3.pandas/1.10-Minutes-to-pandas/10 Minutes to pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 十分钟入门pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coding:utf8\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个一篇针对pandas新手的简短入门，想要了解更多复杂的内容，参阅[*Cookbook*](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常，我们首先要导入以下几个库："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过传递一个list来创建**Series**，pandas会默认创建整型索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过传递一个numpy array，日期索引以及列标签来创建一个**DataFrame**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<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>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326"
      ]
     },
     "execution_count": 5,
     "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": {},
   "source": [
    "通过传递一个能够被转换为类似series的dict对象来创建一个**DataFrame**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>2013-01-02</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>2013-01-02</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>2013-01-02</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>2013-01-02</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>"
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({ 'A' : 1.,\n",
    "                     'B' : pd.Timestamp('20130102'),\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": {},
   "source": [
    "可以看到各列的数据类型为："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看frame中头部和尾部的几行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显示索引、列名以及底层的numpy数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.90052384, -0.30251543, -0.54176245,  1.56291588],\n",
       "       [-0.8841172 , -0.65074073,  0.21734508,  0.26891483],\n",
       "       [ 0.22082238,  0.79052719,  0.69217223,  0.72344092],\n",
       "       [ 1.2602764 ,  1.0002968 ,  0.80980141, -0.38971272],\n",
       "       [ 1.67938067,  1.46860938,  0.36064787, -0.24084994],\n",
       "       [ 0.56786654,  0.23535244,  1.117395  , -0.60432593]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "describe()能对数据做一个快速统计汇总"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>0.323951</td>\n",
       "      <td>0.423588</td>\n",
       "      <td>0.442600</td>\n",
       "      <td>0.220064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.071708</td>\n",
       "      <td>0.809463</td>\n",
       "      <td>0.579465</td>\n",
       "      <td>0.815219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>-0.604326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.607882</td>\n",
       "      <td>-0.168048</td>\n",
       "      <td>0.253171</td>\n",
       "      <td>-0.352497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.394344</td>\n",
       "      <td>0.512940</td>\n",
       "      <td>0.526410</td>\n",
       "      <td>0.014032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.087174</td>\n",
       "      <td>0.947854</td>\n",
       "      <td>0.780394</td>\n",
       "      <td>0.609809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>1.562916</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   0.323951  0.423588  0.442600  0.220064\n",
       "std    1.071708  0.809463  0.579465  0.815219\n",
       "min   -0.900524 -0.650741 -0.541762 -0.604326\n",
       "25%   -0.607882 -0.168048  0.253171 -0.352497\n",
       "50%    0.394344  0.512940  0.526410  0.014032\n",
       "75%    1.087174  0.947854  0.780394  0.609809\n",
       "max    1.679381  1.468609  1.117395  1.562916"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数据做转置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.884117</td>\n",
       "      <td>0.220822</td>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.679381</td>\n",
       "      <td>0.567867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.235352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.541762</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>1.117395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1.562916</td>\n",
       "      <td>0.268915</td>\n",
       "      <td>0.723441</td>\n",
       "      <td>-0.389713</td>\n",
       "      <td>-0.240850</td>\n",
       "      <td>-0.604326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A   -0.900524   -0.884117    0.220822    1.260276    1.679381    0.567867\n",
       "B   -0.302515   -0.650741    0.790527    1.000297    1.468609    0.235352\n",
       "C   -0.541762    0.217345    0.692172    0.809801    0.360648    1.117395\n",
       "D    1.562916    0.268915    0.723441   -0.389713   -0.240850   -0.604326"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按轴进行排序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.562916</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.900524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.268915</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>-0.884117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.723441</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.220822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.389713</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>1.260276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.240850</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>1.679381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.604326</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>0.567867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01  1.562916 -0.541762 -0.302515 -0.900524\n",
       "2013-01-02  0.268915  0.217345 -0.650741 -0.884117\n",
       "2013-01-03  0.723441  0.692172  0.790527  0.220822\n",
       "2013-01-04 -0.389713  0.809801  1.000297  1.260276\n",
       "2013-01-05 -0.240850  0.360648  1.468609  1.679381\n",
       "2013-01-06 -0.604326  1.117395  0.235352  0.567867"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按值进行排序 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915\n",
       "2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择某一列数据，它会返回一个**Series**，等同于**df.A**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.900524\n",
       "2013-01-02   -0.884117\n",
       "2013-01-03    0.220822\n",
       "2013-01-04    1.260276\n",
       "2013-01-05    1.679381\n",
       "2013-01-06    0.567867\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过使用**[ ]**进行切片选取："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.900524 -0.302515 -0.541762  1.562916\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20130102':'20130104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过标签选取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过标签进行交叉选取："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.900524\n",
       "B   -0.302515\n",
       "C   -0.541762\n",
       "D    1.562916\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用标签对多个轴进行选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>B</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01 -0.900524 -0.302515\n",
       "2013-01-02 -0.884117 -0.650741\n",
       "2013-01-03  0.220822  0.790527\n",
       "2013-01-04  1.260276  1.000297\n",
       "2013-01-05  1.679381  1.468609\n",
       "2013-01-06  0.567867  0.235352"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01 -0.900524 -0.302515\n",
       "2013-01-02 -0.884117 -0.650741\n",
       "2013-01-03  0.220822  0.790527"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进行标签切片，包含两个端点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-02 -0.884117 -0.650741\n",
       "2013-01-03  0.220822  0.790527\n",
       "2013-01-04  1.260276  1.000297"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102':'20130104',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于返回的对象进行降维处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.884117\n",
       "B   -0.650741\n",
       "Name: 2013-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取一个标量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9005238449408509"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "快速获取标量（与上面的方法等价）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9005238449408509"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过位置选取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过传递整型的位置进行选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.260276\n",
       "B    1.000297\n",
       "C    0.809801\n",
       "D   -0.389713\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过整型的位置切片进行选取，与python/numpy形式相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-04  1.260276  1.000297\n",
       "2013-01-05  1.679381  1.468609"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只对行进行切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只对列进行切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2013-01-01 -0.302515 -0.541762\n",
       "2013-01-02 -0.650741  0.217345\n",
       "2013-01-03  0.790527  0.692172\n",
       "2013-01-04  1.000297  0.809801\n",
       "2013-01-05  1.468609  0.360648\n",
       "2013-01-06  0.235352  1.117395"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只获取某个值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6507407272837356"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "快速获取某个值（与上面的方法等价）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6507407272837356"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 布尔索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用某列的值来选取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用**where**操作来选取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.562916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01       NaN       NaN       NaN  1.562916\n",
       "2013-01-02       NaN       NaN  0.217345  0.268915\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441\n",
       "2013-01-04  1.260276  1.000297  0.809801       NaN\n",
       "2013-01-05  1.679381  1.468609  0.360648       NaN\n",
       "2013-01-06  0.567867  0.235352  1.117395       NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用**isin()**方法来过滤数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01 -0.900524 -0.302515 -0.541762  1.562916    one\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915    one\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441    two\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713  three\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850   four\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326  three"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441   two\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850  four"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two', 'four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 赋值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "赋值一个新的列，通过索引来自动对齐数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-02    1\n",
       "2013-01-03    2\n",
       "2013-01-04    3\n",
       "2013-01-05    4\n",
       "2013-01-06    5\n",
       "2013-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102',periods=6))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.900524</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2013-01-01 -0.900524 -0.302515 -0.541762  1.562916  NaN\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915  1.0\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441  2.0\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713  3.0\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850  4.0\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326  5.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['F'] = s1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过标签赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.302515</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>1.562916</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>0.268915</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2013-01-01  0.000000 -0.302515 -0.541762  1.562916  NaN\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915  1.0\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441  2.0\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713  3.0\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850  4.0\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326  5.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0], 'A'] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过位置赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.562916</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
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       "      <td>0.268915</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>0.723441</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>-0.389713</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>-0.240850</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</td>\n",
       "      <td>-0.604326</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2013-01-01  0.000000  0.000000 -0.541762  1.562916  NaN\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  0.268915  1.0\n",
       "2013-01-03  0.220822  0.790527  0.692172  0.723441  2.0\n",
       "2013-01-04  1.260276  1.000297  0.809801 -0.389713  3.0\n",
       "2013-01-05  1.679381  1.468609  0.360648 -0.240850  4.0\n",
       "2013-01-06  0.567867  0.235352  1.117395 -0.604326  5.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0,1] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过传递numpy array赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2013-01-01</th>\n",
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       "      <th>2013-01-02</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.679381</td>\n",
       "      <td>1.468609</td>\n",
       "      <td>0.360648</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.567867</td>\n",
       "      <td>0.235352</td>\n",
       "      <td>1.117395</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",
       "2013-01-01  0.000000  0.000000 -0.541762  5  NaN\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  5  1.0\n",
       "2013-01-03  0.220822  0.790527  0.692172  5  2.0\n",
       "2013-01-04  1.260276  1.000297  0.809801  5  3.0\n",
       "2013-01-05  1.679381  1.468609  0.360648  5  4.0\n",
       "2013-01-06  0.567867  0.235352  1.117395  5  5.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'D'] = np.array([5] * len(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过**where**操作来赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>-5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>-0.217345</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.220822</td>\n",
       "      <td>-0.790527</td>\n",
       "      <td>-0.692172</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.260276</td>\n",
       "      <td>-1.000297</td>\n",
       "      <td>-0.809801</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.679381</td>\n",
       "      <td>-1.468609</td>\n",
       "      <td>-0.360648</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.567867</td>\n",
       "      <td>-0.235352</td>\n",
       "      <td>-1.117395</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",
       "2013-01-01  0.000000  0.000000 -0.541762 -5  NaN\n",
       "2013-01-02 -0.884117 -0.650741 -0.217345 -5 -1.0\n",
       "2013-01-03 -0.220822 -0.790527 -0.692172 -5 -2.0\n",
       "2013-01-04 -1.260276 -1.000297 -0.809801 -5 -3.0\n",
       "2013-01-05 -1.679381 -1.468609 -0.360648 -5 -4.0\n",
       "2013-01-06 -0.567867 -0.235352 -1.117395 -5 -5.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2[df2 > 0] = -df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 缺失值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在pandas中，用**np.nan**来代表缺失值，这些值默认不会参与运算。\n",
    "\n",
    "reindex()允许你修改、增加、删除指定轴上的索引，并返回一个数据副本。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</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",
       "2013-01-01  0.000000  0.000000 -0.541762  5  NaN  1.0\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  5  1.0  1.0\n",
       "2013-01-03  0.220822  0.790527  0.692172  5  2.0  NaN\n",
       "2013-01-04  1.260276  1.000297  0.809801  5  3.0  NaN"
      ]
     },
     "execution_count": 44,
     "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": {},
   "source": [
    "剔除所有包含缺失值的行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</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",
       "2013-01-02 -0.884117 -0.650741  0.217345  5  1.0  1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>0.217345</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.220822</td>\n",
       "      <td>0.790527</td>\n",
       "      <td>0.692172</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.260276</td>\n",
       "      <td>1.000297</td>\n",
       "      <td>0.809801</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",
       "2013-01-01  0.000000  0.000000 -0.541762  5  5.0  1.0\n",
       "2013-01-02 -0.884117 -0.650741  0.217345  5  1.0  1.0\n",
       "2013-01-03  0.220822  0.790527  0.692172  5  2.0  5.0\n",
       "2013-01-04  1.260276  1.000297  0.809801  5  3.0  5.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取值是否为**nan**的布尔标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2013-01-01</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>2013-01-02</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>2013-01-03</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>2013-01-04</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",
       "2013-01-01  False  False  False  False   True  False\n",
       "2013-01-02  False  False  False  False  False  False\n",
       "2013-01-03  False  False  False  False  False   True\n",
       "2013-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运算过程中，通常不包含缺失值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进行描述性统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.474038\n",
       "B    0.474008\n",
       "C    0.442600\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对其他轴进行同样的运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.114559\n",
       "2013-01-02    0.936497\n",
       "2013-01-03    1.740704\n",
       "2013-01-04    2.214075\n",
       "2013-01-05    2.501728\n",
       "2013-01-06    2.384123\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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计算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    NaN\n",
       "2013-01-02    NaN\n",
       "2013-01-03    1.0\n",
       "2013-01-04    3.0\n",
       "2013-01-05    5.0\n",
       "2013-01-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2013-01-01</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>2013-01-02</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>2013-01-03</th>\n",
       "      <td>-0.779178</td>\n",
       "      <td>-0.209473</td>\n",
       "      <td>-0.307828</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.739724</td>\n",
       "      <td>-1.999703</td>\n",
       "      <td>-2.190199</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-3.320619</td>\n",
       "      <td>-3.531391</td>\n",
       "      <td>-4.639352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</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",
       "2013-01-01       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-02       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-03 -0.779178 -0.209473 -0.307828  4.0  1.0\n",
       "2013-01-04 -1.739724 -1.999703 -2.190199  2.0  0.0\n",
       "2013-01-05 -3.320619 -3.531391 -4.639352  0.0 -1.0\n",
       "2013-01-06       NaN       NaN       NaN  NaN  NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply 函数作用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过apply()对函数作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.541762</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.884117</td>\n",
       "      <td>-0.650741</td>\n",
       "      <td>-0.324417</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.663295</td>\n",
       "      <td>0.139786</td>\n",
       "      <td>0.367755</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.596982</td>\n",
       "      <td>1.140083</td>\n",
       "      <td>1.177556</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>2.276362</td>\n",
       "      <td>2.608693</td>\n",
       "      <td>1.538204</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>2.844229</td>\n",
       "      <td>2.844045</td>\n",
       "      <td>2.655599</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",
       "2013-01-01  0.000000  0.000000 -0.541762   5   NaN\n",
       "2013-01-02 -0.884117 -0.650741 -0.324417  10   1.0\n",
       "2013-01-03 -0.663295  0.139786  0.367755  15   3.0\n",
       "2013-01-04  0.596982  1.140083  1.177556  20   6.0\n",
       "2013-01-05  2.276362  2.608693  1.538204  25  10.0\n",
       "2013-01-06  2.844229  2.844045  2.655599  30  15.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.563498\n",
       "B    2.119350\n",
       "C    1.659157\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x:x.max()-x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 频数统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    0\n",
       "2    5\n",
       "3    1\n",
       "4    6\n",
       "5    1\n",
       "6    1\n",
       "7    1\n",
       "8    5\n",
       "9    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4\n",
       "5    2\n",
       "6    1\n",
       "4    1\n",
       "2    1\n",
       "0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字符串方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于Series对象，在其str属性中有着一系列的字符串处理方法。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": 56,
   "metadata": {},
   "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": 56,
     "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": {},
   "source": [
    "# 合并"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concat 连接"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas中提供了大量的方法能够轻松对Series，DataFrame和Panel对象进行不同满足逻辑关系的合并操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过**concat()**来连接pandas对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1.560690</td>\n",
       "      <td>2.253479</td>\n",
       "      <td>1.728586</td>\n",
       "      <td>1.224112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.237557</td>\n",
       "      <td>-1.571768</td>\n",
       "      <td>-1.687004</td>\n",
       "      <td>-0.226474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.591146</td>\n",
       "      <td>-0.054644</td>\n",
       "      <td>0.600806</td>\n",
       "      <td>0.076132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.567678</td>\n",
       "      <td>0.426496</td>\n",
       "      <td>-0.972487</td>\n",
       "      <td>0.200211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.073311</td>\n",
       "      <td>-1.566767</td>\n",
       "      <td>-0.533602</td>\n",
       "      <td>1.366468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.244767</td>\n",
       "      <td>1.612232</td>\n",
       "      <td>1.934717</td>\n",
       "      <td>-0.403805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.640917</td>\n",
       "      <td>0.640549</td>\n",
       "      <td>1.257238</td>\n",
       "      <td>0.043773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.545405</td>\n",
       "      <td>1.771884</td>\n",
       "      <td>-0.273687</td>\n",
       "      <td>2.441483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.440476</td>\n",
       "      <td>0.567536</td>\n",
       "      <td>2.379072</td>\n",
       "      <td>1.152354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.047853</td>\n",
       "      <td>-0.440427</td>\n",
       "      <td>-1.382389</td>\n",
       "      <td>0.647217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.560690  2.253479  1.728586  1.224112\n",
       "1 -1.237557 -1.571768 -1.687004 -0.226474\n",
       "2 -0.591146 -0.054644  0.600806  0.076132\n",
       "3 -0.567678  0.426496 -0.972487  0.200211\n",
       "4 -2.073311 -1.566767 -0.533602  1.366468\n",
       "5  2.244767  1.612232  1.934717 -0.403805\n",
       "6 -2.640917  0.640549  1.257238  0.043773\n",
       "7  1.545405  1.771884 -0.273687  2.441483\n",
       "8 -0.440476  0.567536  2.379072  1.152354\n",
       "9 -0.047853 -0.440427 -1.382389  0.647217"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10,4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0  1.560690  2.253479  1.728586  1.224112\n",
       " 1 -1.237557 -1.571768 -1.687004 -0.226474\n",
       " 2 -0.591146 -0.054644  0.600806  0.076132,\n",
       "           0         1         2         3\n",
       " 3 -0.567678  0.426496 -0.972487  0.200211\n",
       " 4 -2.073311 -1.566767 -0.533602  1.366468\n",
       " 5  2.244767  1.612232  1.934717 -0.403805\n",
       " 6 -2.640917  0.640549  1.257238  0.043773,\n",
       "           0         1         2         3\n",
       " 7  1.545405  1.771884 -0.273687  2.441483\n",
       " 8 -0.440476  0.567536  2.379072  1.152354\n",
       " 9 -0.047853 -0.440427 -1.382389  0.647217]"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>1.560690</td>\n",
       "      <td>2.253479</td>\n",
       "      <td>1.728586</td>\n",
       "      <td>1.224112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.237557</td>\n",
       "      <td>-1.571768</td>\n",
       "      <td>-1.687004</td>\n",
       "      <td>-0.226474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.591146</td>\n",
       "      <td>-0.054644</td>\n",
       "      <td>0.600806</td>\n",
       "      <td>0.076132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.567678</td>\n",
       "      <td>0.426496</td>\n",
       "      <td>-0.972487</td>\n",
       "      <td>0.200211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.073311</td>\n",
       "      <td>-1.566767</td>\n",
       "      <td>-0.533602</td>\n",
       "      <td>1.366468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.244767</td>\n",
       "      <td>1.612232</td>\n",
       "      <td>1.934717</td>\n",
       "      <td>-0.403805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.640917</td>\n",
       "      <td>0.640549</td>\n",
       "      <td>1.257238</td>\n",
       "      <td>0.043773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.545405</td>\n",
       "      <td>1.771884</td>\n",
       "      <td>-0.273687</td>\n",
       "      <td>2.441483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.440476</td>\n",
       "      <td>0.567536</td>\n",
       "      <td>2.379072</td>\n",
       "      <td>1.152354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.047853</td>\n",
       "      <td>-0.440427</td>\n",
       "      <td>-1.382389</td>\n",
       "      <td>0.647217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.560690  2.253479  1.728586  1.224112\n",
       "1 -1.237557 -1.571768 -1.687004 -0.226474\n",
       "2 -0.591146 -0.054644  0.600806  0.076132\n",
       "3 -0.567678  0.426496 -0.972487  0.200211\n",
       "4 -2.073311 -1.566767 -0.533602  1.366468\n",
       "5  2.244767  1.612232  1.934717 -0.403805\n",
       "6 -2.640917  0.640549  1.257238  0.043773\n",
       "7  1.545405  1.771884 -0.273687  2.441483\n",
       "8 -0.440476  0.567536  2.379072  1.152354\n",
       "9 -0.047853 -0.440427 -1.382389  0.647217"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join 合并"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类似于SQL中的合并(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key':['foo', 'foo'], 'lval':[1,2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key':['foo', 'foo'], 'lval':[4,5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append 添加"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将若干行添加到dataFrame后面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.415810</td>\n",
       "      <td>-1.106857</td>\n",
       "      <td>-0.687920</td>\n",
       "      <td>2.422911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.696149</td>\n",
       "      <td>-1.235975</td>\n",
       "      <td>0.201409</td>\n",
       "      <td>1.424596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.540622</td>\n",
       "      <td>0.121096</td>\n",
       "      <td>-0.861667</td>\n",
       "      <td>-0.171690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.163904</td>\n",
       "      <td>1.324567</td>\n",
       "      <td>-0.768324</td>\n",
       "      <td>-0.205520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.581152</td>\n",
       "      <td>-0.079061</td>\n",
       "      <td>0.251810</td>\n",
       "      <td>-0.195755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.254246</td>\n",
       "      <td>1.604556</td>\n",
       "      <td>0.766464</td>\n",
       "      <td>-1.090743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.608609</td>\n",
       "      <td>1.000765</td>\n",
       "      <td>-0.407980</td>\n",
       "      <td>0.034970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-3.111914</td>\n",
       "      <td>2.163344</td>\n",
       "      <td>0.619885</td>\n",
       "      <td>-0.705518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.415810 -1.106857 -0.687920  2.422911\n",
       "1  0.696149 -1.235975  0.201409  1.424596\n",
       "2 -0.540622  0.121096 -0.861667 -0.171690\n",
       "3  0.163904  1.324567 -0.768324 -0.205520\n",
       "4 -1.581152 -0.079061  0.251810 -0.195755\n",
       "5  1.254246  1.604556  0.766464 -1.090743\n",
       "6  0.608609  1.000765 -0.407980  0.034970\n",
       "7 -3.111914  2.163344  0.619885 -0.705518"
      ]
     },
     "execution_count": 63,
     "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": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.163904\n",
       "B    1.324567\n",
       "C   -0.768324\n",
       "D   -0.205520\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "      <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>0.415810</td>\n",
       "      <td>-1.106857</td>\n",
       "      <td>-0.687920</td>\n",
       "      <td>2.422911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.696149</td>\n",
       "      <td>-1.235975</td>\n",
       "      <td>0.201409</td>\n",
       "      <td>1.424596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.540622</td>\n",
       "      <td>0.121096</td>\n",
       "      <td>-0.861667</td>\n",
       "      <td>-0.171690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.163904</td>\n",
       "      <td>1.324567</td>\n",
       "      <td>-0.768324</td>\n",
       "      <td>-0.205520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.581152</td>\n",
       "      <td>-0.079061</td>\n",
       "      <td>0.251810</td>\n",
       "      <td>-0.195755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.254246</td>\n",
       "      <td>1.604556</td>\n",
       "      <td>0.766464</td>\n",
       "      <td>-1.090743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.608609</td>\n",
       "      <td>1.000765</td>\n",
       "      <td>-0.407980</td>\n",
       "      <td>0.034970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-3.111914</td>\n",
       "      <td>2.163344</td>\n",
       "      <td>0.619885</td>\n",
       "      <td>-0.705518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.163904</td>\n",
       "      <td>1.324567</td>\n",
       "      <td>-0.768324</td>\n",
       "      <td>-0.205520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.415810 -1.106857 -0.687920  2.422911\n",
       "1  0.696149 -1.235975  0.201409  1.424596\n",
       "2 -0.540622  0.121096 -0.861667 -0.171690\n",
       "3  0.163904  1.324567 -0.768324 -0.205520\n",
       "4 -1.581152 -0.079061  0.251810 -0.195755\n",
       "5  1.254246  1.604556  0.766464 -1.090743\n",
       "6  0.608609  1.000765 -0.407980  0.034970\n",
       "7 -3.111914  2.163344  0.619885 -0.705518\n",
       "8  0.163904  1.324567 -0.768324 -0.205520"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于“group by”操作，我们通常是指以下一个或几个步骤：\n",
    "* **划分** 按照某些标准将数据分为不同的组\n",
    "* **应用** 对每组数据分别执行一个函数\n",
    "* **组合** 将结果组合到一个数据结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.190663</td>\n",
       "      <td>0.589384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1.056331</td>\n",
       "      <td>0.035044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.723645</td>\n",
       "      <td>0.372672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-1.306869</td>\n",
       "      <td>0.435296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.673661</td>\n",
       "      <td>-1.292242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.823728</td>\n",
       "      <td>0.837556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.638573</td>\n",
       "      <td>2.453041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.508922</td>\n",
       "      <td>0.578740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.190663  0.589384\n",
       "1  bar    one  1.056331  0.035044\n",
       "2  foo    two  0.723645  0.372672\n",
       "3  bar  three -1.306869  0.435296\n",
       "4  foo    two  0.673661 -1.292242\n",
       "5  bar    two -0.823728  0.837556\n",
       "6  foo    one  0.638573  2.453041\n",
       "7  bar  three  0.508922  0.578740"
      ]
     },
     "execution_count": 66,
     "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": {},
   "source": [
    "分组并对每个分组应用sum函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.565344</td>\n",
       "      <td>1.886637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>2.226542</td>\n",
       "      <td>2.122855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -0.565344  1.886637\n",
       "foo  2.226542  2.122855"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按多个列分组形成层级索引，然后应用函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>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>1.056331</td>\n",
       "      <td>0.035044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.797947</td>\n",
       "      <td>1.014036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.823728</td>\n",
       "      <td>0.837556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.829236</td>\n",
       "      <td>3.042425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.397306</td>\n",
       "      <td>-0.919570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.056331  0.035044\n",
       "    three -0.797947  1.014036\n",
       "    two   -0.823728  0.837556\n",
       "foo one    0.829236  3.042425\n",
       "    two    1.397306 -0.919570"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 变形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 堆叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "                     'foo', 'foo', 'qux', 'qux'],\n",
    "                    ['one', 'two', 'one', 'two',\n",
    "                     'one', 'two', 'one', 'two']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>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>-0.551894</td>\n",
       "      <td>0.358046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.773283</td>\n",
       "      <td>-0.489393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.461588</td>\n",
       "      <td>-1.561085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.304456</td>\n",
       "      <td>-0.241811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.551894  0.358046\n",
       "      two    -0.773283 -0.489393\n",
       "baz   one    -0.461588 -1.561085\n",
       "      two     2.304456 -0.241811"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[:4]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**stack()**方法对DataFrame的列“压缩”一个层级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.551894\n",
       "               B    0.358046\n",
       "       two     A   -0.773283\n",
       "               B   -0.489393\n",
       "baz    one     A   -0.461588\n",
       "               B   -1.561085\n",
       "       two     A    2.304456\n",
       "               B   -0.241811\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = df2.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于一个“堆叠过的”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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>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>-0.551894</td>\n",
       "      <td>0.358046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.773283</td>\n",
       "      <td>-0.489393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.461588</td>\n",
       "      <td>-1.561085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.304456</td>\n",
       "      <td>-0.241811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.551894  0.358046\n",
       "      two    -0.773283 -0.489393\n",
       "baz   one    -0.461588 -1.561085\n",
       "      two     2.304456 -0.241811"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.551894</td>\n",
       "      <td>-0.773283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.358046</td>\n",
       "      <td>-0.489393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.461588</td>\n",
       "      <td>2.304456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.561085</td>\n",
       "      <td>-0.241811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.551894 -0.773283\n",
       "      B  0.358046 -0.489393\n",
       "baz   A -0.461588  2.304456\n",
       "      B -1.561085 -0.241811"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.551894</td>\n",
       "      <td>-0.461588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.358046</td>\n",
       "      <td>-1.561085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.773283</td>\n",
       "      <td>2.304456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.489393</td>\n",
       "      <td>-0.241811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.551894 -0.461588\n",
       "       B  0.358046 -1.561085\n",
       "two    A -0.773283  2.304456\n",
       "       B -0.489393 -0.241811"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.169373</td>\n",
       "      <td>-1.070179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.484603</td>\n",
       "      <td>-1.627113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.289207</td>\n",
       "      <td>0.635274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.034032</td>\n",
       "      <td>-0.553259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.536531</td>\n",
       "      <td>-0.088784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.977931</td>\n",
       "      <td>-0.211193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.486196</td>\n",
       "      <td>-0.377266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.108725</td>\n",
       "      <td>0.050407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.562988</td>\n",
       "      <td>-0.195356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.510381</td>\n",
       "      <td>-2.050069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.005697</td>\n",
       "      <td>0.708523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.942276</td>\n",
       "      <td>0.769442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo -0.169373 -1.070179\n",
       "1     one  B  foo -0.484603 -1.627113\n",
       "2     two  C  foo -1.289207  0.635274\n",
       "3   three  A  bar  0.034032 -0.553259\n",
       "4     one  B  bar  0.536531 -0.088784\n",
       "5     one  C  bar -0.977931 -0.211193\n",
       "6     two  A  foo  1.486196 -0.377266\n",
       "7   three  B  foo -1.108725  0.050407\n",
       "8     one  C  foo  0.562988 -0.195356\n",
       "9     one  A  bar  0.510381 -2.050069\n",
       "10    two  B  bar  0.005697  0.708523\n",
       "11  three  C  bar  0.942276  0.769442"
      ]
     },
     "execution_count": 77,
     "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": "markdown",
   "metadata": {},
   "source": [
    " 我们可以轻松地从这个数据得到透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.510381</td>\n",
       "      <td>-0.169373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.536531</td>\n",
       "      <td>-0.484603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.977931</td>\n",
       "      <td>0.562988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>0.034032</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.108725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.942276</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>1.486196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.005697</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.289207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A  0.510381 -0.169373\n",
       "      B  0.536531 -0.484603\n",
       "      C -0.977931  0.562988\n",
       "three A  0.034032       NaN\n",
       "      B       NaN -1.108725\n",
       "      C  0.942276       NaN\n",
       "two   A       NaN  1.486196\n",
       "      B  0.005697       NaN\n",
       "      C       NaN -1.289207"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 时间序列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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）。这在金融领域很常见，但又不限于此。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:00:01',\n",
       "               '2012-01-01 00:00:02', '2012-01-01 00:00:03',\n",
       "               '2012-01-01 00:00:04', '2012-01-01 00:00:05',\n",
       "               '2012-01-01 00:00:06', '2012-01-01 00:00:07',\n",
       "               '2012-01-01 00:00:08', '2012-01-01 00:00:09',\n",
       "               '2012-01-01 00:00:10', '2012-01-01 00:00:11',\n",
       "               '2012-01-01 00:00:12', '2012-01-01 00:00:13',\n",
       "               '2012-01-01 00:00:14', '2012-01-01 00:00:15',\n",
       "               '2012-01-01 00:00:16', '2012-01-01 00:00:17',\n",
       "               '2012-01-01 00:00:18', '2012-01-01 00:00:19',\n",
       "               '2012-01-01 00:00:20', '2012-01-01 00:00:21',\n",
       "               '2012-01-01 00:00:22', '2012-01-01 00:00:23',\n",
       "               '2012-01-01 00:00:24', '2012-01-01 00:00:25',\n",
       "               '2012-01-01 00:00:26', '2012-01-01 00:00:27',\n",
       "               '2012-01-01 00:00:28', '2012-01-01 00:00:29',\n",
       "               '2012-01-01 00:00:30', '2012-01-01 00:00:31',\n",
       "               '2012-01-01 00:00:32', '2012-01-01 00:00:33',\n",
       "               '2012-01-01 00:00:34', '2012-01-01 00:00:35',\n",
       "               '2012-01-01 00:00:36', '2012-01-01 00:00:37',\n",
       "               '2012-01-01 00:00:38', '2012-01-01 00:00:39',\n",
       "               '2012-01-01 00:00:40', '2012-01-01 00:00:41',\n",
       "               '2012-01-01 00:00:42', '2012-01-01 00:00:43',\n",
       "               '2012-01-01 00:00:44', '2012-01-01 00:00:45',\n",
       "               '2012-01-01 00:00:46', '2012-01-01 00:00:47',\n",
       "               '2012-01-01 00:00:48', '2012-01-01 00:00:49',\n",
       "               '2012-01-01 00:00:50', '2012-01-01 00:00:51',\n",
       "               '2012-01-01 00:00:52', '2012-01-01 00:00:53',\n",
       "               '2012-01-01 00:00:54', '2012-01-01 00:00:55',\n",
       "               '2012-01-01 00:00:56', '2012-01-01 00:00:57',\n",
       "               '2012-01-01 00:00:58', '2012-01-01 00:00:59',\n",
       "               '2012-01-01 00:01:00', '2012-01-01 00:01:01',\n",
       "               '2012-01-01 00:01:02', '2012-01-01 00:01:03',\n",
       "               '2012-01-01 00:01:04', '2012-01-01 00:01:05',\n",
       "               '2012-01-01 00:01:06', '2012-01-01 00:01:07',\n",
       "               '2012-01-01 00:01:08', '2012-01-01 00:01:09',\n",
       "               '2012-01-01 00:01:10', '2012-01-01 00:01:11',\n",
       "               '2012-01-01 00:01:12', '2012-01-01 00:01:13',\n",
       "               '2012-01-01 00:01:14', '2012-01-01 00:01:15',\n",
       "               '2012-01-01 00:01:16', '2012-01-01 00:01:17',\n",
       "               '2012-01-01 00:01:18', '2012-01-01 00:01:19',\n",
       "               '2012-01-01 00:01:20', '2012-01-01 00:01:21',\n",
       "               '2012-01-01 00:01:22', '2012-01-01 00:01:23',\n",
       "               '2012-01-01 00:01:24', '2012-01-01 00:01:25',\n",
       "               '2012-01-01 00:01:26', '2012-01-01 00:01:27',\n",
       "               '2012-01-01 00:01:28', '2012-01-01 00:01:29',\n",
       "               '2012-01-01 00:01:30', '2012-01-01 00:01:31',\n",
       "               '2012-01-01 00:01:32', '2012-01-01 00:01:33',\n",
       "               '2012-01-01 00:01:34', '2012-01-01 00:01:35',\n",
       "               '2012-01-01 00:01:36', '2012-01-01 00:01:37',\n",
       "               '2012-01-01 00:01:38', '2012-01-01 00:01:39'],\n",
       "              dtype='datetime64[ns]', freq='S')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01 00:00:00    100\n",
       "2012-01-01 00:00:01     47\n",
       "2012-01-01 00:00:02    473\n",
       "2012-01-01 00:00:03    136\n",
       "2012-01-01 00:00:04    192\n",
       "2012-01-01 00:00:05    372\n",
       "2012-01-01 00:00:06    459\n",
       "2012-01-01 00:00:07    450\n",
       "2012-01-01 00:00:08    244\n",
       "2012-01-01 00:00:09    383\n",
       "2012-01-01 00:00:10    250\n",
       "2012-01-01 00:00:11     53\n",
       "2012-01-01 00:00:12    364\n",
       "2012-01-01 00:00:13    139\n",
       "2012-01-01 00:00:14    239\n",
       "2012-01-01 00:00:15    494\n",
       "2012-01-01 00:00:16    370\n",
       "2012-01-01 00:00:17    329\n",
       "2012-01-01 00:00:18     48\n",
       "2012-01-01 00:00:19    278\n",
       "2012-01-01 00:00:20    100\n",
       "2012-01-01 00:00:21    443\n",
       "2012-01-01 00:00:22    348\n",
       "2012-01-01 00:00:23     25\n",
       "2012-01-01 00:00:24    257\n",
       "2012-01-01 00:00:25    245\n",
       "2012-01-01 00:00:26     60\n",
       "2012-01-01 00:00:27     12\n",
       "2012-01-01 00:00:28    312\n",
       "2012-01-01 00:00:29    154\n",
       "                      ... \n",
       "2012-01-01 00:01:10     31\n",
       "2012-01-01 00:01:11    225\n",
       "2012-01-01 00:01:12    364\n",
       "2012-01-01 00:01:13    171\n",
       "2012-01-01 00:01:14    351\n",
       "2012-01-01 00:01:15    459\n",
       "2012-01-01 00:01:16    363\n",
       "2012-01-01 00:01:17    369\n",
       "2012-01-01 00:01:18    421\n",
       "2012-01-01 00:01:19    389\n",
       "2012-01-01 00:01:20    408\n",
       "2012-01-01 00:01:21     63\n",
       "2012-01-01 00:01:22     55\n",
       "2012-01-01 00:01:23    368\n",
       "2012-01-01 00:01:24    439\n",
       "2012-01-01 00:01:25     14\n",
       "2012-01-01 00:01:26    220\n",
       "2012-01-01 00:01:27    250\n",
       "2012-01-01 00:01:28    495\n",
       "2012-01-01 00:01:29    391\n",
       "2012-01-01 00:01:30     79\n",
       "2012-01-01 00:01:31    408\n",
       "2012-01-01 00:01:32    232\n",
       "2012-01-01 00:01:33    413\n",
       "2012-01-01 00:01:34    373\n",
       "2012-01-01 00:01:35    355\n",
       "2012-01-01 00:01:36    308\n",
       "2012-01-01 00:01:37     51\n",
       "2012-01-01 00:01:38    375\n",
       "2012-01-01 00:01:39    185\n",
       "Freq: S, Length: 100, dtype: int32"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).sum()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2012-01-01    26203\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5Min', how='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时区表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\n",
       "               '2012-03-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/6/2012', periods=5, freq='D')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06    0.523781\n",
       "2012-03-07   -0.670822\n",
       "2012-03-08    0.934826\n",
       "2012-03-09    0.002239\n",
       "2012-03-10   -0.091952\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    0.523781\n",
       "2012-03-07 00:00:00+00:00   -0.670822\n",
       "2012-03-08 00:00:00+00:00    0.934826\n",
       "2012-03-09 00:00:00+00:00    0.002239\n",
       "2012-03-10 00:00:00+00:00   -0.091952\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时区转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00    0.523781\n",
       "2012-03-06 19:00:00-05:00   -0.670822\n",
       "2012-03-07 19:00:00-05:00    0.934826\n",
       "2012-03-08 19:00:00-05:00    0.002239\n",
       "2012-03-09 19:00:00-05:00   -0.091952\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间跨度转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',\n",
       "               '2012-05-31'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31    1.296132\n",
       "2012-02-29    1.023936\n",
       "2012-03-31   -0.249774\n",
       "2012-04-30    1.007810\n",
       "2012-05-31   -0.051413\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01    1.296132\n",
       "2012-02    1.023936\n",
       "2012-03   -0.249774\n",
       "2012-04    1.007810\n",
       "2012-05   -0.051413\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ts.to_period()\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    1.296132\n",
       "2012-02-01    1.023936\n",
       "2012-03-01   -0.249774\n",
       "2012-04-01    1.007810\n",
       "2012-05-01   -0.051413\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": 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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2',\n",
       "             '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4',\n",
       "             '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2',\n",
       "             '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4',\n",
       "             '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2',\n",
       "             '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4',\n",
       "             '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2',\n",
       "             '2000Q3', '2000Q4'],\n",
       "            dtype='period[Q-NOV]', freq='Q-NOV')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')\n",
    "prng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990Q1    0.467059\n",
       "1990Q2   -0.745741\n",
       "1990Q3    0.106234\n",
       "1990Q4   -0.306761\n",
       "1991Q1   -2.012034\n",
       "1991Q2   -2.033417\n",
       "1991Q3   -1.116709\n",
       "1991Q4    0.031495\n",
       "1992Q1    0.407074\n",
       "1992Q2    0.988878\n",
       "1992Q3   -0.195428\n",
       "1992Q4   -0.144770\n",
       "1993Q1    0.733104\n",
       "1993Q2   -0.742351\n",
       "1993Q3   -0.464713\n",
       "1993Q4   -1.247317\n",
       "1994Q1   -0.108473\n",
       "1994Q2    0.188728\n",
       "1994Q3    0.052446\n",
       "1994Q4   -0.004046\n",
       "1995Q1    1.075490\n",
       "1995Q2   -0.436009\n",
       "1995Q3    0.747165\n",
       "1995Q4    2.433366\n",
       "1996Q1   -0.471832\n",
       "1996Q2   -1.337675\n",
       "1996Q3    0.931348\n",
       "1996Q4   -0.461312\n",
       "1997Q1    0.041943\n",
       "1997Q2   -1.003802\n",
       "1997Q3   -0.572660\n",
       "1997Q4   -0.959966\n",
       "1998Q1   -1.864559\n",
       "1998Q2   -2.196461\n",
       "1998Q3   -1.317999\n",
       "1998Q4   -0.796643\n",
       "1999Q1    1.183292\n",
       "1999Q2   -0.046480\n",
       "1999Q3   -0.727018\n",
       "1999Q4   -0.972557\n",
       "2000Q1    0.541060\n",
       "2000Q2    1.221401\n",
       "2000Q3   -1.407534\n",
       "2000Q4   -0.773754\n",
       "Freq: Q-NOV, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(prng)), index = prng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-02-01 09:00    0.467059\n",
       "1990-05-01 09:00   -0.745741\n",
       "1990-08-01 09:00    0.106234\n",
       "1990-11-01 09:00   -0.306761\n",
       "1991-02-01 09:00   -2.012034\n",
       "1991-05-01 09:00   -2.033417\n",
       "1991-08-01 09:00   -1.116709\n",
       "1991-11-01 09:00    0.031495\n",
       "1992-02-01 09:00    0.407074\n",
       "1992-05-01 09:00    0.988878\n",
       "1992-08-01 09:00   -0.195428\n",
       "1992-11-01 09:00   -0.144770\n",
       "1993-02-01 09:00    0.733104\n",
       "1993-05-01 09:00   -0.742351\n",
       "1993-08-01 09:00   -0.464713\n",
       "1993-11-01 09:00   -1.247317\n",
       "1994-02-01 09:00   -0.108473\n",
       "1994-05-01 09:00    0.188728\n",
       "1994-08-01 09:00    0.052446\n",
       "1994-11-01 09:00   -0.004046\n",
       "1995-02-01 09:00    1.075490\n",
       "1995-05-01 09:00   -0.436009\n",
       "1995-08-01 09:00    0.747165\n",
       "1995-11-01 09:00    2.433366\n",
       "1996-02-01 09:00   -0.471832\n",
       "1996-05-01 09:00   -1.337675\n",
       "1996-08-01 09:00    0.931348\n",
       "1996-11-01 09:00   -0.461312\n",
       "1997-02-01 09:00    0.041943\n",
       "1997-05-01 09:00   -1.003802\n",
       "1997-08-01 09:00   -0.572660\n",
       "1997-11-01 09:00   -0.959966\n",
       "1998-02-01 09:00   -1.864559\n",
       "1998-05-01 09:00   -2.196461\n",
       "1998-08-01 09:00   -1.317999\n",
       "1998-11-01 09:00   -0.796643\n",
       "1999-02-01 09:00    1.183292\n",
       "1999-05-01 09:00   -0.046480\n",
       "1999-08-01 09:00   -0.727018\n",
       "1999-11-01 09:00   -0.972557\n",
       "2000-02-01 09:00    0.541060\n",
       "2000-05-01 09:00    1.221401\n",
       "2000-08-01 09:00   -1.407534\n",
       "2000-11-01 09:00   -0.773754\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index = (prng.asfreq('M', 'end') ) .asfreq('H', 'start') +9\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从版本0.15开始，pandas在**DataFrame**中开始包括分类数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 93,
     "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": {},
   "source": [
    "把raw_grade转换为分类类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "重命名类别名为更有意义的名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对分类重新排序，并添加缺失的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "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": 96,
     "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": {},
   "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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"grade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按分类分组时，也会显示空的分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     2\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2472e962d68>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2472e95eef0>"
      ]
     },
     "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": {},
   "source": [
    "对于DataFrame类型，**plot()**能很方便地画出所有列及其标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x247309914e0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24730a068d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x247309911d0>"
      ]
     },
     "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",
    "plt.figure(); df.plot(); plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取数据的I/O"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写入一个csv文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从一个csv文件读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.547915</td>\n",
       "      <td>0.758504</td>\n",
       "      <td>-0.791553</td>\n",
       "      <td>-0.014276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-1.478652</td>\n",
       "      <td>-1.824863</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>-1.067864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-3.070727</td>\n",
       "      <td>-1.655967</td>\n",
       "      <td>-0.551411</td>\n",
       "      <td>-2.188238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>-3.317105</td>\n",
       "      <td>-1.810778</td>\n",
       "      <td>0.354000</td>\n",
       "      <td>-1.995760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-2.822308</td>\n",
       "      <td>-2.757077</td>\n",
       "      <td>1.210057</td>\n",
       "      <td>-1.250592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>-2.223865</td>\n",
       "      <td>-3.256931</td>\n",
       "      <td>3.658672</td>\n",
       "      <td>-1.232510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>-0.559411</td>\n",
       "      <td>-2.842820</td>\n",
       "      <td>4.860993</td>\n",
       "      <td>-0.418853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000-01-08</td>\n",
       "      <td>-0.524812</td>\n",
       "      <td>-2.748762</td>\n",
       "      <td>6.280814</td>\n",
       "      <td>0.170616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-09</td>\n",
       "      <td>0.204528</td>\n",
       "      <td>-0.865022</td>\n",
       "      <td>5.560456</td>\n",
       "      <td>0.875871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>0.688895</td>\n",
       "      <td>-1.704024</td>\n",
       "      <td>5.226062</td>\n",
       "      <td>0.444483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>1.272138</td>\n",
       "      <td>-2.478377</td>\n",
       "      <td>2.763524</td>\n",
       "      <td>-1.229797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000-01-12</td>\n",
       "      <td>1.255657</td>\n",
       "      <td>-2.994073</td>\n",
       "      <td>2.869688</td>\n",
       "      <td>0.745504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000-01-13</td>\n",
       "      <td>-0.001803</td>\n",
       "      <td>-2.295823</td>\n",
       "      <td>2.738087</td>\n",
       "      <td>2.640307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000-01-14</td>\n",
       "      <td>0.144209</td>\n",
       "      <td>-0.926810</td>\n",
       "      <td>4.087308</td>\n",
       "      <td>0.164138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-15</td>\n",
       "      <td>1.304811</td>\n",
       "      <td>0.625530</td>\n",
       "      <td>3.542700</td>\n",
       "      <td>-1.499446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000-01-16</td>\n",
       "      <td>0.682026</td>\n",
       "      <td>0.552441</td>\n",
       "      <td>3.201389</td>\n",
       "      <td>-0.155674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2000-01-17</td>\n",
       "      <td>-1.065321</td>\n",
       "      <td>-0.339092</td>\n",
       "      <td>3.792634</td>\n",
       "      <td>-1.754946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>-1.268418</td>\n",
       "      <td>-3.512773</td>\n",
       "      <td>3.656896</td>\n",
       "      <td>-0.132124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-19</td>\n",
       "      <td>-1.936390</td>\n",
       "      <td>-1.069188</td>\n",
       "      <td>2.554887</td>\n",
       "      <td>0.413930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-01-20</td>\n",
       "      <td>-1.138678</td>\n",
       "      <td>-2.993870</td>\n",
       "      <td>3.094198</td>\n",
       "      <td>-0.956574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000-01-21</td>\n",
       "      <td>-0.096721</td>\n",
       "      <td>-4.141108</td>\n",
       "      <td>3.341248</td>\n",
       "      <td>-0.436886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000-01-22</td>\n",
       "      <td>-0.678763</td>\n",
       "      <td>-5.012663</td>\n",
       "      <td>0.823576</td>\n",
       "      <td>1.303377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000-01-23</td>\n",
       "      <td>-2.498525</td>\n",
       "      <td>-6.075938</td>\n",
       "      <td>0.302908</td>\n",
       "      <td>-0.294204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2000-01-24</td>\n",
       "      <td>-0.937785</td>\n",
       "      <td>-6.768487</td>\n",
       "      <td>-0.078521</td>\n",
       "      <td>-0.165799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-01-25</td>\n",
       "      <td>-0.252338</td>\n",
       "      <td>-6.882860</td>\n",
       "      <td>0.545684</td>\n",
       "      <td>-1.108641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>-0.772653</td>\n",
       "      <td>-7.080129</td>\n",
       "      <td>0.764647</td>\n",
       "      <td>-3.041450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2000-01-27</td>\n",
       "      <td>-0.348831</td>\n",
       "      <td>-6.035375</td>\n",
       "      <td>0.507673</td>\n",
       "      <td>-2.522836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>1.751721</td>\n",
       "      <td>-5.756738</td>\n",
       "      <td>-0.469014</td>\n",
       "      <td>-2.825118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>0.799938</td>\n",
       "      <td>-4.559172</td>\n",
       "      <td>-0.086763</td>\n",
       "      <td>-3.243101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2000-01-30</td>\n",
       "      <td>1.149650</td>\n",
       "      <td>-4.433331</td>\n",
       "      <td>1.476689</td>\n",
       "      <td>-2.479956</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>0.466756</td>\n",
       "      <td>38.780786</td>\n",
       "      <td>-6.743120</td>\n",
       "      <td>-15.206051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2002-08-29</td>\n",
       "      <td>1.903199</td>\n",
       "      <td>36.687258</td>\n",
       "      <td>-5.720742</td>\n",
       "      <td>-14.335689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2002-08-30</td>\n",
       "      <td>1.432801</td>\n",
       "      <td>37.186065</td>\n",
       "      <td>-6.274877</td>\n",
       "      <td>-14.879621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2002-08-31</td>\n",
       "      <td>2.218021</td>\n",
       "      <td>36.200395</td>\n",
       "      <td>-5.638178</td>\n",
       "      <td>-15.625389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>3.405318</td>\n",
       "      <td>36.987365</td>\n",
       "      <td>-4.441912</td>\n",
       "      <td>-14.162031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2002-09-02</td>\n",
       "      <td>3.854252</td>\n",
       "      <td>38.283545</td>\n",
       "      <td>-4.845949</td>\n",
       "      <td>-15.449302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2002-09-03</td>\n",
       "      <td>4.328033</td>\n",
       "      <td>37.123804</td>\n",
       "      <td>-6.006344</td>\n",
       "      <td>-14.158929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2002-09-04</td>\n",
       "      <td>5.219103</td>\n",
       "      <td>37.203486</td>\n",
       "      <td>-6.035182</td>\n",
       "      <td>-14.461389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2002-09-05</td>\n",
       "      <td>4.884547</td>\n",
       "      <td>38.192836</td>\n",
       "      <td>-6.447570</td>\n",
       "      <td>-13.541204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>2002-09-06</td>\n",
       "      <td>5.817435</td>\n",
       "      <td>37.669998</td>\n",
       "      <td>-5.983732</td>\n",
       "      <td>-12.238303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2002-09-07</td>\n",
       "      <td>7.953334</td>\n",
       "      <td>37.503576</td>\n",
       "      <td>-4.783659</td>\n",
       "      <td>-12.092902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>2002-09-08</td>\n",
       "      <td>8.575181</td>\n",
       "      <td>37.377622</td>\n",
       "      <td>-3.154240</td>\n",
       "      <td>-12.895208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>8.242240</td>\n",
       "      <td>38.155500</td>\n",
       "      <td>-3.285132</td>\n",
       "      <td>-13.483775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2002-09-10</td>\n",
       "      <td>9.699693</td>\n",
       "      <td>37.421024</td>\n",
       "      <td>-1.763105</td>\n",
       "      <td>-13.397276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2002-09-11</td>\n",
       "      <td>7.228027</td>\n",
       "      <td>38.070563</td>\n",
       "      <td>-1.250151</td>\n",
       "      <td>-14.455654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2002-09-12</td>\n",
       "      <td>7.276336</td>\n",
       "      <td>38.503057</td>\n",
       "      <td>-1.110047</td>\n",
       "      <td>-15.362900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2002-09-13</td>\n",
       "      <td>6.955453</td>\n",
       "      <td>37.038613</td>\n",
       "      <td>-0.571942</td>\n",
       "      <td>-14.527590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>2002-09-14</td>\n",
       "      <td>6.232800</td>\n",
       "      <td>37.254276</td>\n",
       "      <td>-1.893050</td>\n",
       "      <td>-14.385401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>2002-09-15</td>\n",
       "      <td>5.331802</td>\n",
       "      <td>36.671504</td>\n",
       "      <td>-1.782282</td>\n",
       "      <td>-13.645305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>2002-09-16</td>\n",
       "      <td>5.825638</td>\n",
       "      <td>36.920733</td>\n",
       "      <td>-2.718137</td>\n",
       "      <td>-13.875984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2002-09-17</td>\n",
       "      <td>5.972137</td>\n",
       "      <td>36.785577</td>\n",
       "      <td>-3.923763</td>\n",
       "      <td>-13.281418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>2002-09-18</td>\n",
       "      <td>5.154685</td>\n",
       "      <td>34.789858</td>\n",
       "      <td>-4.426825</td>\n",
       "      <td>-13.805227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2002-09-19</td>\n",
       "      <td>4.228035</td>\n",
       "      <td>34.400350</td>\n",
       "      <td>-4.431980</td>\n",
       "      <td>-14.520423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2002-09-20</td>\n",
       "      <td>3.047640</td>\n",
       "      <td>36.244469</td>\n",
       "      <td>-5.093584</td>\n",
       "      <td>-15.106412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>2.984361</td>\n",
       "      <td>38.095349</td>\n",
       "      <td>-5.560222</td>\n",
       "      <td>-16.584216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>3.187098</td>\n",
       "      <td>37.169742</td>\n",
       "      <td>-6.132250</td>\n",
       "      <td>-14.910771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>1.772470</td>\n",
       "      <td>36.399488</td>\n",
       "      <td>-6.257600</td>\n",
       "      <td>-13.668261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>1.886272</td>\n",
       "      <td>34.543307</td>\n",
       "      <td>-6.644915</td>\n",
       "      <td>-15.213739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>2.918438</td>\n",
       "      <td>33.722114</td>\n",
       "      <td>-5.736130</td>\n",
       "      <td>-14.919711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>2.608268</td>\n",
       "      <td>33.473087</td>\n",
       "      <td>-5.111581</td>\n",
       "      <td>-15.969800</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 -0.547915   0.758504 -0.791553  -0.014276\n",
       "1    2000-01-02 -1.478652  -1.824863  0.001416  -1.067864\n",
       "2    2000-01-03 -3.070727  -1.655967 -0.551411  -2.188238\n",
       "3    2000-01-04 -3.317105  -1.810778  0.354000  -1.995760\n",
       "4    2000-01-05 -2.822308  -2.757077  1.210057  -1.250592\n",
       "5    2000-01-06 -2.223865  -3.256931  3.658672  -1.232510\n",
       "6    2000-01-07 -0.559411  -2.842820  4.860993  -0.418853\n",
       "7    2000-01-08 -0.524812  -2.748762  6.280814   0.170616\n",
       "8    2000-01-09  0.204528  -0.865022  5.560456   0.875871\n",
       "9    2000-01-10  0.688895  -1.704024  5.226062   0.444483\n",
       "10   2000-01-11  1.272138  -2.478377  2.763524  -1.229797\n",
       "11   2000-01-12  1.255657  -2.994073  2.869688   0.745504\n",
       "12   2000-01-13 -0.001803  -2.295823  2.738087   2.640307\n",
       "13   2000-01-14  0.144209  -0.926810  4.087308   0.164138\n",
       "14   2000-01-15  1.304811   0.625530  3.542700  -1.499446\n",
       "15   2000-01-16  0.682026   0.552441  3.201389  -0.155674\n",
       "16   2000-01-17 -1.065321  -0.339092  3.792634  -1.754946\n",
       "17   2000-01-18 -1.268418  -3.512773  3.656896  -0.132124\n",
       "18   2000-01-19 -1.936390  -1.069188  2.554887   0.413930\n",
       "19   2000-01-20 -1.138678  -2.993870  3.094198  -0.956574\n",
       "20   2000-01-21 -0.096721  -4.141108  3.341248  -0.436886\n",
       "21   2000-01-22 -0.678763  -5.012663  0.823576   1.303377\n",
       "22   2000-01-23 -2.498525  -6.075938  0.302908  -0.294204\n",
       "23   2000-01-24 -0.937785  -6.768487 -0.078521  -0.165799\n",
       "24   2000-01-25 -0.252338  -6.882860  0.545684  -1.108641\n",
       "25   2000-01-26 -0.772653  -7.080129  0.764647  -3.041450\n",
       "26   2000-01-27 -0.348831  -6.035375  0.507673  -2.522836\n",
       "27   2000-01-28  1.751721  -5.756738 -0.469014  -2.825118\n",
       "28   2000-01-29  0.799938  -4.559172 -0.086763  -3.243101\n",
       "29   2000-01-30  1.149650  -4.433331  1.476689  -2.479956\n",
       "..          ...       ...        ...       ...        ...\n",
       "970  2002-08-28  0.466756  38.780786 -6.743120 -15.206051\n",
       "971  2002-08-29  1.903199  36.687258 -5.720742 -14.335689\n",
       "972  2002-08-30  1.432801  37.186065 -6.274877 -14.879621\n",
       "973  2002-08-31  2.218021  36.200395 -5.638178 -15.625389\n",
       "974  2002-09-01  3.405318  36.987365 -4.441912 -14.162031\n",
       "975  2002-09-02  3.854252  38.283545 -4.845949 -15.449302\n",
       "976  2002-09-03  4.328033  37.123804 -6.006344 -14.158929\n",
       "977  2002-09-04  5.219103  37.203486 -6.035182 -14.461389\n",
       "978  2002-09-05  4.884547  38.192836 -6.447570 -13.541204\n",
       "979  2002-09-06  5.817435  37.669998 -5.983732 -12.238303\n",
       "980  2002-09-07  7.953334  37.503576 -4.783659 -12.092902\n",
       "981  2002-09-08  8.575181  37.377622 -3.154240 -12.895208\n",
       "982  2002-09-09  8.242240  38.155500 -3.285132 -13.483775\n",
       "983  2002-09-10  9.699693  37.421024 -1.763105 -13.397276\n",
       "984  2002-09-11  7.228027  38.070563 -1.250151 -14.455654\n",
       "985  2002-09-12  7.276336  38.503057 -1.110047 -15.362900\n",
       "986  2002-09-13  6.955453  37.038613 -0.571942 -14.527590\n",
       "987  2002-09-14  6.232800  37.254276 -1.893050 -14.385401\n",
       "988  2002-09-15  5.331802  36.671504 -1.782282 -13.645305\n",
       "989  2002-09-16  5.825638  36.920733 -2.718137 -13.875984\n",
       "990  2002-09-17  5.972137  36.785577 -3.923763 -13.281418\n",
       "991  2002-09-18  5.154685  34.789858 -4.426825 -13.805227\n",
       "992  2002-09-19  4.228035  34.400350 -4.431980 -14.520423\n",
       "993  2002-09-20  3.047640  36.244469 -5.093584 -15.106412\n",
       "994  2002-09-21  2.984361  38.095349 -5.560222 -16.584216\n",
       "995  2002-09-22  3.187098  37.169742 -6.132250 -14.910771\n",
       "996  2002-09-23  1.772470  36.399488 -6.257600 -13.668261\n",
       "997  2002-09-24  1.886272  34.543307 -6.644915 -15.213739\n",
       "998  2002-09-25  2.918438  33.722114 -5.736130 -14.919711\n",
       "999  2002-09-26  2.608268  33.473087 -5.111581 -15.969800\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HDF5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HDFStores的读写"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写入一个HDF5 Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_hdf('data/foo.h5', 'df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从一个HDF5 Store读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.547915</td>\n",
       "      <td>0.758504</td>\n",
       "      <td>-0.791553</td>\n",
       "      <td>-0.014276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.478652</td>\n",
       "      <td>-1.824863</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>-1.067864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-3.070727</td>\n",
       "      <td>-1.655967</td>\n",
       "      <td>-0.551411</td>\n",
       "      <td>-2.188238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-3.317105</td>\n",
       "      <td>-1.810778</td>\n",
       "      <td>0.354000</td>\n",
       "      <td>-1.995760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-2.822308</td>\n",
       "      <td>-2.757077</td>\n",
       "      <td>1.210057</td>\n",
       "      <td>-1.250592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-2.223865</td>\n",
       "      <td>-3.256931</td>\n",
       "      <td>3.658672</td>\n",
       "      <td>-1.232510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-0.559411</td>\n",
       "      <td>-2.842820</td>\n",
       "      <td>4.860993</td>\n",
       "      <td>-0.418853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-0.524812</td>\n",
       "      <td>-2.748762</td>\n",
       "      <td>6.280814</td>\n",
       "      <td>0.170616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>0.204528</td>\n",
       "      <td>-0.865022</td>\n",
       "      <td>5.560456</td>\n",
       "      <td>0.875871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>0.688895</td>\n",
       "      <td>-1.704024</td>\n",
       "      <td>5.226062</td>\n",
       "      <td>0.444483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>1.272138</td>\n",
       "      <td>-2.478377</td>\n",
       "      <td>2.763524</td>\n",
       "      <td>-1.229797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>1.255657</td>\n",
       "      <td>-2.994073</td>\n",
       "      <td>2.869688</td>\n",
       "      <td>0.745504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-0.001803</td>\n",
       "      <td>-2.295823</td>\n",
       "      <td>2.738087</td>\n",
       "      <td>2.640307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>0.144209</td>\n",
       "      <td>-0.926810</td>\n",
       "      <td>4.087308</td>\n",
       "      <td>0.164138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>1.304811</td>\n",
       "      <td>0.625530</td>\n",
       "      <td>3.542700</td>\n",
       "      <td>-1.499446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>0.682026</td>\n",
       "      <td>0.552441</td>\n",
       "      <td>3.201389</td>\n",
       "      <td>-0.155674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>-1.065321</td>\n",
       "      <td>-0.339092</td>\n",
       "      <td>3.792634</td>\n",
       "      <td>-1.754946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>-1.268418</td>\n",
       "      <td>-3.512773</td>\n",
       "      <td>3.656896</td>\n",
       "      <td>-0.132124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>-1.936390</td>\n",
       "      <td>-1.069188</td>\n",
       "      <td>2.554887</td>\n",
       "      <td>0.413930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>-1.138678</td>\n",
       "      <td>-2.993870</td>\n",
       "      <td>3.094198</td>\n",
       "      <td>-0.956574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>-0.096721</td>\n",
       "      <td>-4.141108</td>\n",
       "      <td>3.341248</td>\n",
       "      <td>-0.436886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>-0.678763</td>\n",
       "      <td>-5.012663</td>\n",
       "      <td>0.823576</td>\n",
       "      <td>1.303377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>-2.498525</td>\n",
       "      <td>-6.075938</td>\n",
       "      <td>0.302908</td>\n",
       "      <td>-0.294204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>-0.937785</td>\n",
       "      <td>-6.768487</td>\n",
       "      <td>-0.078521</td>\n",
       "      <td>-0.165799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>-0.252338</td>\n",
       "      <td>-6.882860</td>\n",
       "      <td>0.545684</td>\n",
       "      <td>-1.108641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>-0.772653</td>\n",
       "      <td>-7.080129</td>\n",
       "      <td>0.764647</td>\n",
       "      <td>-3.041450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>-0.348831</td>\n",
       "      <td>-6.035375</td>\n",
       "      <td>0.507673</td>\n",
       "      <td>-2.522836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>1.751721</td>\n",
       "      <td>-5.756738</td>\n",
       "      <td>-0.469014</td>\n",
       "      <td>-2.825118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>0.799938</td>\n",
       "      <td>-4.559172</td>\n",
       "      <td>-0.086763</td>\n",
       "      <td>-3.243101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>1.149650</td>\n",
       "      <td>-4.433331</td>\n",
       "      <td>1.476689</td>\n",
       "      <td>-2.479956</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>0.466756</td>\n",
       "      <td>38.780786</td>\n",
       "      <td>-6.743120</td>\n",
       "      <td>-15.206051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>1.903199</td>\n",
       "      <td>36.687258</td>\n",
       "      <td>-5.720742</td>\n",
       "      <td>-14.335689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>1.432801</td>\n",
       "      <td>37.186065</td>\n",
       "      <td>-6.274877</td>\n",
       "      <td>-14.879621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>2.218021</td>\n",
       "      <td>36.200395</td>\n",
       "      <td>-5.638178</td>\n",
       "      <td>-15.625389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>3.405318</td>\n",
       "      <td>36.987365</td>\n",
       "      <td>-4.441912</td>\n",
       "      <td>-14.162031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>3.854252</td>\n",
       "      <td>38.283545</td>\n",
       "      <td>-4.845949</td>\n",
       "      <td>-15.449302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>4.328033</td>\n",
       "      <td>37.123804</td>\n",
       "      <td>-6.006344</td>\n",
       "      <td>-14.158929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>5.219103</td>\n",
       "      <td>37.203486</td>\n",
       "      <td>-6.035182</td>\n",
       "      <td>-14.461389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>4.884547</td>\n",
       "      <td>38.192836</td>\n",
       "      <td>-6.447570</td>\n",
       "      <td>-13.541204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>5.817435</td>\n",
       "      <td>37.669998</td>\n",
       "      <td>-5.983732</td>\n",
       "      <td>-12.238303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>7.953334</td>\n",
       "      <td>37.503576</td>\n",
       "      <td>-4.783659</td>\n",
       "      <td>-12.092902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>8.575181</td>\n",
       "      <td>37.377622</td>\n",
       "      <td>-3.154240</td>\n",
       "      <td>-12.895208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>8.242240</td>\n",
       "      <td>38.155500</td>\n",
       "      <td>-3.285132</td>\n",
       "      <td>-13.483775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>9.699693</td>\n",
       "      <td>37.421024</td>\n",
       "      <td>-1.763105</td>\n",
       "      <td>-13.397276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>7.228027</td>\n",
       "      <td>38.070563</td>\n",
       "      <td>-1.250151</td>\n",
       "      <td>-14.455654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>7.276336</td>\n",
       "      <td>38.503057</td>\n",
       "      <td>-1.110047</td>\n",
       "      <td>-15.362900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>6.955453</td>\n",
       "      <td>37.038613</td>\n",
       "      <td>-0.571942</td>\n",
       "      <td>-14.527590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>6.232800</td>\n",
       "      <td>37.254276</td>\n",
       "      <td>-1.893050</td>\n",
       "      <td>-14.385401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>5.331802</td>\n",
       "      <td>36.671504</td>\n",
       "      <td>-1.782282</td>\n",
       "      <td>-13.645305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>5.825638</td>\n",
       "      <td>36.920733</td>\n",
       "      <td>-2.718137</td>\n",
       "      <td>-13.875984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>5.972137</td>\n",
       "      <td>36.785577</td>\n",
       "      <td>-3.923763</td>\n",
       "      <td>-13.281418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>5.154685</td>\n",
       "      <td>34.789858</td>\n",
       "      <td>-4.426825</td>\n",
       "      <td>-13.805227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>4.228035</td>\n",
       "      <td>34.400350</td>\n",
       "      <td>-4.431980</td>\n",
       "      <td>-14.520423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>3.047640</td>\n",
       "      <td>36.244469</td>\n",
       "      <td>-5.093584</td>\n",
       "      <td>-15.106412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>2.984361</td>\n",
       "      <td>38.095349</td>\n",
       "      <td>-5.560222</td>\n",
       "      <td>-16.584216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>3.187098</td>\n",
       "      <td>37.169742</td>\n",
       "      <td>-6.132250</td>\n",
       "      <td>-14.910771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>1.772470</td>\n",
       "      <td>36.399488</td>\n",
       "      <td>-6.257600</td>\n",
       "      <td>-13.668261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>1.886272</td>\n",
       "      <td>34.543307</td>\n",
       "      <td>-6.644915</td>\n",
       "      <td>-15.213739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>2.918438</td>\n",
       "      <td>33.722114</td>\n",
       "      <td>-5.736130</td>\n",
       "      <td>-14.919711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>2.608268</td>\n",
       "      <td>33.473087</td>\n",
       "      <td>-5.111581</td>\n",
       "      <td>-15.969800</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 -0.547915   0.758504 -0.791553  -0.014276\n",
       "2000-01-02 -1.478652  -1.824863  0.001416  -1.067864\n",
       "2000-01-03 -3.070727  -1.655967 -0.551411  -2.188238\n",
       "2000-01-04 -3.317105  -1.810778  0.354000  -1.995760\n",
       "2000-01-05 -2.822308  -2.757077  1.210057  -1.250592\n",
       "2000-01-06 -2.223865  -3.256931  3.658672  -1.232510\n",
       "2000-01-07 -0.559411  -2.842820  4.860993  -0.418853\n",
       "2000-01-08 -0.524812  -2.748762  6.280814   0.170616\n",
       "2000-01-09  0.204528  -0.865022  5.560456   0.875871\n",
       "2000-01-10  0.688895  -1.704024  5.226062   0.444483\n",
       "2000-01-11  1.272138  -2.478377  2.763524  -1.229797\n",
       "2000-01-12  1.255657  -2.994073  2.869688   0.745504\n",
       "2000-01-13 -0.001803  -2.295823  2.738087   2.640307\n",
       "2000-01-14  0.144209  -0.926810  4.087308   0.164138\n",
       "2000-01-15  1.304811   0.625530  3.542700  -1.499446\n",
       "2000-01-16  0.682026   0.552441  3.201389  -0.155674\n",
       "2000-01-17 -1.065321  -0.339092  3.792634  -1.754946\n",
       "2000-01-18 -1.268418  -3.512773  3.656896  -0.132124\n",
       "2000-01-19 -1.936390  -1.069188  2.554887   0.413930\n",
       "2000-01-20 -1.138678  -2.993870  3.094198  -0.956574\n",
       "2000-01-21 -0.096721  -4.141108  3.341248  -0.436886\n",
       "2000-01-22 -0.678763  -5.012663  0.823576   1.303377\n",
       "2000-01-23 -2.498525  -6.075938  0.302908  -0.294204\n",
       "2000-01-24 -0.937785  -6.768487 -0.078521  -0.165799\n",
       "2000-01-25 -0.252338  -6.882860  0.545684  -1.108641\n",
       "2000-01-26 -0.772653  -7.080129  0.764647  -3.041450\n",
       "2000-01-27 -0.348831  -6.035375  0.507673  -2.522836\n",
       "2000-01-28  1.751721  -5.756738 -0.469014  -2.825118\n",
       "2000-01-29  0.799938  -4.559172 -0.086763  -3.243101\n",
       "2000-01-30  1.149650  -4.433331  1.476689  -2.479956\n",
       "...              ...        ...       ...        ...\n",
       "2002-08-28  0.466756  38.780786 -6.743120 -15.206051\n",
       "2002-08-29  1.903199  36.687258 -5.720742 -14.335689\n",
       "2002-08-30  1.432801  37.186065 -6.274877 -14.879621\n",
       "2002-08-31  2.218021  36.200395 -5.638178 -15.625389\n",
       "2002-09-01  3.405318  36.987365 -4.441912 -14.162031\n",
       "2002-09-02  3.854252  38.283545 -4.845949 -15.449302\n",
       "2002-09-03  4.328033  37.123804 -6.006344 -14.158929\n",
       "2002-09-04  5.219103  37.203486 -6.035182 -14.461389\n",
       "2002-09-05  4.884547  38.192836 -6.447570 -13.541204\n",
       "2002-09-06  5.817435  37.669998 -5.983732 -12.238303\n",
       "2002-09-07  7.953334  37.503576 -4.783659 -12.092902\n",
       "2002-09-08  8.575181  37.377622 -3.154240 -12.895208\n",
       "2002-09-09  8.242240  38.155500 -3.285132 -13.483775\n",
       "2002-09-10  9.699693  37.421024 -1.763105 -13.397276\n",
       "2002-09-11  7.228027  38.070563 -1.250151 -14.455654\n",
       "2002-09-12  7.276336  38.503057 -1.110047 -15.362900\n",
       "2002-09-13  6.955453  37.038613 -0.571942 -14.527590\n",
       "2002-09-14  6.232800  37.254276 -1.893050 -14.385401\n",
       "2002-09-15  5.331802  36.671504 -1.782282 -13.645305\n",
       "2002-09-16  5.825638  36.920733 -2.718137 -13.875984\n",
       "2002-09-17  5.972137  36.785577 -3.923763 -13.281418\n",
       "2002-09-18  5.154685  34.789858 -4.426825 -13.805227\n",
       "2002-09-19  4.228035  34.400350 -4.431980 -14.520423\n",
       "2002-09-20  3.047640  36.244469 -5.093584 -15.106412\n",
       "2002-09-21  2.984361  38.095349 -5.560222 -16.584216\n",
       "2002-09-22  3.187098  37.169742 -6.132250 -14.910771\n",
       "2002-09-23  1.772470  36.399488 -6.257600 -13.668261\n",
       "2002-09-24  1.886272  34.543307 -6.644915 -15.213739\n",
       "2002-09-25  2.918438  33.722114 -5.736130 -14.919711\n",
       "2002-09-26  2.608268  33.473087 -5.111581 -15.969800\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('data/foo.h5', 'df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MS Excel的读写"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写入一个Excel文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('data/foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从一个excel文件读入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>-0.547915</td>\n",
       "      <td>0.758504</td>\n",
       "      <td>-0.791553</td>\n",
       "      <td>-0.014276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.478652</td>\n",
       "      <td>-1.824863</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>-1.067864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-3.070727</td>\n",
       "      <td>-1.655967</td>\n",
       "      <td>-0.551411</td>\n",
       "      <td>-2.188238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-3.317105</td>\n",
       "      <td>-1.810778</td>\n",
       "      <td>0.354000</td>\n",
       "      <td>-1.995760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-2.822308</td>\n",
       "      <td>-2.757077</td>\n",
       "      <td>1.210057</td>\n",
       "      <td>-1.250592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-2.223865</td>\n",
       "      <td>-3.256931</td>\n",
       "      <td>3.658672</td>\n",
       "      <td>-1.232510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-0.559411</td>\n",
       "      <td>-2.842820</td>\n",
       "      <td>4.860993</td>\n",
       "      <td>-0.418853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-0.524812</td>\n",
       "      <td>-2.748762</td>\n",
       "      <td>6.280814</td>\n",
       "      <td>0.170616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>0.204528</td>\n",
       "      <td>-0.865022</td>\n",
       "      <td>5.560456</td>\n",
       "      <td>0.875871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>0.688895</td>\n",
       "      <td>-1.704024</td>\n",
       "      <td>5.226062</td>\n",
       "      <td>0.444483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>1.272138</td>\n",
       "      <td>-2.478377</td>\n",
       "      <td>2.763524</td>\n",
       "      <td>-1.229797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>1.255657</td>\n",
       "      <td>-2.994073</td>\n",
       "      <td>2.869688</td>\n",
       "      <td>0.745504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>-0.001803</td>\n",
       "      <td>-2.295823</td>\n",
       "      <td>2.738087</td>\n",
       "      <td>2.640307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>0.144209</td>\n",
       "      <td>-0.926810</td>\n",
       "      <td>4.087308</td>\n",
       "      <td>0.164138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>1.304811</td>\n",
       "      <td>0.625530</td>\n",
       "      <td>3.542700</td>\n",
       "      <td>-1.499446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>0.682026</td>\n",
       "      <td>0.552441</td>\n",
       "      <td>3.201389</td>\n",
       "      <td>-0.155674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>-1.065321</td>\n",
       "      <td>-0.339092</td>\n",
       "      <td>3.792634</td>\n",
       "      <td>-1.754946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>-1.268418</td>\n",
       "      <td>-3.512773</td>\n",
       "      <td>3.656896</td>\n",
       "      <td>-0.132124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>-1.936390</td>\n",
       "      <td>-1.069188</td>\n",
       "      <td>2.554887</td>\n",
       "      <td>0.413930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>-1.138678</td>\n",
       "      <td>-2.993870</td>\n",
       "      <td>3.094198</td>\n",
       "      <td>-0.956574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>-0.096721</td>\n",
       "      <td>-4.141108</td>\n",
       "      <td>3.341248</td>\n",
       "      <td>-0.436886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>-0.678763</td>\n",
       "      <td>-5.012663</td>\n",
       "      <td>0.823576</td>\n",
       "      <td>1.303377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>-2.498525</td>\n",
       "      <td>-6.075938</td>\n",
       "      <td>0.302908</td>\n",
       "      <td>-0.294204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>-0.937785</td>\n",
       "      <td>-6.768487</td>\n",
       "      <td>-0.078521</td>\n",
       "      <td>-0.165799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>-0.252338</td>\n",
       "      <td>-6.882860</td>\n",
       "      <td>0.545684</td>\n",
       "      <td>-1.108641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>-0.772653</td>\n",
       "      <td>-7.080129</td>\n",
       "      <td>0.764647</td>\n",
       "      <td>-3.041450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>-0.348831</td>\n",
       "      <td>-6.035375</td>\n",
       "      <td>0.507673</td>\n",
       "      <td>-2.522836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>1.751721</td>\n",
       "      <td>-5.756738</td>\n",
       "      <td>-0.469014</td>\n",
       "      <td>-2.825118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>0.799938</td>\n",
       "      <td>-4.559172</td>\n",
       "      <td>-0.086763</td>\n",
       "      <td>-3.243101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>1.149650</td>\n",
       "      <td>-4.433331</td>\n",
       "      <td>1.476689</td>\n",
       "      <td>-2.479956</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>0.466756</td>\n",
       "      <td>38.780786</td>\n",
       "      <td>-6.743120</td>\n",
       "      <td>-15.206051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>1.903199</td>\n",
       "      <td>36.687258</td>\n",
       "      <td>-5.720742</td>\n",
       "      <td>-14.335689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>1.432801</td>\n",
       "      <td>37.186065</td>\n",
       "      <td>-6.274877</td>\n",
       "      <td>-14.879621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>2.218021</td>\n",
       "      <td>36.200395</td>\n",
       "      <td>-5.638178</td>\n",
       "      <td>-15.625389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>3.405318</td>\n",
       "      <td>36.987365</td>\n",
       "      <td>-4.441912</td>\n",
       "      <td>-14.162031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>3.854252</td>\n",
       "      <td>38.283545</td>\n",
       "      <td>-4.845949</td>\n",
       "      <td>-15.449302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>4.328033</td>\n",
       "      <td>37.123804</td>\n",
       "      <td>-6.006344</td>\n",
       "      <td>-14.158929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>5.219103</td>\n",
       "      <td>37.203486</td>\n",
       "      <td>-6.035182</td>\n",
       "      <td>-14.461389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>4.884547</td>\n",
       "      <td>38.192836</td>\n",
       "      <td>-6.447570</td>\n",
       "      <td>-13.541204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>5.817435</td>\n",
       "      <td>37.669998</td>\n",
       "      <td>-5.983732</td>\n",
       "      <td>-12.238303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>7.953334</td>\n",
       "      <td>37.503576</td>\n",
       "      <td>-4.783659</td>\n",
       "      <td>-12.092902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>8.575181</td>\n",
       "      <td>37.377622</td>\n",
       "      <td>-3.154240</td>\n",
       "      <td>-12.895208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>8.242240</td>\n",
       "      <td>38.155500</td>\n",
       "      <td>-3.285132</td>\n",
       "      <td>-13.483775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>9.699693</td>\n",
       "      <td>37.421024</td>\n",
       "      <td>-1.763105</td>\n",
       "      <td>-13.397276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>7.228027</td>\n",
       "      <td>38.070563</td>\n",
       "      <td>-1.250151</td>\n",
       "      <td>-14.455654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>7.276336</td>\n",
       "      <td>38.503057</td>\n",
       "      <td>-1.110047</td>\n",
       "      <td>-15.362900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>6.955453</td>\n",
       "      <td>37.038613</td>\n",
       "      <td>-0.571942</td>\n",
       "      <td>-14.527590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>6.232800</td>\n",
       "      <td>37.254276</td>\n",
       "      <td>-1.893050</td>\n",
       "      <td>-14.385401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>5.331802</td>\n",
       "      <td>36.671504</td>\n",
       "      <td>-1.782282</td>\n",
       "      <td>-13.645305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>5.825638</td>\n",
       "      <td>36.920733</td>\n",
       "      <td>-2.718137</td>\n",
       "      <td>-13.875984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>5.972137</td>\n",
       "      <td>36.785577</td>\n",
       "      <td>-3.923763</td>\n",
       "      <td>-13.281418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>5.154685</td>\n",
       "      <td>34.789858</td>\n",
       "      <td>-4.426825</td>\n",
       "      <td>-13.805227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>4.228035</td>\n",
       "      <td>34.400350</td>\n",
       "      <td>-4.431980</td>\n",
       "      <td>-14.520423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>3.047640</td>\n",
       "      <td>36.244469</td>\n",
       "      <td>-5.093584</td>\n",
       "      <td>-15.106412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>2.984361</td>\n",
       "      <td>38.095349</td>\n",
       "      <td>-5.560222</td>\n",
       "      <td>-16.584216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>3.187098</td>\n",
       "      <td>37.169742</td>\n",
       "      <td>-6.132250</td>\n",
       "      <td>-14.910771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>1.772470</td>\n",
       "      <td>36.399488</td>\n",
       "      <td>-6.257600</td>\n",
       "      <td>-13.668261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>1.886272</td>\n",
       "      <td>34.543307</td>\n",
       "      <td>-6.644915</td>\n",
       "      <td>-15.213739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>2.918438</td>\n",
       "      <td>33.722114</td>\n",
       "      <td>-5.736130</td>\n",
       "      <td>-14.919711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>2.608268</td>\n",
       "      <td>33.473087</td>\n",
       "      <td>-5.111581</td>\n",
       "      <td>-15.969800</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 -0.547915   0.758504 -0.791553  -0.014276\n",
       "2000-01-02 -1.478652  -1.824863  0.001416  -1.067864\n",
       "2000-01-03 -3.070727  -1.655967 -0.551411  -2.188238\n",
       "2000-01-04 -3.317105  -1.810778  0.354000  -1.995760\n",
       "2000-01-05 -2.822308  -2.757077  1.210057  -1.250592\n",
       "2000-01-06 -2.223865  -3.256931  3.658672  -1.232510\n",
       "2000-01-07 -0.559411  -2.842820  4.860993  -0.418853\n",
       "2000-01-08 -0.524812  -2.748762  6.280814   0.170616\n",
       "2000-01-09  0.204528  -0.865022  5.560456   0.875871\n",
       "2000-01-10  0.688895  -1.704024  5.226062   0.444483\n",
       "2000-01-11  1.272138  -2.478377  2.763524  -1.229797\n",
       "2000-01-12  1.255657  -2.994073  2.869688   0.745504\n",
       "2000-01-13 -0.001803  -2.295823  2.738087   2.640307\n",
       "2000-01-14  0.144209  -0.926810  4.087308   0.164138\n",
       "2000-01-15  1.304811   0.625530  3.542700  -1.499446\n",
       "2000-01-16  0.682026   0.552441  3.201389  -0.155674\n",
       "2000-01-17 -1.065321  -0.339092  3.792634  -1.754946\n",
       "2000-01-18 -1.268418  -3.512773  3.656896  -0.132124\n",
       "2000-01-19 -1.936390  -1.069188  2.554887   0.413930\n",
       "2000-01-20 -1.138678  -2.993870  3.094198  -0.956574\n",
       "2000-01-21 -0.096721  -4.141108  3.341248  -0.436886\n",
       "2000-01-22 -0.678763  -5.012663  0.823576   1.303377\n",
       "2000-01-23 -2.498525  -6.075938  0.302908  -0.294204\n",
       "2000-01-24 -0.937785  -6.768487 -0.078521  -0.165799\n",
       "2000-01-25 -0.252338  -6.882860  0.545684  -1.108641\n",
       "2000-01-26 -0.772653  -7.080129  0.764647  -3.041450\n",
       "2000-01-27 -0.348831  -6.035375  0.507673  -2.522836\n",
       "2000-01-28  1.751721  -5.756738 -0.469014  -2.825118\n",
       "2000-01-29  0.799938  -4.559172 -0.086763  -3.243101\n",
       "2000-01-30  1.149650  -4.433331  1.476689  -2.479956\n",
       "...              ...        ...       ...        ...\n",
       "2002-08-28  0.466756  38.780786 -6.743120 -15.206051\n",
       "2002-08-29  1.903199  36.687258 -5.720742 -14.335689\n",
       "2002-08-30  1.432801  37.186065 -6.274877 -14.879621\n",
       "2002-08-31  2.218021  36.200395 -5.638178 -15.625389\n",
       "2002-09-01  3.405318  36.987365 -4.441912 -14.162031\n",
       "2002-09-02  3.854252  38.283545 -4.845949 -15.449302\n",
       "2002-09-03  4.328033  37.123804 -6.006344 -14.158929\n",
       "2002-09-04  5.219103  37.203486 -6.035182 -14.461389\n",
       "2002-09-05  4.884547  38.192836 -6.447570 -13.541204\n",
       "2002-09-06  5.817435  37.669998 -5.983732 -12.238303\n",
       "2002-09-07  7.953334  37.503576 -4.783659 -12.092902\n",
       "2002-09-08  8.575181  37.377622 -3.154240 -12.895208\n",
       "2002-09-09  8.242240  38.155500 -3.285132 -13.483775\n",
       "2002-09-10  9.699693  37.421024 -1.763105 -13.397276\n",
       "2002-09-11  7.228027  38.070563 -1.250151 -14.455654\n",
       "2002-09-12  7.276336  38.503057 -1.110047 -15.362900\n",
       "2002-09-13  6.955453  37.038613 -0.571942 -14.527590\n",
       "2002-09-14  6.232800  37.254276 -1.893050 -14.385401\n",
       "2002-09-15  5.331802  36.671504 -1.782282 -13.645305\n",
       "2002-09-16  5.825638  36.920733 -2.718137 -13.875984\n",
       "2002-09-17  5.972137  36.785577 -3.923763 -13.281418\n",
       "2002-09-18  5.154685  34.789858 -4.426825 -13.805227\n",
       "2002-09-19  4.228035  34.400350 -4.431980 -14.520423\n",
       "2002-09-20  3.047640  36.244469 -5.093584 -15.106412\n",
       "2002-09-21  2.984361  38.095349 -5.560222 -16.584216\n",
       "2002-09-22  3.187098  37.169742 -6.132250 -14.910771\n",
       "2002-09-23  1.772470  36.399488 -6.257600 -13.668261\n",
       "2002-09-24  1.886272  34.543307 -6.644915 -15.213739\n",
       "2002-09-25  2.918438  33.722114 -5.736130 -14.919711\n",
       "2002-09-26  2.608268  33.473087 -5.111581 -15.969800\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('data/foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])"
   ]
  }
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