2-pandas_missing_values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理丢失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有两种丢失数据：\n",
    "- None\n",
    "- np.nan(NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. None\n",
    "\n",
    "None是Python自带的，其类型为python object。因此，None不能参与到任何计算中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看None的数据类型\n",
    "type(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-3fd8740bf8ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mNone\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'int'"
     ]
    }
   ],
   "source": [
    "None + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. np.nan（NaN）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np.nan是浮点类型，能参与到计算中。但计算的结果总是NaN。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看np.nan的数据类型\n",
    "type(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. pandas中的None与NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) pandas中None与np.nan都视作np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "   0   1   2   3   4   5   6   7   8   9   10  11\n",
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       "3  44  25  66  57  56  39  36  96  64   4  88  21\n",
       "4   9  99  52  77  42   8  36  66  13  84  49  92\n",
       "5  41  59  39  99  27  56   8  69  50  39   3  93\n",
       "6  88  68  62   4  22  84  55  41  94  14   9  25\n",
       "7  33  67  83  55  23  46  83  12  41  72  30  71\n",
       "8  98  36  47  30  54  13  33  12  37  42  16  39\n",
       "9  20   2  25  92   9  40  28  66  90  48  54  53"
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "df = DataFrame(data=np.random.randint(0,100,size=(10,12)))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>27.0</td>\n",
       "      <td>56.0</td>\n",
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       "      <td>30</td>\n",
       "      <td>71</td>\n",
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       "      <th>8</th>\n",
       "      <td>98</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
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       "      <td>13.0</td>\n",
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       "      <td>16</td>\n",
       "      <td>39</td>\n",
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       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>92</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
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       "   0     1   2   3     4     5     6   7     8   9   10  11\n",
       "0  33  65.0   2  91  72.0  25.0  18.0  58  85.0  99  16   9\n",
       "1  39   NaN  76  28   5.0   9.0  56.0  88  63.0   9  96  82\n",
       "2  47  53.0  82  26  42.0  46.0  36.0   0   NaN  50  86   3\n",
       "3  44  25.0  66  57  56.0   NaN  36.0  96  64.0   4  88  21\n",
       "4   9  99.0  52  77   NaN   8.0  36.0  66  13.0  84  49  92\n",
       "5  41  59.0  39  99  27.0  56.0   NaN  69  50.0  39   3  93\n",
       "6  88  68.0  62   4  22.0  84.0  55.0  41  94.0  14   9  25\n",
       "7  33  67.0  83  55  23.0  46.0  83.0  12  41.0  72  30  71\n",
       "8  98  36.0  47  30  54.0  13.0  33.0  12  37.0  42  16  39\n",
       "9  20   2.0  25  92   9.0  40.0  28.0  66  90.0  48  54  53"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将某些数组元素赋值为nan\n",
    "df.iloc[1,1] = None\n",
    "df.iloc[5,6] = None\n",
    "df.iloc[3,5] = None\n",
    "df.iloc[4,4] = None\n",
    "df.iloc[2,8] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将空对应的行删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0      1      2      3      4      5      6      7      8      9   \\\n",
       "0  False  False  False  False  False  False  False  False  False  False   \n",
       "1  False   True  False  False  False  False  False  False  False  False   \n",
       "2  False  False  False  False  False  False  False  False   True  False   \n",
       "3  False  False  False  False  False   True  False  False  False  False   \n",
       "4  False  False  False  False   True  False  False  False  False  False   \n",
       "5  False  False  False  False  False  False   True  False  False  False   \n",
       "6  False  False  False  False  False  False  False  False  False  False   \n",
       "7  False  False  False  False  False  False  False  False  False  False   \n",
       "8  False  False  False  False  False  False  False  False  False  False   \n",
       "9  False  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "      10     11  \n",
       "0  False  False  \n",
       "1  False  False  \n",
       "2  False  False  \n",
       "3  False  False  \n",
       "4  False  False  \n",
       "5  False  False  \n",
       "6  False  False  \n",
       "7  False  False  \n",
       "8  False  False  \n",
       "9  False  False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.空值检测\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5     True\n",
       "6    False\n",
       "7    False\n",
       "8    False\n",
       "9    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_index = df.loc[df.isnull().any(axis=1)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>71</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>90.0</td>\n",
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       "      <td>54</td>\n",
       "      <td>53</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0     1   2   3     4     5     6   7     8   9   10  11\n",
       "0  33  65.0   2  91  72.0  25.0  18.0  58  85.0  99  16   9\n",
       "6  88  68.0  62   4  22.0  84.0  55.0  41  94.0  14   9  25\n",
       "7  33  67.0  83  55  23.0  46.0  83.0  12  41.0  72  30  71\n",
       "8  98  36.0  47  30  54.0  13.0  33.0  12  37.0  42  16  39\n",
       "9  20   2.0  25  92   9.0  40.0  28.0  66  90.0  48  54  53"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(labels=drop_index,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notnull().all(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>30</td>\n",
       "      <td>71</td>\n",
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       "      <td>98</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
       "      <td>54.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
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       "      <th>9</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>92</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>66</td>\n",
       "      <td>90.0</td>\n",
       "      <td>48</td>\n",
       "      <td>54</td>\n",
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      ],
      "text/plain": [
       "   0     1   2   3     4     5     6   7     8   9   10  11\n",
       "0  33  65.0   2  91  72.0  25.0  18.0  58  85.0  99  16   9\n",
       "6  88  68.0  62   4  22.0  84.0  55.0  41  94.0  14   9  25\n",
       "7  33  67.0  83  55  23.0  46.0  83.0  12  41.0  72  30  71\n",
       "8  98  36.0  47  30  54.0  13.0  33.0  12  37.0  42  16  39\n",
       "9  20   2.0  25  92   9.0  40.0  28.0  66  90.0  48  54  53"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.notnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 总结：\n",
    "    - isnull():True空值  False非空\n",
    "    - notnull(): True非空  False空\n",
    "    \n",
    "    - 如何检测df中哪些行中存在控制？\n",
    "        - df.isnull().any(axis=1):  True行中存在空 ,  False行中不存在空\n",
    "        - df.notnull().all(axis=1):  False行中存在空 ,  True行中不存在空"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) pandas处理空值操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``isnull()``\n",
    "- ``notnull()``\n",
    "- ``dropna()``: 过滤丢失数据\n",
    "- ``fillna()``: 填充丢失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#创建DataFrame，给其中某些元素赋值为nan\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1)判断函数\n",
    "- ``isnull()``\n",
    "- ``notnull()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- df.notnull/isnull().any()/all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#过滤df中的空值（只保留没有空值的行）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df.dropna() 可以选择过滤的是行还是列（默认为行）:axis中0表示行，1表示的列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "   0     1   2   3     4     5     6   7     8   9   10  11\n",
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       "6  88  68.0  62   4  22.0  84.0  55.0  41  94.0  14   9  25\n",
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       "8  98  36.0  47  30  54.0  13.0  33.0  12  37.0  42  16  39\n",
       "9  20   2.0  25  92   9.0  40.0  28.0  66  90.0  48  54  53"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 填充函数 Series/DataFrame\n",
    "- ``fillna()``:value和method参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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   "source": [
    "df.iloc[0,1] = None\n",
    "df.iloc[2,1] = None\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "   0     1   2   3     4     5     6   7     8   9   10  11\n",
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     },
     "execution_count": 38,
     "metadata": {},
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   "source": [
    "df.fillna(method='bfill',axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以选择前向填充还是后向填充"
   ]
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    "method 控制填充的方式 bfill ffill"
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    "============================================\n",
    "\n",
    "练习7：\n",
    "\n",
    "1. 简述None与NaN的区别\n",
    "\n",
    "2. 假设张三李四参加模拟考试，但张三因为突然想明白人生放弃了英语考试，因此记为None，请据此创建一个DataFrame,命名为ddd3\n",
    "\n",
    "3. 老师决定根据用数学的分数填充张三的英语成绩，如何实现？\n",
    "    用李四的英语成绩填充张三的英语成绩？\n",
    "\n",
    "============================================"
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