第2章 索引.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第2章 索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.read_csv('data/table.csv',index_col='ID')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、单级索引\n",
    "### 1. loc方法、iloc方法、[]操作符\n",
    "#### 最常用的索引方法可能就是这三类，其中iloc表示位置索引，loc表示标签索引，[]也具有很大的便利性，各有特点\n",
    "#### （a）loc方法\n",
    "#### ① 单行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School          S_1\n",
       "Class           C_1\n",
       "Gender            M\n",
       "Address    street_2\n",
       "Height          186\n",
       "Weight           82\n",
       "Math           87.2\n",
       "Physics          B+\n",
       "Name: 1103, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1103]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ② 多行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
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       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
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       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
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       "      <th>2304</th>\n",
       "      <td>S_2</td>\n",
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       "      <td>A-</td>\n",
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      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "2304    S_2   C_3      F  street_6     164      81  95.5      A-"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df.loc[[1102,2304]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （注意：所有在loc中使用的切片全部包含右端点！这是因为如果作为Pandas的使用者，那么肯定不太关心最后一个标签再往后一位是什么，但是如果是左闭右开，那么就很麻烦，先要知道再后面一列的名字是什么，非常不方便，因此Pandas中将loc设计为左右全闭）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>85.2</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>187</td>\n",
       "      <td>69</td>\n",
       "      <td>61.7</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>174</td>\n",
       "      <td>84</td>\n",
       "      <td>83.3</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2102</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>161</td>\n",
       "      <td>61</td>\n",
       "      <td>50.6</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>157</td>\n",
       "      <td>61</td>\n",
       "      <td>52.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1304    S_1   C_3      M  street_2     195      70  85.2       A\n",
       "1305    S_1   C_3      F  street_5     187      69  61.7      B-\n",
       "2101    S_2   C_1      M  street_7     174      84  83.3       C\n",
       "2102    S_2   C_1      F  street_6     161      61  50.6      B+\n",
       "2103    S_2   C_1      M  street_4     157      61  52.5      B-"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1304:2103].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "      <th>2402</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
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       "      <td>S_2</td>\n",
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       "      <td>S_2</td>\n",
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       "      <th>2304</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>164</td>\n",
       "      <td>81</td>\n",
       "      <td>95.5</td>\n",
       "      <td>A-</td>\n",
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       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_7</td>\n",
       "      <td>190</td>\n",
       "      <td>99</td>\n",
       "      <td>65.9</td>\n",
       "      <td>C</td>\n",
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      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "2402    S_2   C_4      M  street_7     166      82  48.7       B\n",
       "2401    S_2   C_4      F  street_2     192      62  45.3       A\n",
       "2305    S_2   C_3      M  street_4     187      73  48.9       B\n",
       "2304    S_2   C_3      F  street_6     164      81  95.5      A-\n",
       "2303    S_2   C_3      F  street_7     190      99  65.9       C"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[2402::-1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ③ 单列索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101    173\n",
       "1102    192\n",
       "1103    186\n",
       "1104    167\n",
       "1105    159\n",
       "Name: Height, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'Height'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ④ 多列索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1101</th>\n",
       "      <td>173</td>\n",
       "      <td>34.0</td>\n",
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       "      <th>1102</th>\n",
       "      <td>192</td>\n",
       "      <td>32.5</td>\n",
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       "      <th>1103</th>\n",
       "      <td>186</td>\n",
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       "      <th>1104</th>\n",
       "      <td>167</td>\n",
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       "      <th>1105</th>\n",
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      "text/plain": [
       "      Height  Math\n",
       "ID                \n",
       "1101     173  34.0\n",
       "1102     192  32.5\n",
       "1103     186  87.2\n",
       "1104     167  80.4\n",
       "1105     159  84.8"
      ]
     },
     "execution_count": 7,
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   "source": [
    "df.loc[:,['Height','Math']].head()"
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height  Weight  Math\n",
       "ID                        \n",
       "1101     173      63  34.0\n",
       "1102     192      73  32.5\n",
       "1103     186      82  87.2\n",
       "1104     167      81  80.4\n",
       "1105     159      64  84.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'Height':'Math'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑤ 联合索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1102</th>\n",
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       "      <td>32.5</td>\n",
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       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>58.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height  Weight  Math\n",
       "ID                        \n",
       "1102     192      73  32.5\n",
       "1105     159      64  84.8\n",
       "1203     160      53  58.8\n",
       "1301     161      68  31.5\n",
       "1304     195      70  85.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1102:2401:3,'Height':'Math'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑥ 函数式索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>188</td>\n",
       "      <td>68</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
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       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_6</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1201    S_1   C_2      M  street_5     188      68  97.0      A-\n",
       "1203    S_1   C_2      M  street_6     160      53  58.8      A+\n",
       "1301    S_1   C_3      M  street_4     161      68  31.5      B+"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[lambda x:x['Gender']=='M'].head()\n",
    "#loc中使用的函数，传入参数就是前面的df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这里的例子表示，loc中能够传入函数，并且函数的输入值是整张表，输出为标量、切片、合法列表（元素出现在索引中）、合法索引\n",
    "def f(x):\n",
    "    return [1101,1103]\n",
    "df.loc[f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑦ 布尔索引（将重点在第2节介绍）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1301</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>188</td>\n",
       "      <td>82</td>\n",
       "      <td>49.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>174</td>\n",
       "      <td>84</td>\n",
       "      <td>83.3</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+\n",
       "1202    S_1   C_2      F  street_4     176      94  63.5      B-\n",
       "1301    S_1   C_3      M  street_4     161      68  31.5      B+\n",
       "1303    S_1   C_3      M  street_7     188      82  49.7       B\n",
       "2101    S_2   C_1      M  street_7     174      84  83.3       C"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Address'].isin(['street_7','street_4'])].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>street_4</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>188</td>\n",
       "      <td>82</td>\n",
       "      <td>49.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>174</td>\n",
       "      <td>84</td>\n",
       "      <td>83.3</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+\n",
       "1202    S_1   C_2      F  street_4     176      94  63.5      B-\n",
       "1301    S_1   C_3      M  street_4     161      68  31.5      B+\n",
       "1303    S_1   C_3      M  street_7     188      82  49.7       B\n",
       "2101    S_2   C_1      M  street_7     174      84  83.3       C"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[True if i[-1]=='4' or i[-1]=='7' else False for i in df['Address'].values]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 小节：本质上说，loc中能传入的只有布尔列表和索引子集构成的列表，只要把握这个原则就很容易理解上面那些操作\n",
    "#### （b）iloc方法（注意与loc不同，切片右端点不包含）\n",
    "#### ① 单行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School          S_1\n",
       "Class           C_1\n",
       "Gender            F\n",
       "Address    street_2\n",
       "Height          167\n",
       "Weight           81\n",
       "Math           80.4\n",
       "Physics          B-\n",
       "Name: 1104, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ② 多行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ③ 单列索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101    street_1\n",
       "1102    street_2\n",
       "1103    street_2\n",
       "1104    street_2\n",
       "1105    street_4\n",
       "Name: Address, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,3].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ④ 多列索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physics</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Address</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>A+</td>\n",
       "      <td>63</td>\n",
       "      <td>street_1</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>B+</td>\n",
       "      <td>73</td>\n",
       "      <td>street_2</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>B+</td>\n",
       "      <td>82</td>\n",
       "      <td>street_2</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>B-</td>\n",
       "      <td>81</td>\n",
       "      <td>street_2</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>B+</td>\n",
       "      <td>64</td>\n",
       "      <td>street_4</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Physics  Weight   Address Class\n",
       "ID                                  \n",
       "1101      A+      63  street_1   C_1\n",
       "1102      B+      73  street_2   C_1\n",
       "1103      B+      82  street_2   C_1\n",
       "1104      B-      81  street_2   C_1\n",
       "1105      B+      64  street_4   C_1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,7::-2].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑤ 混合索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physics</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Address</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>B-</td>\n",
       "      <td>81</td>\n",
       "      <td>street_2</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>A+</td>\n",
       "      <td>53</td>\n",
       "      <td>street_6</td>\n",
       "      <td>C_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>A-</td>\n",
       "      <td>57</td>\n",
       "      <td>street_1</td>\n",
       "      <td>C_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>C</td>\n",
       "      <td>84</td>\n",
       "      <td>street_7</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>A</td>\n",
       "      <td>81</td>\n",
       "      <td>street_4</td>\n",
       "      <td>C_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Physics  Weight   Address Class\n",
       "ID                                  \n",
       "1104      B-      81  street_2   C_1\n",
       "1203      A+      53  street_6   C_2\n",
       "1302      A-      57  street_1   C_3\n",
       "2101       C      84  street_7   C_1\n",
       "2105       A      81  street_4   C_1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3::4,7::-2].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑥ 函数式索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[lambda x:[3]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 小节：iloc中接收的参数只能为整数或整数列表或布尔列表，不能使用布尔Series，如果要用就必须如下把values拿出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.iloc[df['School']=='S_1'].head() #报错\n",
    "df.iloc[(df['School']=='S_1').values].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c） []操作符\n",
    "#### （c.1）Series的[]操作\n",
    "#### ① 单元素索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(df['Math'],index=df.index)\n",
    "s[1101]\n",
    "#使用的是索引标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ② 多行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101    34.0\n",
       "1102    32.5\n",
       "1103    87.2\n",
       "1104    80.4\n",
       "Name: Math, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0:4]\n",
    "#使用的是绝对位置的整数切片，与元素无关，这里容易混淆"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ③ 函数式索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "2102    50.6\n",
       "1301    31.5\n",
       "1105    84.8\n",
       "Name: Math, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[lambda x: x.index[16::-6]]\n",
    "#注意使用lambda函数时，直接切片(如：s[lambda x: 16::-6])就报错，此时使用的不是绝对位置切片，而是元素切片，非常易错"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ④ 布尔索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1103    87.2\n",
       "1104    80.4\n",
       "1105    84.8\n",
       "1201    97.0\n",
       "1302    87.7\n",
       "1304    85.2\n",
       "2101    83.3\n",
       "2205    85.4\n",
       "2304    95.5\n",
       "Name: Math, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s>80]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【注意】如果不想陷入困境，请不要在行索引为浮点时使用[]操作符，因为在Series中[]的浮点切片并不是进行位置比较，而是值比较，非常特殊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1\n",
       "3    2\n",
       "5    3\n",
       "6    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_int = pd.Series([1,2,3,4],index=[1,3,5,6])\n",
    "s_float = pd.Series([1,2,3,4],index=[1.,3.,5.,6.])\n",
    "s_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    3\n",
       "6    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_int[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1\n",
       "3.0    2\n",
       "5.0    3\n",
       "6.0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    2\n",
       "5.0    3\n",
       "6.0    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#注意和s_int[2:]结果不一样了，因为2这里是元素而不是位置\n",
    "s_float[2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c.2）DataFrame的[]操作\n",
    "#### ① 单行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:2]\n",
    "#这里非常容易写成df['label']，会报错\n",
    "#同Series使用了绝对位置切片\n",
    "#如果想要获得某一个元素，可用如下get_loc方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = df.index.get_loc(1102)\n",
    "df[row:row+1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ② 多行索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用切片，如果是选取指定的某几行，推荐使用loc，否则很可能报错\n",
    "df[3:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ③ 单列索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101    S_1\n",
       "1102    S_1\n",
       "1103    S_1\n",
       "1104    S_1\n",
       "1105    S_1\n",
       "Name: School, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['School'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ④ 多列索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School  Math\n",
       "ID               \n",
       "1101    S_1  34.0\n",
       "1102    S_1  32.5\n",
       "1103    S_1  87.2\n",
       "1104    S_1  80.4\n",
       "1105    S_1  84.8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['School','Math']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑤函数式索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Math Physics\n",
       "ID                \n",
       "1101  34.0      A+\n",
       "1102  32.5      B+\n",
       "1103  87.2      B+\n",
       "1104  80.4      B-\n",
       "1105  84.8      B+"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[lambda x:['Math','Physics']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ⑥ 布尔索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
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       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>176</td>\n",
       "      <td>94</td>\n",
       "      <td>63.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>162</td>\n",
       "      <td>63</td>\n",
       "      <td>33.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+\n",
       "1202    S_1   C_2      F  street_4     176      94  63.5      B-\n",
       "1204    S_1   C_2      F  street_5     162      63  33.8       B"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Gender']=='F'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 小节：一般来说，[]操作符常用于列选择或布尔选择，尽量避免行的选择\n",
    "### 2. 布尔索引\n",
    "#### （a）布尔符号：'&','|','~'：分别代表和and，或or，取反not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>62</td>\n",
       "      <td>45.3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>160</td>\n",
       "      <td>84</td>\n",
       "      <td>67.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "2401    S_2   C_4      F  street_2     192      62  45.3       A\n",
       "2404    S_2   C_4      F  street_2     160      84  67.7       B"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gender']=='F')&(df['Address']=='street_2')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_1</td>\n",
       "      <td>175</td>\n",
       "      <td>57</td>\n",
       "      <td>87.7</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>188</td>\n",
       "      <td>82</td>\n",
       "      <td>49.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>85.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1201    S_1   C_2      M  street_5     188      68  97.0      A-\n",
       "1302    S_1   C_3      F  street_1     175      57  87.7      A-\n",
       "1303    S_1   C_3      M  street_7     188      82  49.7       B\n",
       "1304    S_1   C_3      M  street_2     195      70  85.2       A"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Math']>85)|(df['Address']=='street_7')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
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       "      <th>1202</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>street_4</td>\n",
       "      <td>176</td>\n",
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       "      <td>63.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
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       "      <th>1203</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
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       "      <td>street_6</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>162</td>\n",
       "      <td>63</td>\n",
       "      <td>33.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>167</td>\n",
       "      <td>63</td>\n",
       "      <td>68.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1202    S_1   C_2      F  street_4     176      94  63.5      B-\n",
       "1203    S_1   C_2      M  street_6     160      53  58.8      A+\n",
       "1204    S_1   C_2      F  street_5     162      63  33.8       B\n",
       "1205    S_1   C_2      F  street_6     167      63  68.4      B-"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~((df['Math']>75)|(df['Address']=='street_1'))].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### loc和[]中相应位置都能使用布尔列表选择："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Physics</th>\n",
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       "      <th>ID</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>B-</td>\n",
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       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Physics\n",
       "ID          \n",
       "1103      B+\n",
       "1104      B-\n",
       "1105      B+\n",
       "1201      A-\n",
       "1202      B-"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Math']>60,df.columns=='Physics'].head()\n",
    "#思考：为什么df.loc[df['Math']>60,(df[:8]['Address']=='street_6').values].head()得到和上述结果一样？values能去掉吗？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b） isin方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>170</td>\n",
       "      <td>81</td>\n",
       "      <td>34.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>155</td>\n",
       "      <td>91</td>\n",
       "      <td>73.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "2105    S_2   C_1      M  street_4     170      81  34.2       A\n",
       "2203    S_2   C_2      M  street_4     155      91  73.8      A+"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Address'].isin(['street_1','street_4'])&df['Physics'].isin(['A','A+'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Address</th>\n",
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       "      <th>Physics</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>170</td>\n",
       "      <td>81</td>\n",
       "      <td>34.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>155</td>\n",
       "      <td>91</td>\n",
       "      <td>73.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "2105    S_2   C_1      M  street_4     170      81  34.2       A\n",
       "2203    S_2   C_2      M  street_4     155      91  73.8      A+"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#上面也可以用字典方式写：\n",
    "df[df[['Address','Physics']].isin({'Address':['street_1','street_4'],'Physics':['A','A+']}).all(1)]\n",
    "#all与&的思路是类似的，其中的1代表按照跨列方向判断是否全为True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 快速标量索引\n",
    "#### 当只需要取一个元素时，at和iat方法能够提供更快的实现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S_1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'S_1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'S_1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'S_1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.at[1101,'School'])\n",
    "display(df.loc[1101,'School'])\n",
    "display(df.iat[0,0])\n",
    "display(df.iloc[0,0])\n",
    "#可尝试去掉注释对比时间\n",
    "#%timeit df.at[1101,'School']\n",
    "#%timeit df.loc[1101,'School']\n",
    "#%timeit df.iat[0,0]\n",
    "#%timeit df.iloc[0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 区间索引\n",
    "#### 此处介绍并不是说只能在单级索引中使用区间索引，只是作为一种特殊类型的索引方式，在此处先行介绍\n",
    "#### （a）利用interval_range方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],\n",
       "              closed='right',\n",
       "              dtype='interval[int64]')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.interval_range(start=0,end=5)\n",
    "#closed参数可选'left''right''both''neither'，默认左开右闭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IntervalIndex([(0, 5], (5, 10], (10, 15], (15, 20], (20, 25], (25, 30], (30, 35], (35, 40]],\n",
       "              closed='right',\n",
       "              dtype='interval[int64]')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.interval_range(start=0,periods=8,freq=5)\n",
    "#periods参数控制区间个数，freq控制步长"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）利用cut将数值列转为区间为元素的分类变量，例如统计数学成绩的区间情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101      (0, 40]\n",
       "1102      (0, 40]\n",
       "1103    (80, 100]\n",
       "1104    (80, 100]\n",
       "1105    (80, 100]\n",
       "Name: Math, dtype: category\n",
       "Categories (4, interval[int64]): [(0, 40] < (40, 60] < (60, 80] < (80, 100]]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_interval = pd.cut(df['Math'],bins=[0,40,60,80,100])\n",
    "#注意，如果没有类型转换，此时并不是区间类型，而是category类型\n",
    "math_interval.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）区间索引的选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Math_interval</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 40]</th>\n",
       "      <td>1101</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 40]</th>\n",
       "      <td>1102</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1103</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1104</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1105</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  Math\n",
       "Math_interval            \n",
       "(0, 40]        1101  34.0\n",
       "(0, 40]        1102  32.5\n",
       "(80, 100]      1103  87.2\n",
       "(80, 100]      1104  80.4\n",
       "(80, 100]      1105  84.8"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i = df.join(math_interval,rsuffix='_interval')[['Math','Math_interval']]\\\n",
    "            .reset_index().set_index('Math_interval')\n",
    "df_i.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Math_interval</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1202</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1205</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1305</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>2104</td>\n",
       "      <td>72.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>2202</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  Math\n",
       "Math_interval            \n",
       "(60, 80]       1202  63.5\n",
       "(60, 80]       1205  68.4\n",
       "(60, 80]       1305  61.7\n",
       "(60, 80]       2104  72.2\n",
       "(60, 80]       2202  68.5"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i.loc[65].head()\n",
    "#包含该值就会被选中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Math_interval</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1202</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1205</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1305</td>\n",
       "      <td>61.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>2104</td>\n",
       "      <td>72.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>2202</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  Math\n",
       "Math_interval            \n",
       "(60, 80]       1202  63.5\n",
       "(60, 80]       1205  68.4\n",
       "(60, 80]       1305  61.7\n",
       "(60, 80]       2104  72.2\n",
       "(60, 80]       2202  68.5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i.loc[[65,90]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果想要选取某个区间，先要把分类变量转为区间变量，再使用overlap方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Math_interval</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1103</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1104</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1105</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 100]</th>\n",
       "      <td>1201</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 80]</th>\n",
       "      <td>1202</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  Math\n",
       "Math_interval            \n",
       "(80, 100]      1103  87.2\n",
       "(80, 100]      1104  80.4\n",
       "(80, 100]      1105  84.8\n",
       "(80, 100]      1201  97.0\n",
       "(60, 80]       1202  63.5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_i.loc[pd.Interval(70,75)].head() 报错\n",
    "df_i[df_i.index.astype('interval').overlaps(pd.Interval(70, 85))].head()\n",
    "#只要索引与(70,85]这个区间有交集就会被选中，注意pd.Interval默认构造区间都是左开右闭，可选closed参数right,left,both,neither"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、多级索引\n",
    "### 1. 创建多级索引\n",
    "#### （a）通过from_tuple或from_arrays\n",
    "#### ① 直接创建元组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('A', 'a'),\n",
       "            ('A', 'b'),\n",
       "            ('B', 'a'),\n",
       "            ('B', 'b')],\n",
       "           names=['Upper', 'Lower'])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = [('A','a'),('A','b'),('B','a'),('B','b')]\n",
    "mul_index = pd.MultiIndex.from_tuples(tuples, names=('Upper', 'Lower'))\n",
    "mul_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>perfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score\n",
       "Upper Lower         \n",
       "A     a      perfect\n",
       "      b         good\n",
       "B     a         fair\n",
       "      b          bad"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Score':['perfect','good','fair','bad']},index=mul_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ② 利用zip创建元组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>perfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score\n",
       "Upper Lower         \n",
       "A     a      perfect\n",
       "      b         good\n",
       "B     a         fair\n",
       "      b          bad"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1 = list('AABB')\n",
    "L2 = list('abab')\n",
    "tuples = list(zip(L1,L2))\n",
    "mul_index = pd.MultiIndex.from_tuples(tuples, names=('Upper', 'Lower'))\n",
    "pd.DataFrame({'Score':['perfect','good','fair','bad']},index=mul_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ③ 通过Array创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>perfect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>fair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Score\n",
       "Upper Lower         \n",
       "A     a      perfect\n",
       "      b         good\n",
       "B     a         fair\n",
       "      b          bad"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrays = [['A','a'],['A','b'],['B','a'],['B','b']]\n",
    "mul_index = pd.MultiIndex.from_tuples(arrays, names=('Upper', 'Lower'))\n",
    "pd.DataFrame({'Score':['perfect','good','fair','bad']},index=mul_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('A', 'a'),\n",
       "            ('A', 'b'),\n",
       "            ('B', 'a'),\n",
       "            ('B', 'b')],\n",
       "           names=['Upper', 'Lower'])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_index\n",
    "#由此看出内部自动转成元组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）通过from_product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('A', 'a'),\n",
       "            ('A', 'b'),\n",
       "            ('B', 'a'),\n",
       "            ('B', 'b')],\n",
       "           names=['Upper', 'Lower'])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1 = ['A','B']\n",
    "L2 = ['a','b']\n",
    "pd.MultiIndex.from_product([L1,L2],names=('Upper', 'Lower'))\n",
    "#两两相乘"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）指定df中的列创建（set_index方法）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C_1</th>\n",
       "      <th>street_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_1   street_1    S_1      M     173      63  34.0      A+\n",
       "      street_2    S_1      F     192      73  32.5      B+\n",
       "      street_2    S_1      M     186      82  87.2      B+\n",
       "      street_2    S_1      F     167      81  80.4      B-\n",
       "      street_4    S_1      F     159      64  84.8      B+"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul = df.set_index(['Class','Address'])\n",
    "df_using_mul.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 多层索引切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C_1</th>\n",
       "      <th>street_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_1   street_1    S_1      M     173      63  34.0      A+\n",
       "      street_2    S_1      F     192      73  32.5      B+\n",
       "      street_2    S_1      M     186      82  87.2      B+\n",
       "      street_2    S_1      F     167      81  80.4      B-\n",
       "      street_4    S_1      F     159      64  84.8      B+"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）一般切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C_2</th>\n",
       "      <th>street_5</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>188</td>\n",
       "      <td>68</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_5</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>162</td>\n",
       "      <td>63</td>\n",
       "      <td>33.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_5</th>\n",
       "      <td>S_2</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>100</td>\n",
       "      <td>39.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_2   street_5    S_1      M     188      68  97.0      A-\n",
       "      street_5    S_1      F     162      63  33.8       B\n",
       "      street_5    S_2      M     193     100  39.1       B"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_using_mul.loc['C_2','street_5']\n",
    "#当索引不排序时，单个索引会报出性能警告\n",
    "#df_using_mul.index.is_lexsorted()\n",
    "#该函数检查是否排序\n",
    "df_using_mul.sort_index().loc['C_2','street_5']\n",
    "#df_using_mul.sort_index().index.is_lexsorted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">C_2</th>\n",
       "      <th>street_6</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_6</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>167</td>\n",
       "      <td>63</td>\n",
       "      <td>68.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>194</td>\n",
       "      <td>77</td>\n",
       "      <td>68.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "      <td>85.4</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C_3</th>\n",
       "      <th>street_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>175</td>\n",
       "      <td>57</td>\n",
       "      <td>87.7</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>85.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>157</td>\n",
       "      <td>78</td>\n",
       "      <td>72.3</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_2</td>\n",
       "      <td>M</td>\n",
       "      <td>187</td>\n",
       "      <td>73</td>\n",
       "      <td>48.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_2   street_6    S_1      M     160      53  58.8      A+\n",
       "      street_6    S_1      F     167      63  68.4      B-\n",
       "      street_7    S_2      F     194      77  68.5      B+\n",
       "      street_7    S_2      F     183      76  85.4       B\n",
       "C_3   street_1    S_1      F     175      57  87.7      A-\n",
       "      street_2    S_1      M     195      70  85.2       A\n",
       "      street_4    S_1      M     161      68  31.5      B+\n",
       "      street_4    S_2      F     157      78  72.3      B+\n",
       "      street_4    S_2      M     187      73  48.9       B"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_using_mul.loc[('C_2','street_5'):] 报错\n",
    "#当不排序时，不能使用多层切片\n",
    "df_using_mul.sort_index().loc[('C_2','street_6'):('C_3','street_4')]\n",
    "#注意此处由于使用了loc，因此仍然包含右端点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">C_2</th>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>194</td>\n",
       "      <td>77</td>\n",
       "      <td>68.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "      <td>85.4</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C_3</th>\n",
       "      <th>street_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>175</td>\n",
       "      <td>57</td>\n",
       "      <td>87.7</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>85.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_2   street_7    S_2      F     194      77  68.5      B+\n",
       "      street_7    S_2      F     183      76  85.4       B\n",
       "C_3   street_1    S_1      F     175      57  87.7      A-\n",
       "      street_2    S_1      M     195      70  85.2       A\n",
       "      street_4    S_1      M     161      68  31.5      B+"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul.sort_index().loc[('C_2','street_7'):'C_3'].head()\n",
    "#非元组也是合法的，表示选中该层所有元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）第一类特殊情况：由元组构成列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">C_2</th>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>194</td>\n",
       "      <td>77</td>\n",
       "      <td>68.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "      <td>85.4</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_3</th>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>195</td>\n",
       "      <td>70</td>\n",
       "      <td>85.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_2   street_7    S_2      F     194      77  68.5      B+\n",
       "      street_7    S_2      F     183      76  85.4       B\n",
       "C_3   street_2    S_1      M     195      70  85.2       A"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul.sort_index().loc[[('C_2','street_7'),('C_3','street_2')]]\n",
    "#表示选出某几个元素，精确到最内层索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）第二类特殊情况：由列表构成元组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">C_2</th>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>176</td>\n",
       "      <td>94</td>\n",
       "      <td>63.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_2</td>\n",
       "      <td>M</td>\n",
       "      <td>155</td>\n",
       "      <td>91</td>\n",
       "      <td>73.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>194</td>\n",
       "      <td>77</td>\n",
       "      <td>68.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "      <td>85.4</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C_3</th>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>157</td>\n",
       "      <td>78</td>\n",
       "      <td>72.3</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_2</td>\n",
       "      <td>M</td>\n",
       "      <td>187</td>\n",
       "      <td>73</td>\n",
       "      <td>48.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>188</td>\n",
       "      <td>82</td>\n",
       "      <td>49.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_7</th>\n",
       "      <td>S_2</td>\n",
       "      <td>F</td>\n",
       "      <td>190</td>\n",
       "      <td>99</td>\n",
       "      <td>65.9</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_2   street_4    S_1      F     176      94  63.5      B-\n",
       "      street_4    S_2      M     155      91  73.8      A+\n",
       "      street_7    S_2      F     194      77  68.5      B+\n",
       "      street_7    S_2      F     183      76  85.4       B\n",
       "C_3   street_4    S_1      M     161      68  31.5      B+\n",
       "      street_4    S_2      F     157      78  72.3      B+\n",
       "      street_4    S_2      M     187      73  48.9       B\n",
       "      street_7    S_1      M     188      82  49.7       B\n",
       "      street_7    S_2      F     190      99  65.9       C"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul.sort_index().loc[(['C_2','C_3'],['street_4','street_7']),:]\n",
    "#选出第一层在‘C_2’和'C_3'中且第二层在'street_4'和'street_7'中的行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 多层索引中的slice对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>0.903231</td>\n",
       "      <td>0.347113</td>\n",
       "      <td>0.613984</td>\n",
       "      <td>0.855879</td>\n",
       "      <td>0.837101</td>\n",
       "      <td>0.819969</td>\n",
       "      <td>0.583898</td>\n",
       "      <td>0.129336</td>\n",
       "      <td>0.681962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.020348</td>\n",
       "      <td>0.409778</td>\n",
       "      <td>0.594827</td>\n",
       "      <td>0.854630</td>\n",
       "      <td>0.087908</td>\n",
       "      <td>0.499946</td>\n",
       "      <td>0.554276</td>\n",
       "      <td>0.721452</td>\n",
       "      <td>0.538893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.411393</td>\n",
       "      <td>0.028585</td>\n",
       "      <td>0.901497</td>\n",
       "      <td>0.500408</td>\n",
       "      <td>0.354749</td>\n",
       "      <td>0.308252</td>\n",
       "      <td>0.319632</td>\n",
       "      <td>0.772193</td>\n",
       "      <td>0.120076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>0.201583</td>\n",
       "      <td>0.480175</td>\n",
       "      <td>0.423258</td>\n",
       "      <td>0.239614</td>\n",
       "      <td>0.381462</td>\n",
       "      <td>0.849265</td>\n",
       "      <td>0.380623</td>\n",
       "      <td>0.286677</td>\n",
       "      <td>0.449948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.191132</td>\n",
       "      <td>0.787541</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>0.546501</td>\n",
       "      <td>0.076944</td>\n",
       "      <td>0.764933</td>\n",
       "      <td>0.727802</td>\n",
       "      <td>0.656632</td>\n",
       "      <td>0.771932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.289220</td>\n",
       "      <td>0.310385</td>\n",
       "      <td>0.046243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d         e         f         d         e         f   \n",
       "Upper Lower                                                               \n",
       "A     a      0.903231  0.347113  0.613984  0.855879  0.837101  0.819969   \n",
       "      b      0.020348  0.409778  0.594827  0.854630  0.087908  0.499946   \n",
       "      c      0.411393  0.028585  0.901497  0.500408  0.354749  0.308252   \n",
       "B     a      0.201583  0.480175  0.423258  0.239614  0.381462  0.849265   \n",
       "      b      0.191132  0.787541  0.325968  0.546501  0.076944  0.764933   \n",
       "      c      0.830845  0.053417  0.530750  0.699251  0.435809  0.504183   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d         e         f  \n",
       "Upper Lower                                \n",
       "A     a      0.583898  0.129336  0.681962  \n",
       "      b      0.554276  0.721452  0.538893  \n",
       "      c      0.319632  0.772193  0.120076  \n",
       "B     a      0.380623  0.286677  0.449948  \n",
       "      b      0.727802  0.656632  0.771932  \n",
       "      c      0.289220  0.310385  0.046243  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1,L2 = ['A','B'],['a','b','c']\n",
    "mul_index1 = pd.MultiIndex.from_product([L1,L2],names=('Upper', 'Lower'))\n",
    "L3,L4 = ['D','E','F'],['d','e','f']\n",
    "mul_index2 = pd.MultiIndex.from_product([L3,L4],names=('Big', 'Small'))\n",
    "df_s = pd.DataFrame(np.random.rand(6,9),index=mul_index1,columns=mul_index2)\n",
    "df_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx=pd.IndexSlice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IndexSlice本质上是对多个Slice对象的包装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(slice(1, 9, 2), slice('A', 'C', None), slice('start', 'end', 2))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[1:9:2,'A':'C','start':'end':2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 索引Slice可以与loc一起完成切片操作，主要有两种用法\n",
    "#### （a）loc[idx[\\*,\\*]]型\n",
    "#### 第一个星号表示行，第二个表示列，且使用布尔索引时，需要索引对齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"2\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>0.201583</td>\n",
       "      <td>0.423258</td>\n",
       "      <td>0.239614</td>\n",
       "      <td>0.381462</td>\n",
       "      <td>0.849265</td>\n",
       "      <td>0.449948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.191132</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>0.546501</td>\n",
       "      <td>0.076944</td>\n",
       "      <td>0.764933</td>\n",
       "      <td>0.771932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.046243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                   E                             F\n",
       "Small               d         f         d         e         f         f\n",
       "Upper Lower                                                            \n",
       "B     a      0.201583  0.423258  0.239614  0.381462  0.849265  0.449948\n",
       "      b      0.191132  0.325968  0.546501  0.076944  0.764933  0.771932\n",
       "      c      0.830845  0.530750  0.699251  0.435809  0.504183  0.046243"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子1\n",
    "df_s.loc[idx['B':,df_s.iloc[0]>0.6]]\n",
    "#df_s.loc[idx['B':,df_s.iloc[:,0]>0.6]] #索引没有对齐报错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>a</th>\n",
       "      <td>0.903231</td>\n",
       "      <td>0.347113</td>\n",
       "      <td>0.613984</td>\n",
       "      <td>0.855879</td>\n",
       "      <td>0.837101</td>\n",
       "      <td>0.819969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                    \n",
       "Small               d         e         f         d         e         f\n",
       "Upper Lower                                                            \n",
       "A     a      0.903231  0.347113  0.613984  0.855879  0.837101  0.819969\n",
       "B     c      0.830845  0.053417  0.530750  0.699251  0.435809  0.504183"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子2\n",
    "df_s.loc[idx[df_s.iloc[:,0]>0.6,:('E','f')]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）loc[idx[\\*,\\*],idx[\\*,\\*]]型\n",
    "#### 这里与上面的区别在于（a）中的loc是没有逗号隔开的，但（b）是用逗号隔开，前面一个idx表示行索引，后面一个idx为列索引\n",
    "#### 这种用法非常灵活，因此多举几个例子方便理解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.903231</td>\n",
       "      <td>0.347113</td>\n",
       "      <td>0.613984</td>\n",
       "      <td>0.855879</td>\n",
       "      <td>0.837101</td>\n",
       "      <td>0.819969</td>\n",
       "      <td>0.583898</td>\n",
       "      <td>0.129336</td>\n",
       "      <td>0.681962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.020348</td>\n",
       "      <td>0.409778</td>\n",
       "      <td>0.594827</td>\n",
       "      <td>0.854630</td>\n",
       "      <td>0.087908</td>\n",
       "      <td>0.499946</td>\n",
       "      <td>0.554276</td>\n",
       "      <td>0.721452</td>\n",
       "      <td>0.538893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.411393</td>\n",
       "      <td>0.028585</td>\n",
       "      <td>0.901497</td>\n",
       "      <td>0.500408</td>\n",
       "      <td>0.354749</td>\n",
       "      <td>0.308252</td>\n",
       "      <td>0.319632</td>\n",
       "      <td>0.772193</td>\n",
       "      <td>0.120076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big           D                             E                             F  \\\n",
       "Small         d         e         f         d         e         f         d   \n",
       "Lower                                                                         \n",
       "a      0.903231  0.347113  0.613984  0.855879  0.837101  0.819969  0.583898   \n",
       "b      0.020348  0.409778  0.594827  0.854630  0.087908  0.499946  0.554276   \n",
       "c      0.411393  0.028585  0.901497  0.500408  0.354749  0.308252  0.319632   \n",
       "\n",
       "Big                        \n",
       "Small         e         f  \n",
       "Lower                      \n",
       "a      0.129336  0.681962  \n",
       "b      0.721452  0.538893  \n",
       "c      0.772193  0.120076  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子1\n",
    "df_s.loc[idx['A'],idx['D':]]\n",
    "#后面的层出现，则前面的层必须出现\n",
    "#df_s.loc[idx['a'],idx['D':]] #报错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>b</th>\n",
       "      <td>0.020348</td>\n",
       "      <td>0.409778</td>\n",
       "      <td>0.594827</td>\n",
       "      <td>0.854630</td>\n",
       "      <td>0.087908</td>\n",
       "      <td>0.499946</td>\n",
       "      <td>0.554276</td>\n",
       "      <td>0.721452</td>\n",
       "      <td>0.538893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.411393</td>\n",
       "      <td>0.028585</td>\n",
       "      <td>0.901497</td>\n",
       "      <td>0.500408</td>\n",
       "      <td>0.354749</td>\n",
       "      <td>0.308252</td>\n",
       "      <td>0.319632</td>\n",
       "      <td>0.772193</td>\n",
       "      <td>0.120076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>b</th>\n",
       "      <td>0.191132</td>\n",
       "      <td>0.787541</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>0.546501</td>\n",
       "      <td>0.076944</td>\n",
       "      <td>0.764933</td>\n",
       "      <td>0.727802</td>\n",
       "      <td>0.656632</td>\n",
       "      <td>0.771932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.289220</td>\n",
       "      <td>0.310385</td>\n",
       "      <td>0.046243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d         e         f         d         e         f   \n",
       "Upper Lower                                                               \n",
       "A     b      0.020348  0.409778  0.594827  0.854630  0.087908  0.499946   \n",
       "      c      0.411393  0.028585  0.901497  0.500408  0.354749  0.308252   \n",
       "B     b      0.191132  0.787541  0.325968  0.546501  0.076944  0.764933   \n",
       "      c      0.830845  0.053417  0.530750  0.699251  0.435809  0.504183   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d         e         f  \n",
       "Upper Lower                                \n",
       "A     b      0.554276  0.721452  0.538893  \n",
       "      c      0.319632  0.772193  0.120076  \n",
       "B     b      0.727802  0.656632  0.771932  \n",
       "      c      0.289220  0.310385  0.046243  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子2\n",
    "df_s.loc[idx[:'B','b':],:] #举这个例子是为了说明①可以在相应level使用切片②某一个idx可以用:代替表示全选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Upper  Lower\n",
       "A      a         True\n",
       "       b        False\n",
       "       c        False\n",
       "B      a        False\n",
       "       b        False\n",
       "       c         True\n",
       "Name: (D, d), dtype: bool"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子3\n",
    "df_s.iloc[:,0]>0.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>d</th>\n",
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       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
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       "      <th>d</th>\n",
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       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>A</th>\n",
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       "      <td>0.903231</td>\n",
       "      <td>0.347113</td>\n",
       "      <td>0.613984</td>\n",
       "      <td>0.855879</td>\n",
       "      <td>0.837101</td>\n",
       "      <td>0.819969</td>\n",
       "      <td>0.583898</td>\n",
       "      <td>0.129336</td>\n",
       "      <td>0.681962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.289220</td>\n",
       "      <td>0.310385</td>\n",
       "      <td>0.046243</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d         e         f         d         e         f   \n",
       "Upper Lower                                                               \n",
       "A     a      0.903231  0.347113  0.613984  0.855879  0.837101  0.819969   \n",
       "B     c      0.830845  0.053417  0.530750  0.699251  0.435809  0.504183   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d         e         f  \n",
       "Upper Lower                                \n",
       "A     a      0.583898  0.129336  0.681962  \n",
       "B     c      0.289220  0.310385  0.046243  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s.loc[idx[:'B',df_s.iloc[:,0]>0.6],:] #这个例子表示相应位置还可以使用布尔索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>0.903231</td>\n",
       "      <td>0.347113</td>\n",
       "      <td>0.613984</td>\n",
       "      <td>0.855879</td>\n",
       "      <td>0.837101</td>\n",
       "      <td>0.819969</td>\n",
       "      <td>0.583898</td>\n",
       "      <td>0.129336</td>\n",
       "      <td>0.681962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.411393</td>\n",
       "      <td>0.028585</td>\n",
       "      <td>0.901497</td>\n",
       "      <td>0.500408</td>\n",
       "      <td>0.354749</td>\n",
       "      <td>0.308252</td>\n",
       "      <td>0.319632</td>\n",
       "      <td>0.772193</td>\n",
       "      <td>0.120076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>0.201583</td>\n",
       "      <td>0.480175</td>\n",
       "      <td>0.423258</td>\n",
       "      <td>0.239614</td>\n",
       "      <td>0.381462</td>\n",
       "      <td>0.849265</td>\n",
       "      <td>0.380623</td>\n",
       "      <td>0.286677</td>\n",
       "      <td>0.449948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.191132</td>\n",
       "      <td>0.787541</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>0.546501</td>\n",
       "      <td>0.076944</td>\n",
       "      <td>0.764933</td>\n",
       "      <td>0.727802</td>\n",
       "      <td>0.656632</td>\n",
       "      <td>0.771932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.289220</td>\n",
       "      <td>0.310385</td>\n",
       "      <td>0.046243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d         e         f         d         e         f   \n",
       "Upper Lower                                                               \n",
       "A     a      0.903231  0.347113  0.613984  0.855879  0.837101  0.819969   \n",
       "      c      0.411393  0.028585  0.901497  0.500408  0.354749  0.308252   \n",
       "B     a      0.201583  0.480175  0.423258  0.239614  0.381462  0.849265   \n",
       "      b      0.191132  0.787541  0.325968  0.546501  0.076944  0.764933   \n",
       "      c      0.830845  0.053417  0.530750  0.699251  0.435809  0.504183   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d         e         f  \n",
       "Upper Lower                                \n",
       "A     a      0.583898  0.129336  0.681962  \n",
       "      c      0.319632  0.772193  0.120076  \n",
       "B     a      0.380623  0.286677  0.449948  \n",
       "      b      0.727802  0.656632  0.771932  \n",
       "      c      0.289220  0.310385  0.046243  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子4\n",
    "#特别要注意，（b）中的布尔索引是可以索引不对齐的，只需要长度一样，比如下面这个例子\n",
    "df_s.loc[idx[:'B',(df_s.iloc[0]>0.6)[:6]],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
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       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
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       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.53075</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.28922</td>\n",
       "      <td>0.310385</td>\n",
       "      <td>0.046243</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                            E                      \\\n",
       "Small               d         e        f         d         e         f   \n",
       "Upper Lower                                                              \n",
       "B     c      0.830845  0.053417  0.53075  0.699251  0.435809  0.504183   \n",
       "\n",
       "Big                F                      \n",
       "Small              d         e         f  \n",
       "Upper Lower                               \n",
       "B     c      0.28922  0.310385  0.046243  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子5\n",
    "df_s.loc[idx[:'B','c':,(df_s.iloc[:,0]>0.6)],:]\n",
    "#idx中层数k1大于df层数k2时，idx前k2个参数若相应位置是元素或者元素切片，则表示相应df层的元素筛选，同时也可以选择用同长度bool序列\n",
    "#idx后面多出来的参数只能选择同bool序列，这样设计的目的是可以将元素筛选和条件筛选同时运用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
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       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>a</th>\n",
       "      <td>0.903231</td>\n",
       "      <td>0.347113</td>\n",
       "      <td>0.613984</td>\n",
       "      <td>0.855879</td>\n",
       "      <td>0.837101</td>\n",
       "      <td>0.819969</td>\n",
       "      <td>0.583898</td>\n",
       "      <td>0.129336</td>\n",
       "      <td>0.681962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th>c</th>\n",
       "      <td>0.830845</td>\n",
       "      <td>0.053417</td>\n",
       "      <td>0.530750</td>\n",
       "      <td>0.699251</td>\n",
       "      <td>0.435809</td>\n",
       "      <td>0.504183</td>\n",
       "      <td>0.289220</td>\n",
       "      <td>0.310385</td>\n",
       "      <td>0.046243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d         e         f         d         e         f   \n",
       "Upper Lower                                                               \n",
       "A     a      0.903231  0.347113  0.613984  0.855879  0.837101  0.819969   \n",
       "B     c      0.830845  0.053417  0.530750  0.699251  0.435809  0.504183   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d         e         f  \n",
       "Upper Lower                                \n",
       "A     a      0.583898  0.129336  0.681962  \n",
       "B     c      0.289220  0.310385  0.046243  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#例子6\n",
    "df_s.loc[idx[:'B',(df_s.iloc[:,0]>0.6),(df_s.iloc[:,0]>0.6)],:] #这个就不是元素筛选而是条件筛选\n",
    "#df_s.loc[idx[:'B',(df_s.iloc[:,0]>0.6),'c',:]] #报错\n",
    "#df_s.loc[idx[:'c','B',(df_s.iloc[:,0]>0.6),:]] #报错"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 索引层的交换\n",
    "#### （a）swaplevel方法（两层交换）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>School</th>\n",
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       "      <th>Weight</th>\n",
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       "      <th>Physics</th>\n",
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       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">C_1</th>\n",
       "      <th>street_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Class Address                                             \n",
       "C_1   street_1    S_1      M     173      63  34.0      A+\n",
       "      street_2    S_1      F     192      73  32.5      B+\n",
       "      street_2    S_1      M     186      82  87.2      B+\n",
       "      street_2    S_1      F     167      81  80.4      B-\n",
       "      street_4    S_1      F     159      64  84.8      B+"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">street_1</th>\n",
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       "      <td>S_1</td>\n",
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       "      <td>173</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
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       "    <tr>\n",
       "      <th>C_2</th>\n",
       "      <td>S_2</td>\n",
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       "      <td>175</td>\n",
       "      <td>74</td>\n",
       "      <td>47.2</td>\n",
       "      <td>B-</td>\n",
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       "    <tr>\n",
       "      <th>C_3</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>175</td>\n",
       "      <td>57</td>\n",
       "      <td>87.7</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">street_2</th>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               School Gender  Height  Weight  Math Physics\n",
       "Address  Class                                            \n",
       "street_1 C_1      S_1      M     173      63  34.0      A+\n",
       "         C_2      S_2      M     175      74  47.2      B-\n",
       "         C_3      S_1      F     175      57  87.7      A-\n",
       "street_2 C_1      S_1      F     192      73  32.5      B+\n",
       "         C_1      S_1      M     186      82  87.2      B+"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_using_mul.swaplevel(i=1,j=0,axis=0).sort_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）reorder_levels方法（多层交换）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Address</th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">S_1</th>\n",
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       "      <th>street_1</th>\n",
       "      <td>M</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
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       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
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       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
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       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>street_2</th>\n",
       "      <td>F</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <td>F</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gender  Height  Weight  Math Physics\n",
       "School Class Address                                      \n",
       "S_1    C_1   street_1      M     173      63  34.0      A+\n",
       "             street_2      F     192      73  32.5      B+\n",
       "             street_2      M     186      82  87.2      B+\n",
       "             street_2      F     167      81  80.4      B-\n",
       "             street_4      F     159      64  84.8      B+"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_muls = df.set_index(['School','Class','Address'])\n",
    "df_muls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th rowspan=\"3\" valign=\"top\">street_1</th>\n",
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       "      <td>34.0</td>\n",
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       "      <th>C_3</th>\n",
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       "      <th>S_2</th>\n",
       "      <th>C_2</th>\n",
       "      <td>M</td>\n",
       "      <td>175</td>\n",
       "      <td>74</td>\n",
       "      <td>47.2</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">street_2</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">S_1</th>\n",
       "      <th>C_1</th>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_1</th>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gender  Height  Weight  Math Physics\n",
       "Address  School Class                                     \n",
       "street_1 S_1    C_1        M     173      63  34.0      A+\n",
       "                C_3        F     175      57  87.7      A-\n",
       "         S_2    C_2        M     175      74  47.2      B-\n",
       "street_2 S_1    C_1        F     192      73  32.5      B+\n",
       "                C_1        M     186      82  87.2      B+"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_muls.reorder_levels([2,0,1],axis=0).sort_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>M</td>\n",
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       "      <td>34.0</td>\n",
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       "      <th>C_3</th>\n",
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       "      <th>S_2</th>\n",
       "      <th>C_2</th>\n",
       "      <td>M</td>\n",
       "      <td>175</td>\n",
       "      <td>74</td>\n",
       "      <td>47.2</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">street_2</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">S_1</th>\n",
       "      <th>C_1</th>\n",
       "      <td>F</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_1</th>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Gender  Height  Weight  Math Physics\n",
       "Address  School Class                                     \n",
       "street_1 S_1    C_1        M     173      63  34.0      A+\n",
       "                C_3        F     175      57  87.7      A-\n",
       "         S_2    C_2        M     175      74  47.2      B-\n",
       "street_2 S_1    C_1        F     192      73  32.5      B+\n",
       "                C_1        M     186      82  87.2      B+"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#如果索引有name，可以直接使用name\n",
    "df_muls.reorder_levels(['Address','School','Class'],axis=0).sort_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、索引设定\n",
    "### 1. index_col参数\n",
    "#### index_col是read_csv中的一个参数，而不是某一个方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>34.0</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">street_2</th>\n",
       "      <th>S_1</th>\n",
       "      <td>C_1</td>\n",
       "      <td>1102</td>\n",
       "      <td>F</td>\n",
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       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_1</th>\n",
       "      <td>C_1</td>\n",
       "      <td>1103</td>\n",
       "      <td>M</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_1</th>\n",
       "      <td>C_1</td>\n",
       "      <td>1104</td>\n",
       "      <td>F</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street_4</th>\n",
       "      <th>S_1</th>\n",
       "      <td>C_1</td>\n",
       "      <td>1105</td>\n",
       "      <td>F</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Class    ID Gender  Height  Weight  Math Physics\n",
       "Address  School                                                 \n",
       "street_1 S_1      C_1  1101      M     173      63  34.0      A+\n",
       "street_2 S_1      C_1  1102      F     192      73  32.5      B+\n",
       "         S_1      C_1  1103      M     186      82  87.2      B+\n",
       "         S_1      C_1  1104      F     167      81  80.4      B-\n",
       "street_4 S_1      C_1  1105      F     159      64  84.8      B+"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/table.csv',index_col=['Address','School']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. reindex和reindex_like\n",
    "#### reindex是指重新索引，它的重要特性在于索引对齐，很多时候用于重新排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
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       "      <th>1102</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>166.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>48.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1   173.0    63.0  34.0      A+\n",
       "1203    S_1   C_2      M  street_6   160.0    53.0  58.8      A+\n",
       "1206    NaN   NaN    NaN       NaN     NaN     NaN   NaN     NaN\n",
       "2402    S_2   C_4      M  street_7   166.0    82.0  48.7       B"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(index=[1101,1203,1206,2402])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>173</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>192</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>186</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>167</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>159</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height Gender  Average\n",
       "ID                          \n",
       "1101     173      M      NaN\n",
       "1102     192      F      NaN\n",
       "1103     186      M      NaN\n",
       "1104     167      F      NaN\n",
       "1105     159      F      NaN"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(columns=['Height','Gender','Average']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可以选择缺失值的填充方法：fill_value和method（bfill/ffill/nearest），其中method参数必须索引单调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_6</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>166</td>\n",
       "      <td>82</td>\n",
       "      <td>48.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1203    S_1   C_2      M  street_6     160      53  58.8      A+\n",
       "1206    S_1   C_3      M  street_4     161      68  31.5      B+\n",
       "2402    S_2   C_4      M  street_7     166      82  48.7       B"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(index=[1101,1203,1206,2402],method='bfill')\n",
    "#这里的单调是指df必须索引经过排序，否则报错\n",
    "#bfill表示用所在索引1206的后一个有效行填充，ffill为前一个有效行，nearest是指最近的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_6</td>\n",
       "      <td>160</td>\n",
       "      <td>53</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>167</td>\n",
       "      <td>63</td>\n",
       "      <td>68.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>166</td>\n",
       "      <td>82</td>\n",
       "      <td>48.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1203    S_1   C_2      M  street_6     160      53  58.8      A+\n",
       "1206    S_1   C_2      F  street_6     167      63  68.4      B-\n",
       "2402    S_2   C_4      M  street_7     166      82  48.7       B"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(index=[1101,1203,1206,2402],method='nearest')\n",
    "#数值上1205比1301更接近1206，因此用前者填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### reindex_like的作用为生成一个横纵索引完全与参数列表一致的DataFrame，数据使用被调用的表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Weight  Height\n",
       "ID                  \n",
       "1101     0.0     0.0\n",
       "1102     0.0     0.0\n",
       "1103     0.0     0.0\n",
       "1104     0.0     0.0\n",
       "1105     NaN     NaN"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = pd.DataFrame({'Weight':np.zeros(5),\n",
    "                        'Height':np.zeros(5),\n",
    "                        'ID':[1101,1104,1103,1106,1102]}).set_index('ID')\n",
    "df_temp.reindex_like(df[0:5][['Weight','Height']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果df_temp单调还可以使用method参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Weight  Height\n",
       "ID                  \n",
       "1101       0       0\n",
       "1102       4       4\n",
       "1103       2       2\n",
       "1104       1       1\n",
       "1105       3       3"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = pd.DataFrame({'Weight':range(5),\n",
    "                        'Height':range(5),\n",
    "                        'ID':[1101,1104,1103,1106,1102]}).set_index('ID').sort_index()\n",
    "df_temp.reindex_like(df[0:5][['Weight','Height']],method='bfill')\n",
    "#可以自行检验这里的1105的值是否是由bfill规则填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. set_index和reset_index\n",
    "#### 先介绍set_index：从字面意思看，就是将某些列作为索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用表内列作为索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      School Gender   Address  Height  Weight  Math Physics\n",
       "Class                                                      \n",
       "C_1      S_1      M  street_1     173      63  34.0      A+\n",
       "C_1      S_1      F  street_2     192      73  32.5      B+\n",
       "C_1      S_1      M  street_2     186      82  87.2      B+\n",
       "C_1      S_1      F  street_2     167      81  80.4      B-\n",
       "C_1      S_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Class').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用append参数可以将当前索引维持不变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>School</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
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       "      <th>1102</th>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
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       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <th>C_1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           School Gender   Address  Height  Weight  Math Physics\n",
       "ID   Class                                                      \n",
       "1101 C_1      S_1      M  street_1     173      63  34.0      A+\n",
       "1102 C_1      S_1      F  street_2     192      73  32.5      B+\n",
       "1103 C_1      S_1      M  street_2     186      82  87.2      B+\n",
       "1104 C_1      S_1      F  street_2     167      81  80.4      B-\n",
       "1105 C_1      S_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Class',append=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 当使用与表长相同的列作为索引（需要先转化为Series，否则报错）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Address</th>\n",
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       "      <th>2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  School Class Gender   Address  Height  Weight  Math Physics\n",
       "0    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "2    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "3    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "4    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(pd.Series(range(df.shape[0]))).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可以直接添加多级索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1.0</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      School Class Gender   Address  Height  Weight  Math Physics\n",
       "0 1.0    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1 1.0    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "2 1.0    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "3 1.0    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "4 1.0    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index([pd.Series(range(df.shape[0])),pd.Series(np.ones(df.shape[0]))]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 下面介绍reset_index方法，它的主要功能是将索引重置\n",
    "#### 默认状态直接恢复到自然数索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1101</td>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1102</td>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1103</td>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1104</td>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1105</td>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID School Class Gender   Address  Height  Weight  Math Physics\n",
       "0  1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1  1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "2  1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "3  1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "4  1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用level参数指定哪一层被reset，用col_level参数指定set到哪一层："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>0.077679</td>\n",
       "      <td>0.567787</td>\n",
       "      <td>0.665333</td>\n",
       "      <td>0.942349</td>\n",
       "      <td>0.531474</td>\n",
       "      <td>0.330951</td>\n",
       "      <td>0.882092</td>\n",
       "      <td>0.275882</td>\n",
       "      <td>0.650953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.770243</td>\n",
       "      <td>0.313352</td>\n",
       "      <td>0.220805</td>\n",
       "      <td>0.027873</td>\n",
       "      <td>0.761497</td>\n",
       "      <td>0.119895</td>\n",
       "      <td>0.310588</td>\n",
       "      <td>0.198915</td>\n",
       "      <td>0.472835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.160599</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>0.929504</td>\n",
       "      <td>0.750928</td>\n",
       "      <td>0.097759</td>\n",
       "      <td>0.675912</td>\n",
       "      <td>0.686486</td>\n",
       "      <td>0.614004</td>\n",
       "      <td>0.167216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>0.968565</td>\n",
       "      <td>0.406914</td>\n",
       "      <td>0.173109</td>\n",
       "      <td>0.533618</td>\n",
       "      <td>0.014341</td>\n",
       "      <td>0.701709</td>\n",
       "      <td>0.704982</td>\n",
       "      <td>0.623265</td>\n",
       "      <td>0.677072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.687038</td>\n",
       "      <td>0.017382</td>\n",
       "      <td>0.105115</td>\n",
       "      <td>0.025243</td>\n",
       "      <td>0.605660</td>\n",
       "      <td>0.349725</td>\n",
       "      <td>0.018865</td>\n",
       "      <td>0.078166</td>\n",
       "      <td>0.920426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d         e         f         d         e         f   \n",
       "Upper Lower                                                               \n",
       "A     a      0.077679  0.567787  0.665333  0.942349  0.531474  0.330951   \n",
       "      b      0.770243  0.313352  0.220805  0.027873  0.761497  0.119895   \n",
       "      c      0.160599  0.974000  0.929504  0.750928  0.097759  0.675912   \n",
       "B     a      0.968565  0.406914  0.173109  0.533618  0.014341  0.701709   \n",
       "      b      0.687038  0.017382  0.105115  0.025243  0.605660  0.349725   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d         e         f  \n",
       "Upper Lower                                \n",
       "A     a      0.882092  0.275882  0.650953  \n",
       "      b      0.310588  0.198915  0.472835  \n",
       "      c      0.686486  0.614004  0.167216  \n",
       "B     a      0.704982  0.623265  0.677072  \n",
       "      b      0.018865  0.078166  0.920426  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L1,L2 = ['A','B','C'],['a','b','c']\n",
    "mul_index1 = pd.MultiIndex.from_product([L1,L2],names=('Upper', 'Lower'))\n",
    "L3,L4 = ['D','E','F'],['d','e','f']\n",
    "mul_index2 = pd.MultiIndex.from_product([L3,L4],names=('Big', 'Small'))\n",
    "df_temp = pd.DataFrame(np.random.rand(9,9),index=mul_index1,columns=mul_index2)\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Big</th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <th>Lower</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>a</td>\n",
       "      <td>0.077679</td>\n",
       "      <td>0.567787</td>\n",
       "      <td>0.665333</td>\n",
       "      <td>0.942349</td>\n",
       "      <td>0.531474</td>\n",
       "      <td>0.330951</td>\n",
       "      <td>0.882092</td>\n",
       "      <td>0.275882</td>\n",
       "      <td>0.650953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>b</td>\n",
       "      <td>0.770243</td>\n",
       "      <td>0.313352</td>\n",
       "      <td>0.220805</td>\n",
       "      <td>0.027873</td>\n",
       "      <td>0.761497</td>\n",
       "      <td>0.119895</td>\n",
       "      <td>0.310588</td>\n",
       "      <td>0.198915</td>\n",
       "      <td>0.472835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>c</td>\n",
       "      <td>0.160599</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>0.929504</td>\n",
       "      <td>0.750928</td>\n",
       "      <td>0.097759</td>\n",
       "      <td>0.675912</td>\n",
       "      <td>0.686486</td>\n",
       "      <td>0.614004</td>\n",
       "      <td>0.167216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>a</td>\n",
       "      <td>0.968565</td>\n",
       "      <td>0.406914</td>\n",
       "      <td>0.173109</td>\n",
       "      <td>0.533618</td>\n",
       "      <td>0.014341</td>\n",
       "      <td>0.701709</td>\n",
       "      <td>0.704982</td>\n",
       "      <td>0.623265</td>\n",
       "      <td>0.677072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>b</td>\n",
       "      <td>0.687038</td>\n",
       "      <td>0.017382</td>\n",
       "      <td>0.105115</td>\n",
       "      <td>0.025243</td>\n",
       "      <td>0.605660</td>\n",
       "      <td>0.349725</td>\n",
       "      <td>0.018865</td>\n",
       "      <td>0.078166</td>\n",
       "      <td>0.920426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small Lower         d         e         f         d         e         f   \n",
       "Upper                                                                     \n",
       "A         a  0.077679  0.567787  0.665333  0.942349  0.531474  0.330951   \n",
       "A         b  0.770243  0.313352  0.220805  0.027873  0.761497  0.119895   \n",
       "A         c  0.160599  0.974000  0.929504  0.750928  0.097759  0.675912   \n",
       "B         a  0.968565  0.406914  0.173109  0.533618  0.014341  0.701709   \n",
       "B         b  0.687038  0.017382  0.105115  0.025243  0.605660  0.349725   \n",
       "\n",
       "Big           F                      \n",
       "Small         d         e         f  \n",
       "Upper                                \n",
       "A      0.882092  0.275882  0.650953  \n",
       "A      0.310588  0.198915  0.472835  \n",
       "A      0.686486  0.614004  0.167216  \n",
       "B      0.704982  0.623265  0.677072  \n",
       "B      0.018865  0.078166  0.920426  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp1 = df_temp.reset_index(level=1,col_level=1)\n",
    "df_temp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([( '', 'Lower'),\n",
       "            ('D',     'd'),\n",
       "            ('D',     'e'),\n",
       "            ('D',     'f'),\n",
       "            ('E',     'd'),\n",
       "            ('E',     'e'),\n",
       "            ('E',     'f'),\n",
       "            ('F',     'd'),\n",
       "            ('F',     'e'),\n",
       "            ('F',     'f')],\n",
       "           names=['Big', 'Small'])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp1.columns\n",
    "#看到的确插入了level2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'], dtype='object', name='Upper')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp1.index\n",
    "#最内层索引被移出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. rename_axis和rename\n",
    "#### rename_axis是针对多级索引的方法，作用是修改某一层的索引名，而不是索引标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>BigBig</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>LowerLower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
       "      <th>a</th>\n",
       "      <td>0.077679</td>\n",
       "      <td>0.567787</td>\n",
       "      <td>0.665333</td>\n",
       "      <td>0.942349</td>\n",
       "      <td>0.531474</td>\n",
       "      <td>0.330951</td>\n",
       "      <td>0.882092</td>\n",
       "      <td>0.275882</td>\n",
       "      <td>0.650953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.770243</td>\n",
       "      <td>0.313352</td>\n",
       "      <td>0.220805</td>\n",
       "      <td>0.027873</td>\n",
       "      <td>0.761497</td>\n",
       "      <td>0.119895</td>\n",
       "      <td>0.310588</td>\n",
       "      <td>0.198915</td>\n",
       "      <td>0.472835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.160599</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>0.929504</td>\n",
       "      <td>0.750928</td>\n",
       "      <td>0.097759</td>\n",
       "      <td>0.675912</td>\n",
       "      <td>0.686486</td>\n",
       "      <td>0.614004</td>\n",
       "      <td>0.167216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>0.968565</td>\n",
       "      <td>0.406914</td>\n",
       "      <td>0.173109</td>\n",
       "      <td>0.533618</td>\n",
       "      <td>0.014341</td>\n",
       "      <td>0.701709</td>\n",
       "      <td>0.704982</td>\n",
       "      <td>0.623265</td>\n",
       "      <td>0.677072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.687038</td>\n",
       "      <td>0.017382</td>\n",
       "      <td>0.105115</td>\n",
       "      <td>0.025243</td>\n",
       "      <td>0.605660</td>\n",
       "      <td>0.349725</td>\n",
       "      <td>0.018865</td>\n",
       "      <td>0.078166</td>\n",
       "      <td>0.920426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.693014</td>\n",
       "      <td>0.931630</td>\n",
       "      <td>0.483892</td>\n",
       "      <td>0.384802</td>\n",
       "      <td>0.782509</td>\n",
       "      <td>0.162382</td>\n",
       "      <td>0.542573</td>\n",
       "      <td>0.315541</td>\n",
       "      <td>0.602177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
       "      <th>a</th>\n",
       "      <td>0.133081</td>\n",
       "      <td>0.769785</td>\n",
       "      <td>0.892641</td>\n",
       "      <td>0.122432</td>\n",
       "      <td>0.094235</td>\n",
       "      <td>0.638547</td>\n",
       "      <td>0.456789</td>\n",
       "      <td>0.749265</td>\n",
       "      <td>0.250103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.526646</td>\n",
       "      <td>0.710174</td>\n",
       "      <td>0.754488</td>\n",
       "      <td>0.323552</td>\n",
       "      <td>0.290120</td>\n",
       "      <td>0.659110</td>\n",
       "      <td>0.325425</td>\n",
       "      <td>0.444771</td>\n",
       "      <td>0.168545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.905280</td>\n",
       "      <td>0.490078</td>\n",
       "      <td>0.735828</td>\n",
       "      <td>0.574289</td>\n",
       "      <td>0.460427</td>\n",
       "      <td>0.755454</td>\n",
       "      <td>0.692325</td>\n",
       "      <td>0.571639</td>\n",
       "      <td>0.145983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "BigBig                   D                             E                      \\\n",
       "Small                    d         e         f         d         e         f   \n",
       "Upper LowerLower                                                               \n",
       "A     a           0.077679  0.567787  0.665333  0.942349  0.531474  0.330951   \n",
       "      b           0.770243  0.313352  0.220805  0.027873  0.761497  0.119895   \n",
       "      c           0.160599  0.974000  0.929504  0.750928  0.097759  0.675912   \n",
       "B     a           0.968565  0.406914  0.173109  0.533618  0.014341  0.701709   \n",
       "      b           0.687038  0.017382  0.105115  0.025243  0.605660  0.349725   \n",
       "      c           0.693014  0.931630  0.483892  0.384802  0.782509  0.162382   \n",
       "C     a           0.133081  0.769785  0.892641  0.122432  0.094235  0.638547   \n",
       "      b           0.526646  0.710174  0.754488  0.323552  0.290120  0.659110   \n",
       "      c           0.905280  0.490078  0.735828  0.574289  0.460427  0.755454   \n",
       "\n",
       "BigBig                   F                      \n",
       "Small                    d         e         f  \n",
       "Upper LowerLower                                \n",
       "A     a           0.882092  0.275882  0.650953  \n",
       "      b           0.310588  0.198915  0.472835  \n",
       "      c           0.686486  0.614004  0.167216  \n",
       "B     a           0.704982  0.623265  0.677072  \n",
       "      b           0.018865  0.078166  0.920426  \n",
       "      c           0.542573  0.315541  0.602177  \n",
       "C     a           0.456789  0.749265  0.250103  \n",
       "      b           0.325425  0.444771  0.168545  \n",
       "      c           0.692325  0.571639  0.145983  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.rename_axis(index={'Lower':'LowerLower'},columns={'Big':'BigBig'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### rename方法用于修改列或者行索引标签，而不是索引名："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Big</th>\n",
       "      <th colspan=\"3\" halign=\"left\">D</th>\n",
       "      <th colspan=\"3\" halign=\"left\">E</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Small</th>\n",
       "      <th>d</th>\n",
       "      <th>changed_e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>changed_e</th>\n",
       "      <th>f</th>\n",
       "      <th>d</th>\n",
       "      <th>changed_e</th>\n",
       "      <th>f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <th>Lower</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">T</th>\n",
       "      <th>a</th>\n",
       "      <td>0.077679</td>\n",
       "      <td>0.567787</td>\n",
       "      <td>0.665333</td>\n",
       "      <td>0.942349</td>\n",
       "      <td>0.531474</td>\n",
       "      <td>0.330951</td>\n",
       "      <td>0.882092</td>\n",
       "      <td>0.275882</td>\n",
       "      <td>0.650953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.770243</td>\n",
       "      <td>0.313352</td>\n",
       "      <td>0.220805</td>\n",
       "      <td>0.027873</td>\n",
       "      <td>0.761497</td>\n",
       "      <td>0.119895</td>\n",
       "      <td>0.310588</td>\n",
       "      <td>0.198915</td>\n",
       "      <td>0.472835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.160599</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>0.929504</td>\n",
       "      <td>0.750928</td>\n",
       "      <td>0.097759</td>\n",
       "      <td>0.675912</td>\n",
       "      <td>0.686486</td>\n",
       "      <td>0.614004</td>\n",
       "      <td>0.167216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>a</th>\n",
       "      <td>0.968565</td>\n",
       "      <td>0.406914</td>\n",
       "      <td>0.173109</td>\n",
       "      <td>0.533618</td>\n",
       "      <td>0.014341</td>\n",
       "      <td>0.701709</td>\n",
       "      <td>0.704982</td>\n",
       "      <td>0.623265</td>\n",
       "      <td>0.677072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.687038</td>\n",
       "      <td>0.017382</td>\n",
       "      <td>0.105115</td>\n",
       "      <td>0.025243</td>\n",
       "      <td>0.605660</td>\n",
       "      <td>0.349725</td>\n",
       "      <td>0.018865</td>\n",
       "      <td>0.078166</td>\n",
       "      <td>0.920426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Big                 D                             E                      \\\n",
       "Small               d changed_e         f         d changed_e         f   \n",
       "Upper Lower                                                               \n",
       "T     a      0.077679  0.567787  0.665333  0.942349  0.531474  0.330951   \n",
       "      b      0.770243  0.313352  0.220805  0.027873  0.761497  0.119895   \n",
       "      c      0.160599  0.974000  0.929504  0.750928  0.097759  0.675912   \n",
       "B     a      0.968565  0.406914  0.173109  0.533618  0.014341  0.701709   \n",
       "      b      0.687038  0.017382  0.105115  0.025243  0.605660  0.349725   \n",
       "\n",
       "Big                 F                      \n",
       "Small               d changed_e         f  \n",
       "Upper Lower                                \n",
       "T     a      0.882092  0.275882  0.650953  \n",
       "      b      0.310588  0.198915  0.472835  \n",
       "      c      0.686486  0.614004  0.167216  \n",
       "B     a      0.704982  0.623265  0.677072  \n",
       "      b      0.018865  0.078166  0.920426  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.rename(index={'A':'T'},columns={'e':'changed_e'}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、常用索引型函数\n",
    "### 1. where函数\n",
    "#### 当对条件为False的单元进行填充："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
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       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1   173.0    63.0  34.0      A+\n",
       "1102    NaN   NaN    NaN       NaN     NaN     NaN   NaN     NaN\n",
       "1103    S_1   C_1      M  street_2   186.0    82.0  87.2      B+\n",
       "1104    NaN   NaN    NaN       NaN     NaN     NaN   NaN     NaN\n",
       "1105    NaN   NaN    NaN       NaN     NaN     NaN   NaN     NaN"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.where(df['Gender']=='M').head()\n",
    "#不满足条件的行全部被设置为NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 通过这种方法筛选结果和[]操作符的结果完全一致："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>188.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>161.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1   173.0    63.0  34.0      A+\n",
       "1103    S_1   C_1      M  street_2   186.0    82.0  87.2      B+\n",
       "1201    S_1   C_2      M  street_5   188.0    68.0  97.0      A-\n",
       "1203    S_1   C_2      M  street_6   160.0    53.0  58.8      A+\n",
       "1301    S_1   C_3      M  street_4   161.0    68.0  31.5      B+"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.where(df['Gender']=='M').dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 第一个参数为布尔条件，第二个参数为填充值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>0.804438</td>\n",
       "      <td>0.956796</td>\n",
       "      <td>0.182926</td>\n",
       "      <td>0.728754</td>\n",
       "      <td>0.810268</td>\n",
       "      <td>0.254977</td>\n",
       "      <td>0.635681</td>\n",
       "      <td>0.0883274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>87.200000</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>0.216128</td>\n",
       "      <td>0.677674</td>\n",
       "      <td>0.290603</td>\n",
       "      <td>0.000361722</td>\n",
       "      <td>0.697820</td>\n",
       "      <td>0.679540</td>\n",
       "      <td>0.930052</td>\n",
       "      <td>0.290292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>0.478766</td>\n",
       "      <td>0.802287</td>\n",
       "      <td>0.745546</td>\n",
       "      <td>0.900654</td>\n",
       "      <td>0.749546</td>\n",
       "      <td>0.573542</td>\n",
       "      <td>0.108087</td>\n",
       "      <td>0.00666063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        School     Class    Gender      Address      Height     Weight  \\\n",
       "ID                                                                       \n",
       "1101       S_1       C_1         M     street_1  173.000000  63.000000   \n",
       "1102  0.804438  0.956796  0.182926     0.728754    0.810268   0.254977   \n",
       "1103       S_1       C_1         M     street_2  186.000000  82.000000   \n",
       "1104  0.216128  0.677674  0.290603  0.000361722    0.697820   0.679540   \n",
       "1105  0.478766  0.802287  0.745546     0.900654    0.749546   0.573542   \n",
       "\n",
       "           Math     Physics  \n",
       "ID                           \n",
       "1101  34.000000          A+  \n",
       "1102   0.635681   0.0883274  \n",
       "1103  87.200000          B+  \n",
       "1104   0.930052    0.290292  \n",
       "1105   0.108087  0.00666063  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.where(df['Gender']=='M',np.random.rand(df.shape[0],df.shape[1])).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. mask函数\n",
    "#### mask函数与where功能上相反，其余完全一致，即对条件为True的单元进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
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       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>176.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2   192.0    73.0  32.5      B+\n",
       "1104    S_1   C_1      F  street_2   167.0    81.0  80.4      B-\n",
       "1105    S_1   C_1      F  street_4   159.0    64.0  84.8      B+\n",
       "1202    S_1   C_2      F  street_4   176.0    94.0  63.5      B-\n",
       "1204    S_1   C_2      F  street_5   162.0    63.0  33.8       B"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mask(df['Gender']=='M').dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
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       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>0.682213</td>\n",
       "      <td>0.17613</td>\n",
       "      <td>0.81589</td>\n",
       "      <td>0.899976</td>\n",
       "      <td>0.779533</td>\n",
       "      <td>0.768027</td>\n",
       "      <td>0.824438</td>\n",
       "      <td>0.169901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>0.555236</td>\n",
       "      <td>0.758632</td>\n",
       "      <td>0.12173</td>\n",
       "      <td>0.374172</td>\n",
       "      <td>0.385267</td>\n",
       "      <td>0.264608</td>\n",
       "      <td>0.992286</td>\n",
       "      <td>0.00513714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>80.400000</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>84.800000</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        School     Class   Gender   Address      Height     Weight       Math  \\\n",
       "ID                                                                              \n",
       "1101  0.682213   0.17613  0.81589  0.899976    0.779533   0.768027   0.824438   \n",
       "1102       S_1       C_1        F  street_2  192.000000  73.000000  32.500000   \n",
       "1103  0.555236  0.758632  0.12173  0.374172    0.385267   0.264608   0.992286   \n",
       "1104       S_1       C_1        F  street_2  167.000000  81.000000  80.400000   \n",
       "1105       S_1       C_1        F  street_4  159.000000  64.000000  84.800000   \n",
       "\n",
       "         Physics  \n",
       "ID                \n",
       "1101    0.169901  \n",
       "1102          B+  \n",
       "1103  0.00513714  \n",
       "1104          B-  \n",
       "1105          B+  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mask(df['Gender']=='M',np.random.rand(df.shape[0],df.shape[1])).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. query函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
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       "      <td>34.0</td>\n",
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       "      <th>1102</th>\n",
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       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
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       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
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       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81</td>\n",
       "      <td>80.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64</td>\n",
       "      <td>84.8</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "1104    S_1   C_1      F  street_2     167      81  80.4      B-\n",
       "1105    S_1   C_1      F  street_4     159      64  84.8      B+"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### query函数中的布尔表达式中，下面的符号都是合法的：行列索引名、字符串、and/not/or/&/|/~/not in/in/==/!=、四则运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>188</td>\n",
       "      <td>82</td>\n",
       "      <td>49.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>164</td>\n",
       "      <td>81</td>\n",
       "      <td>95.5</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>166</td>\n",
       "      <td>82</td>\n",
       "      <td>48.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1303    S_1   C_3      M  street_7     188      82  49.7       B\n",
       "2304    S_2   C_3      F  street_6     164      81  95.5      A-\n",
       "2402    S_2   C_4      M  street_7     166      82  48.7       B"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('(Address in [\"street_6\",\"street_7\"])&(Weight>(70+10))&(ID in [1303,2304,2402])')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、重复元素处理\n",
    "### 1. duplicated方法\n",
    "#### 该方法返回了是否重复的布尔列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101    False\n",
       "1102     True\n",
       "1103     True\n",
       "1104     True\n",
       "1105     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated('Class').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可选参数keep默认为first，即首次出现设为不重复，若为last，则最后一次设为不重复，若为False，则所有重复项为True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "2401     True\n",
       "2402     True\n",
       "2403     True\n",
       "2404     True\n",
       "2405    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated('Class',keep='last').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "1101    True\n",
       "1102    True\n",
       "1103    True\n",
       "1104    True\n",
       "1105    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated('Class',keep=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. drop_duplicates方法\n",
    "#### 从名字上看出为剔除重复项，这在后面章节中的分组操作中可能是有用的，例如需要保留每组的第一个值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>188</td>\n",
       "      <td>68</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>62</td>\n",
       "      <td>45.3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1201    S_1   C_2      M  street_5     188      68  97.0      A-\n",
       "1301    S_1   C_3      M  street_4     161      68  31.5      B+\n",
       "2401    S_2   C_4      F  street_2     192      62  45.3       A"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates('Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 参数与duplicate函数类似："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>170</td>\n",
       "      <td>81</td>\n",
       "      <td>34.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_7</td>\n",
       "      <td>183</td>\n",
       "      <td>76</td>\n",
       "      <td>85.4</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2305</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>187</td>\n",
       "      <td>73</td>\n",
       "      <td>48.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>193</td>\n",
       "      <td>54</td>\n",
       "      <td>47.6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "2105    S_2   C_1      M  street_4     170      81  34.2       A\n",
       "2205    S_2   C_2      F  street_7     183      76  85.4       B\n",
       "2305    S_2   C_3      M  street_4     187      73  48.9       B\n",
       "2405    S_2   C_4      F  street_6     193      54  47.6       B"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates('Class',keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在传入多列时等价于将多列共同视作一个多级索引，比较重复项："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
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       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>63</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>188</td>\n",
       "      <td>68</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>161</td>\n",
       "      <td>68</td>\n",
       "      <td>31.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>174</td>\n",
       "      <td>84</td>\n",
       "      <td>83.3</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>193</td>\n",
       "      <td>100</td>\n",
       "      <td>39.1</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>157</td>\n",
       "      <td>78</td>\n",
       "      <td>72.3</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>62</td>\n",
       "      <td>45.3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1101    S_1   C_1      M  street_1     173      63  34.0      A+\n",
       "1201    S_1   C_2      M  street_5     188      68  97.0      A-\n",
       "1301    S_1   C_3      M  street_4     161      68  31.5      B+\n",
       "2101    S_2   C_1      M  street_7     174      84  83.3       C\n",
       "2201    S_2   C_2      M  street_5     193     100  39.1       B\n",
       "2301    S_2   C_3      F  street_4     157      78  72.3      B+\n",
       "2401    S_2   C_4      F  street_2     192      62  45.3       A"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(['School','Class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 六、抽样函数\n",
    "#### 这里的抽样函数指的就是sample函数\n",
    "#### （a）n为样本量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School</th>\n",
       "      <th>Class</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Math</th>\n",
       "      <th>Physics</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2403</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>158</td>\n",
       "      <td>60</td>\n",
       "      <td>59.7</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>187</td>\n",
       "      <td>69</td>\n",
       "      <td>61.7</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>155</td>\n",
       "      <td>91</td>\n",
       "      <td>73.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>164</td>\n",
       "      <td>81</td>\n",
       "      <td>95.5</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "2403    S_2   C_4      F  street_6     158      60  59.7      B+\n",
       "1305    S_1   C_3      F  street_5     187      69  61.7      B-\n",
       "2203    S_2   C_2      M  street_4     155      91  73.8      A+\n",
       "2304    S_2   C_3      F  street_6     164      81  95.5      A-\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）frac为抽样比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>157</td>\n",
       "      <td>61</td>\n",
       "      <td>52.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "2103    S_2   C_1      M  street_4     157      61  52.5      B-"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(frac=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）replace为是否放回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Address</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>160</td>\n",
       "      <td>84</td>\n",
       "      <td>67.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>62</td>\n",
       "      <td>45.3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>187</td>\n",
       "      <td>69</td>\n",
       "      <td>61.7</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>175</td>\n",
       "      <td>74</td>\n",
       "      <td>47.2</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>157</td>\n",
       "      <td>61</td>\n",
       "      <td>52.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "2404    S_2   C_4      F  street_2     160      84  67.7       B\n",
       "2401    S_2   C_4      F  street_2     192      62  45.3       A\n",
       "1305    S_1   C_3      F  street_5     187      69  61.7      B-\n",
       "2204    S_2   C_2      M  street_1     175      74  47.2      B-\n",
       "2103    S_2   C_1      M  street_4     157      61  52.5      B-"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=df.shape[0],replace=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=35,replace=True).index.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （d）axis为抽样维度，默认为0，即抽行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>173</td>\n",
       "      <td>A+</td>\n",
       "      <td>S_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>192</td>\n",
       "      <td>B+</td>\n",
       "      <td>S_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>186</td>\n",
       "      <td>B+</td>\n",
       "      <td>S_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>167</td>\n",
       "      <td>B-</td>\n",
       "      <td>S_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>159</td>\n",
       "      <td>B+</td>\n",
       "      <td>S_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height Physics School\n",
       "ID                         \n",
       "1101     173      A+    S_1\n",
       "1102     192      B+    S_1\n",
       "1103     186      B+    S_1\n",
       "1104     167      B-    S_1\n",
       "1105     159      B+    S_1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=3,axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （e）weights为样本权重，自动归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>F</td>\n",
       "      <td>street_1</td>\n",
       "      <td>175</td>\n",
       "      <td>57</td>\n",
       "      <td>87.7</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>170</td>\n",
       "      <td>81</td>\n",
       "      <td>34.2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1302    S_1   C_3      F  street_1     175      57  87.7      A-\n",
       "1102    S_1   C_1      F  street_2     192      73  32.5      B+\n",
       "2105    S_2   C_1      M  street_4     170      81  34.2       A"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=3,weights=np.random.rand(df.shape[0])).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>82</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_4</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>193</td>\n",
       "      <td>54</td>\n",
       "      <td>47.6</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>167</td>\n",
       "      <td>63</td>\n",
       "      <td>68.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     School Class Gender   Address  Height  Weight  Math Physics\n",
       "ID                                                              \n",
       "1103    S_1   C_1      M  street_2     186      82  87.2      B+\n",
       "2405    S_2   C_4      F  street_6     193      54  47.6       B\n",
       "1205    S_1   C_2      F  street_6     167      63  68.4      B-"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#以某一列为权重，这在抽样理论中很常见\n",
    "#抽到的概率与Math数值成正比\n",
    "df.sample(n=3,weights=df['Math']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 七、问题与练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 问题\n",
    "#### 【问题一】 如何更改列或行的顺序？如何交换奇偶行（列）的顺序？\n",
    "#### 【问题二】 如果要选出DataFrame的某个子集，请给出尽可能多的方法实现。\n",
    "#### 【问题三】 query函数比其他索引方法的速度更慢吗？在什么场合使用什么索引最高效？\n",
    "#### 【问题四】 单级索引能使用Slice对象吗？能的话怎么使用，请给出一个例子。\n",
    "#### 【问题五】 如何快速找出某一列的缺失值所在索引？\n",
    "#### 【问题六】 索引设定中的所有方法分别适用于哪些场合？怎么直接把某个DataFrame的索引换成任意给定同长度的索引？\n",
    "#### 【问题七】 对于多层索引，怎么对内层进行条件筛选？\n",
    "#### 【问题八】 swaplevel中的axis参数为1时，代表什么意思？i和j只能是数值型吗？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 练习\n",
    "#### 【练习一】 现有一份关于UFO的数据集，请解决下列问题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>shape</th>\n",
       "      <th>duration (seconds)</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>0</th>\n",
       "      <td>10/10/1949 20:30</td>\n",
       "      <td>cylinder</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>29.883056</td>\n",
       "      <td>-97.941111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/10/1949 21:00</td>\n",
       "      <td>light</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>29.384210</td>\n",
       "      <td>-98.581082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/10/1955 17:00</td>\n",
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       "      <td>20.0</td>\n",
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       "      <td>-2.916667</td>\n",
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       "      <th>3</th>\n",
       "      <td>10/10/1956 21:00</td>\n",
       "      <td>circle</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.978333</td>\n",
       "      <td>-96.645833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/10/1960 20:00</td>\n",
       "      <td>light</td>\n",
       "      <td>900.0</td>\n",
       "      <td>21.418056</td>\n",
       "      <td>-157.803611</td>\n",
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      "text/plain": [
       "           datetime     shape  duration (seconds)   latitude   longitude\n",
       "0  10/10/1949 20:30  cylinder              2700.0  29.883056  -97.941111\n",
       "1  10/10/1949 21:00     light              7200.0  29.384210  -98.581082\n",
       "2  10/10/1955 17:00    circle                20.0  53.200000   -2.916667\n",
       "3  10/10/1956 21:00    circle                20.0  28.978333  -96.645833\n",
       "4  10/10/1960 20:00     light               900.0  21.418056 -157.803611"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/UFO.csv').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）在所有被观测时间超过60s的时间中，哪个形状最多？\n",
    "#### （b）对经纬度进行划分：-180°至180°以30°为一个经度划分，-90°至90°以18°为一个维度划分，请问哪个区域中报告的UFO事件数量最多？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【练习二】 现有一份关于口袋妖怪的数据集，请解决下列问题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>#</th>\n",
       "      <th>Name</th>\n",
       "      <th>Type 1</th>\n",
       "      <th>Type 2</th>\n",
       "      <th>Total</th>\n",
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       "      <th>Sp. Atk</th>\n",
       "      <th>Sp. Def</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>318</td>\n",
       "      <td>45</td>\n",
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       "      <td>Poison</td>\n",
       "      <td>405</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
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       "      <td>82</td>\n",
       "      <td>83</td>\n",
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       "      <td>100</td>\n",
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       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>625</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
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       "      <td>Charmander</td>\n",
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       "      <td>309</td>\n",
       "      <td>39</td>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "   #                   Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
       "0  1              Bulbasaur  Grass  Poison    318  45      49       49   \n",
       "1  2                Ivysaur  Grass  Poison    405  60      62       63   \n",
       "2  3               Venusaur  Grass  Poison    525  80      82       83   \n",
       "3  3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
       "4  4             Charmander   Fire     NaN    309  39      52       43   \n",
       "\n",
       "   Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
       "0       65       65     45           1      False  \n",
       "1       80       80     60           1      False  \n",
       "2      100      100     80           1      False  \n",
       "3      122      120     80           1      False  \n",
       "4       60       50     65           1      False  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/Pokemon.csv').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）双属性的Pokemon占总体比例的多少？\n",
    "#### （b）在所有种族值（Total）不小于580的Pokemon中，非神兽（Legendary=False）的比例为多少？\n",
    "#### （c）在第一属性为格斗系（Fighting）的Pokemon中，物攻排名前三高的是哪些？\n",
    "#### （d）请问六项种族指标（HP、物攻、特攻、物防、特防、速度）极差的均值最大的是哪个属性（只考虑第一属性，且均值是对属性而言）？\n",
    "#### （e）哪个属性（只考虑第一属性）神兽占总Pokemon的比例最高？该属性神兽的种族值也是最高的吗？"
   ]
  }
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