homework7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 删除重复数据，并输出去重前后的数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "去重前数据量：20358，去重后数据量：19779\n"
     ]
    }
   ],
   "source": [
    "#用pandas里面的drop_duplicates()函数去删除重复的数据\n",
    "#.shape会返回数据集里面的行数和列数，.shape[0]会返回行数\n",
    "import pandas as pd\n",
    "file_path = r\"C:\\Users\\大头\\Desktop\\github_bot_raw_data.csv\"\n",
    "raw_data = pd.read_csv(file_path)\n",
    "columns = [\n",
    "    'actor_id',  # GitHub用户的ID (示例值: 1081405)\n",
    "    'label',  # 用户标签（\"Human\"或\"Bot\"） (示例值: Human)\n",
    "    'login',  # GitHub用户的登录名 (示例值: dlazesz)\n",
    "    'id',  # 用户的GitHub ID (示例值: 1081405)\n",
    "    'node_id',  # 用户的GitHub节点ID (示例值: MDQ6VXNlcjEwODE0MDU=)\n",
    "    'avatar_url',  # GitHub头像URL (示例值: https://avatars.githubusercontent.com/u/1081405?v=4)\n",
    "    'gravatar_id',  # Gravatar ID (示例值: None)\n",
    "    'url',  # GitHub用户的URL (示例值: https://api.github.com/users/dlazesz)\n",
    "    'html_url',  # GitHub用户的HTML URL (示例值: https://github.com/dlazesz)\n",
    "    'followers_url',  # GitHub用户的粉丝URL (示例值: https://api.github.com/users/dlazesz/followers)\n",
    "    'following_url',  # GitHub用户的关注URL (示例值: https://api.github.com/users/dlazesz/following{/other_user})\n",
    "    'gists_url',  # 用户的GitHub Gists URL (示例值: https://api.github.com/users/dlazesz/gists{/gist_id})\n",
    "    'starred_url',  # 用户的GitHub Starred URL (示例值: https://api.github.com/users/dlazesz/starred{/owner}{/repo})\n",
    "    'subscriptions_url',  # 用户的GitHub订阅URL (示例值: https://api.github.com/users/dlazesz/subscriptions)\n",
    "    'organizations_url',  # 用户的GitHub组织URL (示例值: https://api.github.com/users/dlazesz/orgs)\n",
    "    'repos_url',  # 用户的GitHub仓库URL (示例值: https://api.github.com/users/dlazesz/repos)\n",
    "    'events_url',  # 用户的GitHub事件URL (示例值: https://api.github.com/users/dlazesz/events{/privacy})\n",
    "    'received_events_url',  # 用户的GitHub接收事件URL (示例值: https://api.github.com/users/dlazesz/received_events)\n",
    "    'type',  # 用户类型，通常为\"User\" (示例值: User)\n",
    "    'site_admin',  # 表示用户是否是GitHub网站管理员的标志 (示例值: False)\n",
    "    'name',  # 用户的姓名 (示例值: Indig Balázs)\n",
    "    'company',  # 用户所在公司 (示例值: None)\n",
    "    'blog',  # 用户的博客 (示例值: None)\n",
    "    'location',  # 用户的位置 (示例值: None)\n",
    "    'email',  # 用户的电子邮件 (示例值: None)\n",
    "    'hireable',  # 表示用户是否愿意被雇佣的标志 (示例值: None)\n",
    "    'bio',  # 用户在其GitHub资料中提供的自我介绍或个人简介 (示例值: None)\n",
    "    'twitter_username',  # 用户的Twitter用户名 (示例值: None)\n",
    "    'public_repos',  # 用户在GitHub上的公共代码仓库数量 (示例值: 26)\n",
    "    'public_gists',  # 用户的公共Gists数量 (示例值: 1)\n",
    "    'followers',  # 关注该用户的其他GitHub用户数量 (示例值: 5)\n",
    "    'following',  # 该用户关注的其他GitHub用户数量 (示例值: 1)\n",
    "    'created_at',  # 用户的GitHub帐户创建日期 (示例值: 2011-09-26T17:27:03Z)\n",
    "    'updated_at',  # 用户的GitHub帐户最后更新日期 (示例值: 2023-10-13T11:21:10Z)\n",
    "]\n",
    "data = raw_data[columns]\n",
    "pre_count = data.shape[0]\n",
    "data = data.drop_duplicates()\n",
    "after_count = data.shape[0]\n",
    "print(f\"去重前数据量：{pre_count}，去重后数据量：{after_count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 缺失值处理\n",
    "首先，去掉 gravatar_id 列，并查看各列的缺失值的情况\n",
    "其次，将可转化成 boolean 变量的列字段转化成 boolean 变量（转成布尔类型是为了便于处理缺失字段，如是否存在公司、位置等等），文本数据用空字符串填充空值......\n",
    "最后，再次看各列有无缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "去掉gravatar_id列后，各列缺失值情况：\n",
      " actor_id                   0\n",
      "label                      0\n",
      "login                      0\n",
      "id                         0\n",
      "node_id                    0\n",
      "avatar_url                 0\n",
      "url                        0\n",
      "html_url                   0\n",
      "followers_url              0\n",
      "following_url              0\n",
      "gists_url                  0\n",
      "starred_url                0\n",
      "subscriptions_url          0\n",
      "organizations_url          0\n",
      "repos_url                  0\n",
      "events_url                 0\n",
      "received_events_url        0\n",
      "type                       0\n",
      "site_admin                 0\n",
      "name                    2654\n",
      "company                 9229\n",
      "blog                   11601\n",
      "location                7287\n",
      "email                  12079\n",
      "hireable               16956\n",
      "bio                    11262\n",
      "twitter_username       15297\n",
      "public_repos               0\n",
      "public_gists               0\n",
      "followers                  0\n",
      "following                  0\n",
      "created_at                 0\n",
      "updated_at                 0\n",
      "dtype: int64\n",
      "转化boolean后，各列缺失值情况：\n",
      " actor_id                   0\n",
      "label                      0\n",
      "login                      0\n",
      "id                         0\n",
      "node_id                    0\n",
      "avatar_url                 0\n",
      "url                        0\n",
      "html_url                   0\n",
      "followers_url              0\n",
      "following_url              0\n",
      "gists_url                  0\n",
      "starred_url                0\n",
      "subscriptions_url          0\n",
      "organizations_url          0\n",
      "repos_url                  0\n",
      "events_url                 0\n",
      "received_events_url        0\n",
      "type                       0\n",
      "site_admin                 0\n",
      "name                    2654\n",
      "company                    0\n",
      "blog                   11601\n",
      "location                   0\n",
      "email                      0\n",
      "hireable               16956\n",
      "bio                        0\n",
      "twitter_username           0\n",
      "public_repos               0\n",
      "public_gists               0\n",
      "followers                  0\n",
      "following                  0\n",
      "created_at                 0\n",
      "updated_at                 0\n",
      "has_company                0\n",
      "has_location               0\n",
      "has_email                  0\n",
      "has_bio                    0\n",
      "has_twitter                0\n",
      "is_hireable                0\n",
      "is_site_admin              0\n",
      "dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\大头\\AppData\\Local\\Temp\\ipykernel_12548\\2764982414.py:54: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  data['is_hireable'] = data['hireable'].fillna(False)  # 缺失值填为 False\n"
     ]
    }
   ],
   "source": [
    "#用isnull()函数统计缺失值数量\n",
    "import pandas as pd\n",
    "file_path = r\"C:\\Users\\大头\\Desktop\\github_bot_raw_data.csv\"\n",
    "raw_data = pd.read_csv(file_path)\n",
    "columns = [\n",
    "    'actor_id',  # GitHub用户的ID (示例值: 1081405)\n",
    "    'label',  # 用户标签（\"Human\"或\"Bot\"） (示例值: Human)\n",
    "    'login',  # GitHub用户的登录名 (示例值: dlazesz)\n",
    "    'id',  # 用户的GitHub ID (示例值: 1081405)\n",
    "    'node_id',  # 用户的GitHub节点ID (示例值: MDQ6VXNlcjEwODE0MDU=)\n",
    "    'avatar_url',  # GitHub头像URL (示例值: https://avatars.githubusercontent.com/u/1081405?v=4)\n",
    "    'gravatar_id',  # Gravatar ID (示例值: None)\n",
    "    'url',  # GitHub用户的URL (示例值: https://api.github.com/users/dlazesz)\n",
    "    'html_url',  # GitHub用户的HTML URL (示例值: https://github.com/dlazesz)\n",
    "    'followers_url',  # GitHub用户的粉丝URL (示例值: https://api.github.com/users/dlazesz/followers)\n",
    "    'following_url',  # GitHub用户的关注URL (示例值: https://api.github.com/users/dlazesz/following{/other_user})\n",
    "    'gists_url',  # 用户的GitHub Gists URL (示例值: https://api.github.com/users/dlazesz/gists{/gist_id})\n",
    "    'starred_url',  # 用户的GitHub Starred URL (示例值: https://api.github.com/users/dlazesz/starred{/owner}{/repo})\n",
    "    'subscriptions_url',  # 用户的GitHub订阅URL (示例值: https://api.github.com/users/dlazesz/subscriptions)\n",
    "    'organizations_url',  # 用户的GitHub组织URL (示例值: https://api.github.com/users/dlazesz/orgs)\n",
    "    'repos_url',  # 用户的GitHub仓库URL (示例值: https://api.github.com/users/dlazesz/repos)\n",
    "    'events_url',  # 用户的GitHub事件URL (示例值: https://api.github.com/users/dlazesz/events{/privacy})\n",
    "    'received_events_url',  # 用户的GitHub接收事件URL (示例值: https://api.github.com/users/dlazesz/received_events)\n",
    "    'type',  # 用户类型，通常为\"User\" (示例值: User)\n",
    "    'site_admin',  # 表示用户是否是GitHub网站管理员的标志 (示例值: False)\n",
    "    'name',  # 用户的姓名 (示例值: Indig Balázs)\n",
    "    'company',  # 用户所在公司 (示例值: None)\n",
    "    'blog',  # 用户的博客 (示例值: None)\n",
    "    'location',  # 用户的位置 (示例值: None)\n",
    "    'email',  # 用户的电子邮件 (示例值: None)\n",
    "    'hireable',  # 表示用户是否愿意被雇佣的标志 (示例值: None)\n",
    "    'bio',  # 用户在其GitHub资料中提供的自我介绍或个人简介 (示例值: None)\n",
    "    'twitter_username',  # 用户的Twitter用户名 (示例值: None)\n",
    "    'public_repos',  # 用户在GitHub上的公共代码仓库数量 (示例值: 26)\n",
    "    'public_gists',  # 用户的公共Gists数量 (示例值: 1)\n",
    "    'followers',  # 关注该用户的其他GitHub用户数量 (示例值: 5)\n",
    "    'following',  # 该用户关注的其他GitHub用户数量 (示例值: 1)\n",
    "    'created_at',  # 用户的GitHub帐户创建日期 (示例值: 2011-09-26T17:27:03Z)\n",
    "    'updated_at',  # 用户的GitHub帐户最后更新日期 (示例值: 2023-10-13T11:21:10Z)\n",
    "]\n",
    "data = raw_data[columns]\n",
    "\n",
    "#去掉 gravatar_id 列，并查看各列的缺失值的情况\n",
    "data = data.drop(columns=['gravatar_id'])  #删除gravatar_id列\n",
    "missing_values = data.isnull().sum()  \n",
    "print(\"去掉gravatar_id列后，各列缺失值情况：\\n\", missing_values)  \n",
    "\n",
    "# 将可转化为布尔变量的字段转换为布尔类型\n",
    "data['has_company'] = data['company'].notnull()  # 有公司信息\n",
    "data['has_location'] = data['location'].notnull()  # 有位置信息\n",
    "data['has_email'] = data['email'].notnull()  # 有电子邮件地址\n",
    "data['has_bio'] = data['bio'].apply(lambda x: isinstance(x, str) and x.strip() != \"\")  # 非空字符串\n",
    "data['has_twitter'] = data['twitter_username'].notnull()  # 有 Twitter 用户名\n",
    "data['is_hireable'] = data['hireable'].fillna(False)  # 缺失值填为 False\n",
    "data['is_site_admin'] = data['site_admin'].fillna(False)  # 默认值为 False\n",
    "\n",
    "# 2. 填充缺失值\n",
    "# 文本字段填充空字符串\n",
    "text_fields = ['company', 'location', 'bio', 'email', 'twitter_username']\n",
    "data[text_fields] = data[text_fields].fillna(\"\")\n",
    "missing_values_after = data.isnull().sum()  \n",
    "print(\"转化boolean后，各列缺失值情况：\\n\", missing_values_after)  \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 数据变换，将created_at、updated_at转为时间戳"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.to_datetime()是把数据从其他格式转换为datatime64的形式\n",
    "import pandas as pd\n",
    "file_path = r\"C:\\Users\\大头\\Desktop\\github_bot_raw_data.csv\"\n",
    "raw_data = pd.read_csv(file_path)\n",
    "columns = [\n",
    "    'actor_id',  # GitHub用户的ID (示例值: 1081405)\n",
    "    'label',  # 用户标签（\"Human\"或\"Bot\"） (示例值: Human)\n",
    "    'login',  # GitHub用户的登录名 (示例值: dlazesz)\n",
    "    'id',  # 用户的GitHub ID (示例值: 1081405)\n",
    "    'node_id',  # 用户的GitHub节点ID (示例值: MDQ6VXNlcjEwODE0MDU=)\n",
    "    'avatar_url',  # GitHub头像URL (示例值: https://avatars.githubusercontent.com/u/1081405?v=4)\n",
    "    'gravatar_id',  # Gravatar ID (示例值: None)\n",
    "    'url',  # GitHub用户的URL (示例值: https://api.github.com/users/dlazesz)\n",
    "    'html_url',  # GitHub用户的HTML URL (示例值: https://github.com/dlazesz)\n",
    "    'followers_url',  # GitHub用户的粉丝URL (示例值: https://api.github.com/users/dlazesz/followers)\n",
    "    'following_url',  # GitHub用户的关注URL (示例值: https://api.github.com/users/dlazesz/following{/other_user})\n",
    "    'gists_url',  # 用户的GitHub Gists URL (示例值: https://api.github.com/users/dlazesz/gists{/gist_id})\n",
    "    'starred_url',  # 用户的GitHub Starred URL (示例值: https://api.github.com/users/dlazesz/starred{/owner}{/repo})\n",
    "    'subscriptions_url',  # 用户的GitHub订阅URL (示例值: https://api.github.com/users/dlazesz/subscriptions)\n",
    "    'organizations_url',  # 用户的GitHub组织URL (示例值: https://api.github.com/users/dlazesz/orgs)\n",
    "    'repos_url',  # 用户的GitHub仓库URL (示例值: https://api.github.com/users/dlazesz/repos)\n",
    "    'events_url',  # 用户的GitHub事件URL (示例值: https://api.github.com/users/dlazesz/events{/privacy})\n",
    "    'received_events_url',  # 用户的GitHub接收事件URL (示例值: https://api.github.com/users/dlazesz/received_events)\n",
    "    'type',  # 用户类型，通常为\"User\" (示例值: User)\n",
    "    'site_admin',  # 表示用户是否是GitHub网站管理员的标志 (示例值: False)\n",
    "    'name',  # 用户的姓名 (示例值: Indig Balázs)\n",
    "    'company',  # 用户所在公司 (示例值: None)\n",
    "    'blog',  # 用户的博客 (示例值: None)\n",
    "    'location',  # 用户的位置 (示例值: None)\n",
    "    'email',  # 用户的电子邮件 (示例值: None)\n",
    "    'hireable',  # 表示用户是否愿意被雇佣的标志 (示例值: None)\n",
    "    'bio',  # 用户在其GitHub资料中提供的自我介绍或个人简介 (示例值: None)\n",
    "    'twitter_username',  # 用户的Twitter用户名 (示例值: None)\n",
    "    'public_repos',  # 用户在GitHub上的公共代码仓库数量 (示例值: 26)\n",
    "    'public_gists',  # 用户的公共Gists数量 (示例值: 1)\n",
    "    'followers',  # 关注该用户的其他GitHub用户数量 (示例值: 5)\n",
    "    'following',  # 该用户关注的其他GitHub用户数量 (示例值: 1)\n",
    "    'created_at',  # 用户的GitHub帐户创建日期 (示例值: 2011-09-26T17:27:03Z)\n",
    "    'updated_at',  # 用户的GitHub帐户最后更新日期 (示例值: 2023-10-13T11:21:10Z)\n",
    "]\n",
    "data = raw_data[columns]\n",
    "data['created_at'] = pd.to_datetime(data['created_at'])\n",
    "data['updated_at'] = pd.to_datetime(data['updated_at'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据可视化\n",
    "4.1 可视化bot和hunman类型的情况（展示图表自选，并在报告中说明选择原因、结果分析以及数据洞察）\n",
    "4.2 可视化bot类型账号的created_at情况（展示图表自选，并在报告中说明选择原因、结果分析以及数据洞察）\n",
    "4.3 可视化human类型账号的created_at情况（展示图表自选，并在报告中说明选择原因、结果分析以及数据洞察）\n",
    "4.4 可视化bot类型账号的followers和following情况（展示图表自选，并在报告中说明选择原因、结果分析以及数据洞察）\n",
    "4.5 可视化human类型账号的followers和following情况（展示图表自选，并在报告中说明选择原因、结果分析以及数据洞察）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.1  我需要去可视化bot和human 的类型情况。我认为这个可视化主要是为了去展示在这个数据集中，二者之间的对比。因为类型的种类比较少，只有两种。所以我选择用饼图去直观地展示二者的类别占比。通过面积的大小可以让人一眼就能看出两者之间的比例关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "file_path = r\"C:\\Users\\大头\\Desktop\\github_bot_raw_data.csv\"\n",
    "raw_data = pd.read_csv(file_path)\n",
    "columns = [\n",
    "    'actor_id',  # GitHub用户的ID (示例值: 1081405)\n",
    "    'label',  # 用户标签（\"Human\"或\"Bot\"） (示例值: Human)\n",
    "    'login',  # GitHub用户的登录名 (示例值: dlazesz)\n",
    "    'id',  # 用户的GitHub ID (示例值: 1081405)\n",
    "    'node_id',  # 用户的GitHub节点ID (示例值: MDQ6VXNlcjEwODE0MDU=)\n",
    "    'avatar_url',  # GitHub头像URL (示例值: https://avatars.githubusercontent.com/u/1081405?v=4)\n",
    "    'gravatar_id',  # Gravatar ID (示例值: None)\n",
    "    'url',  # GitHub用户的URL (示例值: https://api.github.com/users/dlazesz)\n",
    "    'html_url',  # GitHub用户的HTML URL (示例值: https://github.com/dlazesz)\n",
    "    'followers_url',  # GitHub用户的粉丝URL (示例值: https://api.github.com/users/dlazesz/followers)\n",
    "    'following_url',  # GitHub用户的关注URL (示例值: https://api.github.com/users/dlazesz/following{/other_user})\n",
    "    'gists_url',  # 用户的GitHub Gists URL (示例值: https://api.github.com/users/dlazesz/gists{/gist_id})\n",
    "    'starred_url',  # 用户的GitHub Starred URL (示例值: https://api.github.com/users/dlazesz/starred{/owner}{/repo})\n",
    "    'subscriptions_url',  # 用户的GitHub订阅URL (示例值: https://api.github.com/users/dlazesz/subscriptions)\n",
    "    'organizations_url',  # 用户的GitHub组织URL (示例值: https://api.github.com/users/dlazesz/orgs)\n",
    "    'repos_url',  # 用户的GitHub仓库URL (示例值: https://api.github.com/users/dlazesz/repos)\n",
    "    'events_url',  # 用户的GitHub事件URL (示例值: https://api.github.com/users/dlazesz/events{/privacy})\n",
    "    'received_events_url',  # 用户的GitHub接收事件URL (示例值: https://api.github.com/users/dlazesz/received_events)\n",
    "    'type',  # 用户类型，通常为\"User\" (示例值: User)\n",
    "    'site_admin',  # 表示用户是否是GitHub网站管理员的标志 (示例值: False)\n",
    "    'name',  # 用户的姓名 (示例值: Indig Balázs)\n",
    "    'company',  # 用户所在公司 (示例值: None)\n",
    "    'blog',  # 用户的博客 (示例值: None)\n",
    "    'location',  # 用户的位置 (示例值: None)\n",
    "    'email',  # 用户的电子邮件 (示例值: None)\n",
    "    'hireable',  # 表示用户是否愿意被雇佣的标志 (示例值: None)\n",
    "    'bio',  # 用户在其GitHub资料中提供的自我介绍或个人简介 (示例值: None)\n",
    "    'twitter_username',  # 用户的Twitter用户名 (示例值: None)\n",
    "    'public_repos',  # 用户在GitHub上的公共代码仓库数量 (示例值: 26)\n",
    "    'public_gists',  # 用户的公共Gists数量 (示例值: 1)\n",
    "    'followers',  # 关注该用户的其他GitHub用户数量 (示例值: 5)\n",
    "    'following',  # 该用户关注的其他GitHub用户数量 (示例值: 1)\n",
    "    'created_at',  # 用户的GitHub帐户创建日期 (示例值: 2011-09-26T17:27:03Z)\n",
    "    'updated_at',  # 用户的GitHub帐户最后更新日期 (示例值: 2023-10-13T11:21:10Z)\n",
    "]\n",
    "data = raw_data[columns]\n",
    "type_counts = data['label'].value_counts() #统计human和bots的数量\n",
    "type_counts.plot.pie(autopct='%1.1f%%', startangle=120, colors=['#ff9999','#66b3ff']) \n",
    "plt.title('Proportion of Bot and Human Types', fontsize=14)\n",
    "plt.ylabel('')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.2+4.3  这两道题本质上是一样的，都是去展示created_at的情况，只不过一个是针对bot，一个是针对human。所以我把两张图放在一起，不仅可以看到两种类型账号creates的时间序列，同时也可以看到两种类型之间账号的对比。因为都是和时间有关，所以我用了时间序列的折线图，并且按年份去划分，可以更直观的看到不同年份的创建账号的数量以及年与年之间的趋势比较。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "file_path = r\"C:\\Users\\大头\\Desktop\\github_bot_raw_data.csv\"\n",
    "raw_data = pd.read_csv(file_path)\n",
    "columns = [\n",
    "    'actor_id',  # GitHub用户的ID (示例值: 1081405)\n",
    "    'label',  # 用户标签（\"Human\"或\"Bot\"） (示例值: Human)\n",
    "    'login',  # GitHub用户的登录名 (示例值: dlazesz)\n",
    "    'id',  # 用户的GitHub ID (示例值: 1081405)\n",
    "    'node_id',  # 用户的GitHub节点ID (示例值: MDQ6VXNlcjEwODE0MDU=)\n",
    "    'avatar_url',  # GitHub头像URL (示例值: https://avatars.githubusercontent.com/u/1081405?v=4)\n",
    "    'gravatar_id',  # Gravatar ID (示例值: None)\n",
    "    'url',  # GitHub用户的URL (示例值: https://api.github.com/users/dlazesz)\n",
    "    'html_url',  # GitHub用户的HTML URL (示例值: https://github.com/dlazesz)\n",
    "    'followers_url',  # GitHub用户的粉丝URL (示例值: https://api.github.com/users/dlazesz/followers)\n",
    "    'following_url',  # GitHub用户的关注URL (示例值: https://api.github.com/users/dlazesz/following{/other_user})\n",
    "    'gists_url',  # 用户的GitHub Gists URL (示例值: https://api.github.com/users/dlazesz/gists{/gist_id})\n",
    "    'starred_url',  # 用户的GitHub Starred URL (示例值: https://api.github.com/users/dlazesz/starred{/owner}{/repo})\n",
    "    'subscriptions_url',  # 用户的GitHub订阅URL (示例值: https://api.github.com/users/dlazesz/subscriptions)\n",
    "    'organizations_url',  # 用户的GitHub组织URL (示例值: https://api.github.com/users/dlazesz/orgs)\n",
    "    'repos_url',  # 用户的GitHub仓库URL (示例值: https://api.github.com/users/dlazesz/repos)\n",
    "    'events_url',  # 用户的GitHub事件URL (示例值: https://api.github.com/users/dlazesz/events{/privacy})\n",
    "    'received_events_url',  # 用户的GitHub接收事件URL (示例值: https://api.github.com/users/dlazesz/received_events)\n",
    "    'type',  # 用户类型，通常为\"User\" (示例值: User)\n",
    "    'site_admin',  # 表示用户是否是GitHub网站管理员的标志 (示例值: False)\n",
    "    'name',  # 用户的姓名 (示例值: Indig Balázs)\n",
    "    'company',  # 用户所在公司 (示例值: None)\n",
    "    'blog',  # 用户的博客 (示例值: None)\n",
    "    'location',  # 用户的位置 (示例值: None)\n",
    "    'email',  # 用户的电子邮件 (示例值: None)\n",
    "    'hireable',  # 表示用户是否愿意被雇佣的标志 (示例值: None)\n",
    "    'bio',  # 用户在其GitHub资料中提供的自我介绍或个人简介 (示例值: None)\n",
    "    'twitter_username',  # 用户的Twitter用户名 (示例值: None)\n",
    "    'public_repos',  # 用户在GitHub上的公共代码仓库数量 (示例值: 26)\n",
    "    'public_gists',  # 用户的公共Gists数量 (示例值: 1)\n",
    "    'followers',  # 关注该用户的其他GitHub用户数量 (示例值: 5)\n",
    "    'following',  # 该用户关注的其他GitHub用户数量 (示例值: 1)\n",
    "    'created_at',  # 用户的GitHub帐户创建日期 (示例值: 2011-09-26T17:27:03Z)\n",
    "    'updated_at',  # 用户的GitHub帐户最后更新日期 (示例值: 2023-10-13T11:21:10Z)\n",
    "]\n",
    "data = raw_data[columns]\n",
    "# 转换时间字段为日期\n",
    "data['created_at'] = pd.to_datetime(data['created_at'])\n",
    "\n",
    "bot_data = data[data['label'] == 'Bot']\n",
    "human_data = data[data['label'] == 'Human']\n",
    "\n",
    "# 把年份信息提取出来，然后统计每个年份出现的数量，并且用sort进行排序\n",
    "bot_created = bot_data['created_at'].dt.year.value_counts().sort_index()\n",
    "human_created = human_data['created_at'].dt.year.value_counts().sort_index()\n",
    "\n",
    "# 绘制折线图\n",
    "plt.figure(figsize=(10, 8))\n",
    "#选了橙色和紫色，因为我喜欢Hhhh（彩蛋boom!）\n",
    "plt.plot(bot_created.index, bot_created.values, label='Bot', color='orange', marker='o')\n",
    "plt.plot(human_created.index, human_created.values, label='Human', color='purple', marker='o')\n",
    "plt.title('Account Creation Trend', fontsize=14)\n",
    "plt.xlabel('Year', fontsize=12)\n",
    "plt.ylabel('Number of Accounts Created', fontsize=12)\n",
    "plt.legend(fontsize=10)\n",
    "#加一下网格线，能够看的更清楚\n",
    "plt.grid(alpha=0.4)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和4.2与4.3一样，我还是选择把4.4和4.5的图一起画，理由和上面一道题一样。就是可视化bot类型和human类型账号的followers和following情况。follower是关注这个用户的，following是这个用户关注的github用户。我在这道题用了箱线图，因为可以更容易展示出数据的一些值，比如中位数，四分位数之类的，而且可以把一些异常值去掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\大头\\AppData\\Local\\Temp\\ipykernel_12548\\1029338855.py:46: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x='label', y='followers', showfliers=False, palette='Set3')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\大头\\AppData\\Local\\Temp\\ipykernel_12548\\1029338855.py:53: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=data, x='label', y='following', showfliers=False, palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "file_path = r\"C:\\Users\\大头\\Desktop\\github_bot_raw_data.csv\"\n",
    "raw_data = pd.read_csv(file_path)\n",
    "columns = [\n",
    "    'actor_id',  # GitHub用户的ID (示例值: 1081405)\n",
    "    'label',  # 用户标签（\"Human\"或\"Bot\"） (示例值: Human)\n",
    "    'login',  # GitHub用户的登录名 (示例值: dlazesz)\n",
    "    'id',  # 用户的GitHub ID (示例值: 1081405)\n",
    "    'node_id',  # 用户的GitHub节点ID (示例值: MDQ6VXNlcjEwODE0MDU=)\n",
    "    'avatar_url',  # GitHub头像URL (示例值: https://avatars.githubusercontent.com/u/1081405?v=4)\n",
    "    'gravatar_id',  # Gravatar ID (示例值: None)\n",
    "    'url',  # GitHub用户的URL (示例值: https://api.github.com/users/dlazesz)\n",
    "    'html_url',  # GitHub用户的HTML URL (示例值: https://github.com/dlazesz)\n",
    "    'followers_url',  # GitHub用户的粉丝URL (示例值: https://api.github.com/users/dlazesz/followers)\n",
    "    'following_url',  # GitHub用户的关注URL (示例值: https://api.github.com/users/dlazesz/following{/other_user})\n",
    "    'gists_url',  # 用户的GitHub Gists URL (示例值: https://api.github.com/users/dlazesz/gists{/gist_id})\n",
    "    'starred_url',  # 用户的GitHub Starred URL (示例值: https://api.github.com/users/dlazesz/starred{/owner}{/repo})\n",
    "    'subscriptions_url',  # 用户的GitHub订阅URL (示例值: https://api.github.com/users/dlazesz/subscriptions)\n",
    "    'organizations_url',  # 用户的GitHub组织URL (示例值: https://api.github.com/users/dlazesz/orgs)\n",
    "    'repos_url',  # 用户的GitHub仓库URL (示例值: https://api.github.com/users/dlazesz/repos)\n",
    "    'events_url',  # 用户的GitHub事件URL (示例值: https://api.github.com/users/dlazesz/events{/privacy})\n",
    "    'received_events_url',  # 用户的GitHub接收事件URL (示例值: https://api.github.com/users/dlazesz/received_events)\n",
    "    'type',  # 用户类型，通常为\"User\" (示例值: User)\n",
    "    'site_admin',  # 表示用户是否是GitHub网站管理员的标志 (示例值: False)\n",
    "    'name',  # 用户的姓名 (示例值: Indig Balázs)\n",
    "    'company',  # 用户所在公司 (示例值: None)\n",
    "    'blog',  # 用户的博客 (示例值: None)\n",
    "    'location',  # 用户的位置 (示例值: None)\n",
    "    'email',  # 用户的电子邮件 (示例值: None)\n",
    "    'hireable',  # 表示用户是否愿意被雇佣的标志 (示例值: None)\n",
    "    'bio',  # 用户在其GitHub资料中提供的自我介绍或个人简介 (示例值: None)\n",
    "    'twitter_username',  # 用户的Twitter用户名 (示例值: None)\n",
    "    'public_repos',  # 用户在GitHub上的公共代码仓库数量 (示例值: 26)\n",
    "    'public_gists',  # 用户的公共Gists数量 (示例值: 1)\n",
    "    'followers',  # 关注该用户的其他GitHub用户数量 (示例值: 5)\n",
    "    'following',  # 该用户关注的其他GitHub用户数量 (示例值: 1)\n",
    "    'created_at',  # 用户的GitHub帐户创建日期 (示例值: 2011-09-26T17:27:03Z)\n",
    "    'updated_at',  # 用户的GitHub帐户最后更新日期 (示例值: 2023-10-13T11:21:10Z)\n",
    "]\n",
    "data = raw_data[columns]\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "# 绘制箱线图\n",
    "sns.boxplot(data=data, x='label', y='followers', showfliers=False, palette='Set3')\n",
    "plt.title('Followers Distribution of Bot and Human', fontsize=14)\n",
    "plt.xlabel('Type', fontsize=12)\n",
    "plt.ylabel('Followers', fontsize=12)\n",
    "plt.yscale('log')  # 使用对数坐标显示较大的差异\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(data=data, x='label', y='following', showfliers=False, palette='Set2')\n",
    "plt.title('Following Distribution of Bot and Human', fontsize=14)\n",
    "plt.xlabel('Type', fontsize=12)\n",
    "plt.ylabel('Following', fontsize=12)\n",
    "plt.yscale('log')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}