检索 ,查询数据-Indexing and Selecting Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检索，查询数据 Indexing and Selecting Data\n",
    "\n",
    "像使用SQL一样使用pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python和Numpy的索引操作符[]和属性操作符. 提供了快速获取pandas数据结构的方式。然而，这两种方式的效率在pandas中可能不是最优的，我们推荐使用优化过的pandas数据获取方法。这些方法都是本节要介绍的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引的不同方式 Different Choices for Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas支持三种类型的多坐标(multi-axis)索引：\n",
    "* **.loc** 是基本的基于label的，当然也可以和一个boolean数组一起使用。\n",
    "* **.iloc** 是基本的基于整数位置(从0到axis的length-1)的，当然也可以和一个boolean数组一起使用。当提供检索的index越界时会有IndexError错误，注意切片索引(slice index)允许越界。\n",
    "* **.ix** 支持基于label和整数位置混合的数据获取方式。默认是基本label的. .ix是最常用的方式，它支持所有.loc和.iloc的输入。如果提供的是纯label或纯整数索引，我们建议使用.loc或 .iloc。\n",
    "\n",
    "\n",
    "以 .loc为例看一下使用方式：\n",
    "\n",
    "**对象类型**  |  **Indexers**\n",
    "\n",
    "Series | s.loc[indexer]\n",
    "\n",
    "DataFrame | df.loc[row_indexer, column_indexer]\n",
    "\n",
    "Panel | p.loc[item_indexer, major_indexer, minor_indexer]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最基本的索引和选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最基本的选择数据方式就是使用[]操作符进行索引，\n",
    "\n",
    "**对象类型** | ** Selection** | **返回值类型**\n",
    "\n",
    "Series | series[label],**这里的label是index名** | 常数\n",
    "\n",
    "DataFrame| frame[colname],**使用列名** | Series对象，相应的colname那一列\n",
    "\n",
    "Panel | panel[itemname] | DataFrame对象,相应的itemname那一个"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面用示例展示一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',\n",
       "               '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('1/1/2000', periods=8)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.177302</td>\n",
       "      <td>-1.546113</td>\n",
       "      <td>-0.952839</td>\n",
       "      <td>0.008122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.049451</td>\n",
       "      <td>0.137660</td>\n",
       "      <td>1.987125</td>\n",
       "      <td>1.246595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.978006</td>\n",
       "      <td>0.532418</td>\n",
       "      <td>-0.847118</td>\n",
       "      <td>2.461356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.798626</td>\n",
       "      <td>-1.377614</td>\n",
       "      <td>0.776687</td>\n",
       "      <td>0.342846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.095023</td>\n",
       "      <td>-1.861150</td>\n",
       "      <td>0.134254</td>\n",
       "      <td>0.661625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.249859</td>\n",
       "      <td>-0.664076</td>\n",
       "      <td>0.881349</td>\n",
       "      <td>1.945613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.995785</td>\n",
       "      <td>0.674858</td>\n",
       "      <td>-0.809902</td>\n",
       "      <td>-0.543680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.295963</td>\n",
       "      <td>-0.698917</td>\n",
       "      <td>0.932431</td>\n",
       "      <td>1.440893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01  0.177302 -1.546113 -0.952839  0.008122\n",
       "2000-01-02 -1.049451  0.137660  1.987125  1.246595\n",
       "2000-01-03 -0.978006  0.532418 -0.847118  2.461356\n",
       "2000-01-04 -0.798626 -1.377614  0.776687  0.342846\n",
       "2000-01-05 -0.095023 -1.861150  0.134254  0.661625\n",
       "2000-01-06  0.249859 -0.664076  0.881349  1.945613\n",
       "2000-01-07  0.995785  0.674858 -0.809902 -0.543680\n",
       "2000-01-08  0.295963 -0.698917  0.932431  1.440893"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  = pd.DataFrame(np.random.randn(8,4), index=dates, columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 2 (items) x 8 (major_axis) x 4 (minor_axis)\n",
       "Items axis: one to two\n",
       "Major_axis axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00\n",
       "Minor_axis axis: A to D"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel = pd.Panel({'one':df, 'two':df-df.mean()})\n",
    "panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们使用最基本的[]操作符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.562650\n",
       "2000-01-02   -1.062558\n",
       "2000-01-03   -1.194126\n",
       "2000-01-04    0.936506\n",
       "2000-01-05   -1.196422\n",
       "2000-01-06    1.436726\n",
       "2000-01-07    0.329280\n",
       "2000-01-08    0.857815\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df['A'] #使用列名\n",
    "s#返回的是 Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Series使用index索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.436726247472784"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[dates[5]] #使用index名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.505721</td>\n",
       "      <td>-0.769480</td>\n",
       "      <td>0.857238</td>\n",
       "      <td>2.124492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.005630</td>\n",
       "      <td>1.230158</td>\n",
       "      <td>-0.849252</td>\n",
       "      <td>-0.512871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-1.137198</td>\n",
       "      <td>0.960215</td>\n",
       "      <td>1.531157</td>\n",
       "      <td>0.940112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.993435</td>\n",
       "      <td>-0.300830</td>\n",
       "      <td>1.046579</td>\n",
       "      <td>1.142567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.139493</td>\n",
       "      <td>-0.819572</td>\n",
       "      <td>0.168454</td>\n",
       "      <td>-0.310352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1.493655</td>\n",
       "      <td>0.409088</td>\n",
       "      <td>-1.424400</td>\n",
       "      <td>-1.870575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.386208</td>\n",
       "      <td>0.825722</td>\n",
       "      <td>-0.845220</td>\n",
       "      <td>-0.275965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.914743</td>\n",
       "      <td>-1.535301</td>\n",
       "      <td>-0.484555</td>\n",
       "      <td>-1.237407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -0.505721 -0.769480  0.857238  2.124492\n",
       "2000-01-02 -1.005630  1.230158 -0.849252 -0.512871\n",
       "2000-01-03 -1.137198  0.960215  1.531157  0.940112\n",
       "2000-01-04  0.993435 -0.300830  1.046579  1.142567\n",
       "2000-01-05 -1.139493 -0.819572  0.168454 -0.310352\n",
       "2000-01-06  1.493655  0.409088 -1.424400 -1.870575\n",
       "2000-01-07  0.386208  0.825722 -0.845220 -0.275965\n",
       "2000-01-08  0.914743 -1.535301 -0.484555 -1.237407"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel['two']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **也可以给[]传递一个column name组成的的list，形如df[[col1,col2]], 如果给出的某个列名不存在，会报错**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>2000-01-01</th>\n",
       "      <td>-0.562650</td>\n",
       "      <td>-1.226827</td>\n",
       "      <td>0.149550</td>\n",
       "      <td>2.333782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.062558</td>\n",
       "      <td>0.772811</td>\n",
       "      <td>-1.556939</td>\n",
       "      <td>-0.303581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-1.194126</td>\n",
       "      <td>0.502868</td>\n",
       "      <td>0.823470</td>\n",
       "      <td>1.149401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.936506</td>\n",
       "      <td>-0.758176</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>1.351857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.196422</td>\n",
       "      <td>-1.276918</td>\n",
       "      <td>-0.539233</td>\n",
       "      <td>-0.101062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1.436726</td>\n",
       "      <td>-0.048258</td>\n",
       "      <td>-2.132088</td>\n",
       "      <td>-1.661286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.329280</td>\n",
       "      <td>0.368375</td>\n",
       "      <td>-1.552907</td>\n",
       "      <td>-0.066676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.857815</td>\n",
       "      <td>-1.992648</td>\n",
       "      <td>-1.192243</td>\n",
       "      <td>-1.028118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -0.562650 -1.226827  0.149550  2.333782\n",
       "2000-01-02 -1.062558  0.772811 -1.556939 -0.303581\n",
       "2000-01-03 -1.194126  0.502868  0.823470  1.149401\n",
       "2000-01-04  0.936506 -0.758176  0.338892  1.351857\n",
       "2000-01-05 -1.196422 -1.276918 -0.539233 -0.101062\n",
       "2000-01-06  1.436726 -0.048258 -2.132088 -1.661286\n",
       "2000-01-07  0.329280  0.368375 -1.552907 -0.066676\n",
       "2000-01-08  0.857815 -1.992648 -1.192243 -1.028118"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.226827</td>\n",
       "      <td>-0.562650</td>\n",
       "      <td>0.149550</td>\n",
       "      <td>2.333782</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.772811</td>\n",
       "      <td>-1.062558</td>\n",
       "      <td>-1.556939</td>\n",
       "      <td>-0.303581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>0.502868</td>\n",
       "      <td>-1.194126</td>\n",
       "      <td>0.823470</td>\n",
       "      <td>1.149401</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.758176</td>\n",
       "      <td>0.936506</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>1.351857</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.276918</td>\n",
       "      <td>-1.196422</td>\n",
       "      <td>-0.539233</td>\n",
       "      <td>-0.101062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.048258</td>\n",
       "      <td>1.436726</td>\n",
       "      <td>-2.132088</td>\n",
       "      <td>-1.661286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.368375</td>\n",
       "      <td>0.329280</td>\n",
       "      <td>-1.552907</td>\n",
       "      <td>-0.066676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-1.992648</td>\n",
       "      <td>0.857815</td>\n",
       "      <td>-1.192243</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.226827 -0.562650  0.149550  2.333782\n",
       "2000-01-02  0.772811 -1.062558 -1.556939 -0.303581\n",
       "2000-01-03  0.502868 -1.194126  0.823470  1.149401\n",
       "2000-01-04 -0.758176  0.936506  0.338892  1.351857\n",
       "2000-01-05 -1.276918 -1.196422 -0.539233 -0.101062\n",
       "2000-01-06 -0.048258  1.436726 -2.132088 -1.661286\n",
       "2000-01-07  0.368375  0.329280 -1.552907 -0.066676\n",
       "2000-01-08 -1.992648  0.857815 -1.192243 -1.028118"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['B', 'A']] = df[['A', 'B']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 通过属性访问 把column作为DataFrame对象的属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "可以直接把Series的index、DataFrame中的column、Panel中的item作为这些对象的属性使用，然后直接访问相应的index、column、item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sa = pd.Series([1,2,3],index=list('abc'))\n",
    "dfa = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa.b #直接把index作为属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-1.226827</td>\n",
       "      <td>-0.562650</td>\n",
       "      <td>0.149550</td>\n",
       "      <td>2.333782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>0.772811</td>\n",
       "      <td>-1.062558</td>\n",
       "      <td>-1.556939</td>\n",
       "      <td>-0.303581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>0.502868</td>\n",
       "      <td>-1.194126</td>\n",
       "      <td>0.823470</td>\n",
       "      <td>1.149401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.758176</td>\n",
       "      <td>0.936506</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>1.351857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.276918</td>\n",
       "      <td>-1.196422</td>\n",
       "      <td>-0.539233</td>\n",
       "      <td>-0.101062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.048258</td>\n",
       "      <td>1.436726</td>\n",
       "      <td>-2.132088</td>\n",
       "      <td>-1.661286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.368375</td>\n",
       "      <td>0.329280</td>\n",
       "      <td>-1.552907</td>\n",
       "      <td>-0.066676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-1.992648</td>\n",
       "      <td>0.857815</td>\n",
       "      <td>-1.192243</td>\n",
       "      <td>-1.028118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -1.226827 -0.562650  0.149550  2.333782\n",
       "2000-01-02  0.772811 -1.062558 -1.556939 -0.303581\n",
       "2000-01-03  0.502868 -1.194126  0.823470  1.149401\n",
       "2000-01-04 -0.758176  0.936506  0.338892  1.351857\n",
       "2000-01-05 -1.276918 -1.196422 -0.539233 -0.101062\n",
       "2000-01-06 -0.048258  1.436726 -2.132088 -1.661286\n",
       "2000-01-07  0.368375  0.329280 -1.552907 -0.066676\n",
       "2000-01-08 -1.992648  0.857815 -1.192243 -1.028118"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -1.226827\n",
       "2000-01-02    0.772811\n",
       "2000-01-03    0.502868\n",
       "2000-01-04   -0.758176\n",
       "2000-01-05   -1.276918\n",
       "2000-01-06   -0.048258\n",
       "2000-01-07    0.368375\n",
       "2000-01-08   -1.992648\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.562650</td>\n",
       "      <td>-1.226827</td>\n",
       "      <td>0.149550</td>\n",
       "      <td>2.333782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.062558</td>\n",
       "      <td>0.772811</td>\n",
       "      <td>-1.556939</td>\n",
       "      <td>-0.303581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-1.194126</td>\n",
       "      <td>0.502868</td>\n",
       "      <td>0.823470</td>\n",
       "      <td>1.149401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.936506</td>\n",
       "      <td>-0.758176</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>1.351857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.196422</td>\n",
       "      <td>-1.276918</td>\n",
       "      <td>-0.539233</td>\n",
       "      <td>-0.101062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>1.436726</td>\n",
       "      <td>-0.048258</td>\n",
       "      <td>-2.132088</td>\n",
       "      <td>-1.661286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.329280</td>\n",
       "      <td>0.368375</td>\n",
       "      <td>-1.552907</td>\n",
       "      <td>-0.066676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.857815</td>\n",
       "      <td>-1.992648</td>\n",
       "      <td>-1.192243</td>\n",
       "      <td>-1.028118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01 -0.562650 -1.226827  0.149550  2.333782\n",
       "2000-01-02 -1.062558  0.772811 -1.556939 -0.303581\n",
       "2000-01-03 -1.194126  0.502868  0.823470  1.149401\n",
       "2000-01-04  0.936506 -0.758176  0.338892  1.351857\n",
       "2000-01-05 -1.196422 -1.276918 -0.539233 -0.101062\n",
       "2000-01-06  1.436726 -0.048258 -2.132088 -1.661286\n",
       "2000-01-07  0.329280  0.368375 -1.552907 -0.066676\n",
       "2000-01-08  0.857815 -1.992648 -1.192243 -1.028118"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel.one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa.a = 5\n",
    "sa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfa.A=list(range(len(dfa.index))) # ok if A already exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.562650</td>\n",
       "      <td>0.149550</td>\n",
       "      <td>2.333782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.062558</td>\n",
       "      <td>-1.556939</td>\n",
       "      <td>-0.303581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.194126</td>\n",
       "      <td>0.823470</td>\n",
       "      <td>1.149401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>3</td>\n",
       "      <td>0.936506</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>1.351857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.196422</td>\n",
       "      <td>-0.539233</td>\n",
       "      <td>-0.101062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>5</td>\n",
       "      <td>1.436726</td>\n",
       "      <td>-2.132088</td>\n",
       "      <td>-1.661286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>6</td>\n",
       "      <td>0.329280</td>\n",
       "      <td>-1.552907</td>\n",
       "      <td>-0.066676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>7</td>\n",
       "      <td>0.857815</td>\n",
       "      <td>-1.192243</td>\n",
       "      <td>-1.028118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D\n",
       "2000-01-01  0 -0.562650  0.149550  2.333782\n",
       "2000-01-02  1 -1.062558 -1.556939 -0.303581\n",
       "2000-01-03  2 -1.194126  0.823470  1.149401\n",
       "2000-01-04  3  0.936506  0.338892  1.351857\n",
       "2000-01-05  4 -1.196422 -0.539233 -0.101062\n",
       "2000-01-06  5  1.436726 -2.132088 -1.661286\n",
       "2000-01-07  6  0.329280 -1.552907 -0.066676\n",
       "2000-01-08  7  0.857815 -1.192243 -1.028118"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.562650</td>\n",
       "      <td>0.149550</td>\n",
       "      <td>2.333782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.062558</td>\n",
       "      <td>-1.556939</td>\n",
       "      <td>-0.303581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.194126</td>\n",
       "      <td>0.823470</td>\n",
       "      <td>1.149401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>3</td>\n",
       "      <td>0.936506</td>\n",
       "      <td>0.338892</td>\n",
       "      <td>1.351857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.196422</td>\n",
       "      <td>-0.539233</td>\n",
       "      <td>-0.101062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>5</td>\n",
       "      <td>1.436726</td>\n",
       "      <td>-2.132088</td>\n",
       "      <td>-1.661286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>6</td>\n",
       "      <td>0.329280</td>\n",
       "      <td>-1.552907</td>\n",
       "      <td>-0.066676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>7</td>\n",
       "      <td>0.857815</td>\n",
       "      <td>-1.192243</td>\n",
       "      <td>-1.028118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A         B         C         D\n",
       "2000-01-01  0 -0.562650  0.149550  2.333782\n",
       "2000-01-02  1 -1.062558 -1.556939 -0.303581\n",
       "2000-01-03  2 -1.194126  0.823470  1.149401\n",
       "2000-01-04  3  0.936506  0.338892  1.351857\n",
       "2000-01-05  4 -1.196422 -0.539233 -0.101062\n",
       "2000-01-06  5  1.436726 -2.132088 -1.661286\n",
       "2000-01-07  6  0.329280 -1.552907 -0.066676\n",
       "2000-01-08  7  0.857815 -1.192243 -1.028118"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfa['A'] = list(range(len(dfa.index)))  # use this form to create a new column\n",
    "dfa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：**使用属性和[] 有一点区别：\n",
    "\n",
    "如果要新建一个column，只能使用[]\n",
    "\n",
    "毕竟属性的含义就是现在存在的！不存在的列名当然不是属性了\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it fails silently, creating a new attribute rather than a new column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用属性要注意的：\n",
    "* 如果一个已经存在的函数和列名相同，则不存在相应的属性哦\n",
    "* 总而言之，属性的适用范围要比[]小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 切片范围 Slicing ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用 [] 还有.iloc切片，这里先介绍使用[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**对于Series来说，使用[]进行切片就像ndarray一样，**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -1.226827\n",
       "2000-01-02    0.772811\n",
       "2000-01-03    0.502868\n",
       "2000-01-04   -0.758176\n",
       "2000-01-05   -1.276918\n",
       "2000-01-06   -0.048258\n",
       "2000-01-07    0.368375\n",
       "2000-01-08   -1.992648\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -1.226827\n",
       "2000-01-02    0.772811\n",
       "2000-01-03    0.502868\n",
       "2000-01-04   -0.758176\n",
       "2000-01-05   -1.276918\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -1.226827\n",
       "2000-01-03    0.502868\n",
       "2000-01-05   -1.276918\n",
       "2000-01-07    0.368375\n",
       "Freq: 2D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-08   -1.992648\n",
       "2000-01-07    0.368375\n",
       "2000-01-06   -0.048258\n",
       "2000-01-05   -1.276918\n",
       "2000-01-04   -0.758176\n",
       "2000-01-03    0.502868\n",
       "2000-01-02    0.772811\n",
       "2000-01-01   -1.226827\n",
       "Freq: -1D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[]不但可以检索，也可以赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s2 = s.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s2[:5]=0 #赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "对于DataFrame对象来说，[]操作符按照行进行切片，非常有用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.177302</td>\n",
       "      <td>-1.546113</td>\n",
       "      <td>-0.952839</td>\n",
       "      <td>0.008122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.049451</td>\n",
       "      <td>0.137660</td>\n",
       "      <td>1.987125</td>\n",
       "      <td>1.246595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.978006</td>\n",
       "      <td>0.532418</td>\n",
       "      <td>-0.847118</td>\n",
       "      <td>2.461356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01  0.177302 -1.546113 -0.952839  0.008122\n",
       "2000-01-02 -1.049451  0.137660  1.987125  1.246595\n",
       "2000-01-03 -0.978006  0.532418 -0.847118  2.461356"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.295963</td>\n",
       "      <td>-0.698917</td>\n",
       "      <td>0.932431</td>\n",
       "      <td>1.440893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.995785</td>\n",
       "      <td>0.674858</td>\n",
       "      <td>-0.809902</td>\n",
       "      <td>-0.543680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.249859</td>\n",
       "      <td>-0.664076</td>\n",
       "      <td>0.881349</td>\n",
       "      <td>1.945613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.095023</td>\n",
       "      <td>-1.861150</td>\n",
       "      <td>0.134254</td>\n",
       "      <td>0.661625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.798626</td>\n",
       "      <td>-1.377614</td>\n",
       "      <td>0.776687</td>\n",
       "      <td>0.342846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.978006</td>\n",
       "      <td>0.532418</td>\n",
       "      <td>-0.847118</td>\n",
       "      <td>2.461356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.049451</td>\n",
       "      <td>0.137660</td>\n",
       "      <td>1.987125</td>\n",
       "      <td>1.246595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.177302</td>\n",
       "      <td>-1.546113</td>\n",
       "      <td>-0.952839</td>\n",
       "      <td>0.008122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-08  0.295963 -0.698917  0.932431  1.440893\n",
       "2000-01-07  0.995785  0.674858 -0.809902 -0.543680\n",
       "2000-01-06  0.249859 -0.664076  0.881349  1.945613\n",
       "2000-01-05 -0.095023 -1.861150  0.134254  0.661625\n",
       "2000-01-04 -0.798626 -1.377614  0.776687  0.342846\n",
       "2000-01-03 -0.978006  0.532418 -0.847118  2.461356\n",
       "2000-01-02 -1.049451  0.137660  1.987125  1.246595\n",
       "2000-01-01  0.177302 -1.546113 -0.952839  0.008122"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用Label进行检索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**警告**：\n",
    "\n",
    "\n",
    ".loc要求检索时输入必须严格遵守index的类型，一旦输入类型不对，将会引起TypeError。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.478902</td>\n",
       "      <td>0.234646</td>\n",
       "      <td>0.628231</td>\n",
       "      <td>0.480590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.744357</td>\n",
       "      <td>0.234170</td>\n",
       "      <td>0.555582</td>\n",
       "      <td>0.117715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>0.612064</td>\n",
       "      <td>0.104215</td>\n",
       "      <td>0.674296</td>\n",
       "      <td>0.842351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.823353</td>\n",
       "      <td>0.829003</td>\n",
       "      <td>0.501923</td>\n",
       "      <td>0.388439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.810892</td>\n",
       "      <td>0.192622</td>\n",
       "      <td>0.606018</td>\n",
       "      <td>0.581612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-01  0.478902  0.234646  0.628231  0.480590\n",
       "2016-01-02  0.744357  0.234170  0.555582  0.117715\n",
       "2016-01-03  0.612064  0.104215  0.674296  0.842351\n",
       "2016-01-04  0.823353  0.829003  0.501923  0.388439\n",
       "2016-01-05  0.810892  0.192622  0.606018  0.581612"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.rand(5,4), columns=list('ABCD'), index=pd.date_range('20160101',periods=5))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-f18421f8c4a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1285\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1286\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1288\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1399\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1400\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1401\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1402\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m   1306\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1307\u001b[0m         indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,\n\u001b[1;32m-> 1308\u001b[1;33m                                        slice_obj.step, kind=self.name)\n\u001b[0m\u001b[0;32m   1309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1310\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\tseries\\index.pyc\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   1503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1504\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1505\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1506\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1507\u001b[0m             \u001b[1;31m# For historical reasons DatetimeIndex by default supports\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   2698\u001b[0m         \"\"\"\n\u001b[0;32m   2699\u001b[0m         start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[1;32m-> 2700\u001b[1;33m                                                  kind=kind)\n\u001b[0m\u001b[0;32m   2701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2702\u001b[0m         \u001b[1;31m# return a slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mslice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   2877\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2878\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2879\u001b[1;33m             \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2880\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2881\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   2816\u001b[0m         \u001b[1;31m# For datetime indices label may be a string that has to be converted\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2817\u001b[0m         \u001b[1;31m# to datetime boundary according to its resolution.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2818\u001b[1;33m         \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mside\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2819\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2820\u001b[0m         \u001b[1;31m# we need to look up the label\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\tseries\\index.pyc\u001b[0m in \u001b[0;36m_maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   1458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1459\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1460\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'slice'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1462\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[1;34m(self, form, key)\u001b[0m\n\u001b[0;32m   1115\u001b[0m                         \"indexers [{key}] of {kind}\".format(\n\u001b[0;32m   1116\u001b[0m                             \u001b[0mform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m                             kind=type(key)))\n\u001b[0m\u001b[0;32m   1118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>"
     ]
    }
   ],
   "source": [
    "df1.loc[2:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入string进行检索没问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.744357</td>\n",
       "      <td>0.234170</td>\n",
       "      <td>0.555582</td>\n",
       "      <td>0.117715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>0.612064</td>\n",
       "      <td>0.104215</td>\n",
       "      <td>0.674296</td>\n",
       "      <td>0.842351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.823353</td>\n",
       "      <td>0.829003</td>\n",
       "      <td>0.501923</td>\n",
       "      <td>0.388439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2016-01-02  0.744357  0.234170  0.555582  0.117715\n",
       "2016-01-03  0.612064  0.104215  0.674296  0.842351\n",
       "2016-01-04  0.823353  0.829003  0.501923  0.388439"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['20160102':'20160104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "细心地你一定发现了，index='20160104'那一行也被检索出来了，没错，**loc检索时范围是闭集合[start,end]**.\n",
    "\n",
    "整型可以作为label检索，这是没问题的，不过要记住此时整型表示的是label而不是index中的下标！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".loc操作是检索时的基本操作，以下输入格式都是合法的：\n",
    "* 一个label，比如：5、'a'. 记住这里的5表示的是index中的一个label而不是index中的一个下标。\n",
    "* label组成的列表或者数组比如['a','b','c']\n",
    "* 切片，比如'a':'f'.注意loc中切片范围是闭集合！\n",
    "* 布尔数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.270268\n",
       "b    1.015481\n",
       "c    0.380879\n",
       "d    0.965170\n",
       "e   -0.218055\n",
       "f    0.224802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(np.random.randn(6), index=list('abcdef'))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    0.380879\n",
       "d    0.965170\n",
       "e   -0.218055\n",
       "f    0.224802\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.loc['c':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0154808822674235"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.loc['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loc同样支持赋值操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.270268\n",
       "b    1.015481\n",
       "c    0.000000\n",
       "d    0.000000\n",
       "e    0.000000\n",
       "f    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.loc['c':]=0\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再来看看DataFramed的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.500635</td>\n",
       "      <td>2.515980</td>\n",
       "      <td>0.968653</td>\n",
       "      <td>-0.764951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.911650</td>\n",
       "      <td>-2.208888</td>\n",
       "      <td>0.389002</td>\n",
       "      <td>0.296063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.326533</td>\n",
       "      <td>-0.548483</td>\n",
       "      <td>-0.225515</td>\n",
       "      <td>0.561847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.061768</td>\n",
       "      <td>-0.299833</td>\n",
       "      <td>-1.081881</td>\n",
       "      <td>-1.389517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.440465</td>\n",
       "      <td>-0.332527</td>\n",
       "      <td>1.633278</td>\n",
       "      <td>-0.096852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.023751</td>\n",
       "      <td>-0.562649</td>\n",
       "      <td>-0.284983</td>\n",
       "      <td>1.629945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "a  0.500635  2.515980  0.968653 -0.764951\n",
       "b  0.911650 -2.208888  0.389002  0.296063\n",
       "c -0.326533 -0.548483 -0.225515  0.561847\n",
       "d  0.061768 -0.299833 -1.081881 -1.389517\n",
       "e  0.440465 -0.332527  1.633278 -0.096852\n",
       "f  1.023751 -0.562649 -0.284983  1.629945"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(6,4), index=list('abcdef'),columns=list('ABCD'))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.500635</td>\n",
       "      <td>2.515980</td>\n",
       "      <td>0.968653</td>\n",
       "      <td>-0.764951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.911650</td>\n",
       "      <td>-2.208888</td>\n",
       "      <td>0.389002</td>\n",
       "      <td>0.296063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.326533</td>\n",
       "      <td>-0.548483</td>\n",
       "      <td>-0.225515</td>\n",
       "      <td>0.561847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.061768</td>\n",
       "      <td>-0.299833</td>\n",
       "      <td>-1.081881</td>\n",
       "      <td>-1.389517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "a  0.500635  2.515980  0.968653 -0.764951\n",
       "b  0.911650 -2.208888  0.389002  0.296063\n",
       "c -0.326533 -0.548483 -0.225515  0.561847\n",
       "d  0.061768 -0.299833 -1.081881 -1.389517"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[['a','b','c','d'],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.500635</td>\n",
       "      <td>2.515980</td>\n",
       "      <td>0.968653</td>\n",
       "      <td>-0.764951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.911650</td>\n",
       "      <td>-2.208888</td>\n",
       "      <td>0.389002</td>\n",
       "      <td>0.296063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.326533</td>\n",
       "      <td>-0.548483</td>\n",
       "      <td>-0.225515</td>\n",
       "      <td>0.561847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.061768</td>\n",
       "      <td>-0.299833</td>\n",
       "      <td>-1.081881</td>\n",
       "      <td>-1.389517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "a  0.500635  2.515980  0.968653 -0.764951\n",
       "b  0.911650 -2.208888  0.389002  0.296063\n",
       "c -0.326533 -0.548483 -0.225515  0.561847\n",
       "d  0.061768 -0.299833 -1.081881 -1.389517"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[['a','b','c','d']] #可以省略 ':'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用切片检索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.061768</td>\n",
       "      <td>-0.299833</td>\n",
       "      <td>-1.081881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.440465</td>\n",
       "      <td>-0.332527</td>\n",
       "      <td>1.633278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.023751</td>\n",
       "      <td>-0.562649</td>\n",
       "      <td>-0.284983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "d  0.061768 -0.299833 -1.081881\n",
       "e  0.440465 -0.332527  1.633278\n",
       "f  1.023751 -0.562649 -0.284983"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['d':,'A':'C'] #注意是闭集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.500635\n",
       "B    2.515980\n",
       "C    0.968653\n",
       "D   -0.764951\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用布尔数组检索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B     True\n",
       "C     True\n",
       "D    False\n",
       "Name: a, dtype: bool"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['a']>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.500635</td>\n",
       "      <td>2.515980</td>\n",
       "      <td>0.968653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.911650</td>\n",
       "      <td>-2.208888</td>\n",
       "      <td>0.389002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-0.326533</td>\n",
       "      <td>-0.548483</td>\n",
       "      <td>-0.225515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.061768</td>\n",
       "      <td>-0.299833</td>\n",
       "      <td>-1.081881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.440465</td>\n",
       "      <td>-0.332527</td>\n",
       "      <td>1.633278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>1.023751</td>\n",
       "      <td>-0.562649</td>\n",
       "      <td>-0.284983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "a  0.500635  2.515980  0.968653\n",
       "b  0.911650 -2.208888  0.389002\n",
       "c -0.326533 -0.548483 -0.225515\n",
       "d  0.061768 -0.299833 -1.081881\n",
       "e  0.440465 -0.332527  1.633278\n",
       "f  1.023751 -0.562649 -0.284983"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[:,df1.loc['a']>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得到DataFrame中的某一个值, 等同于df1.get_value('a','A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50063542438780895"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc['a','A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50063542438780895"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.get_value('a','A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 根据下标进行检索 Selection By Position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "pandas提供了一系列的方法实现基于整型的检索。语义和python、numpy切片几乎一样。下标同样都是从0开始，并且进行的是半闭半开的区间检索[start,end)。如果输入 非整型label当做下标进行检索会引起IndexError。\n",
    "\n",
    "\n",
    ".iloc的合法输入包括：\n",
    "* 一个整数，比如5\n",
    "* 整数组成的列表或者数组，比如[4,3,0]\n",
    "* 整型表示的切片，比如1:7\n",
    "* 布尔数组\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看一下Series使用iloc检索的示例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.280654\n",
       "2   -0.687606\n",
       "4   -1.195345\n",
       "6   -0.384770\n",
       "8   -0.590466\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series(np.random.randn(5),index=list(range(0,10,2)))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.280654\n",
       "2   -0.687606\n",
       "4   -1.195345\n",
       "dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.iloc[:3] #注意检索是半闭半开区间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.38477022333948063"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iloc同样也可以进行赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000000\n",
       "2    0.000000\n",
       "4    0.000000\n",
       "6   -0.384770\n",
       "8   -0.590466\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.iloc[:3]=0\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame的示例:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357496</td>\n",
       "      <td>0.007987</td>\n",
       "      <td>-0.373388</td>\n",
       "      <td>0.713999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.256285</td>\n",
       "      <td>-0.366258</td>\n",
       "      <td>-0.980229</td>\n",
       "      <td>-1.377265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.256183</td>\n",
       "      <td>-0.405019</td>\n",
       "      <td>-0.740001</td>\n",
       "      <td>0.854734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.122789</td>\n",
       "      <td>-1.652925</td>\n",
       "      <td>-2.109178</td>\n",
       "      <td>0.714779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.105426</td>\n",
       "      <td>0.183194</td>\n",
       "      <td>-0.418197</td>\n",
       "      <td>1.454595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.287264</td>\n",
       "      <td>0.318804</td>\n",
       "      <td>0.532221</td>\n",
       "      <td>1.124164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         2         4         6\n",
       "0   0.357496  0.007987 -0.373388  0.713999\n",
       "2  -1.256285 -0.366258 -0.980229 -1.377265\n",
       "4  -0.256183 -0.405019 -0.740001  0.854734\n",
       "6  -1.122789 -1.652925 -2.109178  0.714779\n",
       "8  -1.105426  0.183194 -0.418197  1.454595\n",
       "10  1.287264  0.318804  0.532221  1.124164"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(6,4),index=list(range(0,12,2)), columns=list(range(0,8,2)))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>6</th>\n",
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       "      <td>0.713999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.256285</td>\n",
       "      <td>-0.366258</td>\n",
       "      <td>-0.980229</td>\n",
       "      <td>-1.377265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.256183</td>\n",
       "      <td>-0.405019</td>\n",
       "      <td>-0.740001</td>\n",
       "      <td>0.854734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         2         4         6\n",
       "0  0.357496  0.007987 -0.373388  0.713999\n",
       "2 -1.256285 -0.366258 -0.980229 -1.377265\n",
       "4 -0.256183 -0.405019 -0.740001  0.854734"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进行行和列的检索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.980229</td>\n",
       "      <td>-1.377265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.740001</td>\n",
       "      <td>0.854734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.109178</td>\n",
       "      <td>0.714779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.418197</td>\n",
       "      <td>1.454595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          4         6\n",
       "2 -0.980229 -1.377265\n",
       "4 -0.740001  0.854734\n",
       "6 -2.109178  0.714779\n",
       "8 -0.418197  1.454595"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[1:5,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.366258</td>\n",
       "      <td>-0.980229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.652925</td>\n",
       "      <td>-2.109178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.318804</td>\n",
       "      <td>0.532221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2         4\n",
       "2  -0.366258 -0.980229\n",
       "6  -1.652925 -2.109178\n",
       "10  0.318804  0.532221"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[[1,3,5],[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.256285</td>\n",
       "      <td>-0.366258</td>\n",
       "      <td>-0.980229</td>\n",
       "      <td>-1.377265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.256183</td>\n",
       "      <td>-0.405019</td>\n",
       "      <td>-0.740001</td>\n",
       "      <td>0.854734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         2         4         6\n",
       "2 -1.256285 -0.366258 -0.980229 -1.377265\n",
       "4 -0.256183 -0.405019 -0.740001  0.854734"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007987</td>\n",
       "      <td>-0.373388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.366258</td>\n",
       "      <td>-0.980229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.405019</td>\n",
       "      <td>-0.740001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.652925</td>\n",
       "      <td>-2.109178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.183194</td>\n",
       "      <td>-0.418197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.318804</td>\n",
       "      <td>0.532221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2         4\n",
       "0   0.007987 -0.373388\n",
       "2  -0.366258 -0.980229\n",
       "4  -0.405019 -0.740001\n",
       "6  -1.652925 -2.109178\n",
       "8   0.183194 -0.418197\n",
       "10  0.318804  0.532221"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.36625813479137037"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[1,1]#只检索一个元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意下面两个例子的区别："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.256285\n",
       "2   -0.366258\n",
       "4   -0.980229\n",
       "6   -1.377265\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.256285</td>\n",
       "      <td>-0.366258</td>\n",
       "      <td>-0.980229</td>\n",
       "      <td>-1.377265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         2         4         6\n",
       "2 -1.256285 -0.366258 -0.980229 -1.377265"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如果切片检索时输入的范围越界，没关系，只要pandas版本>=v0.14.0, 就能如同Python/Numpy那样正确处理。**\n",
    "\n",
    "注意：仅限于 切片检索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'd', 'e', 'f']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = list('abcdef')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['e', 'f']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[4:10] #这里x的长度是6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[8:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = pd.Series(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    e\n",
       "5    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    e\n",
       "5    f\n",
       "dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[4:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: object)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[8:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.372023</td>\n",
       "      <td>-1.074524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.061010</td>\n",
       "      <td>-0.205115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.346113</td>\n",
       "      <td>0.256906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.575367</td>\n",
       "      <td>1.030279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.305473</td>\n",
       "      <td>-0.973037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "0 -0.372023 -1.074524\n",
       "1  0.061010 -0.205115\n",
       "2  0.346113  0.256906\n",
       "3  2.575367  1.030279\n",
       "4  1.305473 -0.973037"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.randn(5,2), columns=list('AB'))\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3, 4]"
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     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1.iloc[:,2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.074524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.205115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.256906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.030279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.973037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          B\n",
       "0 -1.074524\n",
       "1 -0.205115\n",
       "2  0.256906\n",
       "3  1.030279\n",
       "4 -0.973037"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.305473</td>\n",
       "      <td>-0.973037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "4  1.305473 -0.973037"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.iloc[4:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面说到，这种优雅处理越界的能力仅限于**输入全是切片**，如果输入是越界的 列表或者整数，则会引起IndexError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "positional indexers are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-110-496782e5248f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1285\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1286\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1288\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1559\u001b[0m                 \u001b[1;31m# validate list bounds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1560\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1562\u001b[0m                 \u001b[1;31m# force an actual list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_list_like\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1497\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1498\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1499\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1501\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
     ]
    }
   ],
   "source": [
    "df1.iloc[[4,5,6]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 输入有切片，有整数，如果越界同样不能处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-111-a87dc3221dcf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1282\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1283\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1284\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1285\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1286\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1503\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1504\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1505\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1506\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1507\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    136\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Too many indexers'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m                 raise ValueError(\"Location based indexing can only have [%s] \"\n\u001b[0;32m    140\u001b[0m                                  \"types\" % self._valid_types)\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_has_valid_type\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1471\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1472\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1473\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1474\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1475\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1485\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1486\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1487\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1488\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "df1.iloc[:,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择随机样本 Selecting Random Samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用sample()方法能够从行或者列中进行随机选择，适用对象包括Series、DataFrame和Panel。sample()方法默认对行进行随机选择，输入可以是整数或者小数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([0,1,2,3,4,5])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    5\n",
       "1    1\n",
       "3    3\n",
       "0    0\n",
       "2    2\n",
       "4    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    4\n",
       "1    1\n",
       "0    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(3) #直接输入整数即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以输入小数，则会随机选择N*frac个样本, 结果进行四舍五入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    5\n",
       "2    2\n",
       "4    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(frac=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Only integers accepted as `n` values",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-117-24c34e460c2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#必须输入frac=0.5\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[0;32m   2555\u001b[0m             \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2556\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfrac\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2557\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Only integers accepted as `n` values\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2558\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfrac\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2559\u001b[0m             \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrac\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0maxis_length\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Only integers accepted as `n` values"
     ]
    }
   ],
   "source": [
    "s.sample(0.5) #必须输入frac=0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    5\n",
       "4    4\n",
       "0    0\n",
       "2    2\n",
       "1    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(frac=0.8) #6*0.8=4.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "5    5\n",
       "4    4\n",
       "2    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(frac=0.7)# 6*0.7=4.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sample()默认进行的无放回抽样，可以利用replace=True参数进行可放回抽样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1\n",
       "3    3\n",
       "5    5\n",
       "4    4\n",
       "2    2\n",
       "0    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(n=6,replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    5\n",
       "5    5\n",
       "5    5\n",
       "5    5\n",
       "3    3\n",
       "1    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(6,replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认情况下，每一行/列都被等可能的采样，如果你想为每一行赋予一个被抽样选择的权重，可以利用weights参数实现。\n",
    "\n",
    "注意：如果weights中各概率相加和不等于1，pandas会先对weights进行归一化，强制转为概率和为1！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([0,1,2,3,4,5])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "example_weights=[0,0,0.2,0.2,0.2,0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    2\n",
       "5    5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(n=3,weights=example_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "example_weights2 = [0.5, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(n=1, weights=example_weights2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Fewer non-zero entries in p than size",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-128-933f0356437e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexample_weights2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#n>1 会报错，\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[0;32m   2567\u001b[0m                              \"provide positive value.\")\n\u001b[0;32m   2568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2569\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis_length\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2570\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_copy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice (numpy\\random\\mtrand\\mtrand.c:16370)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Fewer non-zero entries in p than size"
     ]
    }
   ],
   "source": [
    "s.sample(n=2, weights=example_weights2) #n>1 会报错，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意**：由于sample默认进行的是无放回抽样，所以输入必须n<=行数，除非进行可放回抽样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when 'replace=False'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-130-d71dc28cbaa3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#7不行\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[0;32m   2567\u001b[0m                              \"provide positive value.\")\n\u001b[0;32m   2568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2569\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis_length\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2570\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_copy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice (numpy\\random\\mtrand\\mtrand.c:16292)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
     ]
    }
   ],
   "source": [
    "s.sample(7) #7不行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    3\n",
       "1    1\n",
       "4    4\n",
       "2    2\n",
       "2    2\n",
       "1    1\n",
       "1    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sample(7,replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果是对DataFrame对象进行有权重采样，一个简单 的方法是新增一列用于表示每一行的权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>weight_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  weight_column\n",
       "0     9            0.5\n",
       "1     8            0.4\n",
       "2     7            0.1\n",
       "3     6            0.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'col1':[9,8,7,6], 'weight_column':[0.5, 0.4, 0.1, 0]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>weight_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  weight_column\n",
       "0     9            0.5\n",
       "1     8            0.4\n",
       "2     7            0.1"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample(n=3,weights='weight_column')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对列进行采样, axis=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clo2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clo2  col1\n",
       "0     2     1\n",
       "1     3     2\n",
       "2     4     3"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'col1':[1,2,3], 'clo2':[2,3,4]})\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1\n",
       "0     1\n",
       "1     2\n",
       "2     3"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.sample(1,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们也可以使用random_state参数 为sample内部的随机数生成器提供种子数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clo2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clo2  col1\n",
       "0     2     1\n",
       "1     3     2\n",
       "2     4     3"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'col1':[1,2,3], 'clo2':[2,3,4]})\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意下面两个示例，输出是相同的，因为使用了相同的种子数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clo2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clo2  col1\n",
       "2     4     3\n",
       "1     3     2"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.sample(n=2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clo2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clo2  col1\n",
       "2     4     3\n",
       "1     3     2"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.sample(n=2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clo2</th>\n",
       "      <th>col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clo2  col1\n",
       "1     3     2\n",
       "0     2     1"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.sample(n=2,random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用赋值的方式扩充对象 Setting With Enlargement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用.loc/.ix/[]对不存在的键值进行赋值时，将会导致在对象中添加新的元素，它的键即为赋值时不存在的键。\n",
    "\n",
    "对于Series来说，这是一种有效的添加操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se = pd.Series([1,2,3])\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "5    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se[5]=5\n",
    "se"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame可以在行或者列上扩充数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B\n",
       "0  0  1\n",
       "1  2  3\n",
       "2  4  5"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfi = pd.DataFrame(np.arange(6).reshape(3,2),columns=['A','B'])\n",
    "dfi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfi.loc[:,'C']=dfi.loc[:,'A'] #对列进行扩充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  0  1  0\n",
       "1  2  3  2\n",
       "2  4  5  4"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfi.loc[3]=5 #对行进行扩充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  C\n",
       "0  0  1  0\n",
       "1  2  3  2\n",
       "2  4  5  4\n",
       "3  5  5  5"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标量值的快速获取和赋值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果仅仅想获取一个元素，使用[]未免太繁重了。pandas提供了快速获取一个元素的方法：at和iat. 适用于Series、DataFrame和Panel。\n",
    "\n",
    "如果loc方法，at方法的合法输入是label，iat的合法输入是整型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iat[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24985887518963862"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[5],'A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.79862582029106743"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[3,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以进行赋值操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.177302</td>\n",
       "      <td>-1.546113</td>\n",
       "      <td>-0.952839</td>\n",
       "      <td>0.008122</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.049451</td>\n",
       "      <td>0.137660</td>\n",
       "      <td>1.987125</td>\n",
       "      <td>1.246595</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.978006</td>\n",
       "      <td>0.532418</td>\n",
       "      <td>-0.847118</td>\n",
       "      <td>2.461356</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.798626</td>\n",
       "      <td>-1.377614</td>\n",
       "      <td>0.776687</td>\n",
       "      <td>0.342846</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.095023</td>\n",
       "      <td>-1.861150</td>\n",
       "      <td>0.134254</td>\n",
       "      <td>0.661625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.249859</td>\n",
       "      <td>-0.664076</td>\n",
       "      <td>0.881349</td>\n",
       "      <td>1.945613</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.995785</td>\n",
       "      <td>0.674858</td>\n",
       "      <td>-0.809902</td>\n",
       "      <td>-0.543680</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.295963</td>\n",
       "      <td>-0.698917</td>\n",
       "      <td>0.932431</td>\n",
       "      <td>1.440893</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    0\n",
       "2000-01-01  0.177302 -1.546113 -0.952839  0.008122  NaN\n",
       "2000-01-02 -1.049451  0.137660  1.987125  1.246595  NaN\n",
       "2000-01-03 -0.978006  0.532418 -0.847118  2.461356  NaN\n",
       "2000-01-04 -0.798626 -1.377614  0.776687  0.342846  NaN\n",
       "2000-01-05 -0.095023 -1.861150  0.134254  0.661625  NaN\n",
       "2000-01-06  0.249859 -0.664076  0.881349  1.945613  NaN\n",
       "2000-01-07  0.995785  0.674858 -0.809902 -0.543680  NaN\n",
       "2000-01-08  0.295963 -0.698917  0.932431  1.440893  NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN  7.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[-1]+1,0]=7\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 布尔检索 Boolean indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一种常用的操作是使用布尔向量过滤数据。运算符有三个:|(or), &(and), ~(not)。\n",
    "\n",
    "**注意**：运算符的操作数要在圆括号内。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用布尔向量检索Series的操作方式和numpy ndarray一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -3\n",
       "1   -2\n",
       "2   -1\n",
       "3    0\n",
       "4    1\n",
       "5    2\n",
       "6    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(range(-3, 4))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    1\n",
       "5    2\n",
       "6    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -3\n",
       "1   -2\n",
       "4    1\n",
       "5    2\n",
       "6    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[(s<-1) | (s>0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0\n",
       "4    1\n",
       "5    2\n",
       "6    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[~(s<0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame示例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.177302</td>\n",
       "      <td>-1.546113</td>\n",
       "      <td>-0.952839</td>\n",
       "      <td>0.008122</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.249859</td>\n",
       "      <td>-0.664076</td>\n",
       "      <td>0.881349</td>\n",
       "      <td>1.945613</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.995785</td>\n",
       "      <td>0.674858</td>\n",
       "      <td>-0.809902</td>\n",
       "      <td>-0.543680</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.295963</td>\n",
       "      <td>-0.698917</td>\n",
       "      <td>0.932431</td>\n",
       "      <td>1.440893</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   0\n",
       "2000-01-01  0.177302 -1.546113 -0.952839  0.008122 NaN\n",
       "2000-01-06  0.249859 -0.664076  0.881349  1.945613 NaN\n",
       "2000-01-07  0.995785  0.674858 -0.809902 -0.543680 NaN\n",
       "2000-01-08  0.295963 -0.698917  0.932431  1.440893 NaN"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['A'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用列表解析和map方法能够产生更加复杂的选择标准。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>1.952594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>0.964196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>0.752335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>0.897060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>-0.120268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.114347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>six</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.690658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "0    one  x  1.952594\n",
       "1    one  y  0.964196\n",
       "2    two  y  0.752335\n",
       "3  three  x  0.897060\n",
       "4    two  y -0.120268\n",
       "5    one  x -0.114347\n",
       "6    six  x -0.690658"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'a' : ['one', 'one', 'two', 'three', 'two', 'one', 'six'],\n",
    "                    'b' : ['x', 'y', 'y', 'x', 'y', 'x', 'x'],\n",
    "                     'c' : np.random.randn(7)})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "criterion = df2['a'].map(lambda x:x.startswith('t'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>0.752335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>0.897060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>-0.120268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "2    two  y  0.752335\n",
       "3  three  x  0.897060\n",
       "4    two  y -0.120268"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[criterion]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>0.752335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>0.897060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>-0.120268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "2    two  y  0.752335\n",
       "3  three  x  0.897060\n",
       "4    two  y -0.120268"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[[x.startswith('t') for x in df2['a']]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>0.89706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b        c\n",
       "3  three  x  0.89706"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[criterion & (df2['b'] == 'x')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结合loc、iloc等方法可以检索多个坐标下的数据."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x</td>\n",
       "      <td>0.89706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   b        c\n",
       "3  x  0.89706"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[criterion & (df2['b'] == 'x'), 'b':'c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用isin方法检索 Indexing with isin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "isin(is in)\n",
    "\n",
    "对于Series对象来说，使用isin方法时传入一个列表，isin方法会返回一个布尔向量。布尔向量元素为1的前提是列表元素在Series对象中存在。看起来比较拗口，还是看例子吧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = pd.Series(np.arange(5), index=np.arange(5)[::-1],dtype='int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0\n",
       "3    1\n",
       "2    2\n",
       "1    3\n",
       "0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    False\n",
       "3    False\n",
       "2     True\n",
       "1    False\n",
       "0     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isin([2,4,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    2\n",
       "0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s.isin([2,4,6])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index对象中也有isin方法."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0\n",
       "2    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s.index.isin([2,4,6])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    2.0\n",
       "4    0.0\n",
       "6    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[2,4,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame同样有isin方法，参数是数组或字典。二者的区别看例子吧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ids ids2  vals\n",
       "0   a    a     1\n",
       "1   b    n     2\n",
       "2   f    c     3\n",
       "3   n    n     4"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'],\n",
    "                   'ids2':['a', 'n', 'c', 'n']})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values=['a', 'b', 1, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ids   ids2   vals\n",
       "0   True   True   True\n",
       "1   True  False  False\n",
       "2  False  False   True\n",
       "3  False  False  False"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isin(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入一个字典的情形："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = {'ids': ['a', 'b'], 'vals': [1, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ids   ids2   vals\n",
       "0   True  False   True\n",
       "1   True  False  False\n",
       "2  False  False   True\n",
       "3  False  False  False"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isin(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结合isin方法和any() all()可以对DataFrame进行快速查询。比如选择每一列都符合标准的行:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "row_mark = df.isin(values).all(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ids ids2  vals\n",
       "0   a    a     1"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[row_mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "row_mark = df.isin(values).any(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ids ids2  vals\n",
       "0   a    a     1\n",
       "1   b    n     2\n",
       "2   f    c     3"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[row_mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## where()方法 The where() Method and Masking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用布尔向量对Series对象查询时通常返回的是对象的子集。如果想要返回的shape和原对象相同，可以使用where方法。\n",
    "\n",
    "使用布尔向量对DataFrame对象查询返回的shape和原对象相同，这是因为底层用的where方法实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1\n",
       "2    2\n",
       "1    3\n",
       "0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用where方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    NaN\n",
       "3    1.0\n",
       "2    2.0\n",
       "1    3.0\n",
       "0    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.where(s>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ids ids2  vals\n",
       "0  NaN  NaN   NaN\n",
       "1  NaN  NaN   NaN\n",
       "2  NaN  NaN   NaN\n",
       "3  NaN  NaN   NaN"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ids ids2  vals\n",
       "0  NaN  NaN   NaN\n",
       "1  NaN  NaN   NaN\n",
       "2  NaN  NaN   NaN\n",
       "3  NaN  NaN   NaN"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.where(df<0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where方法还有一个可选的other参数，作用是替换返回结果中是False的值，并不会改变原对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ids ids2  vals\n",
       "0   2    2     2\n",
       "1   2    2     2\n",
       "2   2    2     2\n",
       "3   2    2     2"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.where(df<0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ids ids2  vals\n",
       "0   a    a     1\n",
       "1   b    n     2\n",
       "2   f    c     3\n",
       "3   n    n     4"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>ids2</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>a</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>4</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "  ids ids2  vals\n",
       "0   a    a     1\n",
       "1   b    n     2\n",
       "2   f    c     3\n",
       "3   n    n     4"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.where(df<0, df) #将df作为other的参数值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可能想基于某种判断条件来赋值。一种直观的方法是："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0\n",
       "3    1\n",
       "2    2\n",
       "1    3\n",
       "0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = s.copy()\n",
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s2[s2<0]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0\n",
       "3    1\n",
       "2    2\n",
       "1    3\n",
       "0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认情况下，where方法并不会修改原始对象，它返回的是一个修改过的原始对象副本，如果你想直接修改原始对象，方法是将inplace参数设置为True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,5), index=list('abcdef'), columns=list('ABCDE'))\n",
    "df_orig = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_orig.where(df < 0, -df, inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <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>a</th>\n",
       "      <td>-1.196045</td>\n",
       "      <td>1.488123</td>\n",
       "      <td>-1.258859</td>\n",
       "      <td>-1.072124</td>\n",
       "      <td>1.186134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.981695</td>\n",
       "      <td>-0.895986</td>\n",
       "      <td>-0.342973</td>\n",
       "      <td>1.387443</td>\n",
       "      <td>-1.254906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-1.051246</td>\n",
       "      <td>1.700564</td>\n",
       "      <td>-1.647643</td>\n",
       "      <td>0.569339</td>\n",
       "      <td>-0.292161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>-0.668880</td>\n",
       "      <td>-1.762922</td>\n",
       "      <td>0.886166</td>\n",
       "      <td>-0.057681</td>\n",
       "      <td>-0.106489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.049452</td>\n",
       "      <td>-0.427708</td>\n",
       "      <td>0.111594</td>\n",
       "      <td>0.524640</td>\n",
       "      <td>0.372802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>-0.320219</td>\n",
       "      <td>0.179891</td>\n",
       "      <td>0.638859</td>\n",
       "      <td>-1.704587</td>\n",
       "      <td>0.199829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "a -1.196045  1.488123 -1.258859 -1.072124  1.186134\n",
       "b -0.981695 -0.895986 -0.342973  1.387443 -1.254906\n",
       "c -1.051246  1.700564 -1.647643  0.569339 -0.292161\n",
       "d -0.668880 -1.762922  0.886166 -0.057681 -0.106489\n",
       "e -1.049452 -0.427708  0.111594  0.524640  0.372802\n",
       "f -0.320219  0.179891  0.638859 -1.704587  0.199829"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**对齐**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where方法会将输入的布尔条件对齐，因此允许部分检索时的赋值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2[df2[1:4] >0]=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
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       "      <th>E</th>\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.196045</td>\n",
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       "      <td>1.072124</td>\n",
       "      <td>-1.186134</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.387443</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.700564</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.569339</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.886166</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>1.049452</td>\n",
       "      <td>0.427708</td>\n",
       "      <td>-0.111594</td>\n",
       "      <td>-0.524640</td>\n",
       "      <td>-0.372802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.320219</td>\n",
       "      <td>-0.179891</td>\n",
       "      <td>-0.638859</td>\n",
       "      <td>1.704587</td>\n",
       "      <td>-0.199829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "a  1.196045 -1.488123  1.258859  1.072124 -1.186134\n",
       "b  3.000000  3.000000  3.000000 -1.387443  3.000000\n",
       "c  3.000000 -1.700564  3.000000 -0.569339  3.000000\n",
       "d  3.000000  3.000000 -0.886166  3.000000  3.000000\n",
       "e  1.049452  0.427708 -0.111594 -0.524640 -0.372802\n",
       "f  0.320219 -0.179891 -0.638859  1.704587 -0.199829"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.196045</td>\n",
       "      <td>1.196045</td>\n",
       "      <td>1.258859</td>\n",
       "      <td>1.072124</td>\n",
       "      <td>1.196045</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.981695</td>\n",
       "      <td>0.895986</td>\n",
       "      <td>0.342973</td>\n",
       "      <td>0.981695</td>\n",
       "      <td>1.254906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.051246</td>\n",
       "      <td>1.051246</td>\n",
       "      <td>1.647643</td>\n",
       "      <td>1.051246</td>\n",
       "      <td>0.292161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.668880</td>\n",
       "      <td>1.762922</td>\n",
       "      <td>0.668880</td>\n",
       "      <td>0.057681</td>\n",
       "      <td>0.106489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>1.049452</td>\n",
       "      <td>0.427708</td>\n",
       "      <td>1.049452</td>\n",
       "      <td>1.049452</td>\n",
       "      <td>1.049452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.320219</td>\n",
       "      <td>0.320219</td>\n",
       "      <td>0.320219</td>\n",
       "      <td>1.704587</td>\n",
       "      <td>0.320219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D         E\n",
       "a  1.196045  1.196045  1.258859  1.072124  1.196045\n",
       "b  0.981695  0.895986  0.342973  0.981695  1.254906\n",
       "c  1.051246  1.051246  1.647643  1.051246  0.292161\n",
       "d  0.668880  1.762922  0.668880  0.057681  0.106489\n",
       "e  1.049452  0.427708  1.049452  1.049452  1.049452\n",
       "f  0.320219  0.320219  0.320219  1.704587  0.320219"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.where(df2>0, df2['A'], axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**mask**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4   NaN\n",
       "3   NaN\n",
       "2   NaN\n",
       "1   NaN\n",
       "0   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.mask(s>=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.488123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.186134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.387443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.700564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.569339</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.886166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.111594</td>\n",
       "      <td>-0.524640</td>\n",
       "      <td>-0.372802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.179891</td>\n",
       "      <td>-0.638859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.199829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A         B         C         D         E\n",
       "a NaN -1.488123       NaN       NaN -1.186134\n",
       "b NaN       NaN       NaN -1.387443       NaN\n",
       "c NaN -1.700564       NaN -0.569339       NaN\n",
       "d NaN       NaN -0.886166       NaN       NaN\n",
       "e NaN       NaN -0.111594 -0.524640 -0.372802\n",
       "f NaN -0.179891 -0.638859       NaN -0.199829"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mask(df >= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## query()方法 The query() Method (Experimental)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame对象拥有query方法，允许使用表达式检索。\n",
    "\n",
    "比如，检索列'b'的值介于列‘a’和‘c’之间的行。\n",
    "\n",
    "**注意：** 需要安装numexptr。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(n, 3), columns=list('abc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.122199</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.032165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.701562</td>\n",
       "      <td>-0.264389</td>\n",
       "      <td>-0.218722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.249641</td>\n",
       "      <td>-0.491504</td>\n",
       "      <td>-0.505626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.737489</td>\n",
       "      <td>-1.021851</td>\n",
       "      <td>-0.133149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.824726</td>\n",
       "      <td>0.610772</td>\n",
       "      <td>-0.618181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.776072</td>\n",
       "      <td>0.179174</td>\n",
       "      <td>-0.221257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.203382</td>\n",
       "      <td>0.170864</td>\n",
       "      <td>0.311583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.184993</td>\n",
       "      <td>2.168807</td>\n",
       "      <td>-0.525213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.569373</td>\n",
       "      <td>-0.422874</td>\n",
       "      <td>0.025034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.250066</td>\n",
       "      <td>-0.523624</td>\n",
       "      <td>-0.068660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "0  0.122199  0.930233  0.032165\n",
       "1  0.701562 -0.264389 -0.218722\n",
       "2  1.249641 -0.491504 -0.505626\n",
       "3  0.737489 -1.021851 -0.133149\n",
       "4  0.824726  0.610772 -0.618181\n",
       "5  1.776072  0.179174 -0.221257\n",
       "6  0.203382  0.170864  0.311583\n",
       "7 -0.184993  2.168807 -0.525213\n",
       "8 -1.569373 -0.422874  0.025034\n",
       "9 -0.250066 -0.523624 -0.068660"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.569373</td>\n",
       "      <td>-0.422874</td>\n",
       "      <td>0.025034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "8 -1.569373 -0.422874  0.025034"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.a<df.b) & (df.b<df.c)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.569373</td>\n",
       "      <td>-0.422874</td>\n",
       "      <td>0.025034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "8 -1.569373 -0.422874  0.025034"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('(a < b) & (b < c)') #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### MultiIndex query() 语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于DataFrame对象，可以使用MultiIndex，如同操作列名一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors = np.random.choice(['red', 'green'], size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "foods = np.random.choice(['eggs', 'ham'], size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['green', 'red', 'red', 'red', 'green', 'red', 'green', 'red',\n",
       "       'green', 'red'], \n",
       "      dtype='|S5')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ham', 'eggs', 'ham', 'ham', 'ham', 'ham', 'ham', 'eggs', 'ham',\n",
       "       'eggs'], \n",
       "      dtype='|S4')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(n,2), index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th>food</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>-0.302117</td>\n",
       "      <td>-0.329496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>1.511928</td>\n",
       "      <td>0.516101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.496161</td>\n",
       "      <td>-2.188031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.675945</td>\n",
       "      <td>-1.174039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>-0.045403</td>\n",
       "      <td>1.232689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <th>ham</th>\n",
       "      <td>-0.629616</td>\n",
       "      <td>0.898270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>-1.107152</td>\n",
       "      <td>-0.608575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.190136</td>\n",
       "      <td>0.267058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>0.463844</td>\n",
       "      <td>0.753210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.202780</td>\n",
       "      <td>-0.489497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1\n",
       "color food                    \n",
       "green ham  -0.302117 -0.329496\n",
       "red   eggs  1.511928  0.516101\n",
       "      ham  -0.496161 -2.188031\n",
       "      ham  -0.675945 -1.174039\n",
       "green ham  -0.045403  1.232689\n",
       "red   ham  -0.629616  0.898270\n",
       "green ham  -1.107152 -0.608575\n",
       "red   eggs -2.190136  0.267058\n",
       "green ham   0.463844  0.753210\n",
       "red   eggs -2.202780 -0.489497"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th>food</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>1.511928</td>\n",
       "      <td>0.516101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.496161</td>\n",
       "      <td>-2.188031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.675945</td>\n",
       "      <td>-1.174039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.629616</td>\n",
       "      <td>0.898270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.190136</td>\n",
       "      <td>0.267058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.202780</td>\n",
       "      <td>-0.489497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1\n",
       "color food                    \n",
       "red   eggs  1.511928  0.516101\n",
       "      ham  -0.496161 -2.188031\n",
       "      ham  -0.675945 -1.174039\n",
       "      ham  -0.629616  0.898270\n",
       "      eggs -2.190136  0.267058\n",
       "      eggs -2.202780 -0.489497"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('color == \"red\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果index没有名字，可以给他们命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.index.names = [None, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>-0.302117</td>\n",
       "      <td>-0.329496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>1.511928</td>\n",
       "      <td>0.516101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.496161</td>\n",
       "      <td>-2.188031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.675945</td>\n",
       "      <td>-1.174039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>-0.045403</td>\n",
       "      <td>1.232689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <th>ham</th>\n",
       "      <td>-0.629616</td>\n",
       "      <td>0.898270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>-1.107152</td>\n",
       "      <td>-0.608575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.190136</td>\n",
       "      <td>0.267058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <th>ham</th>\n",
       "      <td>0.463844</td>\n",
       "      <td>0.753210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.202780</td>\n",
       "      <td>-0.489497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1\n",
       "green ham  -0.302117 -0.329496\n",
       "red   eggs  1.511928  0.516101\n",
       "      ham  -0.496161 -2.188031\n",
       "      ham  -0.675945 -1.174039\n",
       "green ham  -0.045403  1.232689\n",
       "red   ham  -0.629616  0.898270\n",
       "green ham  -1.107152 -0.608575\n",
       "red   eggs -2.190136  0.267058\n",
       "green ham   0.463844  0.753210\n",
       "red   eggs -2.202780 -0.489497"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">red</th>\n",
       "      <th>eggs</th>\n",
       "      <td>1.511928</td>\n",
       "      <td>0.516101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.496161</td>\n",
       "      <td>-2.188031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.675945</td>\n",
       "      <td>-1.174039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>-0.629616</td>\n",
       "      <td>0.898270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.190136</td>\n",
       "      <td>0.267058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eggs</th>\n",
       "      <td>-2.202780</td>\n",
       "      <td>-0.489497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0         1\n",
       "red eggs  1.511928  0.516101\n",
       "    ham  -0.496161 -2.188031\n",
       "    ham  -0.675945 -1.174039\n",
       "    ham  -0.629616  0.898270\n",
       "    eggs -2.190136  0.267058\n",
       "    eggs -2.202780 -0.489497"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('ilevel_0 == \"red\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ilevl_0意思是 0级index。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### query() 用例 query() Use Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个使用query()的情景是面对DataFrame对象组成的集合，并且这些对象有共同的的列名，则可以利用query方法对这个集合进行统一检索。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.165333</td>\n",
       "      <td>-1.033593</td>\n",
       "      <td>-0.350963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.194190</td>\n",
       "      <td>-1.150226</td>\n",
       "      <td>0.394567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.596311</td>\n",
       "      <td>0.731163</td>\n",
       "      <td>-0.076441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.581372</td>\n",
       "      <td>-0.181338</td>\n",
       "      <td>-0.537066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.400650</td>\n",
       "      <td>-0.557857</td>\n",
       "      <td>0.899903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.953411</td>\n",
       "      <td>-0.339584</td>\n",
       "      <td>-0.326526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.624137</td>\n",
       "      <td>2.367149</td>\n",
       "      <td>1.713632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.082154</td>\n",
       "      <td>0.656102</td>\n",
       "      <td>-0.085876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.403841</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>-1.450854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.545236</td>\n",
       "      <td>-0.277590</td>\n",
       "      <td>0.670381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "0  0.165333 -1.033593 -0.350963\n",
       "1  1.194190 -1.150226  0.394567\n",
       "2  2.596311  0.731163 -0.076441\n",
       "3 -0.581372 -0.181338 -0.537066\n",
       "4 -0.400650 -0.557857  0.899903\n",
       "5 -0.953411 -0.339584 -0.326526\n",
       "6 -0.624137  2.367149  1.713632\n",
       "7 -1.082154  0.656102 -0.085876\n",
       "8  0.403841  0.049296 -1.450854\n",
       "9 -1.545236 -0.277590  0.670381"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(n, 3), columns=list('abc'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.094352</td>\n",
       "      <td>-0.298453</td>\n",
       "      <td>-1.033678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.433829</td>\n",
       "      <td>0.453419</td>\n",
       "      <td>0.025845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.073075</td>\n",
       "      <td>-0.807078</td>\n",
       "      <td>-0.301511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.349793</td>\n",
       "      <td>1.206156</td>\n",
       "      <td>-0.409708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.182058</td>\n",
       "      <td>-0.751007</td>\n",
       "      <td>-1.877473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.073020</td>\n",
       "      <td>0.615864</td>\n",
       "      <td>1.196358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.359860</td>\n",
       "      <td>1.054296</td>\n",
       "      <td>0.174546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.139869</td>\n",
       "      <td>-0.005851</td>\n",
       "      <td>0.100370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.250069</td>\n",
       "      <td>-0.380308</td>\n",
       "      <td>-0.632730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.041732</td>\n",
       "      <td>0.077047</td>\n",
       "      <td>0.368696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.769995</td>\n",
       "      <td>0.593472</td>\n",
       "      <td>1.192260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.863597</td>\n",
       "      <td>-0.064093</td>\n",
       "      <td>1.033223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           a         b         c\n",
       "0  -1.094352 -0.298453 -1.033678\n",
       "1   0.433829  0.453419  0.025845\n",
       "2  -0.073075 -0.807078 -0.301511\n",
       "3  -0.349793  1.206156 -0.409708\n",
       "4  -1.182058 -0.751007 -1.877473\n",
       "5   0.073020  0.615864  1.196358\n",
       "6  -0.359860  1.054296  0.174546\n",
       "7  -1.139869 -0.005851  0.100370\n",
       "8  -0.250069 -0.380308 -0.632730\n",
       "9  -1.041732  0.077047  0.368696\n",
       "10  0.769995  0.593472  1.192260\n",
       "11 -0.863597 -0.064093  1.033223"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.random.randn(n+2, 3), columns=df.columns)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "expr = '0.0 <= a <= c <= 0.5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Empty DataFrame\n",
       " Columns: [a, b, c]\n",
       " Index: [], Empty DataFrame\n",
       " Columns: [a, b, c]\n",
       " Index: []]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map(lambda frame: frame.query(expr), [df, df2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python中query和pandas中query语法比较 query() Python versus pandas Syntax Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "0  2  1  2\n",
       "1  2  6  1\n",
       "2  0  0  3\n",
       "3  1  7  3\n",
       "4  7  7  6\n",
       "5  8  7  6\n",
       "6  1  8  9\n",
       "7  4  7  1\n",
       "8  2  8  3\n",
       "9  4  9  7"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "6  1  8  9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('(a<b) &(b<c)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "6  1  8  9"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.a < df.b) & (df.b < df.c)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "query()可以去掉圆括号, 也可以用and 代替&运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "6  1  8  9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('a < b & b < c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c\n",
       "6  1  8  9"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('a<b and b<c')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### in 和not in 运算符 The in and not in operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "query()也支持Python中的in和not in运算符，实际上是底层调用isin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'),\n",
    "                  'c': np.random.randint(5, size=12),\n",
    "                  'd': np.random.randint(9, size=12)})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>b</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>e</td>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a  b  c  d\n",
       "0   a  a  2  8\n",
       "1   a  a  2  2\n",
       "2   b  a  1  1\n",
       "3   b  a  1  3\n",
       "4   c  b  0  8\n",
       "5   c  b  1  2\n",
       "6   d  b  0  1\n",
       "7   d  b  1  7\n",
       "8   e  c  4  2\n",
       "9   e  c  1  7\n",
       "10  f  c  4  1\n",
       "11  f  c  3  0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "1  a  a  2  2\n",
       "2  b  a  1  1\n",
       "3  b  a  1  3\n",
       "4  c  b  0  8\n",
       "5  c  b  1  2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('a in b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>4</th>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
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       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "1  a  a  2  2\n",
       "2  b  a  1  1\n",
       "3  b  a  1  3\n",
       "4  c  b  0  8\n",
       "5  c  b  1  2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.a.isin(df.b)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>b</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>e</td>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a  b  c  d\n",
       "6   d  b  0  1\n",
       "7   d  b  1  7\n",
       "8   e  c  4  2\n",
       "9   e  c  1  7\n",
       "10  f  c  4  1\n",
       "11  f  c  3  0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df.a.isin(df.b)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "3  b  a  1  3\n",
       "4  c  b  0  8\n",
       "5  c  b  1  2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('a in b and c < d') #更复杂的例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
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       "      <th>3</th>\n",
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       "      <th>5</th>\n",
       "      <td>c</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d</td>\n",
       "      <td>b</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>d</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>e</td>\n",
       "      <td>c</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "3  b  a  1  3\n",
       "4  c  b  0  8\n",
       "5  c  b  1  2\n",
       "6  d  b  0  1\n",
       "7  d  b  1  7\n",
       "9  e  c  1  7"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.b.isin(df.a) & (df.c < df.d)] #Python语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ==和列表对象一起使用 Special use of the == operator with list objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用==/!=将列表和列名直接进行比较，等价于使用in/not in.\n",
    "\n",
    "三种方法功能等价： ==/!= VS in/not in VS isin()/~isin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      ],
      "text/plain": [
       "    a  b  c  d\n",
       "0   a  a  2  8\n",
       "1   a  a  2  2\n",
       "2   b  a  1  1\n",
       "3   b  a  1  3\n",
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       "7   d  b  1  7\n",
       "8   e  c  4  2\n",
       "9   e  c  1  7\n",
       "10  f  c  4  1\n",
       "11  f  c  3  0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('b==[\"a\", \"b\", \"c\"]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    a  b  c  d\n",
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       "11  f  c  3  0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.b.isin([\"a\", \"b\", \"c\"])] #Python语法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "1  a  a  2  2\n",
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       "3  b  a  1  3\n",
       "5  c  b  1  2\n",
       "7  d  b  1  7\n",
       "9  e  c  1  7"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('c == [1, 2]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    a  b  c  d\n",
       "4   c  b  0  8\n",
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      ]
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.query('c != [1, 2]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "1  a  a  2  2\n",
       "2  b  a  1  1\n",
       "3  b  a  1  3\n",
       "5  c  b  1  2\n",
       "7  d  b  1  7\n",
       "9  e  c  1  7"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('[1, 2] in c') #使用in "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    a  b  c  d\n",
       "4   c  b  0  8\n",
       "6   d  b  0  1\n",
       "8   e  c  4  2\n",
       "10  f  c  4  1\n",
       "11  f  c  3  0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('[1, 2] not in c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   a  b  c  d\n",
       "0  a  a  2  8\n",
       "1  a  a  2  2\n",
       "2  b  a  1  1\n",
       "3  b  a  1  3\n",
       "5  c  b  1  2\n",
       "7  d  b  1  7\n",
       "9  e  c  1  7"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.c.isin([1, 2])] #Python语法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 布尔运算符 Boolean Operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用not或者~对布尔表达式进行取非。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.809874</td>\n",
       "      <td>0.825521</td>\n",
       "      <td>1.029453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.051787</td>\n",
       "      <td>0.918937</td>\n",
       "      <td>1.154500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.335353</td>\n",
       "      <td>0.231090</td>\n",
       "      <td>-1.512497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.176560</td>\n",
       "      <td>-0.966830</td>\n",
       "      <td>2.052055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.074463</td>\n",
       "      <td>0.166296</td>\n",
       "      <td>0.576796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.082201</td>\n",
       "      <td>-0.900843</td>\n",
       "      <td>-0.374039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.519903</td>\n",
       "      <td>0.041034</td>\n",
       "      <td>-0.642189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.483423</td>\n",
       "      <td>-0.845009</td>\n",
       "      <td>0.190998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.822515</td>\n",
       "      <td>-0.926675</td>\n",
       "      <td>-0.165761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.884488</td>\n",
       "      <td>1.118452</td>\n",
       "      <td>-1.248411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "0  0.809874  0.825521  1.029453\n",
       "1 -0.051787  0.918937  1.154500\n",
       "2  0.335353  0.231090 -1.512497\n",
       "3  1.176560 -0.966830  2.052055\n",
       "4 -0.074463  0.166296  0.576796\n",
       "5 -0.082201 -0.900843 -0.374039\n",
       "6  1.519903  0.041034 -0.642189\n",
       "7 -0.483423 -0.845009  0.190998\n",
       "8  0.822515 -0.926675 -0.165761\n",
       "9 -0.884488  1.118452 -1.248411"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(n, 3), columns=list('abc'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['bools']=np.random.randn(len(df))>0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.809874</td>\n",
       "      <td>0.825521</td>\n",
       "      <td>1.029453</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.051787</td>\n",
       "      <td>0.918937</td>\n",
       "      <td>1.154500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.335353</td>\n",
       "      <td>0.231090</td>\n",
       "      <td>-1.512497</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.176560</td>\n",
       "      <td>-0.966830</td>\n",
       "      <td>2.052055</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.074463</td>\n",
       "      <td>0.166296</td>\n",
       "      <td>0.576796</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.082201</td>\n",
       "      <td>-0.900843</td>\n",
       "      <td>-0.374039</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.519903</td>\n",
       "      <td>0.041034</td>\n",
       "      <td>-0.642189</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.483423</td>\n",
       "      <td>-0.845009</td>\n",
       "      <td>0.190998</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.822515</td>\n",
       "      <td>-0.926675</td>\n",
       "      <td>-0.165761</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.884488</td>\n",
       "      <td>1.118452</td>\n",
       "      <td>-1.248411</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c  bools\n",
       "0  0.809874  0.825521  1.029453   True\n",
       "1 -0.051787  0.918937  1.154500   True\n",
       "2  0.335353  0.231090 -1.512497   True\n",
       "3  1.176560 -0.966830  2.052055  False\n",
       "4 -0.074463  0.166296  0.576796  False\n",
       "5 -0.082201 -0.900843 -0.374039  False\n",
       "6  1.519903  0.041034 -0.642189  False\n",
       "7 -0.483423 -0.845009  0.190998  False\n",
       "8  0.822515 -0.926675 -0.165761  False\n",
       "9 -0.884488  1.118452 -1.248411  False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.809874</td>\n",
       "      <td>0.825521</td>\n",
       "      <td>1.029453</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.051787</td>\n",
       "      <td>0.918937</td>\n",
       "      <td>1.154500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.335353</td>\n",
       "      <td>0.231090</td>\n",
       "      <td>-1.512497</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c bools\n",
       "0  0.809874  0.825521  1.029453  True\n",
       "1 -0.051787  0.918937  1.154500  True\n",
       "2  0.335353  0.231090 -1.512497  True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('bools')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.176560</td>\n",
       "      <td>-0.966830</td>\n",
       "      <td>2.052055</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.074463</td>\n",
       "      <td>0.166296</td>\n",
       "      <td>0.576796</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.082201</td>\n",
       "      <td>-0.900843</td>\n",
       "      <td>-0.374039</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.519903</td>\n",
       "      <td>0.041034</td>\n",
       "      <td>-0.642189</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.483423</td>\n",
       "      <td>-0.845009</td>\n",
       "      <td>0.190998</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.822515</td>\n",
       "      <td>-0.926675</td>\n",
       "      <td>-0.165761</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.884488</td>\n",
       "      <td>1.118452</td>\n",
       "      <td>-1.248411</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c  bools\n",
       "3  1.176560 -0.966830  2.052055  False\n",
       "4 -0.074463  0.166296  0.576796  False\n",
       "5 -0.082201 -0.900843 -0.374039  False\n",
       "6  1.519903  0.041034 -0.642189  False\n",
       "7 -0.483423 -0.845009  0.190998  False\n",
       "8  0.822515 -0.926675 -0.165761  False\n",
       "9 -0.884488  1.118452 -1.248411  False"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('not bools')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a     b     c bools\n",
       "3  True  True  True  True\n",
       "4  True  True  True  True\n",
       "5  True  True  True  True\n",
       "6  True  True  True  True\n",
       "7  True  True  True  True\n",
       "8  True  True  True  True\n",
       "9  True  True  True  True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('not bools') == df[~df.bools]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "表达式任意复杂都没关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.074463</td>\n",
       "      <td>0.166296</td>\n",
       "      <td>0.576796</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c  bools\n",
       "4 -0.074463  0.166296  0.576796  False"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shorter = df.query('a<b<c and (not bools) or bools>2')\n",
    "shorter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.074463</td>\n",
       "      <td>0.166296</td>\n",
       "      <td>0.576796</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c  bools\n",
       "4 -0.074463  0.166296  0.576796  False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longer = df[(df.a < df.b) & (df.b < df.c) & (~df.bools) | (df.bools > 2)]\n",
    "longer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a     b     c bools\n",
       "4  True  True  True  True"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shorter == longer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### query()的性能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame.query()底层使用numexptr，所以速度要比Python快,特别时当DataFrame对象非常大时。\n",
    "\n",
    " ![](https://ooo.0o0.ooo/2016/04/12/570cbb653ce9a.png)\n",
    " \n",
    " \n",
    " ![](https://ooo.0o0.ooo/2016/04/12/570cbb93e1f13.png)\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 重复数据的确定和删除 Duplicate Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你想确定和去掉DataFrame对象中重复的行，pandas提供了两个方法：duplicated和drop_duplicates. 两个方法的参数都是列名。\n",
    "* duplicated 返回一个布尔向量，长度等于行数，表示每一行是否重复\n",
    "* drop_duplicates 则删除重复的行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认情况下，首次遇到的行被认为是唯一的，以后遇到内容相同的行都被认为是重复的。不过两个方法都有一个keep参数来确定目标行是否被保留。\n",
    "* keep='first'(默认)：标记/去掉重复行除了第一次出现的那一行\n",
    "* keep='last': 标记/去掉重复行除了最后一次出现的那一行\n",
    "* keep=False: 标记/去掉所有重复的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>2.126953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>0.570685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.718881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>0.044910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>0.376090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.205828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>four</td>\n",
       "      <td>x</td>\n",
       "      <td>0.336854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "0    one  x  2.126953\n",
       "1    one  y  0.570685\n",
       "2    two  x -0.718881\n",
       "3    two  y  0.044910\n",
       "4    two  x  0.376090\n",
       "5  three  x -0.205828\n",
       "6   four  x  0.336854"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'two', 'two', 'three', 'four'],\n",
    "                        'b': ['x', 'y', 'x', 'y', 'x', 'x', 'x'],\n",
    "                      'c': np.random.randn(7)})\n",
    "   \n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4     True\n",
       "5    False\n",
       "6    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.duplicated('a') #只观察列a的值是否重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.duplicated('a', keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>2.126953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.718881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.205828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>four</td>\n",
       "      <td>x</td>\n",
       "      <td>0.336854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "0    one  x  2.126953\n",
       "2    two  x -0.718881\n",
       "5  three  x -0.205828\n",
       "6   four  x  0.336854"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop_duplicates('a')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>0.570685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>0.376090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.205828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>four</td>\n",
       "      <td>x</td>\n",
       "      <td>0.336854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "1    one  y  0.570685\n",
       "4    two  x  0.376090\n",
       "5  three  x -0.205828\n",
       "6   four  x  0.336854"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop_duplicates('a', keep='last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.205828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>four</td>\n",
       "      <td>x</td>\n",
       "      <td>0.336854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "5  three  x -0.205828\n",
       "6   four  x  0.336854"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop_duplicates('a', keep=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以传递列名组成的列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.duplicated(['a', 'b']) #此时列a和b两个元素构成每一个检索的基本单位，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>2.126953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>0.570685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.718881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>0.044910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>0.376090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>x</td>\n",
       "      <td>-0.205828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>four</td>\n",
       "      <td>x</td>\n",
       "      <td>0.336854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b         c\n",
       "0    one  x  2.126953\n",
       "1    one  y  0.570685\n",
       "2    two  x -0.718881\n",
       "3    two  y  0.044910\n",
       "4    two  x  0.376090\n",
       "5  three  x -0.205828\n",
       "6   four  x  0.336854"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以检查index值是否重复来去掉重复行，方法是Index.duplicated然后使用切片操作(因为调用Index.duplicated会返回布尔向量)。keep参数同上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'a': np.arange(6),\n",
    "                       'b': np.random.randn(6)},\n",
    "                       index=['a', 'a', 'b', 'c', 'b', 'a'])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>0.370944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>0.392321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.999154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.236476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.318244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>5</td>\n",
       "      <td>1.164510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a         b\n",
       "a  0  0.370944\n",
       "a  1  0.392321\n",
       "b  2 -0.999154\n",
       "c  3 -0.236476\n",
       "b  4 -0.318244\n",
       "a  5  1.164510"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, False,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.index.duplicated() #布尔表达式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>0.370944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.999154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.236476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a         b\n",
       "a  0  0.370944\n",
       "b  2 -0.999154\n",
       "c  3 -0.236476"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3.index.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.236476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.318244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>5</td>\n",
       "      <td>1.164510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a         b\n",
       "c  3 -0.236476\n",
       "b  4 -0.318244\n",
       "a  5  1.164510"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3.index.duplicated(keep='last')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.236476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a         b\n",
       "c  3 -0.236476"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[~df3.index.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 形似字典的get()方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Serires, DataFrame和Panel都有一个get方法来得到一个默认值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,2,3], index=['a', 'b', 'c'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('x', default=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.get('b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## select()方法 The select() Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series, DataFrame和Panel都有select()方法来检索数据，这个方法作为保留手段通常其他方法都不管用的时候才使用。select接受一个函数(在label上进行操作)作为输入返回一个布尔值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 3), columns=list('ABC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.768992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.126865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.508768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.847265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.537149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.610245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.626082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.813772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.300097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.368777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A\n",
       "0  0.768992\n",
       "1 -0.126865\n",
       "2 -0.508768\n",
       "3 -0.847265\n",
       "4 -1.537149\n",
       "5  0.610245\n",
       "6 -0.626082\n",
       "7  0.813772\n",
       "8  0.300097\n",
       "9  1.368777"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(lambda x: x=='A', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## lookup()方法 The lookup()方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入行label和列label，得到一个numpy数组，这就是lookup方法的功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.456863</td>\n",
       "      <td>0.261728</td>\n",
       "      <td>1.313254</td>\n",
       "      <td>0.105114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.041107</td>\n",
       "      <td>1.067823</td>\n",
       "      <td>1.156849</td>\n",
       "      <td>-1.306408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.186713</td>\n",
       "      <td>-1.078201</td>\n",
       "      <td>0.561977</td>\n",
       "      <td>-0.107848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.496749</td>\n",
       "      <td>0.246163</td>\n",
       "      <td>0.496875</td>\n",
       "      <td>0.334775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.076146</td>\n",
       "      <td>-0.459081</td>\n",
       "      <td>-0.646699</td>\n",
       "      <td>-0.143237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.337840</td>\n",
       "      <td>1.284264</td>\n",
       "      <td>-1.327627</td>\n",
       "      <td>0.139834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.161458</td>\n",
       "      <td>0.284919</td>\n",
       "      <td>-1.969045</td>\n",
       "      <td>-0.129893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.361869</td>\n",
       "      <td>-1.292803</td>\n",
       "      <td>0.204441</td>\n",
       "      <td>0.066561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.518187</td>\n",
       "      <td>-1.247654</td>\n",
       "      <td>-0.988123</td>\n",
       "      <td>-0.353545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.391405</td>\n",
       "      <td>0.097482</td>\n",
       "      <td>-0.190093</td>\n",
       "      <td>-0.074410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.023540</td>\n",
       "      <td>-1.345222</td>\n",
       "      <td>0.537438</td>\n",
       "      <td>-1.357927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.108617</td>\n",
       "      <td>-0.888801</td>\n",
       "      <td>-0.142176</td>\n",
       "      <td>-0.143029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.996520</td>\n",
       "      <td>0.569428</td>\n",
       "      <td>0.078876</td>\n",
       "      <td>-0.645631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.845302</td>\n",
       "      <td>-0.642925</td>\n",
       "      <td>1.089828</td>\n",
       "      <td>0.645551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.205784</td>\n",
       "      <td>-0.763532</td>\n",
       "      <td>1.763455</td>\n",
       "      <td>-0.873189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.645096</td>\n",
       "      <td>0.828053</td>\n",
       "      <td>-1.405876</td>\n",
       "      <td>0.974612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.444146</td>\n",
       "      <td>1.831544</td>\n",
       "      <td>0.439983</td>\n",
       "      <td>-0.108334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.126371</td>\n",
       "      <td>-0.196340</td>\n",
       "      <td>-0.411644</td>\n",
       "      <td>0.414911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.721539</td>\n",
       "      <td>-1.932596</td>\n",
       "      <td>-1.595068</td>\n",
       "      <td>-1.966388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.060839</td>\n",
       "      <td>0.731949</td>\n",
       "      <td>-0.082693</td>\n",
       "      <td>1.665486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A         B         C         D\n",
       "0   1.456863  0.261728  1.313254  0.105114\n",
       "1  -1.041107  1.067823  1.156849 -1.306408\n",
       "2  -1.186713 -1.078201  0.561977 -0.107848\n",
       "3   0.496749  0.246163  0.496875  0.334775\n",
       "4  -1.076146 -0.459081 -0.646699 -0.143237\n",
       "5  -0.337840  1.284264 -1.327627  0.139834\n",
       "6   0.161458  0.284919 -1.969045 -0.129893\n",
       "7  -0.361869 -1.292803  0.204441  0.066561\n",
       "8  -1.518187 -1.247654 -0.988123 -0.353545\n",
       "9   0.391405  0.097482 -0.190093 -0.074410\n",
       "10  1.023540 -1.345222  0.537438 -1.357927\n",
       "11 -0.108617 -0.888801 -0.142176 -0.143029\n",
       "12  0.996520  0.569428  0.078876 -0.645631\n",
       "13 -0.845302 -0.642925  1.089828  0.645551\n",
       "14  2.205784 -0.763532  1.763455 -0.873189\n",
       "15  0.645096  0.828053 -1.405876  0.974612\n",
       "16  0.444146  1.831544  0.439983 -0.108334\n",
       "17 -0.126371 -0.196340 -0.411644  0.414911\n",
       "18 -0.721539 -1.932596 -1.595068 -1.966388\n",
       "19  0.060839  0.731949 -0.082693  1.665486"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflookup = pd.DataFrame(np.random.randn(20, 4), columns=list('ABCD'))\n",
    "dflookup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.26172828,  0.56197743, -1.07614596,  0.28491916, -0.35354466])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dflookup.lookup(list(range(0,10,2)), ['B','C','A','B','D'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index对象 Index objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas中的Index类和它的子类可以被当做一个序列可重复集合(ordered multiset)，允许数据重复。然而，如果你想把一个有重复值Index对象转型为一个集合这是不可以的。创建Index最简单的方法就是通过传递一个列表或者其他序列创建。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'e', u'd', u'a', u'b'], dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.Index(['e', 'd', 'a', 'b'])\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'d' in index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "还可以个Index命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'something'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.Index(['e', 'd', 'a', 'b'], name='something')\n",
    "index.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = pd.Index(list(range(5)), name='rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = pd.Index(['A', 'B', 'C'], name='cols')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cols</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rows</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.344614</td>\n",
       "      <td>1.756430</td>\n",
       "      <td>-0.648766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.085694</td>\n",
       "      <td>0.181015</td>\n",
       "      <td>-0.679165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.487108</td>\n",
       "      <td>-0.546367</td>\n",
       "      <td>-0.159887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.124834</td>\n",
       "      <td>0.034413</td>\n",
       "      <td>0.863767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.531960</td>\n",
       "      <td>1.210601</td>\n",
       "      <td>0.703460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cols         A         B         C\n",
       "rows                              \n",
       "0     2.344614  1.756430 -0.648766\n",
       "1     0.085694  0.181015 -0.679165\n",
       "2    -0.487108 -0.546367 -0.159887\n",
       "3     0.124834  0.034413  0.863767\n",
       "4    -0.531960  1.210601  0.703460"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rows\n",
       "0    2.344614\n",
       "1    0.085694\n",
       "2   -0.487108\n",
       "3    0.124834\n",
       "4   -0.531960\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 返回视图VS返回副本 Returning a view versus a copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当对pandas对象赋值时，一定要注意避免链式索引(chained indexing)。看下面的例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">one</th>\n",
       "      <th colspan=\"2\" halign=\"left\">two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>h</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i</td>\n",
       "      <td>j</td>\n",
       "      <td>k</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one          two       \n",
       "  first second first second\n",
       "0     a      b     c      d\n",
       "1     e      f     g      h\n",
       "2     i      j     k      l\n",
       "3     m      n     o      p"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmi = pd.DataFrame([list('abcd'),\n",
    "                        list('efgh'),\n",
    "                        list('ijkl'),\n",
    "                        list('mnop')],\n",
    "                       columns=pd.MultiIndex.from_product([['one','two'],\n",
    "                                                          ['first','second']]))\n",
    "dfmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比较下面两种访问方式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    b\n",
       "1    f\n",
       "2    j\n",
       "3    n\n",
       "Name: second, dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmi['one']['second']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    b\n",
       "1    f\n",
       "2    j\n",
       "3    n\n",
       "Name: (one, second), dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmi.loc[:,('one','second')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面两种方法返回的结果抖一下，那么应该使用哪种方法呢？答案是我们更推荐大家使用方法二。\n",
    "\n",
    "dfmi['one']选择了第一级列然后返回一个DataFrame对象，然后另一个Python操作dfmi_with_one['second']根据'second'检索出了一个Series。对pandas来说，这两个操作是独立、有序执行的。而.loc方法传入一个元组(slice(None),('one','second')),pandas把这当作一个事件执行，所以执行速度更快。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 为什么使用链式索引赋值为报错？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "刚才谈到不推荐使用链式索引是出于性能的考虑。接下来从赋值角度谈一下不推荐使用链式索引。首先，思考Python怎么解释执行下面的代码？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfmi.loc[:,('one','second')]=value\n",
    "#实际是\n",
    "dfmi.loc.__setitem__((slice(None), ('one', 'second')), value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但下面的代码解释后结果却不一样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfmi['one']['second'] = value\n",
    "# becomes\n",
    "dfmi.__getitem__('one').__setitem__('second', value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看到__getitem__了吗？除了最简单的情况，我们很难预测他到底返回的是视图还是副本(哲依赖于数组的内存布局，这是pandas没有硬性要求的)，因此不推荐使用链式索引赋值！\n",
    "\n",
    "而dfmi.loc.__setitem__直接对dfmi进行操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有时候明明没有使用链式索引，也会引起SettingWithCopy警告，这是Pandas设计的bug~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def do_something(df):\n",
    "   foo = df[['bar', 'baz']]  # Is foo a view? A copy? Nobody knows!\n",
    "   # ... many lines here ...\n",
    "   foo['quux'] = value       # We don't know whether this will modify df or not!\n",
    "   return foo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 链式索引中顺序也很重要"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此外，在链式表达式中，不同的顺序也可能导致不同的结果。这里的顺序指的是检索时行和列的顺序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>six</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  c\n",
       "0    one  0\n",
       "1    one  1\n",
       "2    two  2\n",
       "3  three  3\n",
       "4    two  4\n",
       "5    one  5\n",
       "6    six  6"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfb = pd.DataFrame({'a' : ['one', 'one', 'two',\n",
    "                               'three', 'two', 'one', 'six'],\n",
    "                       'c' : np.arange(7)})\n",
    "dfb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "dfb['c'][dfb.a.str.startswith('o')] = 42 #虽然会引起SettingWithCopyWarning 但也能得到正确结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('mode.chained_assignment','warn')\n",
    "dfb[dfb.a.str.startswith('o')]['c'] = 42 #这实际上是对副本赋值！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正确的方式是：老老实实使用.loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aaa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bbb</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ccc</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B\n",
       "0  aaa  1\n",
       "1  bbb  2\n",
       "2  ccc  3"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfc = pd.DataFrame({'A':['aaa','bbb','ccc'],'B':[1,2,3]})\n",
    "dfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bbb</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ccc</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B\n",
       "0   11  1\n",
       "1  bbb  2\n",
       "2  ccc  3"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfc.loc[0,'A'] = 11\n",
    "dfc"
   ]
  }
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