rB7Oc28j2LXfYtoXaq76ku1aOEZNw1NpVGReVUnfnYcbYrY1K3TSUiHtx2N0PJpbvT1X9xxrW4JkVp77sZMtGNxdS1OcPqqXZ98XrxGeNqon1j0wDQ/OHIWyjvno27UDOuT50RwM4a21+8LbsefeRqg7aV99xrRPnvtM04pZVXPMdP1Fp0SaKKuxWOOFLYTAsx9rNw21t6bPRRHTi8tqot4nGpKw4q82hVeZwOp4NDQHMbqiFNsfmoGLR/Sw2Cr+fSf6IDRlWHdU3XMxivIDYRmDb/3fsvD6NjmhSkTTiGgjEVUT0WyLba4hovVEtI6InvN2mM60BEOmAkCJkstl3UzqUBUEoxpdGy7spz/YGk6zVcMfbrRlVK1xwHvj3rdrkaf7SwT16cQqTNXYEopLLsAM9VgX6OEYL7R9zLTe25znTkR+AE8AmA5gBIDriGiEYZshAH4M4BwhxEgA/5OCsdry+wWbcc5D72Ht7mN4Y/WepPe349AJD0bFZDu7DOGUURUlttu3GNy+/3zrbACxedh7FUdEhhYAXX4gTtuituvzgvOGlDtvlGJUKV6z41Hf2IKDx5vQIUnjru77nMFlAJKXMgC0kJGRtthD9UwA1UKIrQBARHMAzASgug+3AHhCCHEEAIQQB7weqBOPL9Q6kF/2h48AAJOHlqNTYZ7dR2y5YmwFVtUcCxcyMO0TozbMWQO6Ye1u68K2FsXQHmloQWFAO3+MBmNM387AYi1XvkCZwNOEw6ytS6uJl24lSRsvJ5uDGPGzt+K+uaQCVQbAzNje9YImynX4hPnch1tkFtM3zxuI687sh5ZgCOcNLUtqnwBQbGI32mJYpgKAWiVRoy9TGQpgKBF9TESfEtE0rwaYKHWNycXMpVZF1KM10+4wPqLbCYEBQIuStbFsx5FwPN04Cap626pH55QK2WjICuneqSCqEjIZth080SYMOxCdXWR2s6vWJR1aknSzl+04AkBrvFFZVox/3nwWihykfN1QZCIJ0ebCMjAXUzYe0QCAIQCmALgOwJ+JqHPMjohuJaIqIqqqra2Nd6xx8WySk0LygkmkfReTO2w7GF947q11+6Leywv6s22HsG5PZBLWGL6REJFtzFe9efxo2nCM69fZs7Rds3Dmuxv2e7LveHHy3ENKGmkyzBitZdn06tzBYcv4KDCJubfFVMgaAH2V930AGM+CGgCvCiFahBDbAGyEZuyjEEI8I4QYL4QYX17uXVzPTFdCFW5yy6KNB/DnD7fqr7WbT7rvtkzb4cVlNfhw80HTdcbCFQDh6lXJZaf2CufFP/vxdsx47KOwF9pi4W37HFIh1cnT0g55CPh9ljeKeHly0ZaYZfHe3LzCyXOX4a9knS858ZmINo39fmPHle7aGTfGfSmAIUQ0gIjyAcwC8Jphm1cAnA8ARFQGLUyz1cuB2mGWQ9y5KP54+03PLsX/zt0AAKjSH9c2HzjO1app4hOltVkqseqbaeTu/0SaLTx3y1lR63qUxIbrjp2MLtcvzPPHxFmv1DXIpRbKszedYdgL2U7o3f78iqgx5PkIJ5uDeGP1npScp+meBJSoXYzMfpU0lFa6L26Rnr/Xhld1Cm8/f7D2HWm2I47GXQjRCuB2APMBbADwghBiHRE9QERX6JvNB3CIiNYDWAjgB0KIQ6katMrynUcw5deLYpZ/6fS+sRu7xKipYVWwwnjLl//8GS753Qcp/Y41Nccw/N63XIUbepZEhKGM+iJm16nRPhBin/xkfrz0PM8eFL1fn4Oe+5JtWhHVTRMrceEpPRDw+7D3WCNuf24F5htCQvFy+ZjeMcvMbmLp4EBd5JozC1PJm6Pfl1ypzsTB2vG/5ozE7YUZU0f2wNQRPbDw7inooheztUXPHUKIeUKIoUKIQUKIX+jL7hNCvKa/FkKIO4UQI4QQo4UQc1I5aBVjQYfk0Xc2JbzPz7ZFi0J5qTjH2JPqkvfLH9eyqX67wPn8GNqzEwDgwZkjQUQY1y8yjWQWClFt0CUje+CuqcNMw3qNLUH8v7c+BwDkGWKzbipUgchEvxqWOHg8OaEvMyOaKRkONZXQ7HjIBhojetunpjrRp0sRtj80A2dUdk1qP0aK8gN45sbxGFBWjHx/ap4OnMj6CtXoPFVz9bZ4UavKAHiWjcBkFlW8a+3uOhxvss+oCoZCOL1/F9xwdiUA4O6pw8Lrdh2O5KkLIVA5ey6ueXpxeNnTN4xHz9JC09Zqw+99K/zauN7nMKFq/Jwalkj2PDV7YsjUuV/fGDHuZsdjyjBtzu4L44yJe20PeQP3uh7Biaw37io/v2JUWLxnYLl3MqhWnXQY7/B6QsuMq578JOr9qJ/Nt93+4+pDUU2YZZGLkX028wR2IWs1v12l3kUar4wVf1IdiX4mG39WPUsZJ87UU+t+JSxj9kQi7X02pDtIJ0LqEqWLHDDukZNvUHkxPvjB+Zg4qBu6WLTGstyLjbd0hzKJxaSGdEw2fb4vtmPRsh32F9whw/zLq7edE1YNlDck45xMF2UyX/XMLxzePWq7h68+Neb73ly7D/vqGh0lBXz6frcq2SzJygaEhNYebuV9F+Nr51QC8F7awA1CiKhmKHJi+yt//hSVs+fieFNr+KrPhmQ2KTNxOM36+Flv3GUXmk6FARARiAgd8vyuMyIkVz+1OGbZVF2M6MaJlUmPk7HHKh75pw+24rOtyc/Nm+mpA8DJZmvjVdYxP0aQakzfzrjjgujshxbD4/aj144Nv1Zj7tcaJu3sdFGG/PRNy3VA9A1EkuzTj+wW1LkoH3n6DSwTYRkr+d6P9aeUfccaFc+97Vv3TGXbZb1xl6gaEwV5vrj1nWWlmsq6PXUozPOlJWTQnmlsCeK+V9earvvFvA249plPk/6OG/+yxHS5WUxc0twaMu2fKTM05A3J6N2q4RbVuBu/q1ux/dOlUWpg4caIqsfMsVqs+atn9w8vS3bCLiQi45W/23jjSgfy2pUdpq4d3zfKQDY0t4a7MGWD556MDEoyZL1x76Q3ROioNEYoDPjR1Bqf527GrDP6oiDgj7pRPPrOJtz5wsqk981EeGl5DV6ois168tLjURswTFTSD+3i1M3BUNTnJNLey3i0caKsTsn0UO35GQOiMzJO798lZt+dlOIoozLp155dqoxB2/EpvSLZIsmqGQZDkT6fMgsnE2GZ9z7X0lTr9Vh1fVNL1DUohLuMoraC1Kf64bRhDlt6S9Ybd1kpWHMkkr1QkOdDY0vyJ+WV4ypw7GQL/vbJ9vCyx97djJeX705630wEo8M5/n8XYOO+ek9Tx8YrqW5PfPm08GtjKqJKS1Agz8T4x3juhrRI1eCq3nqJwYMzKxC697KI4KqM1Rr5/kVDw69Vadlk7XBIiPCY/D4CkblQWarp303z2H+gZyfNW7MPW2sjcwvqMc0Gz12qfg4s6+iwpbdkvXHfe0zLVFBjg88v2YXa+iZs2h87gca0PYz6IAePN+HSxz70NCTw2Lubw6+7FOfj718/E4B1KKOusQXBkMDuI7E9AqRXKz9r3KZC0SkxhmJW3z/VdpxqMY3VJHO/bpH9q7tPdlJaiMh4iQh5fh+aMxCWkU9sPUu1IrKyjvlRlcsNzcHwNtkQc7/69D549bZzMG1Uz7R+b9Ybd5n6aMYbq/fGta/zh0Xr3ZQonXT+vXRn1LqDx7lq1SvMnOdgSHgmZXtMKYz6w3XjAMQaaCMb9cyaj6pjJ3NlodXa3Vq16T2vRM8XqN608clAeu9upKRX7Dxqmos/tEen8GvVnic7N9QaCkXdLPL9voyEZVTdmNEVpTi1T+eodORrnl4cmVBt+7YdRKRJPKeZrDfusphEvVjCcUuXnszbetm2rEwd0asEa39+SVSbtB+9tAYTf/Vu+H1DU/IxfUbDSpxt56FYzaBE+M07G8OvZYm9U6NrOaH4wMyRMes+2KyJyj39QazQlhEzYauPZ1+Aj390geNnAeCn/10Tfj2wrBin9euMkb1Lw8vUJ4Nkc9LrTrZGhY4CfsqIcZdzGAG/Dx3y/DjZHAxrPknCqZBpHls2kbXGvak1iMrZc8Pv/3j96eHXj3xpDABgnMmElRnvb9Iu1gZdiS4v4DNV/dtzLPJoeDLOVEvGGquMlcfe22y6PF6kwqDsawk4e+5yqfoZiRyt9B2+M2WQ5XcH/D68ets5WKOEYyo6dwjrjTjx6sqIAKuAVi4fNRbl0CXrubcYJpADPl9GsmXkDSXgJxTm+9Fgcq3JuYBMCZtlA1lr3BsN+cmTh0ZCKjJW5zbbQlYEytxhWZ1nh1XeNBM/qnG/T5lQfGe9ubjXnqMnHZtmqMhMC7WwTX6nWgmpYhfTlfYkJASCIRH25K0Y07dzwulwalu/kBAxKpPqsUs25m7MDvL7MpOjfVRX1+xYEECHPB8am2ON++6jmqPFpt2arDXuZDPySBqXuxNTihT17VoU00n9Rb0HppGTJicckxhqWObKcRWO+d83/nUJHnxjvWu1TlkZKG/6gOaVAtGyvip2FZD9umrZHN2KC/D3T7ZHtd2TglbJoBrwniWROSXNuMdq0UiSyWz51bwN2HGoIWqOwK3Ojdc8/b6mFt61OB8d8vw4UB95YpaT1S8t11Jn2XG3JmuNu7A5j+UJ6lYbXIZlik3aa6kGQYXDMt6hep8dCwKouueiqPVGud19enjMbaOG7XqzczWkYVe8BCieu4n1+LYehpk6sgeOKjntN02sxOvfneRqTHYs+Wnk96uSu6FQ7HhU455o+m9LMISnP9AM6kZFokEz7gntMmGaW0NhmYhOhQHsONyAI8qE+BdP7xO1PYdlrMle427TaVLG7O57dV1c+5w5NlbP2ioPmmWAvUM1UPkBX8wFaywkko0c3P4FZA2EWrJfYBJLV7EXptJWLt9xBK+ujNQ83H/FSBQEkm+oXtYxYtDVY2MWllHfb02wa9J/lAKyNbsjrQCJki+MihdVqK0g4MeKnUfD7686rQLfuzCmwRtjQdYad9W2fmPSgKh1smgAiDQ3sGNYj06o6NwBs87sF7POysPLlM51bhJ7LG9UyuqNfwJpb6wyXaxQJ8nNZAVUZJ8AM+MmbxZ/X7wDOzzK6DGy4M7JAKLj6GZhGfX8fH5JdLquW46eNBe0cqst7yVGp+kHl0SqOicPLXd84mIiZK1xVyd68gyenartsdFFIdPG/fXYfTS2WAWIffSX3XkykSKWq5iFuPYcjYTUrKZO/vJhfE3Qb78gMlHu5LnPWaoJ0pmFOgaVp77ScHD3jujeqSDqPA+JiBqkxPg+EayaTGci5i6dptnThwMAeijdsJxuyEw0ro4WEU0joo1EVE1Es03W30REtUS0Uv/3De+HGo16ykl5UmU84dfFLopF7DBePP+4WatsPMF57p6xpkabkFTb2i1Q2uBZpfhZ3ZCtKFLmVNyGT8xsZ7LSum45UN8UJashTMIygw03mkSeKPt3i/weWbkLaMY9/Z67djPtqmc2qY6anVQEE4vj0SIiP4AnAEwHMALAdUQ0wmTTfwshxur//uzxOGOQHsWDV45C907mk56AcyXgIYdKU1Vt8r7LRoT39xOluIRJjpIOmtFdcNfk8DK1hN/Ke5QX+78+24GtDg1VbjLINls1ymhrfLj5IH788moA0aqNkr5di7Du55fgDj0WnYhgnkwVffeuyVEpxZmIucubkwy/RBn3LPmbtRXcHK0zAVQLIbYKIZoBzAEwM7XDcoF+zjk9lTpJ/y7caJ+jnOf3YftDM7D9oRn4+qQBMTG/d9bvj9uDZKKRzqYaE7/jwkgIxcobDYYEQiGBn/53LWY+/rHpNjKsoUpJABEt9bEOZeFtYWZF9iwwm1AFgOKCQDh9NJGMGXmNGMMemfHcZXWqbtwV5+poQ3qbXWQ7box7BYBdyvsafZmRLxLRaiJ6kYi8bSVugrzerYSDnrvlLABAk8PJLi8KuypDFaPndMs/qnDlE+aGhXGHECImX/naM/ph0d1T0LU4P6ZhuSTgp7D+TL1FP1QZ1vik+mDMuiHdO6KXRaqrxKxSOVOEQsIy9U8+YZ7AQdIPAAAgAElEQVT24DuonD03ruIjadyNzUN8cXjuas/TZJA3clmHoHru5UoWEeOMG+NudjYZ/+KvA6gUQpwKYAGAv5vuiOhWIqoioqraWnuP2Qk5oWkmyQpExJXcPqYaO+5YoV5b0mN3W0zDmCNMwg0AUFlWHC5AUjtryUKhl5fvdhQX+/rfNA30KpNmLHbZIDIOPUrRcTHjS4a861QgD43VcQJiJ4itKm/NaNKPrXEfbidUN+2vx+j738ZLy2I1+eNF6sqYhWWG9tSu6eE9O8V+kInBjXGvAaB64n0A7FE3EEIcEkLIs+lPAE6HCUKIZ4QQ44UQ48vLy802cY30NgosWpXJk2LvMftCJlno5DbFSr24pv/uA1efYewJCeFYRj5nyU58b84KtARD6NwhUsF62KR5ssrmA1os/hGTfqUEsqyX6F2qxfxLTdrZqVx6ai+88d1JeOp601PeEy4d3QuAdVgGiJ4bAoAJv3oXRxuaUefCow5fS4aYNrksYpJFR08uqnbe2IGI5y6Ne+R3ySeLl78zMenvaQ+4Me5LAQwhogFElA9gFoDX1A2IqJfy9goA0RJuKUB65FbpUfKkeHLRFiy16Tq+qkYr2ijv5O6RT61irXPRpZ5xRsDaI5Xc//p6vLpyj9bAQtn0jjnumpd3L4kNv9h57osd+rZKZ+CMyq4YVVGaUq1urWqzDieag5apj2ZPMGMfeAen3v+24/7fXKtJY8fG3N1py8ghbalNrIhKRWbL+MMx98iYCvWbT5FJJTkTi6NxF0K0ArgdwHxoRvsFIcQ6InqAiK7QN7uDiNYR0SoAdwC4KVUDlkQ8d/OfoIZr1tQcM90GACo6y4YA7ox7h3w/epYUYnqahfdzmZBJzN0O1eCstvnbqlh5vFamq7Jbke3jv0zbTGVNzbZfXYoxfUrR1BrC1X/UGrhb1VccbUg85i21cYzx/HV76rBgwwGzj3hOY0sQJ5paw09a4VCRqlLJqZBx4eoWKISYB2CeYdl9yusfA/ixt0OzR8a5Cyz+4OqJaud7hMJZN+6v0s5FeVyh6iFCWAtAXT6mN15fFYkC/vadTdieQFWo2d+XbLJBfEQY1N26WGnOrRPw/qbalHqRRITCPD8aW4Lhph1W2TBXjquIaRqSLryQXJj8yELsr2tCZz0M9smWQ5g2qpcn+26vZO2tUBp3tTG2FXaPlnLCKB4PLOAnFg7zEGFSVi+pORJtyN/fVJvQBLbZ7rVFFmmWQiDP5qTo27UI10/ob7neK8o6FmDFzshkcJPFeVdkMfcEAG+tte5ItsWhPsANslBQ7R0bL3ICWE6cy/i6UyUxY03WHjkZ83QTK7cLG4ZTKuPw3P0+H0v+ekhIWOtye1WVaO65W58barPoTFJc4I+Srm60yP6ykyHYtN/agDd64KR8aJJmmijdirXruZ9eBZwtxWZtkaw9cuFKNhcXoJ2CpFlJtxN5PsIJ3bgHfOSqHyZjjW2Kn0cXt9mcCpF1yM4uVJROjDc3u7qNL58VK3wHANsPnrB8epXCZ6pQWzxsqT2OPy7S2g0OLC9OaB8qs/QG4VfrKaasJ5M4WXvkZCckN8JJ9p67dUjAimMnW7BhrzYJVZTvD+fmMolhN6Fq5bnfbFACtWKGnkY42CR+TiBLoyeEdYFcOjliqMq0O1VvP3+wacXtyyt2478rdpt8IhKWvM5EEdUNso4A8KbIqDmoNemWN3Wrp6cFd07GRz86P+nvy2Wy1ri/tEw7Wd08ttvNfZrpdTghZ/QBTWvcqZCGsUfYhECsJq4nGBp4mNHcGkIwJEwNO6Dpwlul79nllKeTeWv2Rb23CxX17twBr9x2DqaO6BHV3B2ApTSxzL4x5sm7xWvJ4+2HGrQwncM1Obh7x5h+skw0WZsw2qtzITbur485ic2wq7ILCZuArwsO6kU0m/fXY0gPrpxLBC3P3XydmXE/d0iZaeNq401i6D1v2n7vVpu8bLtQUSZxI4dAFGkd6URLq3Z8vRblOtbQgoMnmlzJI6tPT2pmlEqfLh1MlzPWZJ3n/uHmWlTOnotFG2tdext2aYvaRZz4eE7rpz0GJ9rijLEPjf3qqtEYYZKFYTbXokoMxKOOaBaaiTf3PlW8/4Mp4ddlHfOjGohbYRZOsroCnvpAi5ebyXjIDlhudWrkdscaWjDmgbdx4W/ed/U5p7TiD394Pubeca6rfTERss64P6P3egSc+5jKi9OucbBdGp4bKsu0SaSDDtLBjDUhm8nLvl2L8LtZY6OWfXvKIFPZCTVlcOHn7rWL3lgdmyoo0Db6c/bvFpmkfPeuKeji0DwciG8iWD69mE1cNuuFgmobPjukiR7zgHNVrIqx+5JRxK9v1yJXT+hMNFln3NXUravGmYlTRpBVhF7H3J++IaIjsllPM/vBi6vj2gcTQTjEWFXdnzsvHoqJg8rCT0wqzYq889vr9sWst+LVlbGhADOlykwxSM9CcatQaVrF6uB9m81d3aDn8d/zqnlxlFm9gTFv3s6xkhw3KHrOW2Odl8+4J+uMuypLamycbORvX9O6yth5O8k+fp89SJvYO2tA18R30s4RDsJhcl3v0kJ8V2+VR0QxNQ6qcX/ZIjvEjGU7YrWHkg3Xecm7d03B9odmuBa3S0QywMy4d9Wvm2aLnggXPRoddhECYRVPidpJyopfz98Y9b60yPnphHEm64z7r780JvzaKQ2yp67VbRczTGTibEyfiNd4zXgtH3cuexsJ4/Q3kEU8xQWBKA9fjUcD1mE6p0K3CpPJukRSZNsyVlfAZaf2QlnHfFNHydg/2Ihx0va/K3ZjzpJdUctu/vtSOCH71Uoev26c42cYZ7LOuPcoKcToCk1j26mAyY2jk0jKW0+lwUM6miXnOk5/g+66cf7GudG57UX5gajiJLWZ8sjekUnYLg6yveZjSiqJqs1h5fW3BkXYQzei3hD2GLqNLd4Sq5p5vKkVLy2Pjs/HqxQ5c2zvtPWozXWyzrgDkb6oTo+p0stzSoVMxENbePcUPHfLWSAiEAFfPC31TRtyFae85i7F+dj+0Axce0Zsoc0b350U1mpXY9LDe0aMeyISBna599mIlSPUEgxZHh/1spn40HtR6345z17VO5GiqPsvH4Hfz2Kv3Suy0rgX6xexk1GWa520ZRK5iAeUFWPioDIA2sQtV0knjkDi8x49Swtx/vDuAKInEvfVRTxNJ+M+aXBs45i2Ij/gFVYhzNaQsJTSNcp2qO37AhYd0CTqk5Nb/HwReUpWHk1p3J1OMGn87fIEnvtsZ9JpjH4fxaRzMe5JtmBIGu9mPTa/YW8dPq6OhA0qu9k/5nc3icm7aSDSVnlw5siYZVZPucGQcIytq8h5Dacak3j2KXGjE8W4JzuNux6WcfTc9dVum/wmSsBHrO+eBMlmLMkCnAffWA8hBB58Y33U+p9dHmvsVMz+cm1FfiARbji7EgAwTKmYtvotraGQpeHvadK9SgqXOYnl9bRoPB4KCUtF1URuCIw1WWncpaiQ26e4h9/a6LxREvjYc08Krzx3QJODqDe0PyyxKIB5/fZJ+vdbVahmr7FZ9bOpePX2c8LvAz7ziyUYEpYe8zXj+2KqoXF8SzCEqu2HHdMtpXSvyozHPsTAn8zDKfe9hZDJ9cLa7d7i6mgS0TQi2khE1UQ022a7q4lIENF474YYi18/UZ2zZdJzcQZ8ZHqyMu5w0yDbDtXja2wJYs3uSOu9124/x9IzrSzTwjVG2y6EQGNLKKtj7qUd8lCY5w8X3HWyaGqjxdzNf6jPR/jhtGFRy5qDIXz3+ei+tSUm+/b7CD1KCsKdleoaW7T+t8p+jBQmKF7GmONo3InID+AJANMBjABwHRHFCFwQUSdo/VM/83qQRqSj5pTn7nRxetGoQBuPjz33JNBK/RP/vOphq7nuXxhXgVP7xFaySnwW2VRPva9JXBysb475TLZhpsujEgoJ26wz42m9v64Re481ht/3Li3EG989Fy9/ZyJ+e22kBiXPT9hf14SjDS349v8tw96jjVH7MT5dAdyYw2vcHM0zAVQLIbYKIZoBzAEw02S7BwE8DKDRZJ2nyIvSyXNX15t51l7F4v0+Z/Ejxppk9X0A4IkvnwYAUYbnm5MH2n5GGrVPt0bnbMu2dMbc7mzG6uxsdZhQ7d05usDri3qjbskbd5yLft2KcFq/LvjCuEg6sHrDeHPtvphr7dCJ2CQGp4pzJj7cHM0KAGoJWY2+LAwRjQPQVwjxhodjs0R6ak6eu89HYV2OJpMSaq+8bfbckyMUSj7tUIYd/r10Z3iZU4GZDAMY5SlkyfzirbGFOtmG03ENOnjuHQsC2P7QDMv1VoJexvTT6gPRmjNHTsRKEnPXJW9xczTN/vJhS0ZEPgC/BXCX446IbiWiKiKqqq11r9oXux/3237lLE38yEwCNuhRB6VVu47ig02J/572zLaDJ/DWun1Je+7SUA/uHskQcVu89PLy3VGaKIdOZH84JgaLU13z3BM3qsYbwwBdJbWkMNroG+P0Rxtij7FVvj2TGG6OZg2Avsr7PgBUGb1OAEYBWERE2wFMAPCa2aSqEOIZIcR4IcT48vLYwhHXg6bw/hy3lU0IzCZwpLf9rcmDYtYlwlplIo9xx5ee+gSA+ZNVPMh4bfWB+oQ+f97DC8Ovu7mQ1c0WnDJ+nDx3iZVEgZG/3nQG7r98BEodJB/k33vB+v3hZcN7crMbL3Fj3JcCGEJEA4goH8AsAK/JlUKIY0KIMiFEpRCiEsCnAK4QQlSlZMSINCNwEwkJn7cm20odjEMeabG/uMyd7jUTQXayMpWpjYNuHTXjY2xL55bjTa341ZtaSf2VDlLS2YhVk3i7PHeVT2Zf4Op7BpQV46ZzrPvb/uASLfvm3c+1VMp7XtHkhM+o7MLZMh7jaNyFEK0AbgcwH8AGAC8IIdYR0QNEdEWqB2jGq6s0OddH39nkuG0kIyJ23Xt6ru7Ow8n1geyvV0Au3R4rHcu4I9kJaSflRyvUDI2n9SwZWX3pVmK3LeP0C4JBd567VwVGVu3y2kIz8lzDVZBLCDFPCDFUCDFICPELfdl9QojXTLadkkqvHQC+MFbzrHqUOF/Q8pw0y4yRcXizrj7xcNv5msa4msfLxIdTVy0nCgLRf0OnTBlJB0Ol5bo9x/D4wmoAzlWY2YRVBDMo3MkPqPHwMyvd9S4wkyjI9/vQqSCAJdsO4beqc8a23XOycgbjzqnDsODOyfj0xxc6bmurDOlRlcqVY3PvMT7dHG1w19DZLTPHuPubGHOrH3t3c/j1y9+e6OmYMkGy2TJmdCl2J6G8yKC3r42H0NASxP66JvxeOdZs270nK407AAzu3tFVebidMmRkXXIhAc7PbXu4vW8/fUP0vP/8dZEJviE9cmeCL9E8dzMIhGduOB3/vPlM2+16lBRinKEdoo+iQ3D76hr15WzevSbnrVJYGdLk7J46UtPNkJM8THZz6eie4ddujcXYvp3xPxcNSdWQMo5TLFuLucdnBooLApg6sifOHeKc8WZMR7X6u7Bt957cN+76LzQLyxTpMcE+XZLv/HL5mN7hHF/GPWd62Hv20WvGhl/H44yaPXk9e9MZXgypzWD1cNoaEnH3Ipg9fbjrbZftOBL13uo+wp679+S+cbeJucslXpxWftIKcpj48FJPRE2li0fR8YoxvaPe//ILo8MNQLIddzF3d3+DV247B9+7cEhcmUnGLCgiwo1n94/Zbn9dylVL2h05b9zJJhVSLvPCa3hlpVbX5VXOfHsh2fx2K+L5kxqf3HLxCewn/11jmm7qNlsG0EJY3794aFLj8BHhjgtjw2CbDfIETPLkvHG3q2YNL/PwibDBohEBY06LRxIQEtl1KZkb9sDy3DHu6lEw6rsIIRLKlomHy07tFfXeT4QuRblTAdyWaQfG3bma1Ytw388uH6F/DwuIxYPXnrusLnVbLi+ZMkybHAz4CD1MOhBlLcq5vXjLwahV0pNPZQekXoaOTD7KjeKwbCDnjbs8jcyMrlzmRVimW0ctDum1J5rrNCepKWPkexcOwYYHplmqFVoh48CThpR5Op62hFFf6X1d7G7B5/ZdlZLBqNyazd2tso3cN+42qZBeRmXy9JM4VTHkXMXr40VEMVWnbhjWU2tqYZxczSXkb5R8vk8TWVu162jKvtPYc0Ha+j/dGF1fYDbJyiRHzht3O/mBcLaMB9a9rlGrsHx5ee6Ihx050YxzH34P6/akTu2yrTzpVHTugO0PzcBVp/Vx3jiLUPPc8wzt9NLR+cgYgpGevFFjhlMhvacdGHdrz93LsMwevY3Ynz7cho37EpOdbWt8suUQdh0+iW/+c1nKvoOfdFJLeaeCsIRxq+FGWlygNThJZczdGIaRX2XUnWHj7j25b9xtipi8nPvsrOhXX/K7D7zbcQaRjZNlZ6JUII07Szikjr/oBVmtoegbqVQz/f2scSn7bmNYRhp7Y1Nu7tPhPTl/SG2Fw8LbJP89qfR+ttYex+8WbEpaAyde0uFNNbeGMOPUXlj6k4tS/l3tlUB4Pshw/uhvpRZ+KjBeFvKcMnZ/Ys/dewKZHkCqiWTLxK4THoZlLh7RE/e+ui78PhQSjj1enXj8vc1YsOEAVuoTXl+bOMCxw42XJCvD64aWoEDv0sK0/q72htR3MYZlJKk0qzHZMvr/Rs+ds2i8J+c994jhjpzYy3YcxtGG5rDB9+K06mnI592wL35tdyFEeGIWAH799qawYQciIaYF6/fjzTV7ExtoHPxz8faUf0dLMOS61ymTGCUdNB/u6MnovqXpeA40Ok5ygtX4pMup796T81eVWRHTF/+4GFc8/nEkFTIFXkMoBDQ0t8b1mT99uBWn3v82jjeZf07+hG/8owrf/tfyJEdoT9X2w1i6XRN9SpVTFQoJtIYEG/cU06NTIXwE7DvWiJ2HGrDvWLSOSyq9ZuOftp8e5zdm0XBhk/e4uqqIaBoRbSSiaiKabbL+W0S0hohWEtFHRDTC+6EmRjgV0hCX2Xm4IdxXMhXn1by1ezHivvlx5RA/88E2AMBJCwkDkcbEkqufWhz5XhH/jcoNLSGeTE0HPh8hJIA/vFeN8x5ZiAm/eheAtwkFlt9tuHGUFGrhN+MNncMy3uN4VRGRH8ATAKYDGAHgOhPj/ZwQYrQQYiyAhwE86vlIE0SeNEGTM1mWX3t1Yqk3iZf0Ztlr48gRl5kjxqwGiVWTY6959O2NMctuf26F598jJ/iM+ddMekmlXbW6tmLy3/kU8Bw3LtOZAKqFEFuFEM0A5gCYqW4ghFADzMVITzjPFfmB6EwB1YP/w3vVnn7Xpz+JtP07UK+pQ8aTRXPspBZvP/tX75lmxiTZQ9o1j5kcl/dSUKLeoksPcFgmM6TDWZA37guHd8e7d00OL4+tXGXr7jVurqoKALuU9zX6siiI6DYi2gLNc7/Dm+Elj2yc3KRnfhhTIr30GMo7xupcG1O+3LLrsJZb/lWlLDvdqZCpRj6psHHPLKk0q1I7KOAnDCrvGF5uzKJhz9173FxVZoc9xsoIIZ4QQgwC8CMA95juiOhWIqoioqra2tr4RpoghXnaT2zUTzJjeMbLWJ/ZvowpX27ZcVhr/KE2UkiH595qqBi9XNFa2bzf28pbKWSVz8Y9I6TDV3hRD0+qvWnN4Ji797i5qmoA9FXe9wGwx2b7OQCuNFshhHhGCDFeCDG+vNy5/6IXGD134wnt9Sn16m3nRL1P1Cu94S9LAERnGxgfo82aLyTLkYZIKuaYPqV49Jox4fcrPRaYOtGk/U2KCuIX+mLiQ/07GmkLdpWzZbzHjeVZCmAIEQ0gonwAswC8pm5ARGprlRkANns3xOQo0D33Jum5h4xhGW9PKuNJmmzIobPS2MB4Y9p8wHsNG/UG0qOkMGr8Xh+rRv2Ga9QZYbynU2FskVg6gnw36GFFp+5WbNu9x9HyCCFaAdwOYD6ADQBeEEKsI6IHiOgKfbPbiWgdEa0EcCeAr6ZsxHEi+2o2WsTcvXbdjWGYDnl+PLmoGpWz58bEzN3E0Avz/PjlF0br20d+R6pQb36PfCna2/Pau5JZQRxzTz1m4cHI+Zc6yzqgm2bUe5TEzkdtf2gG7p6qte2LV3+fccaV/IAQYh6AeYZl9ymvv+fxuDxDyppKz92YZej1aW3MjgkKgYff0lILV+w6itP6dQEAfFx9EF/582d44sunYYahFZlKQcAXaRUIgdr6SI/W+kbvc89V42684JKVUzDS3Kp3AuJUyJRjN6+RjlRIKz/m1vMGobQoH1ef3td8AyZhct5lyvf7QBSJuR9uiC7B9j4sE31IQ0KgU2EgPBbJKyt2AwBuey5SaSr7f6rkB3zhiy8YEliy7XB4Xb0iVeAVdnF8rx+d31yrSSjwhGrqMXs6So/8gP5dFl+WH/Dhhgn9OeaeAnJfOIwIQmi52/VNrXj24+2G9d5+n9FzX7rtcNjDVkMqvTtrzQpKCiN/gqL82D9HKCSw60gDAOCJhVvw/JKd4XV1J1PruRvx+kb4j8U7AAABNu4pp8imO1U6hMO4t3D6aVdXldGwA96f2MZz+MlFW8Kvn3p/C443tUIIgd+/q805X3hKj/B6swtg+c4j2K3rqX9cHd3gOBWeuxzDqX1KY9alyrniCtXUYxrTToO9tethzKSWdmXczfDaG+1uMnEkWVVzDKN+Nh//1UMyQHSrs2BIYPqonlGf8ft8lu0A61IQc5/++w8BAN+ePMhkbWqMMFcnph67SetU5piHY+4p+wbGinZv3L22V4V5fjx2nXlnGzkZOn/dvvCyKOMuYjXg/b5IqMRYsv2Xj7a5Htfxplbc9txy/OmDrbZZOlKmYevBEzHr4q2QPdkcxH+qdjl+jp26zJAO+YHenTUp7AuHd0/5dzHRtAvj/viXrduIpSLjxMmYqUWgarw5FBIxBvymiZWRXq8Gw3/4RPTksBkb9tah+kA9Pt1yCHNX78Uv5m3AR4bwjhl9u0Ymd2UBjJn4mh0PvbkBP3hxNT7cbP99/MieeoyGXD1HU/nc1Ku0A5bfezG+M2VwCr+FMaNdGPezB3ZL6/c52aoFG8xLsYNCxGQNVHQuCnvTAR+hc1Eeim0mx4xM//2HuOjRD8L5/kBEoMzIUSWTaMLAruHXY/t21sYXZ0XswePa/tQMHzM4xzn19CrtEHUdBEMibU9MXYvzPU+jZZxpF8bdrDovlcTzuKteYKGQFn/ur6RE5vkJV43TdNrG9euCso4FmDysHBV6to1bipUSf7Lw1cY+8E74dfdOkc5SUvzMqk2bFXP1blGPL7RX31SfEpjU8fytE8KvH56/UWlWk6EBMSmlXRh3s2YQ8RrHeDAWSg20Kb1WbwTBkIDfB/z3OxF9Gr+PMHWkNsnaq7QQrcEQAj4fpgwrR1kcjY2TcdL8ejZLMCSwv64RB483OXwiGun5GynvVIDrzuyXxMiYeOlZot20n/lgK77xjyoAPKGdq7QL4w4AK+69OOq99GTtqkMTxWhI7R5JZXpm5ey52FfXCL/Phy5Ks2giCqcgtoYEWoICAT8h4CO0JigcFu9EmpwHaA0JnPXLdzH+fxfE9flTepWYj0MI9hrTzL66xphlXECUm7Qb496lOB+fPzgNz98yAVef3gen99diyqnQSDdOEJ5aEZszrqLK7Pp9salp0sBrPUdDyPP54Pf5EHQIk6it8aLCP3H+ZCnsFU+rPbVVoLHFoTomNivp5StnxT4pxdNQhske2o1xB7Q0xbMHdcOvvzQm3Nt03pp9Dp9KAIMt++VVo/HkV06z3PyoMsFpVqUKaHHvoBBo1T13HwH1Fo20JVJS1zgopxvajUqDECBWWdOJxpYgfvfupvB7qywbAY73ppufXT4yZhlXCOcm7fav2hJMXbdpo+demOfHpaN7xWi9h7dXPNvdR0+abuPzaW3pDp1oxsZ99diwT+tsWDl7LnYdbjD9jCp3oE6GWtn284dpGvsPzBwV/d1h8afIB9fvqYMVP399PZ5+f2v4/YvLavCuSYaQEILjvWkmP+CLkSJI5fwTkznarXF364UmgtkELgCMsZhYVD3bEovMHj9R2MPv1jEfU4ZGikKWbjdPNXxpeU34tRqf71psPhEbElqDjpjv1h/b1ZqAqh3W6Y2fbTsUft23q2Y4bv57len3sWlPP8ZaCqvzlclu2u1f1U0BUKJcOLwHbppYib997Qy8/J2JUetW3z81ZvsjJyJhmW664X3ze+fit9dG9NT9PgrHsScNKUdHRXBs8ZZD+NqzS6Ly0HccOoHfLYj0THFn3GMrZIGIpsxrqyINuO57dZ3pPgBga22kutWuh6w2ocrmPd34WcunXdBujfulo3s6b5QgpUV5uP+KkZgyrHtYv11SUpgXblwwcZBWVHLpYx+G18uWc6f0KsEXxvUJLz/RHMQS3UMvCPgwvn9kv/9ZVoOFG2tx6EQkRXGdIWzS4uJJRQjztDhpgA8dj/+GqO5uxc4jqJw9F1X67+C61MzAE6jtg3Zr3Pt2yVzhzM2TBgAALju1d8y6woB59WkwFGnUsXl/PYb06BSzjRpXL+8ULWA2Ty8osiMYEpbKjz6KNLR2S5eivKgQwBee/AQA8P4mvTm6xc2ESS3qMberwWCyG1fGnYimEdFGIqomotkm6+8kovVEtJqI3iWi/mb7aUtk8tH01vMGYftDM0wndc9Syv6tuGJMhely1bgbf90KF82tQzYTnInkQs/73rmoORI7QfyH96rD38e2Pf3Is6RPlw74z7fOzuhYmNThaNyJyA/gCQDTAYwAcB0RjTBstgLAeCHEqQBeBPCw1wP1Ghku+f2ssRkbw5rdx2KWjextnxMPaBOqZjQHtZj8C1W7cPVTi6PWqVWiVtkyVmEZIDb33s2jfa/SDjhp0/NVgCdUM4F8Aqw5chLdOlpLVDPZjZtOTPxK+zYAAA95SURBVGcCqBZCbAUAIpoDYCaA9XIDIcRCZftPAVzv5SBTwYSB3bD83ostJxfTgdFzv+XcAa4+p4qAqTS2aPv74YurY9a5aYgREgJ5FkbbuPg755ur/NXF0UBECM5zzyQXsAxvTuMmLFMBYJfyvkZfZsXNAN5MZlDpIpOGHQD6d4uOd9o1VFApzDPfzi69s1lZZyU/ELQJyxiXW9nkU+9/23IMRgQ4zz2TcO/a3MbNX9fs6jO1DkR0PYDxAB6xWH8rEVURUVVtba37UeYoxm5HanqjHQX6pOsb350UtbypVQuBDO7eMeYzr6zcE7PMSMgmLGPMjXab6fLSt61juiGOy2SU7YdiG7IwuYMb414DoK/yvg+AGEtBRBcB+CmAK4QQprKBQohnhBDjhRDjy8vLExlvTlFgKB65cqzdA1EEOblpfPJo0sMyw3vGZtKoWMfcrbNlYqQOXGrynN6/Kz74wfkWX2gtP8yknq9MaPN5D0wSuDHuSwEMIaIBRJQPYBaA19QNiGgcgKehGfYD3g8zN1ELhp696Qz0tikDv2liZeznDd609NzNmmp0cxGCssuWMRJPjnpFF/PfJcDZMpnkyrGxqbhM7uBo3IUQrQBuBzAfwAYALwgh1hHRA0R0hb7ZIwA6AvgPEa0kotcsdsdYUOLQjahzUex6o5ddd1Lzro3GvaxjgSHmbk4w5K5ZcrxZkfJJY1B59ByDlp0T374Y70h3ExsmvbgK8goh5gGYZ1h2n/L6Io/H1e4wM94qZqq5RkP8w5dW45oz+sYY98I8H440OFeX2oVlYreNXbb3mLnoGQAsuPM8lHUsiOr2FBKCwzIZ4OkbTo9qqcjkJu5m8JiU4xQ2aTUpeDIa4pG9taYYxiYehXn+KINvJfkbMunhagYRmWbcNDRb57QP7h47D8CSv5nhkpGpk95g2g6cC9VGsMpdl5hVsxrj4zsPadK/RslhtylvdtkyKgTNc1/4+QFUzp6LbQe1rIs8RSTsolOcc6i5WQfDpA427m0EY+aMkRaTrktGQywzWqQMQaeCABbcORkBv7s0RjdyABed0gNE2j7u+s8qAMD5v16k71fb89QRPfCba+wrf+XTA6tCMkxqYOPeRnAyclt171hNczSq6cqQSjAkMGFgV6z5+SUY3L1jjFSAVRajTKW0489fHQ8CQQig3lCNKvc7fXRPlFpMEE8eWo4xfUrD27JtZ5jUwMY9S/hAV1JUJ14LDAqSo/SYe1CIKB11o6b6Xz/eZvodu4+exBurndUjQZqXPs4gZyzDQXaTpD7d65f3F55QZZjUwMY9S5g9fTiA6B6Y+QEfyhThJzmRumzHkajqQ2NYZq4bA27gL18dj/sv1/TiCABEbMpl2GDb2GsfEUJCKGGZuIfCMIwLOFsmwzwwcyS6FTsr833zvIG4alwFupcURi3/4ukV4X6lx5RG22pjDS8aIF94So/wayLgpeW7cfB4pBBZCOGqaJWIEApFbgSc584wqYE99wxz49mVmHFqL8ftiCjGsAPAMKVpR82Rkzhyohn5AR++fFa/8HIZc+9dGvt5I1eNc5ZAIFCUYQeAyx//CNJk280f+EgL34R4QpVhUgob9yznC+MqMP9/zgu/33usEc2toXArOyBi3AfpgmJmUgaAJgvcw8UNwMwer91dF35ysDPXPtImY11K0zAMkyBs3LMcIsIwJYNG6qmrOjUy5h7wEToWBCwbbYRErPqj6XdaLP/Ov5brY7L+rM8XnYfPjjvDpAY27jlGSJ/kPGtApF2fzJbx+3zwUWwFq8Suh6qKVShlf50WqrHLgNl+sAGbDxzHoRPanADruTNMauAJ1RxDNrFWFSdldevCjQcQDAmsNWnvJ7NXfB7McNrZ6/V76wAA89fu07ZN+tsYhjGDPfccQ6Ynqt71nqMno9ZV7Thi+Tk3nvRxRdv9uVvOilnvVG0LuEubZBgmcdi45wjXT9CyY15aXgMgOsUw34WxlZEaN8JhKv27FaPE0EGqvrHVYmvl+0LOBU8MwyQOG/ccoWOBVrk6b40W7og3lh1KsKjIT4QXvz0xapld0xHJvrpGAMAeG5lghmESh417jmBUjVQdcBmiufPioZafl8bdTbaMSo+SAgztES3n269rkePnPtlyCEBEyZJhGG9h454j3DxpAADg6tP7AIgOd0jJ3zF9O8d8bvP+evz1o21xxdwlRfl+08wZp8YjALBBn1i97YLBrr+PYRj3uDLuRDSNiDYSUTURzTZZfx4RLSeiViK62vthMk5Ig1qYp/1JVZsrM2BCSgrk/326A7sON+Dyxz/CA2+sRygUva0bzJ4EXrntHFtt+gV3To56X5zPCVsMkwocrywi8gN4AsDFAGoALCWi14QQ65XNdgK4CcDdqRgk44z0uP9TpU2o7jocCXeYmet7XlmL/t2K0KjL/IZTKOOIypjJJjg1BulYEH3KdXBoUsIwTGK4cZvOBFAthNgKAEQ0B8BMAGHjLoTYrq9zFgRnUoKsOm3SG2FvU2LZ0os3tsZThcau//NnAIATTc6ZLpLOHWJbAw7t0dF+nAaFylIXIRyGYeLHjXGvALBLeV8DIDa5mckoxhTGIhce8dGGiHHfuL8eQKTK1I58vw9BIdAhP/IdN08agDy/z1GBMs+w3qqpB8MwyeHGuJs9qCck+0REtwK4FQD69evnsDUTD0SkKy5q74sKIob3G5MGYtHGWpzaJ3ZC1YhZr1Yjq342NWbZvZeNcDVOt/1cGYZJDjdXWg2Avsr7PgD2JPJlQohnhBDjhRDjy8vLE9kFY4PqvauTmpOGlGH7QzOiGntYYaU7o9Ih3x/ltceD+jlVzZJhGG9xY9yXAhhCRAOIKB/ALACvpXZYTCKYNdE28vwtE2zXG7srpZIh3e3j8wzDJI6jcRdCtAK4HcB8ABsAvCCEWEdEDxDRFQBARGcQUQ2ALwF4mojWpXLQjDNWSS9DHCY83YRlvMILkTKGYcxxlWQshJgHYJ5h2X3K66XQwjVMG8FqYtMpNJNOz51hmNTBFSQ5SlGCMfFuHWPTG73mX984K66US4Zh4oeNe47y1bMrE/rcXRcP83YgJpwzuCzl38Ew7R3OS8tRvMhmYRgme2HjzjAMk4OwcWcweSjXHDBMrsHGPYd46vrTXG337SmDokS/bjy7f/g1t71jmNyAJ1RziO4lhQDMddtVfjRtOABg7uq5AIDm1khue0GAY+4Mkwuw555DxNtaT9LQHPR4JAzDZBr23HOI0RWluH5CP3xj0sC4PtfQohn3L5/FYm4Mkyuwcc8h/D7C/145Ou7PjepdAgC4+JQeXg+JYZgMwca9HfPsTWfgZEsQ4/p1wZr7p6JTIWurM0yuwMa9HXP+8O7h12zYGSa34AlVhmGYHISNO8MwTA7Cxp1hGCYHYePOMAyTg7BxZxiGyUHYuDMMw+QgbNwZhmFyEDbuDMMwOQgJkZmGyERUD2CjzSb9AOxMcPelAI5lweeS+WyixyebfmO6j00y35ktnwP43LEj3ccmkc8OE0J0ctxKCJGRfwCqHNbXJrHvZ7Lhc0l+Z0LHJ8t+Y1qPTYbGmom/B587beTYJPJZJ9sp/7XlsMzRJD77epZ8LpnPJnp8suk3pvvYJPOd2fI5gM8dO9J9bJL9rCWZDMtUCSHGJ7q+vcPHxxo+Nvbw8bEmG46N2zFm0nN/Jsn17R0+PtbwsbGHj4812XBsXI0xY547wzAMkzracsydYRiGSZC0Gnci+isRHSCitcqyMUS0mIjWENHrRFSiL88nomf15auIaIrymdP15dVE9BhRgs1D2xAeHptFRLSRiFbq/7qbfF3WQUR9iWghEW0gonVE9D19eVcieoeINuv/d9GXk35uVBPRaiI6TdnXV/XtNxPRVzP1m7zC42MTVM6d1zL1m7wigWMzXL/mmojobsO+punXVjURzc7E74mLRNN3Ekz5OQ/AaQDWKsuWApisv/46gAf117cBeFZ/3R3AMgA+/f0SAGcDIABvApiezt/Rxo/NIgDjM/17UnB8egE4TX/dCcAmACMAPAxgtr58NoD/p7++VD83CMAEAJ/py7sC2Kr/30V/3SXTv68tHBt93fFM/54MH5vuAM4A8AsAdyv78QPYAmAggHwAqwCMyPTvs/uXVs9dCPEBgMOGxcMAfKC/fgfAF/XXIwC8q3/uALQUpfFE1AtAiRBisdCO+j8AXJnqsacaL45NGoaZMYQQe4UQy/XX9QA2AKgAMBPA3/XN/o7IuTATwD+ExqcAOuvnziUA3hFCHBZCHIF2XKel8ad4jofHJueI99gIIQ4IIZYCaDHs6kwA1UKIrUKIZgBz9H20WdpCzH0tgCv0118C0Fd/vQrATCIKENEAAKfr6yoA1Cifr9GX5SLxHhvJs/pj9b25ELIyQkSVAMYB+AxADyHEXkC7kKF5XoB2TuxSPibPE6vlOUGSxwYAComoiog+JaKsd5pUXB4bK7LuvGkLxv3rAG4jomXQHpua9eV/hXYAqwD8DsAnAFqhPUoaydWUn3iPDQB8RQgxGsC5+r8b0jriFENEHQG8BOB/hBB1dpuaLBM2y7MeD44NAPQTWg71lwH8jogGeTzMjBDHsbHchcmyNn3eZLxBthDicwBTAYCIhgKYoS9vBfB9uR0RfQJgM4AjAPoou+gDYE+6xptOEjg2EELs1v+vJ6LnoD1O/iO9I08NRJQH7QL9lxDiZX3xfiLqJYTYq4cWDujLaxD9NCPPkxoAUwzLF6Vy3OnAo2MDIYT8fysRLYLm6W5Jw09IGXEeGyssj1lbJeOeu8zmICIfgHsAPKW/LyKiYv31xQBahRDr9UeoeiKaoIccbgTwamZGn1riPTZ6mKZMX54H4DJooZ2sR/9b/wXABiHEo8qq1wDIjJevInIuvAbgRj0zZAKAY/q5Mx/AVCLqomdITNWXZS1eHRv9mBTo+ywDcA6A9Wn5ESkigWNjxVIAQ4hoABHlA5il76Ptks7ZWwDPA9gLbbKiBsDNAL4HbQZ7E4CHECmsqoSmGrkBwAIA/ZX9jIdmtLYAeFx+Jpv/eXFsABRDy5xZDWAdgN8D8Gf6t3l0fCZBewxeDWCl/u9SAN2gTS5v1v/vqm9PAJ7Qz5E1UDKIoIW7qvV/X8v0b2srxwbARP39Kv3/mzP92zJwbHrq118dtESFGmgJHNA/t0k/bj/N9G9z+scVqgzDMDlIxsMyDMMwjPewcWcYhslB2LgzDMPkIGzcGYZhchA27gzDMDkIG3eGYZgchI07wzBMDsLGnWEYJgf5//IqglpiDpykAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd0d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 某只股票对某个参考指数（如标准普尔500指数）的相关系数\n",
    "# 通过计算百分数变化并使用rolling_corr的方式得到该结果\n",
    "spx_px = close_px_all['SPX']\n",
    "spx_rets = spx_px/spx_px.shift(1)-1\n",
    "returns = close_px.pct_change()\n",
    "corr = pd.rolling_corr(returns.AAPL,spx_rets,125,min_periods=100)\n",
    "corr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: pd.rolling_corr is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=125,min_periods=100).corr(other=<Series>)\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe54ca10>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf96bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 如果要一次性计算多只股票与标准普尔500指数的相关系数\n",
    "corr = pd.rolling_corr(returns,spx_rets,125,min_periods=100)\n",
    "corr.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户定义的移动窗口函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rolling_apply函数使你能够在移动窗口上应用自己设计的数组函数，唯一要求：该函数要能从数组的各个片段中产生单个值（即约简）\n",
    "# 比如，rolling_quantile计算样本分位数时，可能对样本中特定值的百分等级感兴趣，可以用percentileofscore来达到这个目的\n",
    "def percentileofscore(a, score, kind='rank'):\n",
    "    a = np.array(a)\n",
    "    n = len(a)\n",
    "\n",
    "    if kind == 'rank':\n",
    "        if not np.any(a == score):\n",
    "            a = np.append(a, score)\n",
    "            a_len = np.array(list(range(len(a))))\n",
    "        else:\n",
    "            a_len = np.array(list(range(len(a)))) + 1.0\n",
    "\n",
    "        a = np.sort(a)\n",
    "        idx = [a == score]\n",
    "        pct = (np.mean(a_len[idx]) / n) * 100.0\n",
    "        return pct\n",
    "\n",
    "    elif kind == 'strict':\n",
    "        return np.sum(a < score) / float(n) * 100\n",
    "    elif kind == 'weak':\n",
    "        return np.sum(a <= score) / float(n) * 100\n",
    "    elif kind == 'mean':\n",
    "        return (np.sum(a < score) + np.sum(a <= score)) * 50 / float(n)\n",
    "    else:\n",
    "        raise ValueError(\"kind can only be 'rank', 'strict', 'weak' or 'mean'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: pd.rolling_apply is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=250,center=False).apply(func=<function>,args=<tuple>,kwargs=<dict>)\n",
      "  \n",
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:16: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf7304f0>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf730190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_at_2percent = lambda x:percentileofscore(x,0.02)\n",
    "result = pd.rolling_apply(returns.AAPL,250,score_at_2percent)\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 性能和内存使用方面的注意事项\n",
    "对于每个数据点，其时间戳需要占用8字节的内存。由于pandas会尽量在多个时间序列之间共享索引，所以创建现有时间序列的视图不会占用更多内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00.000    0.268079\n",
       "2000-01-01 00:00:00.010    0.333374\n",
       "2000-01-01 00:00:00.020   -0.473221\n",
       "2000-01-01 00:00:00.030    0.199182\n",
       "2000-01-01 00:00:00.040    0.016043\n",
       "Freq: 10L, dtype: float64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2000',periods=10000000,freq='10ms')\n",
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "ts[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).ohlc()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>0.268079</td>\n",
       "      <td>4.185414</td>\n",
       "      <td>-4.872265</td>\n",
       "      <td>-0.371660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:15:00</th>\n",
       "      <td>-0.286287</td>\n",
       "      <td>3.981232</td>\n",
       "      <td>-4.325316</td>\n",
       "      <td>-0.866245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:30:00</th>\n",
       "      <td>-0.184067</td>\n",
       "      <td>4.198914</td>\n",
       "      <td>-4.250623</td>\n",
       "      <td>-0.800792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:45:00</th>\n",
       "      <td>1.316344</td>\n",
       "      <td>4.550909</td>\n",
       "      <td>-3.930997</td>\n",
       "      <td>0.806287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 01:00:00</th>\n",
       "      <td>1.911346</td>\n",
       "      <td>4.381481</td>\n",
       "      <td>-4.326433</td>\n",
       "      <td>-1.605036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         open      high       low     close\n",
       "2000-01-01 00:00:00  0.268079  4.185414 -4.872265 -0.371660\n",
       "2000-01-01 00:15:00 -0.286287  3.981232 -4.325316 -0.866245\n",
       "2000-01-01 00:30:00 -0.184067  4.198914 -4.250623 -0.800792\n",
       "2000-01-01 00:45:00  1.316344  4.550909 -3.930997  0.806287\n",
       "2000-01-01 01:00:00  1.911346  4.381481 -4.326433 -1.605036"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('15min',how='ohlc')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).ohlc()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "306 ms ± 15.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ts.resample('15min',how='ohlc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).ohlc()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "469 ms ± 50.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# 运行结果跟聚合结果的相对大小有一定关系，越高频率的聚合所韩飞的时间越多\n",
    "rng = pd.date_range('1/1/2000',periods=10000000,freq='1s')\n",
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "%timeit ts.resample('15s',how='ohlc')"
   ]
  }
 ],
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