{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>third</th>\n",
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       "      <th>a</th>\n",
       "      <td>0.480390</td>\n",
       "      <td>0.429609</td>\n",
       "      <td>0.836108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.062058</td>\n",
       "      <td>0.771786</td>\n",
       "      <td>0.329276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.033609</td>\n",
       "      <td>0.935540</td>\n",
       "      <td>0.969110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.408893</td>\n",
       "      <td>0.093299</td>\n",
       "      <td>0.885355</td>\n",
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      "text/plain": [
       "      first    second     third\n",
       "a  0.480390  0.429609  0.836108\n",
       "b  0.062058  0.771786  0.329276\n",
       "c  0.033609  0.935540  0.969110\n",
       "d  0.408893  0.093299  0.885355"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.random.rand(4, 3), index=['a', 'b', 'c', 'd'], columns=['first', 'second', 'third'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
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   "outputs": [
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       "      first    second\n",
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       "c  0.049161  0.763841"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.random.rand(3, 2), index=['a', 'b', 'c'], columns=['first', 'second'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>c</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
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      "text/plain": [
       "      first    second  third\n",
       "a -0.137538 -0.410204    NaN\n",
       "b  0.028572  0.226190    NaN\n",
       "c -0.015552  0.171699    NaN\n",
       "d       NaN       NaN    NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 两个dataframe运算，无对应元素则为NaN\n",
    "df = df1 - df2\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   first  second  third\n",
       "a  False   False   True\n",
       "b  False   False   True\n",
       "c  False   False   True\n",
       "d   True    True   True"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各元素是否为NaN\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      first    second\n",
       "a -0.137538 -0.410204\n",
       "b  0.028572  0.226190\n",
       "c -0.015552  0.171699\n",
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "# axis=1代表针对列，thresh=2表示删除有效数据小于2的列\n",
    "df.dropna(axis=1, thresh=2, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      first    second\n",
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "# 用0填充缺失值\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
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       "      first    second\n",
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    "df"
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   "metadata": {},
   "outputs": [
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       "      first    second\n",
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   ],
   "source": [
    "# 向前填充\n",
    "df.fillna(method='ffill')"
   ]
  }
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