Lesson9-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_datareader import data, wb # 需要安装 pip install pandas_datareader\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sh_table = pd.read_csv('000001.SS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start = datetime.datetime(2010, 1, 1)\n",
    "end = datetime.datetime(2016,5,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sh = data.DataReader(\"000001.SS\", 'yahoo', start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\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>2010-01-04</th>\n",
       "      <td>3289.750000</td>\n",
       "      <td>3295.279053</td>\n",
       "      <td>3243.319092</td>\n",
       "      <td>3243.760010</td>\n",
       "      <td>3243.760010</td>\n",
       "      <td>109400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3254.468018</td>\n",
       "      <td>3290.511963</td>\n",
       "      <td>3221.461914</td>\n",
       "      <td>3282.178955</td>\n",
       "      <td>3282.178955</td>\n",
       "      <td>126200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3277.517090</td>\n",
       "      <td>3295.867920</td>\n",
       "      <td>3253.043945</td>\n",
       "      <td>3254.215088</td>\n",
       "      <td>3254.215088</td>\n",
       "      <td>123600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2010-01-04  3289.750000  3295.279053  3243.319092  3243.760010  3243.760010   \n",
       "2010-01-05  3254.468018  3290.511963  3221.461914  3282.178955  3282.178955   \n",
       "2010-01-06  3277.517090  3295.867920  3253.043945  3254.215088  3254.215088   \n",
       "\n",
       "            Volume  \n",
       "Date                \n",
       "2010-01-04  109400  \n",
       "2010-01-05  126200  \n",
       "2010-01-06  123600  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(3) # 数据获取成功"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2659.930065</td>\n",
       "      <td>2685.134673</td>\n",
       "      <td>2634.536714</td>\n",
       "      <td>2662.850010</td>\n",
       "      <td>2662.850010</td>\n",
       "      <td>163582.903226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>598.499649</td>\n",
       "      <td>611.277584</td>\n",
       "      <td>582.025113</td>\n",
       "      <td>599.169620</td>\n",
       "      <td>599.169620</td>\n",
       "      <td>143178.486481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1935.515991</td>\n",
       "      <td>1959.157959</td>\n",
       "      <td>1849.652954</td>\n",
       "      <td>1950.011963</td>\n",
       "      <td>1950.011963</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2198.475098</td>\n",
       "      <td>2218.034607</td>\n",
       "      <td>2183.512817</td>\n",
       "      <td>2198.712891</td>\n",
       "      <td>2198.712891</td>\n",
       "      <td>78725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2483.253418</td>\n",
       "      <td>2507.597412</td>\n",
       "      <td>2459.354004</td>\n",
       "      <td>2483.584961</td>\n",
       "      <td>2483.584961</td>\n",
       "      <td>108650.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2979.834473</td>\n",
       "      <td>3002.205322</td>\n",
       "      <td>2946.201538</td>\n",
       "      <td>2978.579712</td>\n",
       "      <td>2978.579712</td>\n",
       "      <td>182675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5174.417969</td>\n",
       "      <td>5178.190918</td>\n",
       "      <td>5103.400879</td>\n",
       "      <td>5166.350098</td>\n",
       "      <td>5166.350098</td>\n",
       "      <td>857100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  1550.000000  1550.000000  1550.000000  1550.000000  1550.000000   \n",
       "mean   2659.930065  2685.134673  2634.536714  2662.850010  2662.850010   \n",
       "std     598.499649   611.277584   582.025113   599.169620   599.169620   \n",
       "min    1935.515991  1959.157959  1849.652954  1950.011963  1950.011963   \n",
       "25%    2198.475098  2218.034607  2183.512817  2198.712891  2198.712891   \n",
       "50%    2483.253418  2507.597412  2459.354004  2483.584961  2483.584961   \n",
       "75%    2979.834473  3002.205322  2946.201538  2978.579712  2978.579712   \n",
       "max    5174.417969  5178.190918  5103.400879  5166.350098  5166.350098   \n",
       "\n",
       "              Volume  \n",
       "count    1550.000000  \n",
       "mean   163582.903226  \n",
       "std    143178.486481  \n",
       "min         0.000000  \n",
       "25%     78725.000000  \n",
       "50%    108650.000000  \n",
       "75%    182675.000000  \n",
       "max    857100.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.describe() # 数据整体概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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lO1FAw4Tn/YNP75cXoPY7HDbY2BmAliSngGpWJSy2K9qlRM86KWUEEwRB6C3Y1la3iWvg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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104426750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sh['Close'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sh = sh.drop('Volume',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2010-01-04</th>\n",
       "      <td>3289.750000</td>\n",
       "      <td>3295.279053</td>\n",
       "      <td>3243.319092</td>\n",
       "      <td>3243.760010</td>\n",
       "      <td>3243.760010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3254.468018</td>\n",
       "      <td>3290.511963</td>\n",
       "      <td>3221.461914</td>\n",
       "      <td>3282.178955</td>\n",
       "      <td>3282.178955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close\n",
       "Date                                                                       \n",
       "2010-01-04  3289.750000  3295.279053  3243.319092  3243.760010  3243.760010\n",
       "2010-01-05  3254.468018  3290.511963  3221.461914  3282.178955  3282.178955"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(2) # Volume列消失了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2010-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close Adj Close\n",
       "Date                                            \n",
       "2010-01-04  False  False  False  False     False\n",
       "2010-01-05  False  False  False  False     False\n",
       "2010-01-06  False  False  False  False     False\n",
       "2010-01-07  False  False  False  False     False\n",
       "2010-01-08  False  False  False  False     False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(sh).head() # or sh.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "change = sh.Close.diff()\n",
    "change.fillna(change.mean(),inplace=True)\n",
    "sh['Change'] = change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2010-01-11</th>\n",
       "      <td>3301.611084</td>\n",
       "      <td>3306.750000</td>\n",
       "      <td>3197.327881</td>\n",
       "      <td>3212.750000</td>\n",
       "      <td>3212.750000</td>\n",
       "      <td>16.752930</td>\n",
       "      <td>4.200850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>3205.705078</td>\n",
       "      <td>3275.201904</td>\n",
       "      <td>3180.084961</td>\n",
       "      <td>3273.966064</td>\n",
       "      <td>3273.966064</td>\n",
       "      <td>61.216064</td>\n",
       "      <td>2.654051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>3204.976074</td>\n",
       "      <td>3232.818115</td>\n",
       "      <td>3165.485107</td>\n",
       "      <td>3172.657959</td>\n",
       "      <td>3172.657959</td>\n",
       "      <td>-101.308105</td>\n",
       "      <td>-2.654927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>3183.381104</td>\n",
       "      <td>3219.016113</td>\n",
       "      <td>3165.594971</td>\n",
       "      <td>3215.550049</td>\n",
       "      <td>3215.550049</td>\n",
       "      <td>42.892090</td>\n",
       "      <td>-1.423383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2010-01-11  3301.611084  3306.750000  3197.327881  3212.750000  3212.750000   \n",
       "2010-01-12  3205.705078  3275.201904  3180.084961  3273.966064  3273.966064   \n",
       "2010-01-13  3204.976074  3232.818115  3165.485107  3172.657959  3172.657959   \n",
       "2010-01-14  3183.381104  3219.016113  3165.594971  3215.550049  3215.550049   \n",
       "\n",
       "                Change  pct_change  \n",
       "Date                                \n",
       "2010-01-11   16.752930    4.200850  \n",
       "2010-01-12   61.216064    2.654051  \n",
       "2010-01-13 -101.308105   -2.654927  \n",
       "2010-01-14   42.892090   -1.423383  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh['pct_change'] = sh.Change.pct_change()\n",
    "sh.iloc[5:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sh['group_index'] = sh.index.map(lambda x: 100*x.year + x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>group_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></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>2010-01-04</th>\n",
       "      <td>3289.750000</td>\n",
       "      <td>3295.279053</td>\n",
       "      <td>3243.319092</td>\n",
       "      <td>3243.760010</td>\n",
       "      <td>3243.760010</td>\n",
       "      <td>-0.270030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3254.468018</td>\n",
       "      <td>3290.511963</td>\n",
       "      <td>3221.461914</td>\n",
       "      <td>3282.178955</td>\n",
       "      <td>3282.178955</td>\n",
       "      <td>38.418945</td>\n",
       "      <td>-143.276366</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3277.517090</td>\n",
       "      <td>3295.867920</td>\n",
       "      <td>3253.043945</td>\n",
       "      <td>3254.215088</td>\n",
       "      <td>3254.215088</td>\n",
       "      <td>-27.963867</td>\n",
       "      <td>-1.727867</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3253.990967</td>\n",
       "      <td>3268.819092</td>\n",
       "      <td>3176.707031</td>\n",
       "      <td>3192.775879</td>\n",
       "      <td>3192.775879</td>\n",
       "      <td>-61.439209</td>\n",
       "      <td>1.197093</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3177.259033</td>\n",
       "      <td>3198.919922</td>\n",
       "      <td>3149.017090</td>\n",
       "      <td>3195.997070</td>\n",
       "      <td>3195.997070</td>\n",
       "      <td>3.221191</td>\n",
       "      <td>-1.052429</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2010-01-04  3289.750000  3295.279053  3243.319092  3243.760010  3243.760010   \n",
       "2010-01-05  3254.468018  3290.511963  3221.461914  3282.178955  3282.178955   \n",
       "2010-01-06  3277.517090  3295.867920  3253.043945  3254.215088  3254.215088   \n",
       "2010-01-07  3253.990967  3268.819092  3176.707031  3192.775879  3192.775879   \n",
       "2010-01-08  3177.259033  3198.919922  3149.017090  3195.997070  3195.997070   \n",
       "\n",
       "               Change  pct_change  group_index  \n",
       "Date                                            \n",
       "2010-01-04  -0.270030         NaN       201001  \n",
       "2010-01-05  38.418945 -143.276366       201001  \n",
       "2010-01-06 -27.963867   -1.727867       201001  \n",
       "2010-01-07 -61.439209    1.197093       201001  \n",
       "2010-01-08   3.221191   -1.052429       201001  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-04', '2010-01-05', '2010-01-06', '2010-01-07',\n",
       "               '2010-01-08', '2010-01-11', '2010-01-12', '2010-01-13',\n",
       "               '2010-01-14', '2010-01-15',\n",
       "               ...\n",
       "               '2016-05-09', '2016-05-10', '2016-05-11', '2016-05-12',\n",
       "               '2016-05-13', '2016-05-16', '2016-05-17', '2016-05-18',\n",
       "               '2016-05-19', '2016-05-20'],\n",
       "              dtype='datetime64[ns]', name=u'Date', length=1550, freq=None)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kkk65pg/knk65pg+krpNdvSK8ncp5m7/+jNWPPxjeX7NiBe4zUwGo3FRBdSt9r639jPr0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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac85790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sh[['Open']].plot();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}