pandas_Mix_file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>VALUE</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-12</td>\n",
       "      <td>3318.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-13</td>\n",
       "      <td>3279.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-14</td>\n",
       "      <td>3121.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-15</td>\n",
       "      <td>3169.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-16</td>\n",
       "      <td>3147.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-19</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-20</td>\n",
       "      <td>2959.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-21</td>\n",
       "      <td>3061.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-22</td>\n",
       "      <td>3032.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-23</td>\n",
       "      <td>2965.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2009-01-26</td>\n",
       "      <td>2967.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2009-01-27</td>\n",
       "      <td>3021.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2009-01-28</td>\n",
       "      <td>3137.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2009-01-29</td>\n",
       "      <td>3036.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2009-01-30</td>\n",
       "      <td>2965.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2009-02-02</td>\n",
       "      <td>2908.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009-02-03</td>\n",
       "      <td>3024.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2009-02-04</td>\n",
       "      <td>2991.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2009-02-05</td>\n",
       "      <td>3056.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2009-02-06</td>\n",
       "      <td>3203.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2009-02-09</td>\n",
       "      <td>3203.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2009-02-10</td>\n",
       "      <td>3042.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2009-02-11</td>\n",
       "      <td>3006.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2009-02-12</td>\n",
       "      <td>2986.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2009-02-13</td>\n",
       "      <td>2957.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2009-02-16</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2009-02-17</td>\n",
       "      <td>2803.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2009-02-18</td>\n",
       "      <td>2765.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2009-02-19</td>\n",
       "      <td>2708.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2009-02-20</td>\n",
       "      <td>2698.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>10820.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2580</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>10850.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>10374.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2582</th>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2583</th>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>10358.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2584</th>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>9951.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2585</th>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>9876.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>2018-12-11</td>\n",
       "      <td>9831.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2587</th>\n",
       "      <td>2018-12-12</td>\n",
       "      <td>9833.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2588</th>\n",
       "      <td>2018-12-13</td>\n",
       "      <td>9672.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2589</th>\n",
       "      <td>2018-12-14</td>\n",
       "      <td>9514.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2590</th>\n",
       "      <td>2018-12-17</td>\n",
       "      <td>9403.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2591</th>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>9445.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2592</th>\n",
       "      <td>2018-12-19</td>\n",
       "      <td>9147.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2593</th>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>9029.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2594</th>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>8874.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2595</th>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>8637.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2596</th>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2597</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>9102.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2598</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>9154.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>9109.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>9170.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>9202.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2603</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>8881.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>9225.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2605</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>9296.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2606</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>9447.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2607</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>9583.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>9624.70</td>\n",
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      ],
      "text/plain": [
       "            DATE     VALUE\n",
       "0     2009-01-12   3318.84\n",
       "1     2009-01-13   3279.26\n",
       "2     2009-01-14   3121.31\n",
       "3     2009-01-15   3169.77\n",
       "4     2009-01-16   3147.60\n",
       "5     2009-01-19         .\n",
       "6     2009-01-20   2959.40\n",
       "7     2009-01-21   3061.98\n",
       "8     2009-01-22   3032.60\n",
       "9     2009-01-23   2965.89\n",
       "10    2009-01-26   2967.59\n",
       "11    2009-01-27   3021.10\n",
       "12    2009-01-28   3137.65\n",
       "13    2009-01-29   3036.31\n",
       "14    2009-01-30   2965.69\n",
       "15    2009-02-02   2908.67\n",
       "16    2009-02-03   3024.61\n",
       "17    2009-02-04   2991.82\n",
       "18    2009-02-05   3056.88\n",
       "19    2009-02-06   3203.74\n",
       "20    2009-02-09   3203.32\n",
       "21    2009-02-10   3042.24\n",
       "22    2009-02-11   3006.93\n",
       "23    2009-02-12   2986.98\n",
       "24    2009-02-13   2957.28\n",
       "25    2009-02-16         .\n",
       "26    2009-02-17   2803.62\n",
       "27    2009-02-18   2765.88\n",
       "28    2009-02-19   2708.30\n",
       "29    2009-02-20   2698.87\n",
       "...          ...       ...\n",
       "2579  2018-11-30  10820.20\n",
       "2580  2018-12-03  10850.44\n",
       "2581  2018-12-04  10374.07\n",
       "2582  2018-12-05         .\n",
       "2583  2018-12-06  10358.70\n",
       "2584  2018-12-07   9951.16\n",
       "2585  2018-12-10   9876.54\n",
       "2586  2018-12-11   9831.00\n",
       "2587  2018-12-12   9833.81\n",
       "2588  2018-12-13   9672.74\n",
       "2589  2018-12-14   9514.11\n",
       "2590  2018-12-17   9403.53\n",
       "2591  2018-12-18   9445.47\n",
       "2592  2018-12-19   9147.66\n",
       "2593  2018-12-20   9029.94\n",
       "2594  2018-12-21   8874.79\n",
       "2595  2018-12-24   8637.15\n",
       "2596  2018-12-25         .\n",
       "2597  2018-12-26   9102.12\n",
       "2598  2018-12-27   9154.24\n",
       "2599  2018-12-28   9109.13\n",
       "2600  2018-12-31   9170.40\n",
       "2601  2019-01-01         .\n",
       "2602  2019-01-02   9202.47\n",
       "2603  2019-01-03   8881.49\n",
       "2604  2019-01-04   9225.02\n",
       "2605  2019-01-07   9296.23\n",
       "2606  2019-01-08   9447.85\n",
       "2607  2019-01-09   9583.25\n",
       "2608  2019-01-10   9624.70\n",
       "\n",
       "[2609 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"DJTA.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
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       "      <th>Close</th>\n",
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       "      <th>2017-02-01</th>\n",
       "      <td>174.5</td>\n",
       "      <td>175.5</td>\n",
       "      <td>173.5</td>\n",
       "      <td>175.5</td>\n",
       "      <td>169.355362</td>\n",
       "      <td>5953500.0</td>\n",
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       "      <th>2017-02-02</th>\n",
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       "      <th>2017-02-03</th>\n",
       "      <td>175.0</td>\n",
       "      <td>178.5</td>\n",
       "      <td>175.0</td>\n",
       "      <td>178.5</td>\n",
       "      <td>172.250336</td>\n",
       "      <td>5470600.0</td>\n",
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       "      <th>2017-02-06</th>\n",
       "      <td>178.5</td>\n",
       "      <td>181.5</td>\n",
       "      <td>178.5</td>\n",
       "      <td>180.5</td>\n",
       "      <td>174.180313</td>\n",
       "      <td>8150800.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>180.5</td>\n",
       "      <td>181.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>173.697815</td>\n",
       "      <td>3648600.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>180.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>178.5</td>\n",
       "      <td>179.0</td>\n",
       "      <td>172.732819</td>\n",
       "      <td>2574200.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>179.5</td>\n",
       "      <td>182.5</td>\n",
       "      <td>179.5</td>\n",
       "      <td>181.0</td>\n",
       "      <td>174.662796</td>\n",
       "      <td>7290700.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>182.0</td>\n",
       "      <td>182.5</td>\n",
       "      <td>181.0</td>\n",
       "      <td>182.5</td>\n",
       "      <td>176.110275</td>\n",
       "      <td>3589100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>182.5</td>\n",
       "      <td>182.5</td>\n",
       "      <td>182.5</td>\n",
       "      <td>182.5</td>\n",
       "      <td>176.110275</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>182.0</td>\n",
       "      <td>182.5</td>\n",
       "      <td>179.0</td>\n",
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      "text/plain": [
       "             Open   High    Low  Close   Adj Close     Volume\n",
       "Date                                                         \n",
       "2017-02-01  174.5  175.5  173.5  175.5  169.355362  5953500.0\n",
       "2017-02-02  175.0  177.0  174.5  175.5  169.355362  5250500.0\n",
       "2017-02-03  175.0  178.5  175.0  178.5  172.250336  5470600.0\n",
       "2017-02-06  178.5  181.5  178.5  180.5  174.180313  8150800.0\n",
       "2017-02-07  180.5  181.0  179.0  180.0  173.697815  3648600.0\n",
       "2017-02-08  180.0  180.0  178.5  179.0  172.732819  2574200.0\n",
       "2017-02-09  179.5  182.5  179.5  181.0  174.662796  7290700.0\n",
       "2017-02-10  182.0  182.5  181.0  182.5  176.110275  3589100.0\n",
       "2017-02-13  182.5  182.5  182.5  182.5  176.110275        0.0\n",
       "2017-02-14  182.0  182.5  179.0  179.0  172.732819  2841700.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbl=pd.read_csv(\"https://github.com/prasertcbs/tutorial/raw/master/eod/bbl.csv\",parse_dates=True,index_col=\"Date\")\n",
    "bbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 10 entries, 2017-02-01 to 2017-02-14\n",
      "Data columns (total 6 columns):\n",
      "Open         10 non-null float64\n",
      "High         10 non-null float64\n",
      "Low          10 non-null float64\n",
      "Close        10 non-null float64\n",
      "Adj Close    10 non-null float64\n",
      "Volume       10 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 560.0 bytes\n"
     ]
    }
   ],
   "source": [
    "bbl.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b38a2cb38>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bbl.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock=[\"bbl\",\"ktb\",\"scb\"]\n",
    "dfs=[]\n",
    "url=\"https://github.com/prasertcbs/tutorial/raw/master/eod/\"\n",
    "dfs=[pd.read_csv(f'{url}{s}.csv',parse_dates=True,index_col=\"Date\") for s in stock]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">bbl</th>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>174.500000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>169.355362</td>\n",
       "      <td>5953500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>174.500000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>169.355362</td>\n",
       "      <td>5250500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>172.250336</td>\n",
       "      <td>5470600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>178.500000</td>\n",
       "      <td>181.500000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>180.500000</td>\n",
       "      <td>174.180313</td>\n",
       "      <td>8150800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>180.500000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>173.697815</td>\n",
       "      <td>3648600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>172.732819</td>\n",
       "      <td>2574200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>179.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>179.500000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>174.662796</td>\n",
       "      <td>7290700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>176.110275</td>\n",
       "      <td>3589100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>182.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>176.110275</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>172.732819</td>\n",
       "      <td>2841700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ktb</th>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.366062</td>\n",
       "      <td>21752200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>40206000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.270405</td>\n",
       "      <td>48912400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>77639200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>49866700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>57530300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>19.100000</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>18.653030</td>\n",
       "      <td>142179400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.557373</td>\n",
       "      <td>55991300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.557373</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>19.500000</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.557373</td>\n",
       "      <td>69731100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">scb</th>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>150.500000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>150.500000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>146.727814</td>\n",
       "      <td>6969200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>152.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>145.762512</td>\n",
       "      <td>6529100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>153.500000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>147.693130</td>\n",
       "      <td>6597700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>153.500000</td>\n",
       "      <td>154.500000</td>\n",
       "      <td>149.141098</td>\n",
       "      <td>9901200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>154.500000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>153.500000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>148.658447</td>\n",
       "      <td>3802600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>153.500000</td>\n",
       "      <td>154.500000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>148.658447</td>\n",
       "      <td>5747300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>154.500000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>153.500000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>148.658447</td>\n",
       "      <td>6157400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>154.500000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>148.658447</td>\n",
       "      <td>9620500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>148.658447</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>155.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>148.658447</td>\n",
       "      <td>9754000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Open        High         Low       Close   Adj Close  \\\n",
       "Symbol Date                                                                     \n",
       "bbl    2017-02-01  174.500000  175.500000  173.500000  175.500000  169.355362   \n",
       "       2017-02-02  175.000000  177.000000  174.500000  175.500000  169.355362   \n",
       "       2017-02-03  175.000000  178.500000  175.000000  178.500000  172.250336   \n",
       "       2017-02-06  178.500000  181.500000  178.500000  180.500000  174.180313   \n",
       "       2017-02-07  180.500000  181.000000  179.000000  180.000000  173.697815   \n",
       "       2017-02-08  180.000000  180.000000  178.500000  179.000000  172.732819   \n",
       "       2017-02-09  179.500000  182.500000  179.500000  181.000000  174.662796   \n",
       "       2017-02-10  182.000000  182.500000  181.000000  182.500000  176.110275   \n",
       "       2017-02-13  182.500000  182.500000  182.500000  182.500000  176.110275   \n",
       "       2017-02-14  182.000000  182.500000  179.000000  179.000000  172.732819   \n",
       "ktb    2017-02-01   19.000000   19.200001   18.900000   19.200001   18.366062   \n",
       "       2017-02-02   19.200001   19.200001   18.900000   19.000000   18.174747   \n",
       "       2017-02-03   19.000000   19.100000   18.900000   19.100000   18.270405   \n",
       "       2017-02-06   19.200001   19.299999   18.900000   19.000000   18.174747   \n",
       "       2017-02-07   18.900000   19.100000   18.799999   19.000000   18.174747   \n",
       "       2017-02-08   19.000000   19.200001   18.900000   19.000000   18.174747   \n",
       "       2017-02-09   19.100000   19.700001   19.100000   19.500000   18.653030   \n",
       "       2017-02-10   19.600000   19.600000   19.400000   19.400000   18.557373   \n",
       "       2017-02-13   19.400000   19.400000   19.400000   19.400000   18.557373   \n",
       "       2017-02-14   19.500000   19.700001   19.200001   19.400000   18.557373   \n",
       "scb    2017-02-01  150.500000  153.000000  150.500000  152.000000  146.727814   \n",
       "       2017-02-02  152.000000  153.000000  151.000000  151.000000  145.762512   \n",
       "       2017-02-03  151.000000  153.500000  151.000000  153.000000  147.693130   \n",
       "       2017-02-06  154.000000  155.000000  153.500000  154.500000  149.141098   \n",
       "       2017-02-07  154.500000  155.000000  153.500000  154.000000  148.658447   \n",
       "       2017-02-08  153.500000  154.500000  153.000000  154.000000  148.658447   \n",
       "       2017-02-09  154.500000  156.000000  153.500000  154.000000  148.658447   \n",
       "       2017-02-10  154.500000  155.000000  154.000000  154.000000  148.658447   \n",
       "       2017-02-13  154.000000  154.000000  154.000000  154.000000  148.658447   \n",
       "       2017-02-14  155.000000  156.000000  153.000000  154.000000  148.658447   \n",
       "\n",
       "                        Volume  \n",
       "Symbol Date                     \n",
       "bbl    2017-02-01    5953500.0  \n",
       "       2017-02-02    5250500.0  \n",
       "       2017-02-03    5470600.0  \n",
       "       2017-02-06    8150800.0  \n",
       "       2017-02-07    3648600.0  \n",
       "       2017-02-08    2574200.0  \n",
       "       2017-02-09    7290700.0  \n",
       "       2017-02-10    3589100.0  \n",
       "       2017-02-13          0.0  \n",
       "       2017-02-14    2841700.0  \n",
       "ktb    2017-02-01   21752200.0  \n",
       "       2017-02-02   40206000.0  \n",
       "       2017-02-03   48912400.0  \n",
       "       2017-02-06   77639200.0  \n",
       "       2017-02-07   49866700.0  \n",
       "       2017-02-08   57530300.0  \n",
       "       2017-02-09  142179400.0  \n",
       "       2017-02-10   55991300.0  \n",
       "       2017-02-13          0.0  \n",
       "       2017-02-14   69731100.0  \n",
       "scb    2017-02-01    6969200.0  \n",
       "       2017-02-02    6529100.0  \n",
       "       2017-02-03    6597700.0  \n",
       "       2017-02-06    9901200.0  \n",
       "       2017-02-07    3802600.0  \n",
       "       2017-02-08    5747300.0  \n",
       "       2017-02-09    6157400.0  \n",
       "       2017-02-10    9620500.0  \n",
       "       2017-02-13          0.0  \n",
       "       2017-02-14    9754000.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df=pd.concat(dfs,keys=stock)\n",
    "df=pd.concat(dfs,keys=stock,names=[\"Symbol\",\"Date\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">bbl</th>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>174.500000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>169.355362</td>\n",
       "      <td>5953500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>174.500000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>169.355362</td>\n",
       "      <td>5250500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>175.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>172.250336</td>\n",
       "      <td>5470600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>178.500000</td>\n",
       "      <td>181.500000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>180.500000</td>\n",
       "      <td>174.180313</td>\n",
       "      <td>8150800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>180.500000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>173.697815</td>\n",
       "      <td>3648600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>178.500000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>172.732819</td>\n",
       "      <td>2574200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>179.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>179.500000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>174.662796</td>\n",
       "      <td>7290700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>176.110275</td>\n",
       "      <td>3589100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>182.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>176.110275</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>172.732819</td>\n",
       "      <td>2841700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ktb</th>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.366062</td>\n",
       "      <td>21752200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>40206000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.270405</td>\n",
       "      <td>48912400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.299999</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>77639200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>49866700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-08</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.174747</td>\n",
       "      <td>57530300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-09</th>\n",
       "      <td>19.100000</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>18.653030</td>\n",
       "      <td>142179400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.557373</td>\n",
       "      <td>55991300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-13</th>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.557373</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-14</th>\n",
       "      <td>19.500000</td>\n",
       "      <td>19.700001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.557373</td>\n",
       "      <td>69731100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Open        High         Low       Close   Adj Close  \\\n",
       "    Date                                                                     \n",
       "bbl 2017-02-01  174.500000  175.500000  173.500000  175.500000  169.355362   \n",
       "    2017-02-02  175.000000  177.000000  174.500000  175.500000  169.355362   \n",
       "    2017-02-03  175.000000  178.500000  175.000000  178.500000  172.250336   \n",
       "    2017-02-06  178.500000  181.500000  178.500000  180.500000  174.180313   \n",
       "    2017-02-07  180.500000  181.000000  179.000000  180.000000  173.697815   \n",
       "    2017-02-08  180.000000  180.000000  178.500000  179.000000  172.732819   \n",
       "    2017-02-09  179.500000  182.500000  179.500000  181.000000  174.662796   \n",
       "    2017-02-10  182.000000  182.500000  181.000000  182.500000  176.110275   \n",
       "    2017-02-13  182.500000  182.500000  182.500000  182.500000  176.110275   \n",
       "    2017-02-14  182.000000  182.500000  179.000000  179.000000  172.732819   \n",
       "ktb 2017-02-01   19.000000   19.200001   18.900000   19.200001   18.366062   \n",
       "    2017-02-02   19.200001   19.200001   18.900000   19.000000   18.174747   \n",
       "    2017-02-03   19.000000   19.100000   18.900000   19.100000   18.270405   \n",
       "    2017-02-06   19.200001   19.299999   18.900000   19.000000   18.174747   \n",
       "    2017-02-07   18.900000   19.100000   18.799999   19.000000   18.174747   \n",
       "    2017-02-08   19.000000   19.200001   18.900000   19.000000   18.174747   \n",
       "    2017-02-09   19.100000   19.700001   19.100000   19.500000   18.653030   \n",
       "    2017-02-10   19.600000   19.600000   19.400000   19.400000   18.557373   \n",
       "    2017-02-13   19.400000   19.400000   19.400000   19.400000   18.557373   \n",
       "    2017-02-14   19.500000   19.700001   19.200001   19.400000   18.557373   \n",
       "\n",
       "                     Volume  \n",
       "    Date                     \n",
       "bbl 2017-02-01    5953500.0  \n",
       "    2017-02-02    5250500.0  \n",
       "    2017-02-03    5470600.0  \n",
       "    2017-02-06    8150800.0  \n",
       "    2017-02-07    3648600.0  \n",
       "    2017-02-08    2574200.0  \n",
       "    2017-02-09    7290700.0  \n",
       "    2017-02-10    3589100.0  \n",
       "    2017-02-13          0.0  \n",
       "    2017-02-14    2841700.0  \n",
       "ktb 2017-02-01   21752200.0  \n",
       "    2017-02-02   40206000.0  \n",
       "    2017-02-03   48912400.0  \n",
       "    2017-02-06   77639200.0  \n",
       "    2017-02-07   49866700.0  \n",
       "    2017-02-08   57530300.0  \n",
       "    2017-02-09  142179400.0  \n",
       "    2017-02-10   55991300.0  \n",
       "    2017-02-13          0.0  \n",
       "    2017-02-14   69731100.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([dfs[0],dfs[1]],keys=stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2017-02-01    174.5\n",
       "2017-02-02    175.0\n",
       "2017-02-03    175.0\n",
       "2017-02-06    178.5\n",
       "2017-02-07    180.5\n",
       "2017-02-08    180.0\n",
       "2017-02-09    179.5\n",
       "2017-02-10    182.0\n",
       "2017-02-13    182.5\n",
       "2017-02-14    182.0\n",
       "Name: Open, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"bbl\"][\"Open\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Stock.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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