第6章 缺失数据.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第6章 缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在接下来的两章中，会接触到数据预处理中比较麻烦的类型，即缺失数据和文本数据（尤其是混杂型文本）\n",
    "#### Pandas在步入1.0后，对数据类型也做出了新的尝试，尤其是Nullable类型和String类型，了解这些可能在未来成为主流的新特性是必要的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.085679Z",
     "start_time": "2020-06-23T23:23:23.862438Z"
    }
   },
   "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>ID</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>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>street_1</td>\n",
       "      <td>173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_2</td>\n",
       "      <td>186</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81.0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_1</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_4</td>\n",
       "      <td>159</td>\n",
       "      <td>64.0</td>\n",
       "      <td>84.8</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>159</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>176</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>street_6</td>\n",
       "      <td>160</td>\n",
       "      <td>53.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>162</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_6</td>\n",
       "      <td>167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.4</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  School Class      ID Gender   Address  Height  Weight  Math Physics\n",
       "0    S_1   C_1     NaN      M  street_1     173     NaN  34.0      A+\n",
       "1    S_1   C_1     NaN      F  street_2     192     NaN  32.5      B+\n",
       "2    S_1   C_1  1103.0      M  street_2     186     NaN  87.2      B+\n",
       "3    S_1   NaN     NaN      F  street_2     167    81.0  80.4     NaN\n",
       "4    S_1   C_1  1105.0    NaN  street_4     159    64.0  84.8      A-\n",
       "5    S_1   C_2  1201.0      M  street_5     159    68.0  97.0      A-\n",
       "6    S_1   C_2  1202.0      F  street_4     176    94.0  63.5      B-\n",
       "7    S_1   C_2     NaN      M  street_6     160    53.0  58.8      A+\n",
       "8    S_1   C_2  1204.0      F  street_5     162    63.0  33.8     NaN\n",
       "9    S_1   C_2  1205.0    NaN  street_6     167     NaN  68.4      B-"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('data/table_missing.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、缺失观测及其类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 了解缺失信息\n",
    "#### （a）isna和notna方法\n",
    "#### 对Series使用会返回布尔列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.101707Z",
     "start_time": "2020-06-23T23:23:24.085679Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "Name: Physics, dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Physics'].isna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.117721Z",
     "start_time": "2020-06-23T23:23:24.102708Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "Name: Physics, dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Physics'].notna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对DataFrame使用会返回布尔表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.133736Z",
     "start_time": "2020-06-23T23:23:24.118722Z"
    }
   },
   "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>ID</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   School  Class     ID  Gender  Address  Height  Weight   Math  Physics\n",
       "0   False  False   True   False    False   False    True  False    False\n",
       "1   False  False   True   False    False   False    True  False    False\n",
       "2   False  False  False   False    False   False    True  False    False\n",
       "3   False   True   True   False    False   False   False  False     True\n",
       "4   False  False  False    True    False   False   False  False    False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 但对于DataFrame我们更关心到底每列有多少缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.149750Z",
     "start_time": "2020-06-23T23:23:24.134738Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School      0\n",
       "Class       4\n",
       "ID          6\n",
       "Gender      7\n",
       "Address     0\n",
       "Height      0\n",
       "Weight     13\n",
       "Math        5\n",
       "Physics     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 此外，可以通过第1章中介绍的info函数查看缺失信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.165765Z",
     "start_time": "2020-06-23T23:23:24.149750Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35 entries, 0 to 34\n",
      "Data columns (total 9 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   School   35 non-null     object \n",
      " 1   Class    31 non-null     object \n",
      " 2   ID       29 non-null     float64\n",
      " 3   Gender   28 non-null     object \n",
      " 4   Address  35 non-null     object \n",
      " 5   Height   35 non-null     int64  \n",
      " 6   Weight   22 non-null     float64\n",
      " 7   Math     30 non-null     float64\n",
      " 8   Physics  31 non-null     object \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 2.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）查看缺失值的所以在行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 以最后一列为例，挑出该列缺失值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.181779Z",
     "start_time": "2020-06-23T23:23:24.166766Z"
    }
   },
   "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>ID</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>3</th>\n",
       "      <td>S_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>street_2</td>\n",
       "      <td>167</td>\n",
       "      <td>81.0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_5</td>\n",
       "      <td>162</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_2</td>\n",
       "      <td>195</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>155</td>\n",
       "      <td>91.0</td>\n",
       "      <td>73.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   School Class      ID Gender   Address  Height  Weight  Math Physics\n",
       "3     S_1   NaN     NaN      F  street_2     167    81.0  80.4     NaN\n",
       "8     S_1   C_2  1204.0      F  street_5     162    63.0  33.8     NaN\n",
       "13    S_1   C_3  1304.0    NaN  street_2     195    70.0  85.2     NaN\n",
       "22    S_2   C_2  2203.0      M  street_4     155    91.0  73.8     NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Physics'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）挑选出所有非缺失值列\n",
    "#### 使用all就是全部非缺失值，如果是any就是至少有一个不是缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.197793Z",
     "start_time": "2020-06-23T23:23:24.182780Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna().all(1).head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame.all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs)  \n",
    "[pd.df.all](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.all.html)  \n",
    "axis{0 or ‘index’, 1 or ‘columns’, None}, default 0  \n",
    "indicate which axis or axes should be reduced.\n",
    "\n",
    "* 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.\n",
    "\n",
    "* 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.\n",
    "\n",
    "* None : reduce all axes, return a scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.213808Z",
     "start_time": "2020-06-23T23:23:24.198794Z"
    }
   },
   "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>ID</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>5</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_5</td>\n",
       "      <td>159</td>\n",
       "      <td>68.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_2</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>176</td>\n",
       "      <td>94.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S_1</td>\n",
       "      <td>C_3</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_7</td>\n",
       "      <td>188</td>\n",
       "      <td>82.0</td>\n",
       "      <td>49.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_1</td>\n",
       "      <td>2103.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>157</td>\n",
       "      <td>61.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_2</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_7</td>\n",
       "      <td>194</td>\n",
       "      <td>77.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>2301.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_4</td>\n",
       "      <td>157</td>\n",
       "      <td>78.0</td>\n",
       "      <td>72.3</td>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_7</td>\n",
       "      <td>190</td>\n",
       "      <td>99.0</td>\n",
       "      <td>65.9</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>F</td>\n",
       "      <td>street_6</td>\n",
       "      <td>164</td>\n",
       "      <td>81.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>S_2</td>\n",
       "      <td>C_3</td>\n",
       "      <td>2305.0</td>\n",
       "      <td>M</td>\n",
       "      <td>street_4</td>\n",
       "      <td>187</td>\n",
       "      <td>73.0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   School Class      ID Gender   Address  Height  Weight  Math Physics\n",
       "5     S_1   C_2  1201.0      M  street_5     159    68.0  97.0      A-\n",
       "6     S_1   C_2  1202.0      F  street_4     176    94.0  63.5      B-\n",
       "12    S_1   C_3  1303.0      M  street_7     188    82.0  49.7       B\n",
       "17    S_2   C_1  2103.0      M  street_4     157    61.0  52.5      B-\n",
       "21    S_2   C_2  2202.0      F  street_7     194    77.0  68.5      B+\n",
       "25    S_2   C_3  2301.0      F  street_4     157    78.0  72.3      B+\n",
       "27    S_2   C_3  2303.0      F  street_7     190    99.0  65.9       C\n",
       "28    S_2   C_3  2304.0      F  street_6     164    81.0  95.5      A-\n",
       "29    S_2   C_3  2305.0      M  street_4     187    73.0  48.9       B"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.notna().all(1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 三种缺失符号\n",
    "#### （a）np.nan\n",
    "#### np.nan是一个麻烦的东西，首先它不等与任何东西，甚至不等于自己"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.229822Z",
     "start_time": "2020-06-23T23:23:24.214809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.245836Z",
     "start_time": "2020-06-23T23:23:24.230823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.261850Z",
     "start_time": "2020-06-23T23:23:24.246837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在用equals函数比较时，自动略过两侧全是np.nan的单元格，因此结果不会影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.277865Z",
     "start_time": "2020-06-23T23:23:24.262852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.equals(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 其次，它在numpy中的类型为浮点，由此导致数据集读入时，即使原来是整数的列，只要有缺失值就会变为浮点型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.293879Z",
     "start_time": "2020-06-23T23:23:24.278866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.309894Z",
     "start_time": "2020-06-23T23:23:24.294880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,3]).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.325908Z",
     "start_time": "2020-06-23T23:23:24.309894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,np.nan,3]).dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 此外，对于布尔类型的列表，如果是np.nan填充，那么它的值会自动变为True而不是False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame.bool(self)  [pd.df.bool](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.bool.html)  \n",
    "Return the bool of a single element PandasObject.  \n",
    "This must be a boolean scalar value, either True or False.   \n",
    "Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean\n",
    "\n",
    "* Returns bool\n",
    "* Same single boolean value converted to bool type.\n",
    "- 下面这一段内容，一开始粗心，以为是上面这一段，和助教一番battle后发现，不好意思粗心了。\n",
    "- 我说所以为什么要Int64呢，难道有什么可以忽略大小写的后续能用吗？然后没有一个人理解我的意思，都在觉得我钻牛角尖，我当时就是生气，很生气。\n",
    "- 然后助教 半解释了一堆我已经知道的，又说：大小写可能是为了区分pandas中的64位整型Int64和numpy中的64位整型int64，而numpy的int64对缺失值的支持不好。\n",
    "- 行吧后面这部分还是有点东西，但是我没空（懒得)印证了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**dtypes**  [dtypes](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-dtypes)\n",
    "For the most part, pandas uses NumPy arrays and dtypes for Series or individual columns of a DataFrame.   \n",
    "NumPy provides support for float, int, bool, timedelta64 and datetime64.\n",
    "(note that NumPy does not support timezone-aware datetimes).\n",
    "大概是pandas用的Numpy arrays and dtypes\n",
    "**dtypes python**\n",
    "[然后Numpy use python的，猜的，不会](https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.dtypes.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.341922Z",
     "start_time": "2020-06-23T23:23:24.325908Z"
    }
   },
   "outputs": [],
   "source": [
    "# python中 bool为 False的, 其他均为True, 怀疑这里（pandas)和python是一样的\n",
    "0  \n",
    "0.0  \n",
    "0j  # complex\n",
    "False  \n",
    "''    # 空字符串 \n",
    "()    # 空元组\n",
    "[]    # 空列表\n",
    "set()  # 空集合\n",
    "{}    # 空字典\n",
    "None #系统关键字 代表什么也没有 ，一般用于初始化变量的时候用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.357936Z",
     "start_time": "2020-06-23T23:23:24.342924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,np.nan,3],dtype='bool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 但当修改一个布尔列表时，会改变列表类型，而不是赋值为True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.373951Z",
     "start_time": "2020-06-23T23:23:24.358938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([True,False],dtype='bool')\n",
    "s[1]=np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在所有的表格读取后，无论列是存放什么类型的数据，默认的缺失值全为np.nan类型\n",
    "#### 因此整型列转为浮点；而字符由于无法转化为浮点，因此只能归并为object类型（'O'），原来是浮点型的则类型不变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.389965Z",
     "start_time": "2020-06-23T23:23:24.374952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ID'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.405979Z",
     "start_time": "2020-06-23T23:23:24.390966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Math'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.421994Z",
     "start_time": "2020-06-23T23:23:24.408982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）None\n",
    "#### None比前者稍微好些，至少它会等于自身"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.438008Z",
     "start_time": "2020-06-23T23:23:24.422995Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "None == None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 它的布尔值为False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.454023Z",
     "start_time": "2020-06-23T23:23:24.439010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([None],dtype='bool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 修改布尔列表不会改变数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.470037Z",
     "start_time": "2020-06-23T23:23:24.455024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([True,False],dtype='bool')\n",
    "s[0]=None\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.486052Z",
     "start_time": "2020-06-23T23:23:24.471038Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,0],dtype='bool')\n",
    "s[0]=None\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 在传入数值类型后，会自动变为np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.502066Z",
     "start_time": "2020-06-23T23:23:24.486052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.Series([1,None])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 只有当传入object类型是保持不动，几乎可以认为，除非人工命名None，它基本不会自动出现在Pandas中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.518080Z",
     "start_time": "2020-06-23T23:23:24.503067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.Series([1,None],dtype='O')[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  在使用equals函数时不会被略过，因此下面的情况下返回False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.534094Z",
     "start_time": "2020-06-23T23:23:24.519081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([None]).equals(pd.Series([np.nan]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）NaT\n",
    "#### NaT是针对时间序列的缺失值，是Pandas的内置类型，可以完全看做时序版本的np.nan，与自己不等，且使用equals是也会被跳过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.550108Z",
     "start_time": "2020-06-23T23:23:24.534094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2012-01-01\n",
       "1   2012-01-01\n",
       "2   2012-01-01\n",
       "3   2012-01-01\n",
       "4   2012-01-01\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_time = pd.Series([pd.Timestamp('20120101')]*5)\n",
    "s_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.566123Z",
     "start_time": "2020-06-23T23:23:24.551110Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2012-01-01\n",
       "1   2012-01-01\n",
       "2          NaT\n",
       "3   2012-01-01\n",
       "4   2012-01-01\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_time[2] = None\n",
    "s_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.582137Z",
     "start_time": "2020-06-23T23:23:24.566123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2012-01-01\n",
       "1   2012-01-01\n",
       "2          NaT\n",
       "3   2012-01-01\n",
       "4   2012-01-01\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_time[2] = np.nan\n",
    "s_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.598152Z",
     "start_time": "2020-06-23T23:23:24.583139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2012-01-01\n",
       "1   2012-01-01\n",
       "2          NaT\n",
       "3   2012-01-01\n",
       "4   2012-01-01\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_time[2] = pd.NaT\n",
    "s_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.614166Z",
     "start_time": "2020-06-23T23:23:24.599152Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.nattype.NaTType"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s_time[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.630180Z",
     "start_time": "2020-06-23T23:23:24.614166Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_time[2] == s_time[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.646172Z",
     "start_time": "2020-06-23T23:23:24.631181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_time.equals(s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.661835Z",
     "start_time": "2020-06-23T23:23:24.647173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([True,False],dtype='bool')\n",
    "s[1]=pd.NaT\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Nullable类型与NA符号\n",
    "#### 这是Pandas在1.0新版本中引入的重大改变，其目的就是为了（在若干版本后）解决之前出现的混乱局面，统一缺失值处理方法\n",
    "#### \"The goal of pd.NA is provide a “missing” indicator that can be used consistently across data types (instead of np.nan, None or pd.NaT depending on the data type).\"——User Guide for Pandas v-1.0\n",
    "#### 官方鼓励用户使用新的数据类型和缺失类型pd.NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）Nullable整形\n",
    "#### 对于该种类型而言，它与原来标记int上的符号区别在于首字母大写：'Int'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.669479Z",
     "start_time": "2020-06-23T23:23:24.662836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_original = pd.Series([1, 2], dtype=\"int64\")\n",
    "s_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:05:09.359300Z",
     "start_time": "2020-06-24T00:05:09.348805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new = pd.Series([1, 2], dtype=\"Int64\")\n",
    "s_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 它的好处就在于，其中前面提到的三种缺失值都会被替换为统一的NA符号，且不改变数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.701211Z",
     "start_time": "2020-06-23T23:23:24.686198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_original[1] = np.nan\n",
    "s_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:05:12.553815Z",
     "start_time": "2020-06-24T00:05:12.546809Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    <NA>\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new[1] = np.nan\n",
    "s_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:05:18.232664Z",
     "start_time": "2020-06-24T00:05:18.225143Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.731604Z",
     "start_time": "2020-06-23T23:23:24.716278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    <NA>\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new[1] = None\n",
    "s_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.749304Z",
     "start_time": "2020-06-23T23:23:24.731604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    <NA>\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new[1] = pd.NaT\n",
    "s_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）Nullable布尔\n",
    "#### 对于该种类型而言，作用与上面的类似，记号为boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.765952Z",
     "start_time": "2020-06-23T23:23:24.749304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_original = pd.Series([1, 0], dtype=\"bool\")\n",
    "s_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.781628Z",
     "start_time": "2020-06-23T23:23:24.766066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "dtype: boolean"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new = pd.Series([0, 1], dtype=\"boolean\")\n",
    "s_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.798326Z",
     "start_time": "2020-06-23T23:23:24.781628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_original[0] = np.nan\n",
    "s_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.815988Z",
     "start_time": "2020-06-23T23:23:24.798326Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_original = pd.Series([1, 0], dtype=\"bool\") #此处重新加一句是因为前面赋值改变了bool类型\n",
    "s_original[0] = None\n",
    "s_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.831711Z",
     "start_time": "2020-06-23T23:23:24.816146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1    True\n",
       "dtype: boolean"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new[0] = np.nan\n",
    "s_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.849392Z",
     "start_time": "2020-06-23T23:23:24.831711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1    True\n",
       "dtype: boolean"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new[0] = None\n",
    "s_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.865043Z",
     "start_time": "2020-06-23T23:23:24.849914Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1    True\n",
       "dtype: boolean"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_new[0] = pd.NaT\n",
    "s_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 需要注意的是，含有pd.NA的布尔列表在1.0.2之前的版本作为索引时会报错，这是一个之前的[bug](https://pandas.pydata.org/docs/whatsnew/v1.0.2.html#indexing-with-nullable-boolean-arrays)，现已经修复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.881811Z",
     "start_time": "2020-06-23T23:23:24.865043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['dog','cat'])\n",
    "s[s_new]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）string类型\n",
    "#### 该类型是1.0的一大创新，目的之一就是为了区分开原本含糊不清的object类型，这里将简要地提及string，因为它是第7章的主题内容\n",
    "#### 它本质上也属于Nullable类型，因为并不会因为含有缺失而改变类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.898438Z",
     "start_time": "2020-06-23T23:23:24.882812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    dog\n",
       "1    cat\n",
       "dtype: string"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['dog','cat'],dtype='string')\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.914453Z",
     "start_time": "2020-06-23T23:23:24.899440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1     cat\n",
       "dtype: string"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0] = np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.930467Z",
     "start_time": "2020-06-23T23:23:24.915454Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1     cat\n",
       "dtype: string"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0] = None\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.946481Z",
     "start_time": "2020-06-23T23:23:24.931468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1     cat\n",
       "dtype: string"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0] = pd.NaT\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ？？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 此外，和object类型的一点重要区别就在于，在调用字符方法后，string类型返回的是Nullable类型，object则会根据缺失类型和数据类型而改变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.962496Z",
     "start_time": "2020-06-23T23:23:24.946481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    <NA>\n",
       "2       0\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([\"a\", None, \"b\"], dtype=\"string\")\n",
    "s.str.count('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.978510Z",
     "start_time": "2020-06-23T23:23:24.963498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series([\"a\", None, \"b\"], dtype=\"object\")\n",
    "s2.str.count(\"a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:24.994524Z",
     "start_time": "2020-06-23T23:23:24.979511Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     <NA>\n",
       "2    False\n",
       "dtype: boolean"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.isdigit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.010539Z",
     "start_time": "2020-06-23T23:23:24.994524Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     None\n",
       "2    False\n",
       "dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.str.isdigit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. NA的特性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）逻辑运算\n",
    "#### 只需看该逻辑运算的结果是否依赖pd.NA的取值，如果依赖，则结果还是NA，如果不依赖，则直接计算结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.026553Z",
     "start_time": "2020-06-23T23:23:25.011540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True | pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.042568Z",
     "start_time": "2020-06-23T23:23:25.027554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA | True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.058582Z",
     "start_time": "2020-06-23T23:23:25.043569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False | pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.074596Z",
     "start_time": "2020-06-23T23:23:25.059583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False & pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.114762Z",
     "start_time": "2020-06-23T23:23:25.074596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True & pd.NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 取值不明直接报错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.130776Z",
     "start_time": "2020-06-23T23:23:25.115763Z"
    }
   },
   "outputs": [],
   "source": [
    "#bool(pd.NA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）算术运算和比较运算\n",
    "#### 这里只需记住除了下面两类情况，其他结果都是NA即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.146791Z",
     "start_time": "2020-06-23T23:23:25.131777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA ** 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.162805Z",
     "start_time": "2020-06-23T23:23:25.152796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 ** pd.NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 其他情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.178819Z",
     "start_time": "2020-06-23T23:23:25.165807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.194834Z",
     "start_time": "2020-06-23T23:23:25.179820Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"a\" * pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.210848Z",
     "start_time": "2020-06-23T23:23:25.195834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA == pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.226862Z",
     "start_time": "2020-06-23T23:23:25.211850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.NA < 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.242877Z",
     "start_time": "2020-06-23T23:23:25.227863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(pd.NA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.258891Z",
     "start_time": "2020-06-23T23:23:25.243878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NA>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(pd.NA, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.  convert_dtypes方法\n",
    "#### 这个函数的功能往往就是在读取数据时，就把数据列转为Nullable类型，是1.0的新函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.274905Z",
     "start_time": "2020-06-23T23:23:25.259892Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School      object\n",
       "Class       object\n",
       "ID         float64\n",
       "Gender      object\n",
       "Address     object\n",
       "Height       int64\n",
       "Weight     float64\n",
       "Math       float64\n",
       "Physics     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/table_missing.csv').dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.290919Z",
     "start_time": "2020-06-23T23:23:25.275906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School      string\n",
       "Class       string\n",
       "ID           Int64\n",
       "Gender      string\n",
       "Address     string\n",
       "Height       Int64\n",
       "Weight       Int64\n",
       "Math       float64\n",
       "Physics     string\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/table_missing.csv').convert_dtypes().dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、缺失数据的运算与分组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 加号与乘号规则"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用加法时，缺失值为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.306934Z",
     "start_time": "2020-06-23T23:23:25.291921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([2,3,np.nan,4])\n",
    "s.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用乘法时，缺失值为1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series.prod(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)  \n",
    "[Series.prod](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.prod.html)  \n",
    "Return the product of the values for the requested axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.322948Z",
     "start_time": "2020-06-23T23:23:25.307935Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.prod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用累计函数时，缺失值自动略过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.338962Z",
     "start_time": "2020-06-23T23:23:25.323949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    5.0\n",
       "2    NaN\n",
       "3    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.354977Z",
     "start_time": "2020-06-23T23:23:25.338962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1     6.0\n",
       "2     NaN\n",
       "3    24.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.cumprod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T09:15:56.814803Z",
     "start_time": "2020-06-23T09:15:56.801288Z"
    }
   },
   "source": [
    "Series.pct_change(self: ~ FrameOrSeries, periods=1, fill_method='pad', limit=None, freq=None, **kwargs) → ~FrameOrSeries\n",
    "[Series.pct_change](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.pct_change.html)  \n",
    "Percentage change between the current and a prior element.  \n",
    "Computes the percentage change from the immediately previous row by default.  \n",
    "This is useful in comparing the percentage of change in a time series of elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.370991Z",
     "start_time": "2020-06-23T23:23:25.355978Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1    0.500000\n",
       "2    0.000000\n",
       "3    0.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. groupby方法中的缺失值\n",
    "#### 自动忽略为缺失值的组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.387005Z",
     "start_time": "2020-06-23T23:23:25.371992Z"
    }
   },
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1.660216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.429846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>-0.061430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>-0.678914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.050121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one       two\n",
       "0    A  1.660216\n",
       "1    B  0.429846\n",
       "2    C -0.061430\n",
       "3    D -0.678914\n",
       "4  NaN  2.050121"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g = pd.DataFrame({'one':['A','B','C','D',np.nan],'two':np.random.randn(5)})\n",
    "df_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.403025Z",
     "start_time": "2020-06-23T23:23:25.388007Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': Int64Index([0], dtype='int64'),\n",
       " 'B': Int64Index([1], dtype='int64'),\n",
       " 'C': Int64Index([2], dtype='int64'),\n",
       " 'D': Int64Index([3], dtype='int64')}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_g.groupby('one').groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、填充与剔除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. fillna方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）值填充与前后向填充（分别与ffill方法和bfill方法等价）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.419034Z",
     "start_time": "2020-06-23T23:23:25.404021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         A+\n",
       "1         B+\n",
       "2         B+\n",
       "3    missing\n",
       "4         A-\n",
       "Name: Physics, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Physics'].fillna('missing').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.435048Z",
     "start_time": "2020-06-23T23:23:25.420035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A+\n",
       "1    B+\n",
       "2    B+\n",
       "3    B+\n",
       "4    A-\n",
       "Name: Physics, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Physics'].fillna(method='ffill').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.451063Z",
     "start_time": "2020-06-23T23:23:25.436049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A+\n",
       "1    B+\n",
       "2    B+\n",
       "3    A-\n",
       "4    A-\n",
       "Name: Physics, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Physics'].fillna(method='backfill').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）填充中的对齐特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.472180Z",
     "start_time": "2020-06-23T23:23:25.452064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  2.0  3.0\n",
       "1  3.0  4.0  5.0\n",
       "2  2.0  3.0  4.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f = pd.DataFrame({'A':[1,3,np.nan],'B':[2,4,np.nan],'C':[3,5,np.nan]})\n",
    "df_f.fillna(df_f.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 返回的结果中没有C，根据对齐特点不会被填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.488195Z",
     "start_time": "2020-06-23T23:23:25.473182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  2.0  3.0\n",
       "1  3.0  4.0  5.0\n",
       "2  2.0  3.0  NaN"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.fillna(df_f.mean()[['A','B']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. dropna方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）axis参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T09:21:10.366044Z",
     "start_time": "2020-06-23T09:21:10.360039Z"
    }
   },
   "source": [
    "DataFrame.dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False)  \n",
    "[df.dropna](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html)  \n",
    "axis{0 or ‘index’, 1 or ‘columns’}, default 0  \n",
    "Determine if rows or columns which contain missing values are removed.\n",
    "\n",
    "- 0, or ‘index’ : Drop rows which contain missing values.\n",
    "\n",
    "- 1, or ‘columns’ : Drop columns which contain missing value.\n",
    "\n",
    "- Changed in version 1.0.0: Pass tuple or list to drop on multiple axes. Only a single axis is allowed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.504209Z",
     "start_time": "2020-06-23T23:23:25.489196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A    B  C\n",
       "0 NaN  NaN  3\n",
       "1 NaN  3.0  2\n",
       "2 NaN  2.0  1"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d = pd.DataFrame({'A':[np.nan,np.nan,np.nan],'B':[np.nan,3,2],'C':[3,2,1]})\n",
    "df_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.520197Z",
     "start_time": "2020-06-23T23:23:25.505210Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [A, B, C]\n",
       "Index: []"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.533735Z",
     "start_time": "2020-06-23T23:23:25.521198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C\n",
       "0  3\n",
       "1  2\n",
       "2  1"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）how参数（可以选all或者any，表示全为缺失 去除  和  存在 缺失 去除）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.548949Z",
     "start_time": "2020-06-23T23:23:25.533735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     B  C\n",
       "0  NaN  3\n",
       "1  3.0  2\n",
       "2  2.0  1"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d.dropna(axis=1,how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）subset参数（即在某一组列范围中搜索缺失值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.564588Z",
     "start_time": "2020-06-23T23:23:25.549951Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A    B  C\n",
       "1 NaN  3.0  2\n",
       "2 NaN  2.0  1"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d.dropna(axis=0,subset=['B','C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、插值（interpolation）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 线性插值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （a）索引无关的线性插值\n",
    "#### 默认状态下，interpolate会对缺失的值进行线性插值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.584100Z",
     "start_time": "2020-06-23T23:23:25.565589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1    10.0\n",
       "2    15.0\n",
       "3    -5.0\n",
       "4    -2.0\n",
       "5     NaN\n",
       "6     NaN\n",
       "7    28.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,10,15,-5,-2,np.nan,np.nan,28])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.600115Z",
     "start_time": "2020-06-23T23:23:25.584100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1    10.0\n",
       "2    15.0\n",
       "3    -5.0\n",
       "4    -2.0\n",
       "5     8.0\n",
       "6    18.0\n",
       "7    28.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.895243Z",
     "start_time": "2020-06-23T23:23:25.600263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e54ecd340>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.interpolate().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 此时的插值与索引无关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:25.911258Z",
     "start_time": "2020-06-23T23:23:25.896245Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54      1.0\n",
       "108    10.0\n",
       "149    15.0\n",
       "178    -5.0\n",
       "193    -2.0\n",
       "223     8.0\n",
       "238    18.0\n",
       "288    28.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index = np.sort(np.random.randint(50,300,8))\n",
    "s.interpolate()\n",
    "#值不变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:26.000271Z",
     "start_time": "2020-06-23T23:23:25.912259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e54f9dbb0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.interpolate().plot()\n",
    "#后面三个点不是线性的（如果几乎为线性函数，请重新运行上面的一个代码块，这是随机性导致的）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）与索引有关的插值\n",
    "#### method中的index和time选项可以使插值线性地依赖索引，即插值为索引的线性函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:26.084128Z",
     "start_time": "2020-06-23T23:23:26.000271Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e55fe1bb0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.interpolate(method='index').plot()\n",
    "#可以看到与上面的区别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果索引是时间，那么可以按照时间长短插值，对于时间序列将在第9章详细介绍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:26.100143Z",
     "start_time": "2020-06-23T23:23:26.085129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-05-01     0.0\n",
       "2012-05-07     NaN\n",
       "2012-06-03    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_t = pd.Series([0,np.nan,10]\n",
    "        ,index=[pd.Timestamp('2012-05-01'),pd.Timestamp('2012-05-07'),pd.Timestamp('2012-06-03')])\n",
    "s_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:26.208239Z",
     "start_time": "2020-06-23T23:23:26.101144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e5603b4c0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_t.interpolate().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:23:26.333652Z",
     "start_time": "2020-06-23T23:23:26.209241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24e560a9070>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_t.interpolate(method='time').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 高级插值方法\n",
    "#### 此处的高级指的是与线性插值相比较，例如样条插值、多项式插值、阿基玛插值等（需要安装Scipy），方法详情请看[这里](https://pandas.pydata.org/pandas-docs/version/1.0.0/reference/api/pandas.DataFrame.interpolate.html#pandas.DataFrame.interpolate)\n",
    "#### 关于这部分仅给出一个官方的例子，因为插值方法是数值分析的内容，而不是Pandas中的基本知识："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:27:19.469661Z",
     "start_time": "2020-06-23T23:27:19.360521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "ser = pd.Series(np.arange(1, 10.1, .25) ** 2 + np.random.randn(37))\n",
    "missing = np.array([4, 13, 14, 15, 16, 17, 18, 20, 29])\n",
    "ser[missing] = np.nan\n",
    "methods = ['linear', 'quadratic', 'cubic']\n",
    "df = pd.DataFrame({m: ser.interpolate(method=m) for m in methods})\n",
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. interpolate中的限制参数\n",
    "#### （a）limit表示最多插入多少个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:28:07.962607Z",
     "start_time": "2020-06-23T23:28:07.956601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "4    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,np.nan,np.nan,np.nan,5])\n",
    "s.interpolate(limit=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （b）limit_direction表示插值方向，可选forward,backward,both，默认前向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:28:14.719669Z",
     "start_time": "2020-06-23T23:28:14.703932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    2.0\n",
       "4    3.0\n",
       "5    4.0\n",
       "6    5.0\n",
       "7    NaN\n",
       "8    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([np.nan,np.nan,1,np.nan,np.nan,np.nan,5,np.nan,np.nan,])\n",
    "s.interpolate(limit_direction='backward')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （c）limit_area表示插值区域，可选inside,outside，默认None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:28:20.199917Z",
     "start_time": "2020-06-23T23:28:20.186953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    NaN\n",
       "2    1.0\n",
       "3    2.0\n",
       "4    3.0\n",
       "5    4.0\n",
       "6    5.0\n",
       "7    NaN\n",
       "8    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([np.nan,np.nan,1,np.nan,np.nan,np.nan,5,np.nan,np.nan,])\n",
    "s.interpolate(limit_area='inside')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:28:24.278617Z",
     "start_time": "2020-06-23T23:28:24.266606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    NaN\n",
       "2    1.0\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "6    5.0\n",
       "7    5.0\n",
       "8    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([np.nan,np.nan,1,np.nan,np.nan,np.nan,5,np.nan,np.nan,])\n",
    "s.interpolate(limit_area='outside')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、问题与练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【问题一】 如何删除缺失值占比超过25%的列？\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`data = data.isna().sum()/data.shape[0]>=0.25`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【问题二】 什么是Nullable类型？请谈谈为什么要引入这个设计？\n",
    "使用Nullable类型是一种统一处理缺失值的方法。所有的缺失类型都统一表示为pd.NA。  \n",
    "避免了因为np.nan是float值，在使用缺失值的时候出现类型转换导致数据类型不统一难以处理的情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【问题三】 对于一份有缺失值的数据，可以采取哪些策略或方法深化对它的了解？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 先使用　convert_dtypes　统一数据类型\n",
    "2. 先检查看哪列有缺失值：　df.info()\n",
    "3. 检查每列有多少缺失值：　df.isna().sum()\n",
    "4. 检查某列缺失值所在行情况：　df[df['某列'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【练习一】现有一份虚拟数据集，列类型分别为string/浮点/整型，请解决如下问题：\n",
    "#### （a）请以列类型读入数据，并选出C为缺失值的行。\n",
    "#### （b）现需要将A中的部分单元转为缺失值，单元格中的最小转换概率为25%，且概率大小与所在行B列单元的值成正比。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设A中缺失值占比为prob，题目可以翻译成：\n",
    "1. prob > 0.25\n",
    "2. prob 正比 value in B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T01:39:39.202725Z",
     "start_time": "2020-06-24T01:39:39.181184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.922</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.503</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.938</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.952</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.972</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.572</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.523</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.557</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.695</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.782</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.736</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.682</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.916</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.935</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.823</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.763</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.976</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.684</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.935</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.913</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.552</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.892</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.891</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.960</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.799</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.801</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A      B     C\n",
       "0   not_NaN  0.922   4.0\n",
       "1   not_NaN  0.700   NaN\n",
       "2   not_NaN  0.503   8.0\n",
       "3   not_NaN  0.938   4.0\n",
       "4   not_NaN  0.952  10.0\n",
       "5   not_NaN  0.972   NaN\n",
       "6   not_NaN  0.572   2.0\n",
       "7   not_NaN  0.523  10.0\n",
       "8   not_NaN  0.557  10.0\n",
       "9   not_NaN  0.695   4.0\n",
       "10  not_NaN  0.782   1.0\n",
       "11  not_NaN  0.736   NaN\n",
       "12  not_NaN  0.706   0.0\n",
       "13  not_NaN  0.682   3.0\n",
       "14  not_NaN  0.916   8.0\n",
       "15  not_NaN  0.935   5.0\n",
       "16  not_NaN  0.823   1.0\n",
       "17  not_NaN  0.763   2.0\n",
       "18  not_NaN  0.976   5.0\n",
       "19  not_NaN  0.684   NaN\n",
       "20  not_NaN  0.935   2.0\n",
       "21  not_NaN  0.913   NaN\n",
       "22  not_NaN  0.538   5.0\n",
       "23  not_NaN  0.552   2.0\n",
       "24  not_NaN  0.892   5.0\n",
       "25  not_NaN  0.891   7.0\n",
       "26  not_NaN  0.960   2.0\n",
       "27  not_NaN  0.799   6.0\n",
       "28  not_NaN  0.577   0.0\n",
       "29  not_NaN  0.801   4.0"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/Missing_data_one.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.info() # 如果按以前一般的做法，A的dtype是object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:28:34.512024Z",
     "start_time": "2020-06-23T23:28:34.504016Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.922</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.503</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.938</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.952</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A      B     C\n",
       "0  not_NaN  0.922   4.0\n",
       "1  not_NaN  0.700   NaN\n",
       "2  not_NaN  0.503   8.0\n",
       "3  not_NaN  0.938   4.0\n",
       "4  not_NaN  0.952  10.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/Missing_data_one.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T01:35:22.264278Z",
     "start_time": "2020-06-24T01:35:22.251745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['B'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T01:35:46.227276Z",
     "start_time": "2020-06-24T01:35:46.220758Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9403578528827037"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['B'].max() / data['B'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:18:32.673727Z",
     "start_time": "2020-06-24T00:18:32.653187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.972</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.736</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.684</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>not_NaN</td>\n",
       "      <td>0.913</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A      B   C\n",
       "1   not_NaN  0.700 NaN\n",
       "5   not_NaN  0.972 NaN\n",
       "11  not_NaN  0.736 NaN\n",
       "19  not_NaN  0.684 NaN\n",
       "21  not_NaN  0.913 NaN"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (a)\n",
    "data=pd.read_csv('data/Missing_data_one.csv')\n",
    "data.convert_dtypes().dtypes\n",
    "data_c=data[data['C'].isna()]\n",
    "data_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:32:12.398666Z",
     "start_time": "2020-06-24T00:32:12.374323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A       30 non-null     string \n",
      " 1   B       30 non-null     float64\n",
      " 2   C       25 non-null     Int64  \n",
      "dtypes: Int64(1), float64(1), string(1)\n",
      "memory usage: 878.0 bytes\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data/Missing_data_one.csv').convert_dtypes().info()  # 现在用convert_dtypes 是string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:41:32.716487Z",
     "start_time": "2020-06-23T23:41:32.705128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.503"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_minb=data['B'].min()\n",
    "data_minb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先来看看官方做法，首先他把A的values, B的values zip 成tuple,  \n",
    "然后再转list 传给pd.Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-23T23:41:07.495657Z",
     "start_time": "2020-06-23T23:41:07.480525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not_NaN 0.922\n",
      "not_NaN 0.7\n",
      "not_NaN 0.503\n",
      "not_NaN 0.938\n",
      "not_NaN 0.9520000000000001\n",
      "not_NaN 0.972\n",
      "not_NaN 0.5720000000000001\n",
      "not_NaN 0.523\n",
      "not_NaN 0.557\n",
      "not_NaN 0.695\n",
      "not_NaN 0.782\n",
      "not_NaN 0.736\n",
      "not_NaN 0.706\n",
      "not_NaN 0.682\n",
      "not_NaN 0.9159999999999999\n",
      "not_NaN 0.935\n",
      "not_NaN 0.823\n",
      "not_NaN 0.763\n",
      "not_NaN 0.976\n",
      "not_NaN 0.684\n",
      "not_NaN 0.935\n",
      "not_NaN 0.9129999999999999\n",
      "not_NaN 0.5379999999999999\n",
      "not_NaN 0.552\n",
      "not_NaN 0.892\n",
      "not_NaN 0.8909999999999999\n",
      "not_NaN 0.96\n",
      "not_NaN 0.799\n",
      "not_NaN 0.5770000000000001\n",
      "not_NaN 0.8009999999999999\n"
     ]
    }
   ],
   "source": [
    "for (x, y) in zip(data['A'].values, data['B'].values):\n",
    "    print (x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:08:21.155965Z",
     "start_time": "2020-06-24T00:08:21.138341Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    (1, 2)\n",
       "1    (3, 5)\n",
       "2    (4, 1)\n",
       "dtype: object"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.Series([(1,2),(3,5),(4,1)])\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:00:58.382919Z",
     "start_time": "2020-06-24T00:00:58.377915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    (1, 2)\n",
       "1    (3, 2)\n",
       "2    (4, 1)\n",
       "dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.apply(lambda x:x[0])\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T01:06:34.218981Z",
     "start_time": "2020-06-24T01:06:34.207448Z"
    }
   },
   "source": [
    "他大概是说要么不变，  \n",
    "什么时候不变呢就是 np.random.rand() > say0.26 (0.25*x[1]/data_minb) 的时候，  \n",
    "其他时候就是np.nan。\n",
    "\n",
    "可是这里其实有点问题，np.random.rand()是以给定的形状创建一个数组，并在数组中加入在[0,1]之间均匀分布的随机样本。  \n",
    "也就是会取到<0.25, 此时照样进入条件 np.nan，  \n",
    "?rand = np.random.rand() if np.random.rand()>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T01:06:54.609793Z",
     "start_time": "2020-06-24T01:06:54.603258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4143207522868412"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.rand()\n",
    "a "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T01:27:23.148530Z",
     "start_time": "2020-06-24T01:27:23.133517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def get_rand():\\n    if np.random.rand() > 0.25 :\\n        rand = np.random.rand()\\n    return rand\\n'"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"def get_rand():\n",
    "    if np.random.rand() > 0.25 :\n",
    "        rand = np.random.rand()\n",
    "    return rand\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:20:49.002116Z",
     "start_time": "2020-06-24T00:20:48.986102Z"
    }
   },
   "outputs": [],
   "source": [
    "# (b)\n",
    "data=pd.read_csv('data/Missing_data_one.csv').convert_dtypes()\n",
    "data_minb=data['B'].min()\n",
    "data['A'] = pd.Series(list(zip(data['A'].values,data['B'].values))).apply(lambda x:x[0] if np.random.rand()>0.25*x[1]/data_minb else np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-24T00:20:50.468396Z",
     "start_time": "2020-06-24T00:20:50.456876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.200000\n",
       "B    0.000000\n",
       "C    0.166667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob = data.isna().sum()/data.shape[0]\n",
    "prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 【练习二】 现有一份缺失的数据集，记录了36个人来自的地区、身高、体重、年龄和工资，请解决如下问题：\n",
    "#### （a）统计各列缺失的比例并选出在后三列中至少有两个非缺失值的行。\n",
    "#### （b）请结合身高列和地区列中的数据，对体重进行合理插值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-01T08:33:27.374001Z",
     "start_time": "2020-07-01T08:33:27.199222Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-01T08:33:29.103005Z",
     "start_time": "2020-07-01T08:33:29.082987Z"
    }
   },
   "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>编号</th>\n",
       "      <th>地区</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>年龄</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>157.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>202.00</td>\n",
       "      <td>91.80</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>169.09</td>\n",
       "      <td>62.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>166.61</td>\n",
       "      <td>59.95</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>185.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4242.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   编号 地区      身高     体重    年龄       工资\n",
       "0   1  A  157.50    NaN  47.0  15905.0\n",
       "1   2  B  202.00  91.80  25.0      NaN\n",
       "2   3  C  169.09  62.18   NaN      NaN\n",
       "3   4  A  166.61  59.95  77.0   5434.0\n",
       "4   5  B  185.19    NaN  62.0   4242.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/Missing_data_two.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-01T08:33:31.222111Z",
     "start_time": "2020-07-01T08:33:31.210581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "编号      Int64\n",
       "地区     string\n",
       "身高    float64\n",
       "体重    float64\n",
       "年龄      Int64\n",
       "工资      Int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('data/Missing_data_two.csv')\n",
    "data.convert_dtypes().dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-01T08:33:33.474067Z",
     "start_time": "2020-07-01T08:33:33.442518Z"
    }
   },
   "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>编号</th>\n",
       "      <th>地区</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>年龄</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>157.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>202.00</td>\n",
       "      <td>91.80</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>166.61</td>\n",
       "      <td>59.95</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>185.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "      <td>187.13</td>\n",
       "      <td>78.42</td>\n",
       "      <td>55.0</td>\n",
       "      <td>13959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>163.81</td>\n",
       "      <td>57.43</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>A</td>\n",
       "      <td>183.80</td>\n",
       "      <td>75.42</td>\n",
       "      <td>48.0</td>\n",
       "      <td>19779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>B</td>\n",
       "      <td>179.67</td>\n",
       "      <td>71.70</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "      <td>186.08</td>\n",
       "      <td>77.47</td>\n",
       "      <td>65.0</td>\n",
       "      <td>12433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>163.41</td>\n",
       "      <td>57.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>B</td>\n",
       "      <td>175.99</td>\n",
       "      <td>68.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>A</td>\n",
       "      <td>165.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>B</td>\n",
       "      <td>166.48</td>\n",
       "      <td>59.83</td>\n",
       "      <td>31.0</td>\n",
       "      <td>17673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>C</td>\n",
       "      <td>191.62</td>\n",
       "      <td>82.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>172.83</td>\n",
       "      <td>65.55</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>B</td>\n",
       "      <td>156.99</td>\n",
       "      <td>51.29</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3054.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>C</td>\n",
       "      <td>200.22</td>\n",
       "      <td>90.20</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>A</td>\n",
       "      <td>154.63</td>\n",
       "      <td>49.17</td>\n",
       "      <td>35.0</td>\n",
       "      <td>14559.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>B</td>\n",
       "      <td>157.87</td>\n",
       "      <td>52.08</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>A</td>\n",
       "      <td>165.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>19890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>C</td>\n",
       "      <td>181.78</td>\n",
       "      <td>73.60</td>\n",
       "      <td>63.0</td>\n",
       "      <td>11383.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>A</td>\n",
       "      <td>164.43</td>\n",
       "      <td>57.99</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>C</td>\n",
       "      <td>172.39</td>\n",
       "      <td>65.15</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>B</td>\n",
       "      <td>162.12</td>\n",
       "      <td>55.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>A</td>\n",
       "      <td>183.73</td>\n",
       "      <td>75.36</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>C</td>\n",
       "      <td>181.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>12616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>B</td>\n",
       "      <td>167.28</td>\n",
       "      <td>60.55</td>\n",
       "      <td>64.0</td>\n",
       "      <td>18317.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>B</td>\n",
       "      <td>170.12</td>\n",
       "      <td>63.11</td>\n",
       "      <td>77.0</td>\n",
       "      <td>7398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>C</td>\n",
       "      <td>180.47</td>\n",
       "      <td>72.42</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9554.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    编号 地区      身高     体重    年龄       工资\n",
       "0    1  A  157.50    NaN  47.0  15905.0\n",
       "1    2  B  202.00  91.80  25.0      NaN\n",
       "3    4  A  166.61  59.95  77.0   5434.0\n",
       "4    5  B  185.19    NaN  62.0   4242.0\n",
       "5    6  A  187.13  78.42  55.0  13959.0\n",
       "6    7  C  163.81  57.43  43.0   6533.0\n",
       "7    8  A  183.80  75.42  48.0  19779.0\n",
       "8    9  B  179.67  71.70  65.0   8608.0\n",
       "9   10  C  186.08  77.47  65.0  12433.0\n",
       "10  11  B  163.41  57.07   NaN   6495.0\n",
       "13  14  B  175.99  68.39   NaN  13130.0\n",
       "15  16  A  165.68    NaN  46.0  13683.0\n",
       "16  17  B  166.48  59.83  31.0  17673.0\n",
       "17  18  C  191.62  82.46   NaN  12447.0\n",
       "18  19  A  172.83  65.55  23.0  13768.0\n",
       "19  20  B  156.99  51.29  62.0   3054.0\n",
       "20  21  C  200.22  90.20  41.0      NaN\n",
       "21  22  A  154.63  49.17  35.0  14559.0\n",
       "22  23  B  157.87  52.08  67.0   7398.0\n",
       "23  24  A  165.55    NaN  66.0  19890.0\n",
       "24  25  C  181.78  73.60  63.0  11383.0\n",
       "25  26  A  164.43  57.99  34.0  19899.0\n",
       "27  28  C  172.39  65.15  43.0  10362.0\n",
       "28  29  B  162.12  55.91   NaN  13362.0\n",
       "29  30  A  183.73  75.36  58.0   8270.0\n",
       "30  31  C  181.19    NaN  41.0  12616.0\n",
       "31  32  B  167.28  60.55  64.0  18317.0\n",
       "34  35  B  170.12  63.11  77.0   7398.0\n",
       "35  36  C  180.47  72.42  78.0   9554.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()/data.shape[0]\n",
    "data=data[data.iloc[:,-3:].isna().sum(1)<=1]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-01T08:33:36.807731Z",
     "start_time": "2020-07-01T08:33:36.779185Z"
    }
   },
   "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>编号</th>\n",
       "      <th>地区</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>年龄</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>157.50</td>\n",
       "      <td>53.58</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>202.00</td>\n",
       "      <td>91.80</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>166.61</td>\n",
       "      <td>59.95</td>\n",
       "      <td>77.0</td>\n",
       "      <td>5434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>185.19</td>\n",
       "      <td>81.75</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "      <td>187.13</td>\n",
       "      <td>78.42</td>\n",
       "      <td>55.0</td>\n",
       "      <td>13959.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   编号 地区      身高     体重    年龄       工资\n",
       "0   1  A  157.50  53.58  47.0  15905.0\n",
       "1   2  B  202.00  91.80  25.0      NaN\n",
       "3   4  A  166.61  59.95  77.0   5434.0\n",
       "4   5  B  185.19  81.75  62.0   4242.0\n",
       "5   6  A  187.13  78.42  55.0  13959.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_method_1 = data.copy()\n",
    "for name,group in df_method_1.groupby('地区'):\n",
    "    df_method_1.loc[group.index,'体重'] = group[['身高','体重']].sort_values(by='身高').interpolate()['体重']\n",
    "df_method_1['体重'] = df_method_1['体重'].round(decimals=2)\n",
    "df_method_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "joyfulpd",
   "language": "python",
   "name": "joyfulpd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}