5.3 pandas 知乎用户分析.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "885909af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbb8e773",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./dataset/zhihuyonghu.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f150fda",
   "metadata": {},
   "outputs": [
    {
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       "      <th>followers</th>\n",
       "      <th>following</th>\n",
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       "      <th>name</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1,043,150</td>\n",
       "      <td>203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>李开复</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>计算机软件Facebook人工智能研究所卡耐基梅隆大学机器人</td>\n",
       "      <td>70,609</td>\n",
       "      <td>115</td>\n",
       "      <td>Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士</td>\n",
       "      <td>田渊栋</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>互联网24好玩产品经理北京邮电大学信息工程</td>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>产品经理，关注企业服务、AI、大数据。</td>\n",
       "      <td>易良慧</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>制药</td>\n",
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       "      <td>huangjian</td>\n",
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      "text/plain": [
       "                           detail  followers following  \\\n",
       "0                             NaN  1,043,150       203   \n",
       "1  计算机软件Facebook人工智能研究所卡耐基梅隆大学机器人     70,609       115   \n",
       "2           互联网24好玩产品经理北京邮电大学信息工程        231       273   \n",
       "3                              制药          4        58   \n",
       "4                             NaN          0         6   \n",
       "\n",
       "                                   intro       name  \n",
       "0                                    NaN        李开复  \n",
       "1  Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士        田渊栋  \n",
       "2                    产品经理，关注企业服务、AI、大数据。        易良慧  \n",
       "3                                    NaN  huangjian  \n",
       "4                                     学生         流苏  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "1a55e125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 109381 entries, 0 to 109380\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   detail     43433 non-null   object\n",
      " 1   followers  109381 non-null  object\n",
      " 2   following  109381 non-null  object\n",
      " 3   intro      80359 non-null   object\n",
      " 4   name       109380 non-null  object\n",
      "dtypes: object(5)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3ea0dc1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>李开复</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>计算机软件Facebook人工智能研究所卡耐基梅隆大学机器人</td>\n",
       "      <td>70,609</td>\n",
       "      <td>115</td>\n",
       "      <td>Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士</td>\n",
       "      <td>田渊栋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>互联网24好玩产品经理北京邮电大学信息工程</td>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>产品经理，关注企业服务、AI、大数据。</td>\n",
       "      <td>易良慧</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>制药</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>huangjian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>学生</td>\n",
       "      <td>流苏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109376</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>82</td>\n",
       "      <td>神经病</td>\n",
       "      <td>一诺</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109377</th>\n",
       "      <td>电子商务苏州尚葡科技电子商务运营</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>Life is too short to drink bad wine.</td>\n",
       "      <td>沈钧</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109378</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>专业蜜蜡鉴定vx:kxm0755。</td>\n",
       "      <td>康小蜜</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109379</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>什么也没有</td>\n",
       "      <td>王者农药不能玩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109380</th>\n",
       "      <td>创意艺术Université de Lausanne，瑞士</td>\n",
       "      <td>529</td>\n",
       "      <td>22</td>\n",
       "      <td>我是个谜^。^</td>\n",
       "      <td>私房甜甜猫</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109380 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                detail  followers following  \\\n",
       "0                                  NaN  1,043,150       203   \n",
       "1       计算机软件Facebook人工智能研究所卡耐基梅隆大学机器人     70,609       115   \n",
       "2                互联网24好玩产品经理北京邮电大学信息工程        231       273   \n",
       "3                                   制药          4        58   \n",
       "4                                  NaN          0         6   \n",
       "...                                ...        ...       ...   \n",
       "109376                             NaN          4        82   \n",
       "109377                电子商务苏州尚葡科技电子商务运营         30        50   \n",
       "109378                            电子商务         26         9   \n",
       "109379                             NaN          0         1   \n",
       "109380   创意艺术Université de Lausanne，瑞士        529        22   \n",
       "\n",
       "                                        intro       name  \n",
       "0                                         NaN        李开复  \n",
       "1       Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士        田渊栋  \n",
       "2                         产品经理，关注企业服务、AI、大数据。        易良慧  \n",
       "3                                         NaN  huangjian  \n",
       "4                                          学生         流苏  \n",
       "...                                       ...        ...  \n",
       "109376                                    神经病         一诺  \n",
       "109377   Life is too short to drink bad wine.         沈钧  \n",
       "109378                      专业蜜蜡鉴定vx:kxm0755。        康小蜜  \n",
       "109379                                  什么也没有    王者农药不能玩  \n",
       "109380                                我是个谜^。^      私房甜甜猫  \n",
       "\n",
       "[109380 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(subset=['followers', 'following', 'name'])  # 子表中有一个nan该行就会被删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ade0756d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.fillna('')  # nan值填充为空字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03ad9392",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['intro'] = data.intro + data.detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46287f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['detail']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2273e400",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>following</th>\n",
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       "      <td>李开复</td>\n",
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       "      <th>1</th>\n",
       "      <td>70,609</td>\n",
       "      <td>115</td>\n",
       "      <td>Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士计算机软件Face...</td>\n",
       "      <td>田渊栋</td>\n",
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       "      <td>231</td>\n",
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       "      <td>产品经理，关注企业服务、AI、大数据。互联网24好玩产品经理北京邮电大学信息工程</td>\n",
       "      <td>易良慧</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>学生</td>\n",
       "      <td>流苏</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>109376</th>\n",
       "      <td>4</td>\n",
       "      <td>82</td>\n",
       "      <td>神经病</td>\n",
       "      <td>一诺</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109377</th>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>Life is too short to drink bad wine.电子商务苏州尚葡科技...</td>\n",
       "      <td>沈钧</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109378</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>专业蜜蜡鉴定vx:kxm0755。电子商务</td>\n",
       "      <td>康小蜜</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109379</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>什么也没有</td>\n",
       "      <td>王者农药不能玩</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109380</th>\n",
       "      <td>529</td>\n",
       "      <td>22</td>\n",
       "      <td>我是个谜^。^创意艺术Université de Lausanne，瑞士</td>\n",
       "      <td>私房甜甜猫</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109381 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        followers following  \\\n",
       "0       1,043,150       203   \n",
       "1          70,609       115   \n",
       "2             231       273   \n",
       "3               4        58   \n",
       "4               0         6   \n",
       "...           ...       ...   \n",
       "109376          4        82   \n",
       "109377         30        50   \n",
       "109378         26         9   \n",
       "109379          0         1   \n",
       "109380        529        22   \n",
       "\n",
       "                                                    intro       name  \n",
       "0                                                                李开复  \n",
       "1       Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士计算机软件Face...        田渊栋  \n",
       "2                产品经理，关注企业服务、AI、大数据。互联网24好玩产品经理北京邮电大学信息工程        易良慧  \n",
       "3                                                      制药  huangjian  \n",
       "4                                                      学生         流苏  \n",
       "...                                                   ...        ...  \n",
       "109376                                                神经病         一诺  \n",
       "109377  Life is too short to drink bad wine.电子商务苏州尚葡科技...         沈钧  \n",
       "109378                              专业蜜蜡鉴定vx:kxm0755。电子商务        康小蜜  \n",
       "109379                                              什么也没有    王者农药不能玩  \n",
       "109380               我是个谜^。^创意艺术Université de Lausanne，瑞士      私房甜甜猫  \n",
       "\n",
       "[109381 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db40b8be",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_it = data[data.intro.str.contains('互联网|因特网|计算机')] # | 或"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e2780047",
   "metadata": {},
   "outputs": [
    {
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       "      <td>产品经理，关注企业服务、AI、大数据。互联网24好玩产品经理北京邮电大学信息工程</td>\n",
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       "      <td>7,563</td>\n",
       "      <td>153</td>\n",
       "      <td>FAIR炼丹中Facebook吃饭看paper玩游戏写码卡内基梅隆大学 (Carnegie ...</td>\n",
       "      <td>吴育昕</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6,183</td>\n",
       "      <td>211</td>\n",
       "      <td>The Journey is the Reward.互联网网易Brand Marketing</td>\n",
       "      <td>Kyle君</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1,902</td>\n",
       "      <td>49</td>\n",
       "      <td>小吴Facebook Applied Machine Learning程序员卡内基梅隆大学 ...</td>\n",
       "      <td>吴逸鸣</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109244</th>\n",
       "      <td>614</td>\n",
       "      <td>78</td>\n",
       "      <td>互联网</td>\n",
       "      <td>木三刀</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109256</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>cool互联网</td>\n",
       "      <td>Ella</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109268</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>只是一个性向跟您不同的普通人。互联网IT 男氪金四核苑黄叫兽</td>\n",
       "      <td>黄某某</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109281</th>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>夏普区域运营互联网</td>\n",
       "      <td>Zion.an</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109353</th>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>等待和希望互联网浙江大学高等教育</td>\n",
       "      <td>张大狗</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9924 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       followers following                                              intro  \\\n",
       "1         70,609       115  Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士计算机软件Face...   \n",
       "2            231       273           产品经理，关注企业服务、AI、大数据。互联网24好玩产品经理北京邮电大学信息工程   \n",
       "5          7,563       153  FAIR炼丹中Facebook吃饭看paper玩游戏写码卡内基梅隆大学 (Carnegie ...   \n",
       "6          6,183       211     The Journey is the Reward.互联网网易Brand Marketing   \n",
       "7          1,902        49  小吴Facebook Applied Machine Learning程序员卡内基梅隆大学 ...   \n",
       "...          ...       ...                                                ...   \n",
       "109244       614        78                                                互联网   \n",
       "109256         1         4                                            cool互联网   \n",
       "109268         2        22                     只是一个性向跟您不同的普通人。互联网IT 男氪金四核苑黄叫兽   \n",
       "109281       108         0                                          夏普区域运营互联网   \n",
       "109353        24        34                                   等待和希望互联网浙江大学高等教育   \n",
       "\n",
       "           name  is_edu  \n",
       "1           田渊栋   False  \n",
       "2           易良慧   False  \n",
       "5           吴育昕   False  \n",
       "6         Kyle君   False  \n",
       "7           吴逸鸣   False  \n",
       "...         ...     ...  \n",
       "109244      木三刀   False  \n",
       "109256     Ella   False  \n",
       "109268      黄某某   False  \n",
       "109281  Zion.an   False  \n",
       "109353      张大狗    True  \n",
       "\n",
       "[9924 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "135df644",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luhe\\AppData\\Local\\Temp\\ipykernel_13396\\2951315006.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_it['is_edu'] = data_it.intro.str.contains('教育')\n"
     ]
    }
   ],
   "source": [
    "data_it['is_edu'] = data_it.intro.str.contains('教育')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c7ae184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014510278113663845"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_it.is_edu.sum()/len(data_it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bcc944f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>followers</th>\n",
       "      <th>following</th>\n",
       "      <th>intro</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,043,150</td>\n",
       "      <td>203</td>\n",
       "      <td></td>\n",
       "      <td>李开复</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70,609</td>\n",
       "      <td>115</td>\n",
       "      <td>Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士计算机软件Face...</td>\n",
       "      <td>田渊栋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>产品经理，关注企业服务、AI、大数据。互联网24好玩产品经理北京邮电大学信息工程</td>\n",
       "      <td>易良慧</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>制药</td>\n",
       "      <td>huangjian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>学生</td>\n",
       "      <td>流苏</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   followers following                                              intro  \\\n",
       "0  1,043,150       203                                                      \n",
       "1     70,609       115  Facebook人工智能研究院研究员/研究经理，卡耐基梅隆大学机器人系博士计算机软件Face...   \n",
       "2        231       273           产品经理，关注企业服务、AI、大数据。互联网24好玩产品经理北京邮电大学信息工程   \n",
       "3          4        58                                                 制药   \n",
       "4          0         6                                                 学生   \n",
       "\n",
       "        name  \n",
       "0        李开复  \n",
       "1        田渊栋  \n",
       "2        易良慧  \n",
       "3  huangjian  \n",
       "4         流苏  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ee0381b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_it = data[data.intro.str.contains('互联网|因特网|计算机|产品经理|软件')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "114abec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luhe\\AppData\\Local\\Temp\\ipykernel_13396\\2498431737.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_it['job'] = 'internet'\n"
     ]
    }
   ],
   "source": [
    "data_it['job'] = 'internet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "011b5251",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mon = data[data.intro.str.contains('金融|银行|证券|基金')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "30ede57c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luhe\\AppData\\Local\\Temp\\ipykernel_13396\\783163704.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_mon['job'] = 'bank'\n"
     ]
    }
   ],
   "source": [
    "data_mon['job'] = 'bank'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43049f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_edu = data[data.intro.str.contains('教育|培训|大学|学校|课程')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "acc7d8ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luhe\\AppData\\Local\\Temp\\ipykernel_13396\\172218367.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_edu['job'] = 'edu'\n"
     ]
    }
   ],
   "source": [
    "data_edu['job'] = 'edu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7bdc4442",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new = pd.concat([data_it, data_mon, data_edu])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "60ba671a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>followers</th>\n",
       "      <th>following</th>\n",
       "      <th>intro</th>\n",
       "      <th>name</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32353</td>\n",
       "      <td>32353</td>\n",
       "      <td>32353</td>\n",
       "      <td>32353</td>\n",
       "      <td>32353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5252</td>\n",
       "      <td>1564</td>\n",
       "      <td>25309</td>\n",
       "      <td>26587</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>互联网</td>\n",
       "      <td>[已重置]</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2388</td>\n",
       "      <td>1098</td>\n",
       "      <td>467</td>\n",
       "      <td>29</td>\n",
       "      <td>18938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       followers following  intro   name    job\n",
       "count      32353     32353  32353  32353  32353\n",
       "unique      5252      1564  25309  26587      3\n",
       "top            0         0    互联网  [已重置]    edu\n",
       "freq        2388      1098    467     29  18938"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.describe()  # 非数值数据会自动计算count等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "89b38bde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 32353 entries, 1 to 109360\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   followers  32353 non-null  object\n",
      " 1   following  32353 non-null  object\n",
      " 2   intro      32353 non-null  object\n",
      " 3   name       32353 non-null  object\n",
      " 4   job        32353 non-null  object\n",
      "dtypes: object(5)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5da09054",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_new['intro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c18aa11c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>following</th>\n",
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       "      <th>1</th>\n",
       "      <td>70,609</td>\n",
       "      <td>115</td>\n",
       "      <td>田渊栋</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>易良慧</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7,563</td>\n",
       "      <td>153</td>\n",
       "      <td>吴育昕</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6,183</td>\n",
       "      <td>211</td>\n",
       "      <td>Kyle君</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1,902</td>\n",
       "      <td>49</td>\n",
       "      <td>吴逸鸣</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109349</th>\n",
       "      <td>173</td>\n",
       "      <td>49</td>\n",
       "      <td>吴炎平</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109350</th>\n",
       "      <td>40,257</td>\n",
       "      <td>36</td>\n",
       "      <td>娜娜</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109351</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>蔡乐婷</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109353</th>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>张大狗</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109360</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>失语者</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32353 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       followers following   name       job\n",
       "1         70,609       115    田渊栋  internet\n",
       "2            231       273    易良慧  internet\n",
       "5          7,563       153    吴育昕  internet\n",
       "6          6,183       211  Kyle君  internet\n",
       "7          1,902        49    吴逸鸣  internet\n",
       "...          ...       ...    ...       ...\n",
       "109349       173        49    吴炎平       edu\n",
       "109350    40,257        36     娜娜       edu\n",
       "109351         0         9    蔡乐婷       edu\n",
       "109353        24        34    张大狗       edu\n",
       "109360         7         2    失语者       edu\n",
       "\n",
       "[32353 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "931eaacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu         18938\n",
       "internet    10312\n",
       "bank         3103\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a072d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5766"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data_new.name.duplicated()).sum()  # name 列重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "370a2cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据子表去重，子表相同才会删除\n",
    "data_new.drop_duplicates(subset=['followers', 'following', 'name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d01a2300",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27739 entries, 1 to 109360\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   followers  27739 non-null  object\n",
      " 1   following  27739 non-null  object\n",
      " 2   name       27739 non-null  object\n",
      " 3   job        27739 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e5c5c241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu         14542\n",
       "internet    10312\n",
       "bank         2885\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "77fd4470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>followers</th>\n",
       "      <th>following</th>\n",
       "      <th>name</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70,609</td>\n",
       "      <td>115</td>\n",
       "      <td>田渊栋</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>易良慧</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7,563</td>\n",
       "      <td>153</td>\n",
       "      <td>吴育昕</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6,183</td>\n",
       "      <td>211</td>\n",
       "      <td>Kyle君</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1,902</td>\n",
       "      <td>49</td>\n",
       "      <td>吴逸鸣</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109345</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>没有故事的短暂一生</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109349</th>\n",
       "      <td>173</td>\n",
       "      <td>49</td>\n",
       "      <td>吴炎平</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109350</th>\n",
       "      <td>40,257</td>\n",
       "      <td>36</td>\n",
       "      <td>娜娜</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109351</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>蔡乐婷</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109360</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>失语者</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27739 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       followers following       name       job\n",
       "1         70,609       115        田渊栋  internet\n",
       "2            231       273        易良慧  internet\n",
       "5          7,563       153        吴育昕  internet\n",
       "6          6,183       211      Kyle君  internet\n",
       "7          1,902        49        吴逸鸣  internet\n",
       "...          ...       ...        ...       ...\n",
       "109345         1         7  没有故事的短暂一生       edu\n",
       "109349       173        49        吴炎平       edu\n",
       "109350    40,257        36         娜娜       edu\n",
       "109351         0         9        蔡乐婷       edu\n",
       "109360         7         2        失语者       edu\n",
       "\n",
       "[27739 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6ee770b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new['followers'] = data_new.followers.str.replace(',', '').astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d3f84eb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27739 entries, 1 to 109360\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   followers  27739 non-null  int32 \n",
      " 1   following  27739 non-null  object\n",
      " 2   name       27739 non-null  object\n",
      " 3   job        27739 non-null  object\n",
      "dtypes: int32(1), object(3)\n",
      "memory usage: 975.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b8557371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "bank        2428.999653\n",
       "edu         2634.539609\n",
       "internet    3418.802948\n",
       "Name: followers, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.groupby('job')['followers'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c13e79bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new['following'] = data_new.following.str.replace(',', '').astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bb7be502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "bank        188.935529\n",
       "edu         154.789162\n",
       "internet    190.196955\n",
       "Name: following, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.groupby('job')['following'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "57d7a12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "bank         8568\n",
       "edu         11949\n",
       "internet     9909\n",
       "Name: following, dtype: int32"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.groupby('job')['following'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "51d15fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bank</th>\n",
       "      <td>2885.0</td>\n",
       "      <td>188.935529</td>\n",
       "      <td>459.198038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>8568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edu</th>\n",
       "      <td>14542.0</td>\n",
       "      <td>154.789162</td>\n",
       "      <td>405.841916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>11949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet</th>\n",
       "      <td>10312.0</td>\n",
       "      <td>190.196955</td>\n",
       "      <td>455.408214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>9909.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean         std  min   25%   50%    75%      max\n",
       "job                                                                       \n",
       "bank       2885.0  188.935529  459.198038  0.0  13.0  54.0  163.0   8568.0\n",
       "edu       14542.0  154.789162  405.841916  0.0  12.0  44.0  141.0  11949.0\n",
       "internet  10312.0  190.196955  455.408214  0.0  16.0  57.0  170.0   9909.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.groupby('job')['following'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "43bc4c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th>bank</th>\n",
       "      <td>2885.0</td>\n",
       "      <td>2428.999653</td>\n",
       "      <td>12590.504328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>311622.0</td>\n",
       "      <td>2885.0</td>\n",
       "      <td>188.935529</td>\n",
       "      <td>459.198038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>8568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edu</th>\n",
       "      <td>14542.0</td>\n",
       "      <td>2634.539609</td>\n",
       "      <td>16239.737802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>614813.0</td>\n",
       "      <td>14542.0</td>\n",
       "      <td>154.789162</td>\n",
       "      <td>405.841916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>11949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet</th>\n",
       "      <td>10312.0</td>\n",
       "      <td>3418.802948</td>\n",
       "      <td>21557.178588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>823699.0</td>\n",
       "      <td>10312.0</td>\n",
       "      <td>190.196955</td>\n",
       "      <td>455.408214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>9909.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         followers                                                    \\\n",
       "             count         mean           std  min  25%   50%    75%   \n",
       "job                                                                    \n",
       "bank        2885.0  2428.999653  12590.504328  0.0  4.0  40.0  446.0   \n",
       "edu        14542.0  2634.539609  16239.737802  0.0  5.0  45.0  531.0   \n",
       "internet   10312.0  3418.802948  21557.178588  0.0  7.0  70.0  766.0   \n",
       "\n",
       "                   following                                                  \\\n",
       "               max     count        mean         std  min   25%   50%    75%   \n",
       "job                                                                            \n",
       "bank      311622.0    2885.0  188.935529  459.198038  0.0  13.0  54.0  163.0   \n",
       "edu       614813.0   14542.0  154.789162  405.841916  0.0  12.0  44.0  141.0   \n",
       "internet  823699.0   10312.0  190.196955  455.408214  0.0  16.0  57.0  170.0   \n",
       "\n",
       "                   \n",
       "              max  \n",
       "job                \n",
       "bank       8568.0  \n",
       "edu       11949.0  \n",
       "internet   9909.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new.groupby('job').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "db41de64",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>followers</th>\n",
       "      <th>following</th>\n",
       "      <th>name</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70609</td>\n",
       "      <td>115</td>\n",
       "      <td>田渊栋</td>\n",
       "      <td>internet</td>\n",
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       "      <th>2</th>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>易良慧</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7563</td>\n",
       "      <td>153</td>\n",
       "      <td>吴育昕</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6183</td>\n",
       "      <td>211</td>\n",
       "      <td>Kyle君</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1902</td>\n",
       "      <td>49</td>\n",
       "      <td>吴逸鸣</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>109345</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>没有故事的短暂一生</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109349</th>\n",
       "      <td>173</td>\n",
       "      <td>49</td>\n",
       "      <td>吴炎平</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109350</th>\n",
       "      <td>40257</td>\n",
       "      <td>36</td>\n",
       "      <td>娜娜</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109351</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>蔡乐婷</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109360</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>失语者</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27739 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        followers  following       name       job\n",
       "1           70609        115        田渊栋  internet\n",
       "2             231        273        易良慧  internet\n",
       "5            7563        153        吴育昕  internet\n",
       "6            6183        211      Kyle君  internet\n",
       "7            1902         49        吴逸鸣  internet\n",
       "...           ...        ...        ...       ...\n",
       "109345          1          7  没有故事的短暂一生       edu\n",
       "109349        173         49        吴炎平       edu\n",
       "109350      40257         36         娜娜       edu\n",
       "109351          0          9        蔡乐婷       edu\n",
       "109360          7          2        失语者       edu\n",
       "\n",
       "[27739 rows x 4 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1e6816eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>followers</th>\n",
       "      <th>following</th>\n",
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       "      <th>job</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70609</td>\n",
       "      <td>115</td>\n",
       "      <td>田渊栋</td>\n",
       "      <td>internet</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>231</td>\n",
       "      <td>273</td>\n",
       "      <td>易良慧</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7563</td>\n",
       "      <td>153</td>\n",
       "      <td>吴育昕</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6183</td>\n",
       "      <td>211</td>\n",
       "      <td>Kyle君</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1902</td>\n",
       "      <td>49</td>\n",
       "      <td>吴逸鸣</td>\n",
       "      <td>internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>109345</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>没有故事的短暂一生</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109349</th>\n",
       "      <td>173</td>\n",
       "      <td>49</td>\n",
       "      <td>吴炎平</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109350</th>\n",
       "      <td>40257</td>\n",
       "      <td>36</td>\n",
       "      <td>娜娜</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109351</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>蔡乐婷</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109360</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>失语者</td>\n",
       "      <td>edu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27739 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        followers  following       name       job\n",
       "1           70609        115        田渊栋  internet\n",
       "2             231        273        易良慧  internet\n",
       "5            7563        153        吴育昕  internet\n",
       "6            6183        211      Kyle君  internet\n",
       "7            1902         49        吴逸鸣  internet\n",
       "...           ...        ...        ...       ...\n",
       "109345          1          7  没有故事的短暂一生       edu\n",
       "109349        173         49        吴炎平       edu\n",
       "109350      40257         36         娜娜       edu\n",
       "109351          0          9        蔡乐婷       edu\n",
       "109360          7          2        失语者       edu\n",
       "\n",
       "[27739 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "aece3f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.cut(data_new.followers, bins=[0,10,100,1000,10000,100000,1000000], right=False)).value_counts().plot.bar(rot=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1c4a1315",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = pd.cut(data_new.followers, bins=[0,10,100,1000,10000,100000,1000000], right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "96edf1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='following'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_new.groupby(g)['following'].mean().plot.pie(figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89d13c3f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}