10分钟入门pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[TOC]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 10分钟入门pandas\n",
    "\n",
    "这是一篇针对pandas新手的简短入门。\n",
    "\n",
    "在开始之前，首先导入以下几个库："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 创建对象\n",
    "\n",
    "通过传递一个list来创建**Series **,pandas会默认创建整型index："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "通过传递一个numpy array，日期index以及column标签来创建一个**DataFrame**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
       "               '2016-01-05', '2016-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20160101',periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "通过传递一个dictionary来创建一个**DataFrame**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2016-01-02  1.0  3   test  foo\n",
       "1  1.0 2016-01-02  1.0  3  train  foo\n",
       "2  1.0 2016-01-02  1.0  3   test  foo\n",
       "3  1.0 2016-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({ 'A' : 1.,\n",
    "                     'B' : pd.Timestamp('20160102'),\n",
    "                     'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                     'D' : np.array([3]*4,dtype='int32'),\n",
    "                     'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                     'F' : 'foo' })\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "查看每一列的数据类型："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 查看数据\n",
    "\n",
    "查看DataFrame中前几行和最后几行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "显示index，columns以及底层的numpy数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
       "               '2016-01-05', '2016-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'A', u'B', u'C', u'D'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.92836868,  0.33357795, -0.10563178, -0.71241949],\n",
       "       [-0.5233758 , -0.15095921,  0.17848586, -0.17826755],\n",
       "       [-0.54105741, -0.39732389, -2.58003191, -0.77831501],\n",
       "       [ 1.28208414, -0.20509759, -0.66533112, -0.68633603],\n",
       "       [-1.27995972,  0.27943192,  1.3007739 , -0.69807398],\n",
       "       [ 0.66991176,  0.17242801,  0.98034059,  1.24886146]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "describe()方法能对数据做一个快速统计汇总:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.089329</td>\n",
       "      <td>0.005343</td>\n",
       "      <td>-0.148566</td>\n",
       "      <td>-0.300758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.011086</td>\n",
       "      <td>0.297199</td>\n",
       "      <td>1.390172</td>\n",
       "      <td>0.789985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.536637</td>\n",
       "      <td>-0.191563</td>\n",
       "      <td>-0.525406</td>\n",
       "      <td>-0.708833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.073268</td>\n",
       "      <td>0.010734</td>\n",
       "      <td>0.036427</td>\n",
       "      <td>-0.692205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.863754</td>\n",
       "      <td>0.252681</td>\n",
       "      <td>0.779877</td>\n",
       "      <td>-0.305285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.089329  0.005343 -0.148566 -0.300758\n",
       "std    1.011086  0.297199  1.390172  0.789985\n",
       "min   -1.279960 -0.397324 -2.580032 -0.778315\n",
       "25%   -0.536637 -0.191563 -0.525406 -0.708833\n",
       "50%    0.073268  0.010734  0.036427 -0.692205\n",
       "75%    0.863754  0.252681  0.779877 -0.305285\n",
       "max    1.282084  0.333578  1.300774  1.248861"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对数据进行转置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <th>2016-01-02 00:00:00</th>\n",
       "      <th>2016-01-03 00:00:00</th>\n",
       "      <th>2016-01-04 00:00:00</th>\n",
       "      <th>2016-01-05 00:00:00</th>\n",
       "      <th>2016-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.541057</td>\n",
       "      <td>1.282084</td>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.669912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>0.172428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.105632</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>0.980341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.712419</td>\n",
       "      <td>-0.178268</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>-0.686336</td>\n",
       "      <td>-0.698074</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2016-01-01  2016-01-02  2016-01-03  2016-01-04  2016-01-05  2016-01-06\n",
       "A    0.928369   -0.523376   -0.541057    1.282084   -1.279960    0.669912\n",
       "B    0.333578   -0.150959   -0.397324   -0.205098    0.279432    0.172428\n",
       "C   -0.105632    0.178486   -2.580032   -0.665331    1.300774    0.980341\n",
       "D   -0.712419   -0.178268   -0.778315   -0.686336   -0.698074    1.248861"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "按某一坐标轴进行排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>-0.712419</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>0.928369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.178268</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>-0.523376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.778315</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-0.541057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-0.686336</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>1.282084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.698074</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>-1.279960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1.248861</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.669912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2016-01-01 -0.712419 -0.105632  0.333578  0.928369\n",
       "2016-01-02 -0.178268  0.178486 -0.150959 -0.523376\n",
       "2016-01-03 -0.778315 -2.580032 -0.397324 -0.541057\n",
       "2016-01-04 -0.686336 -0.665331 -0.205098  1.282084\n",
       "2016-01-05 -0.698074  1.300774  0.279432 -1.279960\n",
       "2016-01-06  1.248861  0.980341  0.172428  0.669912"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1,ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "按值进行排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 数据选择\n",
    "\n",
    "注意：虽然标准的Python/Numpy的表达式能完成选择与赋值等功能，但我们仍推荐使用优化过的pandas数据访问方法：.at，.iat，.loc，.iloc和.ix\n",
    "\n",
    "### 选取\n",
    "\n",
    "选择某一列数据，它会返回一个Series，等同于df.A："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    0.928369\n",
       "2016-01-02   -0.523376\n",
       "2016-01-03   -0.541057\n",
       "2016-01-04    1.282084\n",
       "2016-01-05   -1.279960\n",
       "2016-01-06    0.669912\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "使用[]，对行进行切片选取。\n",
    "\n",
    "**注意**，[]内必须是一个范围，而不能仅仅是一个值。否则报错："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3] # 选取范围是[)，前闭合，后开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20160102':'20160104'] # 选取范围是[], 前闭合，后闭合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# []内必须是一个范围，而不能仅仅是一个值。否则报错\n",
    "\n",
    "# df[1]  # 报错\n",
    "\n",
    "# df['20160102'] # 报错\n",
    "# df[dates[0]] # 报错"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 通过label选取\n",
    "\n",
    "**Series的label就是index。DataFrame的label包括index和columns**。\n",
    "\n",
    "通过label选取比使用整形下标选取直观的多。方法很简单，调用.loc[], .loc[]内可以是一个label值，也可以是一个label取值范围。注意切片范围是前后闭合。\n",
    "\n",
    "通过label进行交叉选取："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.928369\n",
       "B    0.333578\n",
       "C   -0.105632\n",
       "D   -0.712419\n",
       "Name: 2016-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "使用label对多个轴进行选取："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2016-01-01  0.928369  0.333578\n",
       "2016-01-02 -0.523376 -0.150959\n",
       "2016-01-03 -0.541057 -0.397324\n",
       "2016-01-04  1.282084 -0.205098\n",
       "2016-01-05 -1.279960  0.279432\n",
       "2016-01-06  0.669912  0.172428"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['A', 'B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "根据label进行切片选择，注意切片范围是前后闭合："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2016-01-02 -0.523376 -0.150959\n",
       "2016-01-03 -0.541057 -0.397324\n",
       "2016-01-04  1.282084 -0.205098"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20160102':'20160104',['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# .loc[] 内的值只能是label，如果传入整数下标，报错\n",
    "# df.loc[0:3, ['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.523376\n",
       "B   -0.150959\n",
       "Name: 2016-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20160102',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "获取一个标量值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9283686787182388"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9283686787182388"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20160101', 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "快速获取一个标量（与上面方法效果相同）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9283686787182388"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 通过下标选取\n",
    "\n",
    "下标(position), 是编程语言中的重要概念。\n",
    "\n",
    "pandas的数据结构也支持用下标进行检索。比如把DataFrame看做一个二维数组，用下标进行检索。\n",
    "\n",
    "用下标进行选取/检索的方法是调用 .iloc[]\n",
    "\n",
    ".loc[]内可以是一个整形下标值，也可以是一个下标取值范围（切片），甚至可以是一个list。注意，如果传入切片范围，范围是前闭合，后开的。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "传入单个整形下标："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.282084\n",
       "B   -0.205098\n",
       "C   -0.665331\n",
       "D   -0.686336\n",
       "Name: 2016-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "传入整型的下标切片，与python/numpy形式相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2016-01-04  1.282084 -0.205098\n",
       "2016-01-05 -1.279960  0.279432"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "传入list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>0.178486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-2.580032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>1.300774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2016-01-02 -0.523376  0.178486\n",
       "2016-01-03 -0.541057 -2.580032\n",
       "2016-01-05 -1.279960  1.300774"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4], [0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "只对行进行切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "只对列进行切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2016-01-01  0.333578 -0.105632\n",
       "2016-01-02 -0.150959  0.178486\n",
       "2016-01-03 -0.397324 -2.580032\n",
       "2016-01-04 -0.205098 -0.665331\n",
       "2016-01-05  0.279432  1.300774\n",
       "2016-01-06  0.172428  0.980341"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "只获取某个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.15095920811674263"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "快速获取某个值（与上面的方法等价）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.15095920811674263"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 布尔索引\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "用某列的值来选取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "用where操作来选取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01  0.928369  0.333578       NaN       NaN\n",
       "2016-01-02       NaN       NaN  0.178486       NaN\n",
       "2016-01-03       NaN       NaN       NaN       NaN\n",
       "2016-01-04  1.282084       NaN       NaN       NaN\n",
       "2016-01-05       NaN  0.279432  1.300774       NaN\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "用isin()方法来过滤数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419    one\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268    one\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315    two\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336  three\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074   four\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861  three"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['E'] = ['one','one','two','three','four','three']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315   two\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074  four"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two','four'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    E\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419  one\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268  one\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315  two"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['one','one','two'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 赋值\n",
    "\n",
    "为DataFrame增加新的一列，通过index来自动对齐数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20160102',periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-02    1\n",
       "2016-01-03    2\n",
       "2016-01-04    3\n",
       "2016-01-05    4\n",
       "2016-01-06    5\n",
       "2016-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-02    1\n",
       "2016-01-03    2\n",
       "2016-01-04    3\n",
       "2016-01-05    4\n",
       "2016-01-06    5\n",
       "2016-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['F'] = s1\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.928369</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2016-01-01  0.928369  0.333578 -0.105632 -0.712419  NaN\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315  2.0\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336  3.0\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074  4.0\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861  5.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # 注意F列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "通过label赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333578</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2016-01-01  0.000000  0.333578 -0.105632 -0.712419  NaN\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315  2.0\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336  3.0\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074  4.0\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861  5.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'A'] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "通过下标赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-0.712419</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>-0.178268</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-0.778315</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-0.686336</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>-0.698074</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>1.248861</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2016-01-01  0.000000  0.000000 -0.105632 -0.712419  NaN\n",
       "2016-01-02 -0.523376 -0.150959  0.178486 -0.178268  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -0.778315  2.0\n",
       "2016-01-04  1.282084 -0.205098 -0.665331 -0.686336  3.0\n",
       "2016-01-05 -1.279960  0.279432  1.300774 -0.698074  4.0\n",
       "2016-01-06  0.669912  0.172428  0.980341  1.248861  5.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0,1] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "通过传递numpy array赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.loc[:,'D'] = np.array([5] * len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2016-01-01  0.000000  0.000000 -0.105632  5  NaN\n",
       "2016-01-02 -0.523376 -0.150959  0.178486  5  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032  5  2.0\n",
       "2016-01-04  1.282084 -0.205098 -0.665331  5  3.0\n",
       "2016-01-05 -1.279960  0.279432  1.300774  5  4.0\n",
       "2016-01-06  0.669912  0.172428  0.980341  5  5.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>-5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>-0.178486</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>-0.279432</td>\n",
       "      <td>-1.300774</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.669912</td>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.980341</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2016-01-01  0.000000  0.000000 -0.105632 -5  NaN\n",
       "2016-01-02 -0.523376 -0.150959 -0.178486 -5 -1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032 -5 -2.0\n",
       "2016-01-04 -1.282084 -0.205098 -0.665331 -5 -3.0\n",
       "2016-01-05 -1.279960 -0.279432 -1.300774 -5 -4.0\n",
       "2016-01-06 -0.669912 -0.172428 -0.980341 -5 -5.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2[df2 > 0] = -df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 缺失值处理\n",
    "\n",
    "在pandas中，用np.nan来填充缺失值，这些值默认不会参与运算。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "reindex()允许你修改、增加、删除指定轴上的index，并返回**数据副本**。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2016-01-01  0.000000  0.000000 -0.105632  5  NaN  1.0\n",
       "2016-01-02 -0.523376 -0.150959  0.178486  5  1.0  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032  5  2.0  NaN\n",
       "2016-01-04  1.282084 -0.205098 -0.665331  5  3.0  NaN"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4],columns=list(df.columns)+['E'])\n",
    "df1.loc[dates[0]:dates[1],'E'] = 1\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "剔除所有包含缺失值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2016-01-02 -0.523376 -0.150959  0.178486  5  1.0  1.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2016-01-01  0.000000  0.000000 -0.105632  5  5.0  1.0\n",
       "2016-01-02 -0.523376 -0.150959  0.178486  5  1.0  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032  5  2.0  5.0\n",
       "2016-01-04  1.282084 -0.205098 -0.665331  5  3.0  5.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "获取值是否为nan的布尔标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>False</td>\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>2016-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2016-01-01  False  False  False  False   True  False\n",
       "2016-01-02  False  False  False  False  False  False\n",
       "2016-01-03  False  False  False  False  False   True\n",
       "2016-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 运算\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 统计\n",
    "\n",
    "运算过程中，缺失值不参与计算。\n",
    "\n",
    "得到描述性统计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.178486</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-0.541057</td>\n",
       "      <td>-0.397324</td>\n",
       "      <td>-2.580032</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1.282084</td>\n",
       "      <td>-0.205098</td>\n",
       "      <td>-0.665331</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.279960</td>\n",
       "      <td>0.279432</td>\n",
       "      <td>1.300774</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.172428</td>\n",
       "      <td>0.980341</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2016-01-01  0.000000  0.000000 -0.105632  5  NaN\n",
       "2016-01-02 -0.523376 -0.150959  0.178486  5  1.0\n",
       "2016-01-03 -0.541057 -0.397324 -2.580032  5  2.0\n",
       "2016-01-04  1.282084 -0.205098 -0.665331  5  3.0\n",
       "2016-01-05 -1.279960  0.279432  1.300774  5  4.0\n",
       "2016-01-06  0.669912  0.172428  0.980341  5  5.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.065400\n",
       "B   -0.050253\n",
       "C   -0.148566\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    -0.392397\n",
       "B    -0.301521\n",
       "C    -0.891394\n",
       "D    30.000000\n",
       "F    15.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对其他轴进行同样的运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    1.223592\n",
       "2016-01-02    1.100830\n",
       "2016-01-03    0.696317\n",
       "2016-01-04    1.682331\n",
       "2016-01-05    1.860049\n",
       "2016-01-06    2.364536\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对于拥有不同维度的对象进行运算时需要对齐。除此之外，pandas会自动沿着指定维度广播。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    NaN\n",
       "2016-01-02    NaN\n",
       "2016-01-03    1.0\n",
       "2016-01-04    3.0\n",
       "2016-01-05    5.0\n",
       "2016-01-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8],index=dates).shift(2)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-1.541057</td>\n",
       "      <td>-1.397324</td>\n",
       "      <td>-3.580032</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-1.717916</td>\n",
       "      <td>-3.205098</td>\n",
       "      <td>-3.665331</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-6.279960</td>\n",
       "      <td>-4.720568</td>\n",
       "      <td>-3.699226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>NaN</td>\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": [
       "                   A         B         C    D    F\n",
       "2016-01-01       NaN       NaN       NaN  NaN  NaN\n",
       "2016-01-02       NaN       NaN       NaN  NaN  NaN\n",
       "2016-01-03 -1.541057 -1.397324 -3.580032  4.0  1.0\n",
       "2016-01-04 -1.717916 -3.205098 -3.665331  2.0  0.0\n",
       "2016-01-05 -6.279960 -4.720568 -3.699226  0.0 -1.0\n",
       "2016-01-06       NaN       NaN       NaN  NaN  NaN"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s,axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Apply函数\n",
    "\n",
    "这是一个高阶函数，它的参数也是函数，类似于map(),把参数函数作用在数据上。\n",
    "\n",
    "默认作用在一列数据上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.105632</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-0.523376</td>\n",
       "      <td>-0.150959</td>\n",
       "      <td>0.072854</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-1.064433</td>\n",
       "      <td>-0.548283</td>\n",
       "      <td>-2.507178</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.217651</td>\n",
       "      <td>-0.753381</td>\n",
       "      <td>-3.172509</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-1.062309</td>\n",
       "      <td>-0.473949</td>\n",
       "      <td>-1.871735</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.392397</td>\n",
       "      <td>-0.301521</td>\n",
       "      <td>-0.891394</td>\n",
       "      <td>30</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D     F\n",
       "2016-01-01  0.000000  0.000000 -0.105632   5   NaN\n",
       "2016-01-02 -0.523376 -0.150959  0.072854  10   1.0\n",
       "2016-01-03 -1.064433 -0.548283 -2.507178  15   3.0\n",
       "2016-01-04  0.217651 -0.753381 -3.172509  20   6.0\n",
       "2016-01-05 -1.062309 -0.473949 -1.871735  25  10.0\n",
       "2016-01-06 -0.392397 -0.301521 -0.891394  30  15.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.562044\n",
       "B    0.676756\n",
       "C    3.880806\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max()-x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 频数统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    6\n",
       "2    0\n",
       "3    0\n",
       "4    6\n",
       "5    6\n",
       "6    3\n",
       "7    1\n",
       "8    2\n",
       "9    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0,7,size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    3\n",
       "0    3\n",
       "2    2\n",
       "3    1\n",
       "1    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 字符串方法\n",
    "\n",
    "对于Series对象，它的str属性包含一系列的字符串处理方法。\n",
    "\n",
    "就如同下段代码一样，能很方便的对array中各个元素进行运算。值得注意的是，在str属性中的模式匹配默认使用正则表达式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n",
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 合并(Merge)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Concat 连接\n",
    "\n",
    "pandas中提供了大量的方法能够轻松对Series，DataFrame和Panel对象进行多种逻辑关系的合并操作。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "通过pd.concat()来连接pandas对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.247905</td>\n",
       "      <td>-2.345166</td>\n",
       "      <td>1.252221</td>\n",
       "      <td>0.670462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.314595</td>\n",
       "      <td>0.619986</td>\n",
       "      <td>2.421725</td>\n",
       "      <td>0.357492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.826127</td>\n",
       "      <td>-0.483145</td>\n",
       "      <td>0.655893</td>\n",
       "      <td>-0.627891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.071736</td>\n",
       "      <td>0.601667</td>\n",
       "      <td>-0.995181</td>\n",
       "      <td>-0.268664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.477940</td>\n",
       "      <td>-1.112391</td>\n",
       "      <td>-0.374552</td>\n",
       "      <td>0.058009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.781169</td>\n",
       "      <td>1.171458</td>\n",
       "      <td>-0.071877</td>\n",
       "      <td>-0.352357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.286301</td>\n",
       "      <td>-0.252232</td>\n",
       "      <td>0.342850</td>\n",
       "      <td>1.783261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.670887</td>\n",
       "      <td>1.295367</td>\n",
       "      <td>-0.720624</td>\n",
       "      <td>-0.795947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.174607</td>\n",
       "      <td>0.357461</td>\n",
       "      <td>1.990180</td>\n",
       "      <td>-0.521559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.561951</td>\n",
       "      <td>-1.539058</td>\n",
       "      <td>-0.640498</td>\n",
       "      <td>1.416332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.247905 -2.345166  1.252221  0.670462\n",
       "1 -0.314595  0.619986  2.421725  0.357492\n",
       "2  1.826127 -0.483145  0.655893 -0.627891\n",
       "3  0.071736  0.601667 -0.995181 -0.268664\n",
       "4 -0.477940 -1.112391 -0.374552  0.058009\n",
       "5 -0.781169  1.171458 -0.071877 -0.352357\n",
       "6 -0.286301 -0.252232  0.342850  1.783261\n",
       "7  1.670887  1.295367 -0.720624 -0.795947\n",
       "8  0.174607  0.357461  1.990180 -0.521559\n",
       "9 -1.561951 -1.539058 -0.640498  1.416332"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0  0.247905 -2.345166  1.252221  0.670462\n",
       " 1 -0.314595  0.619986  2.421725  0.357492\n",
       " 2  1.826127 -0.483145  0.655893 -0.627891,\n",
       "           0         1         2         3\n",
       " 3  0.071736  0.601667 -0.995181 -0.268664\n",
       " 4 -0.477940 -1.112391 -0.374552  0.058009\n",
       " 5 -0.781169  1.171458 -0.071877 -0.352357\n",
       " 6 -0.286301 -0.252232  0.342850  1.783261,\n",
       "           0         1         2         3\n",
       " 7  1.670887  1.295367 -0.720624 -0.795947\n",
       " 8  0.174607  0.357461  1.990180 -0.521559\n",
       " 9 -1.561951 -1.539058 -0.640498  1.416332]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# break it into pieces\n",
    "pieces = [df[:3], df[3:7], df[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.247905</td>\n",
       "      <td>-2.345166</td>\n",
       "      <td>1.252221</td>\n",
       "      <td>0.670462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.314595</td>\n",
       "      <td>0.619986</td>\n",
       "      <td>2.421725</td>\n",
       "      <td>0.357492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.826127</td>\n",
       "      <td>-0.483145</td>\n",
       "      <td>0.655893</td>\n",
       "      <td>-0.627891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.071736</td>\n",
       "      <td>0.601667</td>\n",
       "      <td>-0.995181</td>\n",
       "      <td>-0.268664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.477940</td>\n",
       "      <td>-1.112391</td>\n",
       "      <td>-0.374552</td>\n",
       "      <td>0.058009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.781169</td>\n",
       "      <td>1.171458</td>\n",
       "      <td>-0.071877</td>\n",
       "      <td>-0.352357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.286301</td>\n",
       "      <td>-0.252232</td>\n",
       "      <td>0.342850</td>\n",
       "      <td>1.783261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.670887</td>\n",
       "      <td>1.295367</td>\n",
       "      <td>-0.720624</td>\n",
       "      <td>-0.795947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.174607</td>\n",
       "      <td>0.357461</td>\n",
       "      <td>1.990180</td>\n",
       "      <td>-0.521559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.561951</td>\n",
       "      <td>-1.539058</td>\n",
       "      <td>-0.640498</td>\n",
       "      <td>1.416332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.247905 -2.345166  1.252221  0.670462\n",
       "1 -0.314595  0.619986  2.421725  0.357492\n",
       "2  1.826127 -0.483145  0.655893 -0.627891\n",
       "3  0.071736  0.601667 -0.995181 -0.268664\n",
       "4 -0.477940 -1.112391 -0.374552  0.058009\n",
       "5 -0.781169  1.171458 -0.071877 -0.352357\n",
       "6 -0.286301 -0.252232  0.342850  1.783261\n",
       "7  1.670887  1.295367 -0.720624 -0.795947\n",
       "8  0.174607  0.357461  1.990180 -0.521559\n",
       "9 -1.561951 -1.539058 -0.640498  1.416332"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Join 合并\n",
    "\n",
    "类似于SQL中的合并(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key':['foo','foo'],'lval':[1,2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key':['foo','foo'],'lval':[4,5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval_x  lval_y\n",
       "0  foo       1       4\n",
       "1  foo       1       5\n",
       "2  foo       2       4\n",
       "3  foo       2       5"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "pd.merge()的另一个例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  bar     5"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  bar     2     5"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Append 添加\n",
    "\n",
    "将若干行添加到dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222672</td>\n",
       "      <td>0.443049</td>\n",
       "      <td>-1.257127</td>\n",
       "      <td>0.736807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.375083</td>\n",
       "      <td>-0.024848</td>\n",
       "      <td>-0.976872</td>\n",
       "      <td>-0.605654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.685383</td>\n",
       "      <td>0.229636</td>\n",
       "      <td>-0.970260</td>\n",
       "      <td>2.517385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.255183</td>\n",
       "      <td>-0.482105</td>\n",
       "      <td>0.703707</td>\n",
       "      <td>0.069610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.128585</td>\n",
       "      <td>2.153157</td>\n",
       "      <td>-1.720873</td>\n",
       "      <td>-0.598394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.629814</td>\n",
       "      <td>-0.916473</td>\n",
       "      <td>0.264643</td>\n",
       "      <td>-0.412704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.636748</td>\n",
       "      <td>0.450428</td>\n",
       "      <td>-0.665539</td>\n",
       "      <td>-0.608706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.767546</td>\n",
       "      <td>-0.706155</td>\n",
       "      <td>0.594495</td>\n",
       "      <td>-0.213454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.222672  0.443049 -1.257127  0.736807\n",
       "1 -0.375083 -0.024848 -0.976872 -0.605654\n",
       "2 -0.685383  0.229636 -0.970260  2.517385\n",
       "3 -0.255183 -0.482105  0.703707  0.069610\n",
       "4 -0.128585  2.153157 -1.720873 -0.598394\n",
       "5  0.629814 -0.916473  0.264643 -0.412704\n",
       "6  0.636748  0.450428 -0.665539 -0.608706\n",
       "7  0.767546 -0.706155  0.594495 -0.213454"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.DataFrame(np.random.randn(8,4),columns=['A','B','C','D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.255183\n",
       "B   -0.482105\n",
       "C    0.703707\n",
       "D    0.069610\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222672</td>\n",
       "      <td>0.443049</td>\n",
       "      <td>-1.257127</td>\n",
       "      <td>0.736807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.375083</td>\n",
       "      <td>-0.024848</td>\n",
       "      <td>-0.976872</td>\n",
       "      <td>-0.605654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.685383</td>\n",
       "      <td>0.229636</td>\n",
       "      <td>-0.970260</td>\n",
       "      <td>2.517385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.255183</td>\n",
       "      <td>-0.482105</td>\n",
       "      <td>0.703707</td>\n",
       "      <td>0.069610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.128585</td>\n",
       "      <td>2.153157</td>\n",
       "      <td>-1.720873</td>\n",
       "      <td>-0.598394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.629814</td>\n",
       "      <td>-0.916473</td>\n",
       "      <td>0.264643</td>\n",
       "      <td>-0.412704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.636748</td>\n",
       "      <td>0.450428</td>\n",
       "      <td>-0.665539</td>\n",
       "      <td>-0.608706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.767546</td>\n",
       "      <td>-0.706155</td>\n",
       "      <td>0.594495</td>\n",
       "      <td>-0.213454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.255183</td>\n",
       "      <td>-0.482105</td>\n",
       "      <td>0.703707</td>\n",
       "      <td>0.069610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.222672  0.443049 -1.257127  0.736807\n",
       "1 -0.375083 -0.024848 -0.976872 -0.605654\n",
       "2 -0.685383  0.229636 -0.970260  2.517385\n",
       "3 -0.255183 -0.482105  0.703707  0.069610\n",
       "4 -0.128585  2.153157 -1.720873 -0.598394\n",
       "5  0.629814 -0.916473  0.264643 -0.412704\n",
       "6  0.636748  0.450428 -0.665539 -0.608706\n",
       "7  0.767546 -0.706155  0.594495 -0.213454\n",
       "8 -0.255183 -0.482105  0.703707  0.069610"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s,ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## group(分组)\n",
    "\n",
    "group by 操作过程如下: \n",
    "\n",
    "* **划分** 按照某些标准将数据分为不同的组\n",
    "* **应用** 对每组数据分别应用某个函数\n",
    "* **划分** 将结果组合到一个新的数据结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1.389706</td>\n",
       "      <td>0.194864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.068122</td>\n",
       "      <td>0.548799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.772096</td>\n",
       "      <td>0.656627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.784895</td>\n",
       "      <td>-0.971999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.601806</td>\n",
       "      <td>-0.986536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.778508</td>\n",
       "      <td>-3.408366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.251064</td>\n",
       "      <td>-0.144214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.180176</td>\n",
       "      <td>0.669016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  1.389706  0.194864\n",
       "1  bar    one  1.068122  0.548799\n",
       "2  foo    two -1.772096  0.656627\n",
       "3  bar  three -0.784895 -0.971999\n",
       "4  foo    two -1.601806 -0.986536\n",
       "5  bar    two -0.778508 -3.408366\n",
       "6  foo    one  0.251064 -0.144214\n",
       "7  bar  three  0.180176  0.669016"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', \n",
    "                          'foo', 'bar', 'foo', 'bar'],\n",
    "                   'B' : ['one', 'one', 'two', 'three', \n",
    "                          'two', 'two', 'one', 'three'],\n",
    "                   'C' : np.random.randn(8),\n",
    "                   'D' : np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "分组并对每个分组应用sum函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>-0.315105</td>\n",
       "      <td>-3.162549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-1.733133</td>\n",
       "      <td>-0.279259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -0.315105 -3.162549\n",
       "foo -1.733133 -0.279259"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "按多个column分组形成层级index，然后应用sun函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1.068122</td>\n",
       "      <td>0.548799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.604719</td>\n",
       "      <td>-0.302983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.778508</td>\n",
       "      <td>-3.408366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>1.640770</td>\n",
       "      <td>0.050650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-3.373903</td>\n",
       "      <td>-0.329909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.068122  0.548799\n",
       "    three -0.604719 -0.302983\n",
       "    two   -0.778508 -3.408366\n",
       "foo one    1.640770  0.050650\n",
       "    two   -3.373903 -0.329909"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## reshape(变形)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bar', 'one'),\n",
       " ('bar', 'two'),\n",
       " ('baz', 'one'),\n",
       " ('baz', 'two'),\n",
       " ('foo', 'one'),\n",
       " ('foo', 'two'),\n",
       " ('qux', 'one'),\n",
       " ('qux', 'two')]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "                     'foo', 'foo', 'qux', 'qux'],\n",
    "                    ['one', 'two', 'one', 'two',\n",
    "                     'one', 'two', 'one', 'two']]))\n",
    "tuples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.678641</td>\n",
       "      <td>0.196709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.414739</td>\n",
       "      <td>-1.874639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>0.526168</td>\n",
       "      <td>-0.586098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.794330</td>\n",
       "      <td>0.621687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.678641  0.196709\n",
       "      two     0.414739 -1.874639\n",
       "baz   one     0.526168 -0.586098\n",
       "      two    -0.794330  0.621687"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples,names=['first', 'second'])\n",
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\n",
    "\n",
    "df2 = df[:4]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**stack()**方法对DataFrame的列“压缩”一个层级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.678641\n",
       "               B    0.196709\n",
       "       two     A    0.414739\n",
       "               B   -1.874639\n",
       "baz    one     A    0.526168\n",
       "               B   -0.586098\n",
       "       two     A   -0.794330\n",
       "               B    0.621687\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = df2.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对于一个“stacked” DataFrame或者Series（拥有MultiIndex作为索引），**stack()**的逆操作是**unstack()**，默认反堆叠到上一个层级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.678641</td>\n",
       "      <td>0.196709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.414739</td>\n",
       "      <td>-1.874639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>0.526168</td>\n",
       "      <td>-0.586098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.794330</td>\n",
       "      <td>0.621687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.678641  0.196709\n",
       "      two     0.414739 -1.874639\n",
       "baz   one     0.526168 -0.586098\n",
       "      two    -0.794330  0.621687"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.678641</td>\n",
       "      <td>0.414739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.196709</td>\n",
       "      <td>-1.874639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>0.526168</td>\n",
       "      <td>-0.794330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.586098</td>\n",
       "      <td>0.621687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.678641  0.414739\n",
       "      B  0.196709 -1.874639\n",
       "baz   A  0.526168 -0.794330\n",
       "      B -0.586098  0.621687"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.678641</td>\n",
       "      <td>0.526168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.196709</td>\n",
       "      <td>-0.586098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>0.414739</td>\n",
       "      <td>-0.794330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.874639</td>\n",
       "      <td>0.621687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.678641  0.526168\n",
       "       B  0.196709 -0.586098\n",
       "two    A  0.414739 -0.794330\n",
       "       B -1.874639  0.621687"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 数据透视表(pivot tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.484209</td>\n",
       "      <td>1.372559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.229735</td>\n",
       "      <td>1.155902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.540843</td>\n",
       "      <td>-0.244282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.639626</td>\n",
       "      <td>1.056057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-1.323951</td>\n",
       "      <td>-0.729839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.855990</td>\n",
       "      <td>0.498600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.298377</td>\n",
       "      <td>-0.219432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.363005</td>\n",
       "      <td>-0.471282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.430104</td>\n",
       "      <td>-0.466728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.216707</td>\n",
       "      <td>0.439948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-2.140101</td>\n",
       "      <td>-1.813999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.620069</td>\n",
       "      <td>1.107080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo -0.484209  1.372559\n",
       "1     one  B  foo  1.229735  1.155902\n",
       "2     two  C  foo  0.540843 -0.244282\n",
       "3   three  A  bar  1.639626  1.056057\n",
       "4     one  B  bar -1.323951 -0.729839\n",
       "5     one  C  bar  0.855990  0.498600\n",
       "6     two  A  foo -1.298377 -0.219432\n",
       "7   three  B  foo -0.363005 -0.471282\n",
       "8     one  C  foo -1.430104 -0.466728\n",
       "9     one  A  bar  1.216707  0.439948\n",
       "10    two  B  bar -2.140101 -1.813999\n",
       "11  three  C  bar  1.620069  1.107080"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n",
    "                   'B' : ['A', 'B', 'C'] * 4,\n",
    "                   'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "                   'D' : np.random.randn(12),\n",
    "                   'E' : np.random.randn(12)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>1.216707</td>\n",
       "      <td>-0.484209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.323951</td>\n",
       "      <td>1.229735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.855990</td>\n",
       "      <td>-1.430104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>1.639626</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.363005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.620069</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.298377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-2.140101</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.540843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A  1.216707 -0.484209\n",
       "      B -1.323951  1.229735\n",
       "      C  0.855990 -1.430104\n",
       "three A  1.639626       NaN\n",
       "      B       NaN -0.363005\n",
       "      C  1.620069       NaN\n",
       "two   A       NaN -1.298377\n",
       "      B -2.140101       NaN\n",
       "      C       NaN  0.540843"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 时间序列\n",
    "\n",
    "pandas在对频率转换进行重新采样时拥有着简单，强大而且高效的功能（例如把按秒采样的数据转换为按5分钟采样的数据）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.tseries.index.DatetimeIndex'>\n",
       "[2012-01-01 00:00:00, ..., 2012-01-01 00:01:39]\n",
       "Length: 100, Freq: S, Timezone: None"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2012',periods=100,freq='S')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01 00:00:00    114\n",
       "2012-01-01 00:00:01     62\n",
       "2012-01-01 00:00:02    370\n",
       "2012-01-01 00:00:03    379\n",
       "2012-01-01 00:00:04    360\n",
       "2012-01-01 00:00:05    234\n",
       "2012-01-01 00:00:06    198\n",
       "2012-01-01 00:00:07     55\n",
       "2012-01-01 00:00:08    458\n",
       "2012-01-01 00:00:09    241\n",
       "2012-01-01 00:00:10    117\n",
       "2012-01-01 00:00:11    110\n",
       "2012-01-01 00:00:12     57\n",
       "2012-01-01 00:00:13    293\n",
       "2012-01-01 00:00:14    150\n",
       "...\n",
       "2012-01-01 00:01:25    346\n",
       "2012-01-01 00:01:26    355\n",
       "2012-01-01 00:01:27     27\n",
       "2012-01-01 00:01:28     36\n",
       "2012-01-01 00:01:29     47\n",
       "2012-01-01 00:01:30    394\n",
       "2012-01-01 00:01:31    295\n",
       "2012-01-01 00:01:32    329\n",
       "2012-01-01 00:01:33     48\n",
       "2012-01-01 00:01:34    296\n",
       "2012-01-01 00:01:35     18\n",
       "2012-01-01 00:01:36    373\n",
       "2012-01-01 00:01:37    328\n",
       "2012-01-01 00:01:38    494\n",
       "2012-01-01 00:01:39    457\n",
       "Freq: S, Length: 100"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randint(0,500,len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    24374\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5Min',how='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "时区表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.tseries.index.DatetimeIndex'>\n",
       "[2012-03-06, ..., 2012-03-10]\n",
       "Length: 5, Freq: D, Timezone: None"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/6/2012',periods=5,freq='D')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06   -1.062647\n",
       "2012-03-07   -0.988973\n",
       "2012-03-08    0.018998\n",
       "2012-03-09    0.882671\n",
       "2012-03-10   -0.566935\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)),index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00   -1.062647\n",
       "2012-03-07 00:00:00+00:00   -0.988973\n",
       "2012-03-08 00:00:00+00:00    0.018998\n",
       "2012-03-09 00:00:00+00:00    0.882671\n",
       "2012-03-10 00:00:00+00:00   -0.566935\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "时区转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00   -1.062647\n",
       "2012-03-06 19:00:00-05:00   -0.988973\n",
       "2012-03-07 19:00:00-05:00    0.018998\n",
       "2012-03-08 19:00:00-05:00    0.882671\n",
       "2012-03-09 19:00:00-05:00   -0.566935\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "时间跨度转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.tseries.index.DatetimeIndex'>\n",
       "[2012-01-31, ..., 2012-05-31]\n",
       "Length: 5, Freq: M, Timezone: None"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31   -0.632847\n",
       "2012-02-29   -0.983099\n",
       "2012-03-31    1.646944\n",
       "2012-04-30   -2.136451\n",
       "2012-05-31   -0.048257\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01   -0.632847\n",
       "2012-02   -0.983099\n",
       "2012-03    1.646944\n",
       "2012-04   -2.136451\n",
       "2012-05   -0.048257\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ts.to_period()\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01   -0.632847\n",
       "2012-02-01   -0.983099\n",
       "2012-03-01    1.646944\n",
       "2012-04-01   -2.136451\n",
       "2012-05-01   -0.048257\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "日期与时间戳之间的转换使得可以使用一些方便的算术函数。例如，我们把以11月为年底的季度数据转换为当前季度末月底为始的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.tseries.period.PeriodIndex'>\n",
       "[1990Q1, ..., 2000Q4]\n",
       "Length: 44, Freq: Q-NOV"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')\n",
    "prng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990Q1   -0.529455\n",
       "1990Q2    0.570384\n",
       "1990Q3   -1.544862\n",
       "1990Q4   -0.297993\n",
       "1991Q1   -0.817410\n",
       "1991Q2   -0.409854\n",
       "1991Q3   -2.555214\n",
       "1991Q4    1.032389\n",
       "1992Q1   -0.378980\n",
       "1992Q2   -0.373298\n",
       "1992Q3   -0.290478\n",
       "1992Q4   -1.106722\n",
       "1993Q1   -1.118070\n",
       "1993Q2    1.683016\n",
       "1993Q3    2.278607\n",
       "1993Q4    1.070247\n",
       "1994Q1    0.911208\n",
       "1994Q2    0.632467\n",
       "1994Q3   -0.234640\n",
       "1994Q4   -1.545572\n",
       "1995Q1    0.707218\n",
       "1995Q2   -0.386264\n",
       "1995Q3    1.440627\n",
       "1995Q4    0.751621\n",
       "1996Q1   -1.640999\n",
       "1996Q2    0.125822\n",
       "1996Q3   -0.781528\n",
       "1996Q4    1.900030\n",
       "1997Q1    0.031440\n",
       "1997Q2   -1.521395\n",
       "1997Q3   -0.350913\n",
       "1997Q4   -1.335841\n",
       "1998Q1   -0.506609\n",
       "1998Q2    0.609221\n",
       "1998Q3   -0.092327\n",
       "1998Q4    0.742343\n",
       "1999Q1   -0.490511\n",
       "1999Q2    1.018211\n",
       "1999Q3    1.817720\n",
       "1999Q4    0.560937\n",
       "2000Q1   -1.012795\n",
       "2000Q2   -1.070557\n",
       "2000Q3    0.704798\n",
       "2000Q4    0.353916\n",
       "Freq: Q-NOV, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(prng)), index = prng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-02-01 09:00   -0.529455\n",
       "1990-05-01 09:00    0.570384\n",
       "1990-08-01 09:00   -1.544862\n",
       "1990-11-01 09:00   -0.297993\n",
       "1991-02-01 09:00   -0.817410\n",
       "1991-05-01 09:00   -0.409854\n",
       "1991-08-01 09:00   -2.555214\n",
       "1991-11-01 09:00    1.032389\n",
       "1992-02-01 09:00   -0.378980\n",
       "1992-05-01 09:00   -0.373298\n",
       "1992-08-01 09:00   -0.290478\n",
       "1992-11-01 09:00   -1.106722\n",
       "1993-02-01 09:00   -1.118070\n",
       "1993-05-01 09:00    1.683016\n",
       "1993-08-01 09:00    2.278607\n",
       "1993-11-01 09:00    1.070247\n",
       "1994-02-01 09:00    0.911208\n",
       "1994-05-01 09:00    0.632467\n",
       "1994-08-01 09:00   -0.234640\n",
       "1994-11-01 09:00   -1.545572\n",
       "1995-02-01 09:00    0.707218\n",
       "1995-05-01 09:00   -0.386264\n",
       "1995-08-01 09:00    1.440627\n",
       "1995-11-01 09:00    0.751621\n",
       "1996-02-01 09:00   -1.640999\n",
       "1996-05-01 09:00    0.125822\n",
       "1996-08-01 09:00   -0.781528\n",
       "1996-11-01 09:00    1.900030\n",
       "1997-02-01 09:00    0.031440\n",
       "1997-05-01 09:00   -1.521395\n",
       "1997-08-01 09:00   -0.350913\n",
       "1997-11-01 09:00   -1.335841\n",
       "1998-02-01 09:00   -0.506609\n",
       "1998-05-01 09:00    0.609221\n",
       "1998-08-01 09:00   -0.092327\n",
       "1998-11-01 09:00    0.742343\n",
       "1999-02-01 09:00   -0.490511\n",
       "1999-05-01 09:00    1.018211\n",
       "1999-08-01 09:00    1.817720\n",
       "1999-11-01 09:00    0.560937\n",
       "2000-02-01 09:00   -1.012795\n",
       "2000-05-01 09:00   -1.070557\n",
       "2000-08-01 09:00    0.704798\n",
       "2000-11-01 09:00    0.353916\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index = (prng.asfreq('M', 'end') ) .asfreq('H', 'start') +9\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## categoricals\n",
    "\n",
    "从版本0.15开始，DataFrame中可以包含categorical数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>raw_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade\n",
       "0   1         a\n",
       "1   2         b\n",
       "2   3         b\n",
       "3   4         a\n",
       "4   5         e\n",
       "5   6         e"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'e', 'e']})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "把raw_grade转换为categorical类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    e\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对category进行重命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\",\"good\",\"very bad\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对分类重新排序，并添加缺失的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4     very bad\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad < bad < medium < good < very good]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "排序是按照分类的顺序进行的，而不是字典序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 5</td>\n",
       "      <td> e</td>\n",
       "      <td>  very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 6</td>\n",
       "      <td> e</td>\n",
       "      <td>  very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2</td>\n",
       "      <td> b</td>\n",
       "      <td>      good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 3</td>\n",
       "      <td> b</td>\n",
       "      <td>      good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td> a</td>\n",
       "      <td> very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 4</td>\n",
       "      <td> a</td>\n",
       "      <td> very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "4   5         e   very bad\n",
       "5   6         e   very bad\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "0   1         a  very good\n",
       "3   4         a  very good"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(by=\"grade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "按分类分组时，也会显示空的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad      2\n",
       "bad         NaN\n",
       "medium      NaN\n",
       "good          2\n",
       "very good     2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1069af0d0>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEHCAYAAACtAv3IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1069ba850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "对于DataFrame类型，**plot()**能很方便地画出所有column及其label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106ae2b50>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEHCAYAAACtAv3IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFHoJisFXUFBQ6CUoBl9BQUGhl/D/kAlwugFUDpAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1069c5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000,4),index=ts.index,columns=['A', 'B', 'C', 'D'])\n",
    "df = df.cumsum()\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 获取数据的\n",
    "### CSV\n",
    "\n",
    "写入一个csv文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "从一个csv文件读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>-0.062672</td>\n",
       "      <td>-1.256108</td>\n",
       "      <td>-0.276911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>1.185635</td>\n",
       "      <td>0.278415</td>\n",
       "      <td>0.111969</td>\n",
       "      <td>1.471304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>0.181744</td>\n",
       "      <td>0.862553</td>\n",
       "      <td>2.168972</td>\n",
       "      <td>1.217986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>1.025786</td>\n",
       "      <td>0.444376</td>\n",
       "      <td>1.946407</td>\n",
       "      <td>1.781225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>1.156284</td>\n",
       "      <td>1.556331</td>\n",
       "      <td>2.972193</td>\n",
       "      <td>1.485344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>2.647098</td>\n",
       "      <td>1.406498</td>\n",
       "      <td>2.667766</td>\n",
       "      <td>1.806171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>2.838413</td>\n",
       "      <td>1.148143</td>\n",
       "      <td>4.251299</td>\n",
       "      <td>1.178629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000-01-08</td>\n",
       "      <td>3.264215</td>\n",
       "      <td>2.052171</td>\n",
       "      <td>4.695244</td>\n",
       "      <td>0.635274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-09</td>\n",
       "      <td>4.837534</td>\n",
       "      <td>1.668099</td>\n",
       "      <td>2.820105</td>\n",
       "      <td>-1.641457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>4.785927</td>\n",
       "      <td>1.949996</td>\n",
       "      <td>3.937043</td>\n",
       "      <td>-1.326241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>4.370393</td>\n",
       "      <td>-0.773381</td>\n",
       "      <td>5.357964</td>\n",
       "      <td>-2.355775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000-01-12</td>\n",
       "      <td>4.817681</td>\n",
       "      <td>-1.828862</td>\n",
       "      <td>5.009137</td>\n",
       "      <td>-2.723674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000-01-13</td>\n",
       "      <td>4.056709</td>\n",
       "      <td>-2.298854</td>\n",
       "      <td>5.661288</td>\n",
       "      <td>-2.848931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000-01-14</td>\n",
       "      <td>2.358674</td>\n",
       "      <td>-1.586434</td>\n",
       "      <td>5.171422</td>\n",
       "      <td>-3.659978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-15</td>\n",
       "      <td>2.286226</td>\n",
       "      <td>-1.309240</td>\n",
       "      <td>6.083846</td>\n",
       "      <td>-3.033132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000-01-16</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>0.991229</td>\n",
       "      <td>6.179765</td>\n",
       "      <td>-3.977637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2000-01-17</td>\n",
       "      <td>1.667437</td>\n",
       "      <td>1.482464</td>\n",
       "      <td>6.586782</td>\n",
       "      <td>-4.733009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>-0.704198</td>\n",
       "      <td>1.114435</td>\n",
       "      <td>8.273148</td>\n",
       "      <td>-6.013564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-19</td>\n",
       "      <td>0.521272</td>\n",
       "      <td>0.855078</td>\n",
       "      <td>10.667356</td>\n",
       "      <td>-7.072008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-01-20</td>\n",
       "      <td>0.453339</td>\n",
       "      <td>1.491088</td>\n",
       "      <td>11.326491</td>\n",
       "      <td>-6.707565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000-01-21</td>\n",
       "      <td>2.120191</td>\n",
       "      <td>1.624046</td>\n",
       "      <td>12.256566</td>\n",
       "      <td>-6.946169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000-01-22</td>\n",
       "      <td>2.653590</td>\n",
       "      <td>1.515170</td>\n",
       "      <td>13.133247</td>\n",
       "      <td>-7.462825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000-01-23</td>\n",
       "      <td>3.766077</td>\n",
       "      <td>1.367578</td>\n",
       "      <td>12.350256</td>\n",
       "      <td>-7.643055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2000-01-24</td>\n",
       "      <td>5.390681</td>\n",
       "      <td>1.771788</td>\n",
       "      <td>10.443662</td>\n",
       "      <td>-7.819640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-01-25</td>\n",
       "      <td>6.333766</td>\n",
       "      <td>2.907701</td>\n",
       "      <td>7.415541</td>\n",
       "      <td>-8.294551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>5.745656</td>\n",
       "      <td>3.138093</td>\n",
       "      <td>6.264761</td>\n",
       "      <td>-7.738085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2000-01-27</td>\n",
       "      <td>4.820698</td>\n",
       "      <td>3.079208</td>\n",
       "      <td>8.175576</td>\n",
       "      <td>-7.785478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>2.902801</td>\n",
       "      <td>4.318009</td>\n",
       "      <td>9.259691</td>\n",
       "      <td>-7.447738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>2.562628</td>\n",
       "      <td>5.182195</td>\n",
       "      <td>9.737713</td>\n",
       "      <td>-7.622801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2000-01-30</td>\n",
       "      <td>2.493323</td>\n",
       "      <td>5.992592</td>\n",
       "      <td>9.364377</td>\n",
       "      <td>-6.604847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>2002-08-28</td>\n",
       "      <td>-13.787008</td>\n",
       "      <td>10.349694</td>\n",
       "      <td>20.639172</td>\n",
       "      <td>-16.897246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2002-08-29</td>\n",
       "      <td>-13.961866</td>\n",
       "      <td>9.608443</td>\n",
       "      <td>19.893805</td>\n",
       "      <td>-16.674283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2002-08-30</td>\n",
       "      <td>-14.407201</td>\n",
       "      <td>10.131890</td>\n",
       "      <td>22.030059</td>\n",
       "      <td>-15.462973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2002-08-31</td>\n",
       "      <td>-15.661060</td>\n",
       "      <td>10.139718</td>\n",
       "      <td>22.063381</td>\n",
       "      <td>-16.127232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>-15.893622</td>\n",
       "      <td>9.579937</td>\n",
       "      <td>20.869018</td>\n",
       "      <td>-16.426683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2002-09-02</td>\n",
       "      <td>-14.326294</td>\n",
       "      <td>9.843613</td>\n",
       "      <td>21.372123</td>\n",
       "      <td>-16.396319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2002-09-03</td>\n",
       "      <td>-13.222234</td>\n",
       "      <td>11.503247</td>\n",
       "      <td>21.673593</td>\n",
       "      <td>-17.067178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2002-09-04</td>\n",
       "      <td>-13.874599</td>\n",
       "      <td>11.029781</td>\n",
       "      <td>22.177487</td>\n",
       "      <td>-17.584621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2002-09-05</td>\n",
       "      <td>-14.366766</td>\n",
       "      <td>10.929471</td>\n",
       "      <td>23.749803</td>\n",
       "      <td>-18.250348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>2002-09-06</td>\n",
       "      <td>-14.301658</td>\n",
       "      <td>11.352843</td>\n",
       "      <td>25.016192</td>\n",
       "      <td>-16.140891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2002-09-07</td>\n",
       "      <td>-14.765577</td>\n",
       "      <td>11.554946</td>\n",
       "      <td>21.596048</td>\n",
       "      <td>-16.378724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>2002-09-08</td>\n",
       "      <td>-14.295516</td>\n",
       "      <td>11.904146</td>\n",
       "      <td>21.838288</td>\n",
       "      <td>-16.458944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>-14.869276</td>\n",
       "      <td>11.892539</td>\n",
       "      <td>22.105776</td>\n",
       "      <td>-16.813928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2002-09-10</td>\n",
       "      <td>-14.431293</td>\n",
       "      <td>12.287799</td>\n",
       "      <td>23.208907</td>\n",
       "      <td>-16.393767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2002-09-11</td>\n",
       "      <td>-15.550096</td>\n",
       "      <td>13.010101</td>\n",
       "      <td>22.782674</td>\n",
       "      <td>-17.215624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2002-09-12</td>\n",
       "      <td>-15.301565</td>\n",
       "      <td>13.504414</td>\n",
       "      <td>21.390448</td>\n",
       "      <td>-16.545548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2002-09-13</td>\n",
       "      <td>-15.723424</td>\n",
       "      <td>13.094349</td>\n",
       "      <td>19.957580</td>\n",
       "      <td>-15.729522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>2002-09-14</td>\n",
       "      <td>-14.264989</td>\n",
       "      <td>14.834233</td>\n",
       "      <td>18.490564</td>\n",
       "      <td>-15.643701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>2002-09-15</td>\n",
       "      <td>-13.551818</td>\n",
       "      <td>14.704450</td>\n",
       "      <td>19.560484</td>\n",
       "      <td>-15.715354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>2002-09-16</td>\n",
       "      <td>-13.792679</td>\n",
       "      <td>14.822606</td>\n",
       "      <td>20.930214</td>\n",
       "      <td>-15.642744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2002-09-17</td>\n",
       "      <td>-13.583718</td>\n",
       "      <td>15.564811</td>\n",
       "      <td>23.432460</td>\n",
       "      <td>-16.040966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>2002-09-18</td>\n",
       "      <td>-12.819410</td>\n",
       "      <td>17.180775</td>\n",
       "      <td>20.185889</td>\n",
       "      <td>-15.677880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2002-09-19</td>\n",
       "      <td>-12.801132</td>\n",
       "      <td>17.250503</td>\n",
       "      <td>19.255585</td>\n",
       "      <td>-14.618826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2002-09-20</td>\n",
       "      <td>-11.267237</td>\n",
       "      <td>18.258379</td>\n",
       "      <td>19.270546</td>\n",
       "      <td>-15.589998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>-13.001507</td>\n",
       "      <td>18.570462</td>\n",
       "      <td>20.733814</td>\n",
       "      <td>-15.013118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>-14.067549</td>\n",
       "      <td>18.896325</td>\n",
       "      <td>21.719062</td>\n",
       "      <td>-14.114297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>-12.348151</td>\n",
       "      <td>18.025382</td>\n",
       "      <td>20.937040</td>\n",
       "      <td>-12.093957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>-12.693599</td>\n",
       "      <td>17.232992</td>\n",
       "      <td>20.656773</td>\n",
       "      <td>-12.760468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>-11.666343</td>\n",
       "      <td>18.446673</td>\n",
       "      <td>19.570311</td>\n",
       "      <td>-12.059097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>-12.161885</td>\n",
       "      <td>18.318993</td>\n",
       "      <td>18.654749</td>\n",
       "      <td>-11.719645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0          A          B          C          D\n",
       "0    2000-01-01   0.000241  -0.062672  -1.256108  -0.276911\n",
       "1    2000-01-02   1.185635   0.278415   0.111969   1.471304\n",
       "2    2000-01-03   0.181744   0.862553   2.168972   1.217986\n",
       "3    2000-01-04   1.025786   0.444376   1.946407   1.781225\n",
       "4    2000-01-05   1.156284   1.556331   2.972193   1.485344\n",
       "5    2000-01-06   2.647098   1.406498   2.667766   1.806171\n",
       "6    2000-01-07   2.838413   1.148143   4.251299   1.178629\n",
       "7    2000-01-08   3.264215   2.052171   4.695244   0.635274\n",
       "8    2000-01-09   4.837534   1.668099   2.820105  -1.641457\n",
       "9    2000-01-10   4.785927   1.949996   3.937043  -1.326241\n",
       "10   2000-01-11   4.370393  -0.773381   5.357964  -2.355775\n",
       "11   2000-01-12   4.817681  -1.828862   5.009137  -2.723674\n",
       "12   2000-01-13   4.056709  -2.298854   5.661288  -2.848931\n",
       "13   2000-01-14   2.358674  -1.586434   5.171422  -3.659978\n",
       "14   2000-01-15   2.286226  -1.309240   6.083846  -3.033132\n",
       "15   2000-01-16   2.214300   0.991229   6.179765  -3.977637\n",
       "16   2000-01-17   1.667437   1.482464   6.586782  -4.733009\n",
       "17   2000-01-18  -0.704198   1.114435   8.273148  -6.013564\n",
       "18   2000-01-19   0.521272   0.855078  10.667356  -7.072008\n",
       "19   2000-01-20   0.453339   1.491088  11.326491  -6.707565\n",
       "20   2000-01-21   2.120191   1.624046  12.256566  -6.946169\n",
       "21   2000-01-22   2.653590   1.515170  13.133247  -7.462825\n",
       "22   2000-01-23   3.766077   1.367578  12.350256  -7.643055\n",
       "23   2000-01-24   5.390681   1.771788  10.443662  -7.819640\n",
       "24   2000-01-25   6.333766   2.907701   7.415541  -8.294551\n",
       "25   2000-01-26   5.745656   3.138093   6.264761  -7.738085\n",
       "26   2000-01-27   4.820698   3.079208   8.175576  -7.785478\n",
       "27   2000-01-28   2.902801   4.318009   9.259691  -7.447738\n",
       "28   2000-01-29   2.562628   5.182195   9.737713  -7.622801\n",
       "29   2000-01-30   2.493323   5.992592   9.364377  -6.604847\n",
       "..          ...        ...        ...        ...        ...\n",
       "970  2002-08-28 -13.787008  10.349694  20.639172 -16.897246\n",
       "971  2002-08-29 -13.961866   9.608443  19.893805 -16.674283\n",
       "972  2002-08-30 -14.407201  10.131890  22.030059 -15.462973\n",
       "973  2002-08-31 -15.661060  10.139718  22.063381 -16.127232\n",
       "974  2002-09-01 -15.893622   9.579937  20.869018 -16.426683\n",
       "975  2002-09-02 -14.326294   9.843613  21.372123 -16.396319\n",
       "976  2002-09-03 -13.222234  11.503247  21.673593 -17.067178\n",
       "977  2002-09-04 -13.874599  11.029781  22.177487 -17.584621\n",
       "978  2002-09-05 -14.366766  10.929471  23.749803 -18.250348\n",
       "979  2002-09-06 -14.301658  11.352843  25.016192 -16.140891\n",
       "980  2002-09-07 -14.765577  11.554946  21.596048 -16.378724\n",
       "981  2002-09-08 -14.295516  11.904146  21.838288 -16.458944\n",
       "982  2002-09-09 -14.869276  11.892539  22.105776 -16.813928\n",
       "983  2002-09-10 -14.431293  12.287799  23.208907 -16.393767\n",
       "984  2002-09-11 -15.550096  13.010101  22.782674 -17.215624\n",
       "985  2002-09-12 -15.301565  13.504414  21.390448 -16.545548\n",
       "986  2002-09-13 -15.723424  13.094349  19.957580 -15.729522\n",
       "987  2002-09-14 -14.264989  14.834233  18.490564 -15.643701\n",
       "988  2002-09-15 -13.551818  14.704450  19.560484 -15.715354\n",
       "989  2002-09-16 -13.792679  14.822606  20.930214 -15.642744\n",
       "990  2002-09-17 -13.583718  15.564811  23.432460 -16.040966\n",
       "991  2002-09-18 -12.819410  17.180775  20.185889 -15.677880\n",
       "992  2002-09-19 -12.801132  17.250503  19.255585 -14.618826\n",
       "993  2002-09-20 -11.267237  18.258379  19.270546 -15.589998\n",
       "994  2002-09-21 -13.001507  18.570462  20.733814 -15.013118\n",
       "995  2002-09-22 -14.067549  18.896325  21.719062 -14.114297\n",
       "996  2002-09-23 -12.348151  18.025382  20.937040 -12.093957\n",
       "997  2002-09-24 -12.693599  17.232992  20.656773 -12.760468\n",
       "998  2002-09-25 -11.666343  18.446673  19.570311 -12.059097\n",
       "999  2002-09-26 -12.161885  18.318993  18.654749 -11.719645\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### HDF5\n",
    "\n",
    "HDFStores的读写\n",
    "\n",
    "写入一个HDF5 Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.to_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "从一个HDF5 Store读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.425461</td>\n",
       "      <td> -1.403269</td>\n",
       "      <td> -0.044436</td>\n",
       "      <td>  0.219559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td> 0.308576</td>\n",
       "      <td> -2.057619</td>\n",
       "      <td>  0.634288</td>\n",
       "      <td> -0.452682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td> 0.619000</td>\n",
       "      <td> -2.869349</td>\n",
       "      <td>  1.237154</td>\n",
       "      <td> -0.308545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-1.165187</td>\n",
       "      <td> -2.040167</td>\n",
       "      <td>  1.047757</td>\n",
       "      <td>  0.847036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.214774</td>\n",
       "      <td> -4.301137</td>\n",
       "      <td>  0.571458</td>\n",
       "      <td>  2.713687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td> 1.639842</td>\n",
       "      <td> -3.369141</td>\n",
       "      <td>  0.753631</td>\n",
       "      <td>  2.160821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td> 1.910771</td>\n",
       "      <td> -4.025835</td>\n",
       "      <td> -0.773456</td>\n",
       "      <td>  1.821734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td> 1.563340</td>\n",
       "      <td> -4.742109</td>\n",
       "      <td>  2.518628</td>\n",
       "      <td>  0.588485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td> 0.132218</td>\n",
       "      <td> -4.341313</td>\n",
       "      <td>  1.862238</td>\n",
       "      <td> -0.697081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td> 0.373116</td>\n",
       "      <td> -4.632546</td>\n",
       "      <td>  1.397640</td>\n",
       "      <td> -0.845373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td> 1.112101</td>\n",
       "      <td> -5.740170</td>\n",
       "      <td>  1.803410</td>\n",
       "      <td>  0.575382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td> 0.310180</td>\n",
       "      <td> -5.740034</td>\n",
       "      <td>  1.422615</td>\n",
       "      <td> -0.070605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-0.496999</td>\n",
       "      <td> -6.002253</td>\n",
       "      <td>  1.836140</td>\n",
       "      <td> -1.737801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td> 0.162170</td>\n",
       "      <td> -4.867653</td>\n",
       "      <td>  1.883516</td>\n",
       "      <td> -1.377816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>-0.219185</td>\n",
       "      <td> -6.045063</td>\n",
       "      <td>  2.179289</td>\n",
       "      <td>  0.404574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>-0.492499</td>\n",
       "      <td> -8.239633</td>\n",
       "      <td>  2.340800</td>\n",
       "      <td>  2.083196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>-1.906515</td>\n",
       "      <td> -7.611583</td>\n",
       "      <td>  2.096817</td>\n",
       "      <td>  2.474113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>-1.479602</td>\n",
       "      <td> -7.924990</td>\n",
       "      <td>  3.659178</td>\n",
       "      <td>  1.732523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>-1.877567</td>\n",
       "      <td> -7.676786</td>\n",
       "      <td>  2.962439</td>\n",
       "      <td>  0.808766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td> 0.446554</td>\n",
       "      <td> -8.478972</td>\n",
       "      <td>  2.770583</td>\n",
       "      <td>  1.005155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td> 0.815523</td>\n",
       "      <td> -7.639916</td>\n",
       "      <td>  4.375444</td>\n",
       "      <td>  0.975386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td> 1.232161</td>\n",
       "      <td> -8.011159</td>\n",
       "      <td>  3.948103</td>\n",
       "      <td> -0.071826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td> 0.809309</td>\n",
       "      <td>-10.975806</td>\n",
       "      <td>  3.579664</td>\n",
       "      <td> -0.193101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td> 2.980972</td>\n",
       "      <td>-10.672872</td>\n",
       "      <td>  4.323559</td>\n",
       "      <td> -0.837766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td> 3.361422</td>\n",
       "      <td>-12.640693</td>\n",
       "      <td>  5.046673</td>\n",
       "      <td> -0.518537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td> 1.927294</td>\n",
       "      <td>-11.242640</td>\n",
       "      <td>  3.801135</td>\n",
       "      <td>  0.896068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td> 1.249261</td>\n",
       "      <td>-10.931013</td>\n",
       "      <td>  2.837784</td>\n",
       "      <td>  1.464897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td> 1.281549</td>\n",
       "      <td>-11.003295</td>\n",
       "      <td>  3.951254</td>\n",
       "      <td>  1.179706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td> 0.512112</td>\n",
       "      <td>-10.761742</td>\n",
       "      <td>  2.233165</td>\n",
       "      <td>  0.174430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td> 0.262344</td>\n",
       "      <td> -9.672089</td>\n",
       "      <td>  4.887404</td>\n",
       "      <td>  0.712865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td> 4.625208</td>\n",
       "      <td> -1.794282</td>\n",
       "      <td> -8.259543</td>\n",
       "      <td> -1.588357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td> 4.666648</td>\n",
       "      <td> -1.795497</td>\n",
       "      <td> -8.238837</td>\n",
       "      <td>  0.252956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td> 5.607705</td>\n",
       "      <td> -1.428826</td>\n",
       "      <td> -7.712760</td>\n",
       "      <td>  1.868046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td> 4.946251</td>\n",
       "      <td> -0.112299</td>\n",
       "      <td> -7.518860</td>\n",
       "      <td>  1.777350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td> 3.864572</td>\n",
       "      <td> -0.545530</td>\n",
       "      <td> -6.292253</td>\n",
       "      <td>  1.586294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td> 5.555515</td>\n",
       "      <td> -0.372099</td>\n",
       "      <td> -6.675474</td>\n",
       "      <td> -0.593894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td> 7.088676</td>\n",
       "      <td> -1.012956</td>\n",
       "      <td> -7.077376</td>\n",
       "      <td> -0.086973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td> 7.750305</td>\n",
       "      <td> -1.040951</td>\n",
       "      <td> -6.940778</td>\n",
       "      <td> -1.275356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td> 7.037225</td>\n",
       "      <td> -2.321507</td>\n",
       "      <td> -7.430603</td>\n",
       "      <td> -2.204701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td> 7.219238</td>\n",
       "      <td> -1.307758</td>\n",
       "      <td> -7.643952</td>\n",
       "      <td> -1.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td> 5.001612</td>\n",
       "      <td> -2.704933</td>\n",
       "      <td> -7.839517</td>\n",
       "      <td> -2.209851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td> 6.266836</td>\n",
       "      <td> -2.599260</td>\n",
       "      <td> -7.889995</td>\n",
       "      <td> -1.813327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td> 4.921057</td>\n",
       "      <td> -2.086468</td>\n",
       "      <td> -8.432188</td>\n",
       "      <td> -1.590568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td> 5.409754</td>\n",
       "      <td> -0.320361</td>\n",
       "      <td>-11.189269</td>\n",
       "      <td> -2.413758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td> 5.011992</td>\n",
       "      <td> -0.950910</td>\n",
       "      <td>-11.590882</td>\n",
       "      <td> -3.230179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td> 4.742224</td>\n",
       "      <td> -0.815597</td>\n",
       "      <td> -9.546448</td>\n",
       "      <td> -4.055350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td> 6.101983</td>\n",
       "      <td> -0.774610</td>\n",
       "      <td> -9.722567</td>\n",
       "      <td> -6.045751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td> 7.365744</td>\n",
       "      <td> -1.116305</td>\n",
       "      <td> -9.630651</td>\n",
       "      <td> -5.592564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td> 5.084383</td>\n",
       "      <td> -2.143574</td>\n",
       "      <td> -9.798200</td>\n",
       "      <td> -6.216111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td> 5.535138</td>\n",
       "      <td> -2.309296</td>\n",
       "      <td>-10.247144</td>\n",
       "      <td> -5.912922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td> 6.727144</td>\n",
       "      <td> -1.383390</td>\n",
       "      <td>-10.538517</td>\n",
       "      <td> -6.811851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td> 6.381736</td>\n",
       "      <td> -1.541663</td>\n",
       "      <td>-11.300122</td>\n",
       "      <td> -7.795385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td> 5.797594</td>\n",
       "      <td> -2.066551</td>\n",
       "      <td>-10.043998</td>\n",
       "      <td> -5.295954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td> 5.727980</td>\n",
       "      <td> -2.694832</td>\n",
       "      <td>-10.937890</td>\n",
       "      <td> -6.940196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td> 6.101493</td>\n",
       "      <td> -3.295493</td>\n",
       "      <td>-10.662621</td>\n",
       "      <td> -8.503858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td> 6.281263</td>\n",
       "      <td> -3.543243</td>\n",
       "      <td> -9.669077</td>\n",
       "      <td> -8.634091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td> 6.193919</td>\n",
       "      <td> -3.517716</td>\n",
       "      <td>-11.120515</td>\n",
       "      <td> -9.019951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td> 6.032230</td>\n",
       "      <td> -3.171396</td>\n",
       "      <td>-11.000641</td>\n",
       "      <td>-10.376975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td> 5.692565</td>\n",
       "      <td> -4.190530</td>\n",
       "      <td>-11.027112</td>\n",
       "      <td>-11.042980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td> 5.337877</td>\n",
       "      <td> -3.505361</td>\n",
       "      <td>-11.775212</td>\n",
       "      <td>-10.298722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A          B          C          D\n",
       "2000-01-01 -0.425461  -1.403269  -0.044436   0.219559\n",
       "2000-01-02  0.308576  -2.057619   0.634288  -0.452682\n",
       "2000-01-03  0.619000  -2.869349   1.237154  -0.308545\n",
       "2000-01-04 -1.165187  -2.040167   1.047757   0.847036\n",
       "2000-01-05 -0.214774  -4.301137   0.571458   2.713687\n",
       "2000-01-06  1.639842  -3.369141   0.753631   2.160821\n",
       "2000-01-07  1.910771  -4.025835  -0.773456   1.821734\n",
       "2000-01-08  1.563340  -4.742109   2.518628   0.588485\n",
       "2000-01-09  0.132218  -4.341313   1.862238  -0.697081\n",
       "2000-01-10  0.373116  -4.632546   1.397640  -0.845373\n",
       "2000-01-11  1.112101  -5.740170   1.803410   0.575382\n",
       "2000-01-12  0.310180  -5.740034   1.422615  -0.070605\n",
       "2000-01-13 -0.496999  -6.002253   1.836140  -1.737801\n",
       "2000-01-14  0.162170  -4.867653   1.883516  -1.377816\n",
       "2000-01-15 -0.219185  -6.045063   2.179289   0.404574\n",
       "2000-01-16 -0.492499  -8.239633   2.340800   2.083196\n",
       "2000-01-17 -1.906515  -7.611583   2.096817   2.474113\n",
       "2000-01-18 -1.479602  -7.924990   3.659178   1.732523\n",
       "2000-01-19 -1.877567  -7.676786   2.962439   0.808766\n",
       "2000-01-20  0.446554  -8.478972   2.770583   1.005155\n",
       "2000-01-21  0.815523  -7.639916   4.375444   0.975386\n",
       "2000-01-22  1.232161  -8.011159   3.948103  -0.071826\n",
       "2000-01-23  0.809309 -10.975806   3.579664  -0.193101\n",
       "2000-01-24  2.980972 -10.672872   4.323559  -0.837766\n",
       "2000-01-25  3.361422 -12.640693   5.046673  -0.518537\n",
       "2000-01-26  1.927294 -11.242640   3.801135   0.896068\n",
       "2000-01-27  1.249261 -10.931013   2.837784   1.464897\n",
       "2000-01-28  1.281549 -11.003295   3.951254   1.179706\n",
       "2000-01-29  0.512112 -10.761742   2.233165   0.174430\n",
       "2000-01-30  0.262344  -9.672089   4.887404   0.712865\n",
       "...              ...        ...        ...        ...\n",
       "2002-08-28  4.625208  -1.794282  -8.259543  -1.588357\n",
       "2002-08-29  4.666648  -1.795497  -8.238837   0.252956\n",
       "2002-08-30  5.607705  -1.428826  -7.712760   1.868046\n",
       "2002-08-31  4.946251  -0.112299  -7.518860   1.777350\n",
       "2002-09-01  3.864572  -0.545530  -6.292253   1.586294\n",
       "2002-09-02  5.555515  -0.372099  -6.675474  -0.593894\n",
       "2002-09-03  7.088676  -1.012956  -7.077376  -0.086973\n",
       "2002-09-04  7.750305  -1.040951  -6.940778  -1.275356\n",
       "2002-09-05  7.037225  -2.321507  -7.430603  -2.204701\n",
       "2002-09-06  7.219238  -1.307758  -7.643952  -1.942661\n",
       "2002-09-07  5.001612  -2.704933  -7.839517  -2.209851\n",
       "2002-09-08  6.266836  -2.599260  -7.889995  -1.813327\n",
       "2002-09-09  4.921057  -2.086468  -8.432188  -1.590568\n",
       "2002-09-10  5.409754  -0.320361 -11.189269  -2.413758\n",
       "2002-09-11  5.011992  -0.950910 -11.590882  -3.230179\n",
       "2002-09-12  4.742224  -0.815597  -9.546448  -4.055350\n",
       "2002-09-13  6.101983  -0.774610  -9.722567  -6.045751\n",
       "2002-09-14  7.365744  -1.116305  -9.630651  -5.592564\n",
       "2002-09-15  5.084383  -2.143574  -9.798200  -6.216111\n",
       "2002-09-16  5.535138  -2.309296 -10.247144  -5.912922\n",
       "2002-09-17  6.727144  -1.383390 -10.538517  -6.811851\n",
       "2002-09-18  6.381736  -1.541663 -11.300122  -7.795385\n",
       "2002-09-19  5.797594  -2.066551 -10.043998  -5.295954\n",
       "2002-09-20  5.727980  -2.694832 -10.937890  -6.940196\n",
       "2002-09-21  6.101493  -3.295493 -10.662621  -8.503858\n",
       "2002-09-22  6.281263  -3.543243  -9.669077  -8.634091\n",
       "2002-09-23  6.193919  -3.517716 -11.120515  -9.019951\n",
       "2002-09-24  6.032230  -3.171396 -11.000641 -10.376975\n",
       "2002-09-25  5.692565  -4.190530 -11.027112 -11.042980\n",
       "2002-09-26  5.337877  -3.505361 -11.775212 -10.298722\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Excel表格\n",
    "\n",
    "Excel表格的读写\n",
    "\n",
    "写入一个Excel文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "从一个Excel文件读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.425461</td>\n",
       "      <td> -1.403269</td>\n",
       "      <td> -0.044436</td>\n",
       "      <td>  0.219559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td> 0.308576</td>\n",
       "      <td> -2.057619</td>\n",
       "      <td>  0.634288</td>\n",
       "      <td> -0.452682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td> 0.619000</td>\n",
       "      <td> -2.869349</td>\n",
       "      <td>  1.237154</td>\n",
       "      <td> -0.308545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-1.165187</td>\n",
       "      <td> -2.040167</td>\n",
       "      <td>  1.047757</td>\n",
       "      <td>  0.847036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.214774</td>\n",
       "      <td> -4.301137</td>\n",
       "      <td>  0.571458</td>\n",
       "      <td>  2.713687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td> 1.639842</td>\n",
       "      <td> -3.369141</td>\n",
       "      <td>  0.753631</td>\n",
       "      <td>  2.160821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td> 1.910771</td>\n",
       "      <td> -4.025835</td>\n",
       "      <td> -0.773456</td>\n",
       "      <td>  1.821734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td> 1.563340</td>\n",
       "      <td> -4.742109</td>\n",
       "      <td>  2.518628</td>\n",
       "      <td>  0.588485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td> 0.132218</td>\n",
       "      <td> -4.341313</td>\n",
       "      <td>  1.862238</td>\n",
       "      <td> -0.697081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td> 0.373116</td>\n",
       "      <td> -4.632546</td>\n",
       "      <td>  1.397640</td>\n",
       "      <td> -0.845373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td> 1.112101</td>\n",
       "      <td> -5.740170</td>\n",
       "      <td>  1.803410</td>\n",
       "      <td>  0.575382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td> 0.310180</td>\n",
       "      <td> -5.740034</td>\n",
       "      <td>  1.422615</td>\n",
       "      <td> -0.070605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-0.496999</td>\n",
       "      <td> -6.002253</td>\n",
       "      <td>  1.836140</td>\n",
       "      <td> -1.737801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td> 0.162170</td>\n",
       "      <td> -4.867653</td>\n",
       "      <td>  1.883516</td>\n",
       "      <td> -1.377816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>-0.219185</td>\n",
       "      <td> -6.045063</td>\n",
       "      <td>  2.179289</td>\n",
       "      <td>  0.404574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>-0.492499</td>\n",
       "      <td> -8.239633</td>\n",
       "      <td>  2.340800</td>\n",
       "      <td>  2.083196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>-1.906515</td>\n",
       "      <td> -7.611583</td>\n",
       "      <td>  2.096817</td>\n",
       "      <td>  2.474113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>-1.479602</td>\n",
       "      <td> -7.924990</td>\n",
       "      <td>  3.659178</td>\n",
       "      <td>  1.732523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>-1.877567</td>\n",
       "      <td> -7.676786</td>\n",
       "      <td>  2.962439</td>\n",
       "      <td>  0.808766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td> 0.446554</td>\n",
       "      <td> -8.478972</td>\n",
       "      <td>  2.770583</td>\n",
       "      <td>  1.005155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td> 0.815523</td>\n",
       "      <td> -7.639916</td>\n",
       "      <td>  4.375444</td>\n",
       "      <td>  0.975386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td> 1.232161</td>\n",
       "      <td> -8.011159</td>\n",
       "      <td>  3.948103</td>\n",
       "      <td> -0.071826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td> 0.809309</td>\n",
       "      <td>-10.975806</td>\n",
       "      <td>  3.579664</td>\n",
       "      <td> -0.193101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td> 2.980972</td>\n",
       "      <td>-10.672872</td>\n",
       "      <td>  4.323559</td>\n",
       "      <td> -0.837766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td> 3.361422</td>\n",
       "      <td>-12.640693</td>\n",
       "      <td>  5.046673</td>\n",
       "      <td> -0.518537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td> 1.927294</td>\n",
       "      <td>-11.242640</td>\n",
       "      <td>  3.801135</td>\n",
       "      <td>  0.896068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td> 1.249261</td>\n",
       "      <td>-10.931013</td>\n",
       "      <td>  2.837784</td>\n",
       "      <td>  1.464897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td> 1.281549</td>\n",
       "      <td>-11.003295</td>\n",
       "      <td>  3.951254</td>\n",
       "      <td>  1.179706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td> 0.512112</td>\n",
       "      <td>-10.761742</td>\n",
       "      <td>  2.233165</td>\n",
       "      <td>  0.174430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td> 0.262344</td>\n",
       "      <td> -9.672089</td>\n",
       "      <td>  4.887404</td>\n",
       "      <td>  0.712865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td> 4.625208</td>\n",
       "      <td> -1.794282</td>\n",
       "      <td> -8.259543</td>\n",
       "      <td> -1.588357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td> 4.666648</td>\n",
       "      <td> -1.795497</td>\n",
       "      <td> -8.238837</td>\n",
       "      <td>  0.252956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td> 5.607705</td>\n",
       "      <td> -1.428826</td>\n",
       "      <td> -7.712760</td>\n",
       "      <td>  1.868046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td> 4.946251</td>\n",
       "      <td> -0.112299</td>\n",
       "      <td> -7.518860</td>\n",
       "      <td>  1.777350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td> 3.864572</td>\n",
       "      <td> -0.545530</td>\n",
       "      <td> -6.292253</td>\n",
       "      <td>  1.586294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td> 5.555515</td>\n",
       "      <td> -0.372099</td>\n",
       "      <td> -6.675474</td>\n",
       "      <td> -0.593894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td> 7.088676</td>\n",
       "      <td> -1.012956</td>\n",
       "      <td> -7.077376</td>\n",
       "      <td> -0.086973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td> 7.750305</td>\n",
       "      <td> -1.040951</td>\n",
       "      <td> -6.940778</td>\n",
       "      <td> -1.275356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td> 7.037225</td>\n",
       "      <td> -2.321507</td>\n",
       "      <td> -7.430603</td>\n",
       "      <td> -2.204701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td> 7.219238</td>\n",
       "      <td> -1.307758</td>\n",
       "      <td> -7.643952</td>\n",
       "      <td> -1.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td> 5.001612</td>\n",
       "      <td> -2.704933</td>\n",
       "      <td> -7.839517</td>\n",
       "      <td> -2.209851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td> 6.266836</td>\n",
       "      <td> -2.599260</td>\n",
       "      <td> -7.889995</td>\n",
       "      <td> -1.813327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td> 4.921057</td>\n",
       "      <td> -2.086468</td>\n",
       "      <td> -8.432188</td>\n",
       "      <td> -1.590568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td> 5.409754</td>\n",
       "      <td> -0.320361</td>\n",
       "      <td>-11.189269</td>\n",
       "      <td> -2.413758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td> 5.011992</td>\n",
       "      <td> -0.950910</td>\n",
       "      <td>-11.590882</td>\n",
       "      <td> -3.230179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td> 4.742224</td>\n",
       "      <td> -0.815597</td>\n",
       "      <td> -9.546448</td>\n",
       "      <td> -4.055350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td> 6.101983</td>\n",
       "      <td> -0.774610</td>\n",
       "      <td> -9.722567</td>\n",
       "      <td> -6.045751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td> 7.365744</td>\n",
       "      <td> -1.116305</td>\n",
       "      <td> -9.630651</td>\n",
       "      <td> -5.592564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td> 5.084383</td>\n",
       "      <td> -2.143574</td>\n",
       "      <td> -9.798200</td>\n",
       "      <td> -6.216111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td> 5.535138</td>\n",
       "      <td> -2.309296</td>\n",
       "      <td>-10.247144</td>\n",
       "      <td> -5.912922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td> 6.727144</td>\n",
       "      <td> -1.383390</td>\n",
       "      <td>-10.538517</td>\n",
       "      <td> -6.811851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td> 6.381736</td>\n",
       "      <td> -1.541663</td>\n",
       "      <td>-11.300122</td>\n",
       "      <td> -7.795385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td> 5.797594</td>\n",
       "      <td> -2.066551</td>\n",
       "      <td>-10.043998</td>\n",
       "      <td> -5.295954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td> 5.727980</td>\n",
       "      <td> -2.694832</td>\n",
       "      <td>-10.937890</td>\n",
       "      <td> -6.940196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td> 6.101493</td>\n",
       "      <td> -3.295493</td>\n",
       "      <td>-10.662621</td>\n",
       "      <td> -8.503858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td> 6.281263</td>\n",
       "      <td> -3.543243</td>\n",
       "      <td> -9.669077</td>\n",
       "      <td> -8.634091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td> 6.193919</td>\n",
       "      <td> -3.517716</td>\n",
       "      <td>-11.120515</td>\n",
       "      <td> -9.019951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td> 6.032230</td>\n",
       "      <td> -3.171396</td>\n",
       "      <td>-11.000641</td>\n",
       "      <td>-10.376975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td> 5.692565</td>\n",
       "      <td> -4.190530</td>\n",
       "      <td>-11.027112</td>\n",
       "      <td>-11.042980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td> 5.337877</td>\n",
       "      <td> -3.505361</td>\n",
       "      <td>-11.775212</td>\n",
       "      <td>-10.298722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A          B          C          D\n",
       "2000-01-01 -0.425461  -1.403269  -0.044436   0.219559\n",
       "2000-01-02  0.308576  -2.057619   0.634288  -0.452682\n",
       "2000-01-03  0.619000  -2.869349   1.237154  -0.308545\n",
       "2000-01-04 -1.165187  -2.040167   1.047757   0.847036\n",
       "2000-01-05 -0.214774  -4.301137   0.571458   2.713687\n",
       "2000-01-06  1.639842  -3.369141   0.753631   2.160821\n",
       "2000-01-07  1.910771  -4.025835  -0.773456   1.821734\n",
       "2000-01-08  1.563340  -4.742109   2.518628   0.588485\n",
       "2000-01-09  0.132218  -4.341313   1.862238  -0.697081\n",
       "2000-01-10  0.373116  -4.632546   1.397640  -0.845373\n",
       "2000-01-11  1.112101  -5.740170   1.803410   0.575382\n",
       "2000-01-12  0.310180  -5.740034   1.422615  -0.070605\n",
       "2000-01-13 -0.496999  -6.002253   1.836140  -1.737801\n",
       "2000-01-14  0.162170  -4.867653   1.883516  -1.377816\n",
       "2000-01-15 -0.219185  -6.045063   2.179289   0.404574\n",
       "2000-01-16 -0.492499  -8.239633   2.340800   2.083196\n",
       "2000-01-17 -1.906515  -7.611583   2.096817   2.474113\n",
       "2000-01-18 -1.479602  -7.924990   3.659178   1.732523\n",
       "2000-01-19 -1.877567  -7.676786   2.962439   0.808766\n",
       "2000-01-20  0.446554  -8.478972   2.770583   1.005155\n",
       "2000-01-21  0.815523  -7.639916   4.375444   0.975386\n",
       "2000-01-22  1.232161  -8.011159   3.948103  -0.071826\n",
       "2000-01-23  0.809309 -10.975806   3.579664  -0.193101\n",
       "2000-01-24  2.980972 -10.672872   4.323559  -0.837766\n",
       "2000-01-25  3.361422 -12.640693   5.046673  -0.518537\n",
       "2000-01-26  1.927294 -11.242640   3.801135   0.896068\n",
       "2000-01-27  1.249261 -10.931013   2.837784   1.464897\n",
       "2000-01-28  1.281549 -11.003295   3.951254   1.179706\n",
       "2000-01-29  0.512112 -10.761742   2.233165   0.174430\n",
       "2000-01-30  0.262344  -9.672089   4.887404   0.712865\n",
       "...              ...        ...        ...        ...\n",
       "2002-08-28  4.625208  -1.794282  -8.259543  -1.588357\n",
       "2002-08-29  4.666648  -1.795497  -8.238837   0.252956\n",
       "2002-08-30  5.607705  -1.428826  -7.712760   1.868046\n",
       "2002-08-31  4.946251  -0.112299  -7.518860   1.777350\n",
       "2002-09-01  3.864572  -0.545530  -6.292253   1.586294\n",
       "2002-09-02  5.555515  -0.372099  -6.675474  -0.593894\n",
       "2002-09-03  7.088676  -1.012956  -7.077376  -0.086973\n",
       "2002-09-04  7.750305  -1.040951  -6.940778  -1.275356\n",
       "2002-09-05  7.037225  -2.321507  -7.430603  -2.204701\n",
       "2002-09-06  7.219238  -1.307758  -7.643952  -1.942661\n",
       "2002-09-07  5.001612  -2.704933  -7.839517  -2.209851\n",
       "2002-09-08  6.266836  -2.599260  -7.889995  -1.813327\n",
       "2002-09-09  4.921057  -2.086468  -8.432188  -1.590568\n",
       "2002-09-10  5.409754  -0.320361 -11.189269  -2.413758\n",
       "2002-09-11  5.011992  -0.950910 -11.590882  -3.230179\n",
       "2002-09-12  4.742224  -0.815597  -9.546448  -4.055350\n",
       "2002-09-13  6.101983  -0.774610  -9.722567  -6.045751\n",
       "2002-09-14  7.365744  -1.116305  -9.630651  -5.592564\n",
       "2002-09-15  5.084383  -2.143574  -9.798200  -6.216111\n",
       "2002-09-16  5.535138  -2.309296 -10.247144  -5.912922\n",
       "2002-09-17  6.727144  -1.383390 -10.538517  -6.811851\n",
       "2002-09-18  6.381736  -1.541663 -11.300122  -7.795385\n",
       "2002-09-19  5.797594  -2.066551 -10.043998  -5.295954\n",
       "2002-09-20  5.727980  -2.694832 -10.937890  -6.940196\n",
       "2002-09-21  6.101493  -3.295493 -10.662621  -8.503858\n",
       "2002-09-22  6.281263  -3.543243  -9.669077  -8.634091\n",
       "2002-09-23  6.193919  -3.517716 -11.120515  -9.019951\n",
       "2002-09-24  6.032230  -3.171396 -11.000641 -10.376975\n",
       "2002-09-25  5.692565  -4.190530 -11.027112 -11.042980\n",
       "2002-09-26  5.337877  -3.505361 -11.775212 -10.298722\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])"
   ]
  }
 ],
 "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}