GitHub Data Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee201097",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc113d2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'login': 'aarushiksk', 'id': 92772439, 'node_id': 'U_kgDOBYeYVw', 'avatar_url': 'https://avatars.githubusercontent.com/u/92772439?v=4', 'gravatar_id': '', 'url': 'https://api.github.com/users/aarushiksk', 'html_url': 'https://github.com/aarushiksk', 'followers_url': 'https://api.github.com/users/aarushiksk/followers', 'following_url': 'https://api.github.com/users/aarushiksk/following{/other_user}', 'gists_url': 'https://api.github.com/users/aarushiksk/gists{/gist_id}', 'starred_url': 'https://api.github.com/users/aarushiksk/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/aarushiksk/subscriptions', 'organizations_url': 'https://api.github.com/users/aarushiksk/orgs', 'repos_url': 'https://api.github.com/users/aarushiksk/repos', 'events_url': 'https://api.github.com/users/aarushiksk/events{/privacy}', 'received_events_url': 'https://api.github.com/users/aarushiksk/received_events', 'type': 'User', 'site_admin': False, 'name': 'Aarushi', 'company': None, 'blog': '', 'location': None, 'email': None, 'hireable': None, 'bio': 'Be the Jack of all trades and master of one.', 'twitter_username': None, 'public_repos': 28, 'public_gists': 0, 'followers': 4, 'following': 8, 'created_at': '2021-10-19T04:01:38Z', 'updated_at': '2023-04-18T13:09:25Z'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "username = \"aarushiksk\" #Add your username here\n",
    "url = \"https://api.github.com/users/{}\".format(username)\n",
    "response = requests.get(url)\n",
    "output= json.loads(response.text)\n",
    "print(output)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1713391",
   "metadata": {},
   "source": [
    "# Getting the popular python libraries according to stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5f8503c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status code: 200\n"
     ]
    }
   ],
   "source": [
    "\n",
    "url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'\n",
    "headers = {'Accept': 'application/vnd.github.v3+json'}\n",
    "response = requests.get(url, headers=headers)\n",
    "print(f\"Status code: {response.status_code}\")\n",
    "#Status code 200 depicts that the request has successfully executed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "090f41e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict=response.json() #Convert the response to json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e629d4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['total_count', 'incomplete_results', 'items'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict.keys() #display keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6174d026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Repositories: 10419837\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total Repositories: {dict['total_count']}\") #Prints total repos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c80fe857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Repositories returned: 30\n"
     ]
    }
   ],
   "source": [
    "repository_dict = dict['items']\n",
    "print(f\"Repositories returned: {len(repository_dict)}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e7b088ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "repo1=repository_dict[0]  #Get the details of the first repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "807a9814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 123458551,\n",
       " 'node_id': 'MDEwOlJlcG9zaXRvcnkxMjM0NTg1NTE=',\n",
       " 'name': 'Python-100-Days',\n",
       " 'full_name': 'jackfrued/Python-100-Days',\n",
       " 'private': False,\n",
       " 'owner': {'login': 'jackfrued',\n",
       "  'id': 7474657,\n",
       "  'node_id': 'MDQ6VXNlcjc0NzQ2NTc=',\n",
       "  'avatar_url': 'https://avatars.githubusercontent.com/u/7474657?v=4',\n",
       "  'gravatar_id': '',\n",
       "  'url': 'https://api.github.com/users/jackfrued',\n",
       "  'html_url': 'https://github.com/jackfrued',\n",
       "  'followers_url': 'https://api.github.com/users/jackfrued/followers',\n",
       "  'following_url': 'https://api.github.com/users/jackfrued/following{/other_user}',\n",
       "  'gists_url': 'https://api.github.com/users/jackfrued/gists{/gist_id}',\n",
       "  'starred_url': 'https://api.github.com/users/jackfrued/starred{/owner}{/repo}',\n",
       "  'subscriptions_url': 'https://api.github.com/users/jackfrued/subscriptions',\n",
       "  'organizations_url': 'https://api.github.com/users/jackfrued/orgs',\n",
       "  'repos_url': 'https://api.github.com/users/jackfrued/repos',\n",
       "  'events_url': 'https://api.github.com/users/jackfrued/events{/privacy}',\n",
       "  'received_events_url': 'https://api.github.com/users/jackfrued/received_events',\n",
       "  'type': 'User',\n",
       "  'site_admin': False},\n",
       " 'html_url': 'https://github.com/jackfrued/Python-100-Days',\n",
       " 'description': 'Python - 100天从新手到大师',\n",
       " 'fork': False,\n",
       " 'url': 'https://api.github.com/repos/jackfrued/Python-100-Days',\n",
       " 'forks_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/forks',\n",
       " 'keys_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/keys{/key_id}',\n",
       " 'collaborators_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/collaborators{/collaborator}',\n",
       " 'teams_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/teams',\n",
       " 'hooks_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/hooks',\n",
       " 'issue_events_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/issues/events{/number}',\n",
       " 'events_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/events',\n",
       " 'assignees_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/assignees{/user}',\n",
       " 'branches_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/branches{/branch}',\n",
       " 'tags_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/tags',\n",
       " 'blobs_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/git/blobs{/sha}',\n",
       " 'git_tags_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/git/tags{/sha}',\n",
       " 'git_refs_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/git/refs{/sha}',\n",
       " 'trees_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/git/trees{/sha}',\n",
       " 'statuses_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/statuses/{sha}',\n",
       " 'languages_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/languages',\n",
       " 'stargazers_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/stargazers',\n",
       " 'contributors_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/contributors',\n",
       " 'subscribers_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/subscribers',\n",
       " 'subscription_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/subscription',\n",
       " 'commits_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/commits{/sha}',\n",
       " 'git_commits_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/git/commits{/sha}',\n",
       " 'comments_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/comments{/number}',\n",
       " 'issue_comment_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/issues/comments{/number}',\n",
       " 'contents_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/contents/{+path}',\n",
       " 'compare_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/compare/{base}...{head}',\n",
       " 'merges_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/merges',\n",
       " 'archive_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/{archive_format}{/ref}',\n",
       " 'downloads_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/downloads',\n",
       " 'issues_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/issues{/number}',\n",
       " 'pulls_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/pulls{/number}',\n",
       " 'milestones_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/milestones{/number}',\n",
       " 'notifications_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/notifications{?since,all,participating}',\n",
       " 'labels_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/labels{/name}',\n",
       " 'releases_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/releases{/id}',\n",
       " 'deployments_url': 'https://api.github.com/repos/jackfrued/Python-100-Days/deployments',\n",
       " 'created_at': '2018-03-01T16:05:52Z',\n",
       " 'updated_at': '2023-04-22T04:11:12Z',\n",
       " 'pushed_at': '2023-04-11T06:00:22Z',\n",
       " 'git_url': 'git://github.com/jackfrued/Python-100-Days.git',\n",
       " 'ssh_url': 'git@github.com:jackfrued/Python-100-Days.git',\n",
       " 'clone_url': 'https://github.com/jackfrued/Python-100-Days.git',\n",
       " 'svn_url': 'https://github.com/jackfrued/Python-100-Days',\n",
       " 'homepage': '',\n",
       " 'size': 310266,\n",
       " 'stargazers_count': 134335,\n",
       " 'watchers_count': 134335,\n",
       " 'language': 'Python',\n",
       " 'has_issues': True,\n",
       " 'has_projects': True,\n",
       " 'has_downloads': True,\n",
       " 'has_wiki': True,\n",
       " 'has_pages': False,\n",
       " 'has_discussions': False,\n",
       " 'forks_count': 48814,\n",
       " 'mirror_url': None,\n",
       " 'archived': False,\n",
       " 'disabled': False,\n",
       " 'open_issues_count': 681,\n",
       " 'license': None,\n",
       " 'allow_forking': True,\n",
       " 'is_template': False,\n",
       " 'web_commit_signoff_required': False,\n",
       " 'topics': [],\n",
       " 'visibility': 'public',\n",
       " 'forks': 48814,\n",
       " 'open_issues': 681,\n",
       " 'watchers': 134335,\n",
       " 'default_branch': 'master',\n",
       " 'score': 1.0}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repo1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be93d88",
   "metadata": {},
   "source": [
    "# Print two attributes of different repos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "64b5366c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Name: Python-100-Days\n",
      "Owner: jackfrued\n",
      "Stars: 134335\n",
      "Forks: 48814\n",
      "Open Issues: 681\n",
      "Repository: https://github.com/jackfrued/Python-100-Days\n",
      "Description: Python - 100天从新手到大师\n",
      "\n",
      "Name: Auto-GPT\n",
      "Owner: Significant-Gravitas\n",
      "Stars: 101742\n",
      "Forks: 16075\n",
      "Open Issues: 658\n",
      "Repository: https://github.com/Significant-Gravitas/Auto-GPT\n",
      "Description: An experimental open-source attempt to make GPT-4 fully autonomous.\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for repo in repository_dict:\n",
    "\n",
    "    if(count<2):\n",
    "        print(f\"\\nName: {repo['name']}\")\n",
    "        \n",
    "        print(f\"Owner: {repo['owner']['login']}\")\n",
    "\n",
    "        print(f\"Stars: {repo['stargazers_count']}\")\n",
    "        print(f\"Forks: {repo['forks_count']}\")\n",
    "        print(f\"Open Issues: {repo['open_issues']}\")\n",
    "        print(f\"Repository: {repo['html_url']}\")\n",
    "        print(f\"Description: {repo['description']}\")\n",
    "    count=count+1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd4b450",
   "metadata": {},
   "source": [
    "# Convert the dictionary to a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "40829c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Repo_Name</th>\n",
       "      <th>Repo_Link</th>\n",
       "      <th>Created_at</th>\n",
       "      <th>Forks</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python-100-Days</td>\n",
       "      <td><a href='https://github.com/jackfrued/Python-100-Days'>Python-100-Days</a></td>\n",
       "      <td>2018-03-01T16:05:52Z</td>\n",
       "      <td>48814</td>\n",
       "      <td>134335</td>\n",
       "      <td>jackfrued</td>\n",
       "      <td>Python - 100天从新手到大师</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Auto-GPT</td>\n",
       "      <td><a href='https://github.com/Significant-Gravitas/Auto-GPT'>Auto-GPT</a></td>\n",
       "      <td>2023-03-16T09:21:07Z</td>\n",
       "      <td>16075</td>\n",
       "      <td>101742</td>\n",
       "      <td>Significant-Gravitas</td>\n",
       "      <td>An experimental open-source attempt to make GPT-4 fully autonomous.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cpython</td>\n",
       "      <td><a href='https://github.com/python/cpython'>cpython</a></td>\n",
       "      <td>2017-02-10T19:23:51Z</td>\n",
       "      <td>26582</td>\n",
       "      <td>52050</td>\n",
       "      <td>python</td>\n",
       "      <td>The Python programming language</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>manim</td>\n",
       "      <td><a href='https://github.com/3b1b/manim'>manim</a></td>\n",
       "      <td>2015-03-22T18:50:58Z</td>\n",
       "      <td>5275</td>\n",
       "      <td>50801</td>\n",
       "      <td>3b1b</td>\n",
       "      <td>Animation engine for explanatory math videos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yt-dlp</td>\n",
       "      <td><a href='https://github.com/yt-dlp/yt-dlp'>yt-dlp</a></td>\n",
       "      <td>2020-10-26T04:22:55Z</td>\n",
       "      <td>3762</td>\n",
       "      <td>45667</td>\n",
       "      <td>yt-dlp</td>\n",
       "      <td>A youtube-dl fork with additional features and fixes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>big-list-of-naughty-strings</td>\n",
       "      <td><a href='https://github.com/minimaxir/big-list-of-naughty-strings'>big-list-of-naughty-strings</a></td>\n",
       "      <td>2015-08-08T20:57:20Z</td>\n",
       "      <td>2134</td>\n",
       "      <td>44762</td>\n",
       "      <td>minimaxir</td>\n",
       "      <td>The Big List of Naughty Strings is a list of strings which have a high probability of causing issues when used as user-input data.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d2l-zh</td>\n",
       "      <td><a href='https://github.com/d2l-ai/d2l-zh'>d2l-zh</a></td>\n",
       "      <td>2017-08-23T04:40:24Z</td>\n",
       "      <td>8977</td>\n",
       "      <td>42629</td>\n",
       "      <td>d2l-ai</td>\n",
       "      <td>《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sherlock</td>\n",
       "      <td><a href='https://github.com/sherlock-project/sherlock'>sherlock</a></td>\n",
       "      <td>2018-12-24T14:30:48Z</td>\n",
       "      <td>4821</td>\n",
       "      <td>40424</td>\n",
       "      <td>sherlock-project</td>\n",
       "      <td>🔎 Hunt down social media accounts by username across social networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bert</td>\n",
       "      <td><a href='https://github.com/google-research/bert'>bert</a></td>\n",
       "      <td>2018-10-25T22:57:34Z</td>\n",
       "      <td>9193</td>\n",
       "      <td>33980</td>\n",
       "      <td>google-research</td>\n",
       "      <td>TensorFlow code and pre-trained models for BERT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>whisper</td>\n",
       "      <td><a href='https://github.com/openai/whisper'>whisper</a></td>\n",
       "      <td>2022-09-16T20:02:54Z</td>\n",
       "      <td>3686</td>\n",
       "      <td>33808</td>\n",
       "      <td>openai</td>\n",
       "      <td>Robust Speech Recognition via Large-Scale Weak Supervision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XX-Net</td>\n",
       "      <td><a href='https://github.com/XX-net/XX-Net'>XX-Net</a></td>\n",
       "      <td>2015-01-15T09:35:51Z</td>\n",
       "      <td>7784</td>\n",
       "      <td>31956</td>\n",
       "      <td>XX-net</td>\n",
       "      <td>A proxy tool to bypass GFW.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gym</td>\n",
       "      <td><a href='https://github.com/openai/gym'>gym</a></td>\n",
       "      <td>2016-04-27T14:59:16Z</td>\n",
       "      <td>8364</td>\n",
       "      <td>30914</td>\n",
       "      <td>openai</td>\n",
       "      <td>A toolkit for developing and comparing reinforcement learning algorithms.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>chatgpt_academic</td>\n",
       "      <td><a href='https://github.com/binary-husky/chatgpt_academic'>chatgpt_academic</a></td>\n",
       "      <td>2023-03-20T09:05:13Z</td>\n",
       "      <td>3394</td>\n",
       "      <td>29373</td>\n",
       "      <td>binary-husky</td>\n",
       "      <td>科研工作专用ChatGPT/GLM拓展，特别优化学术Paper润色体验，模块化设计支持自定义快捷按钮&函数插件，支持代码块表格显示，Tex公式双显示，新增Python和C++项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持gpt-3.5/gpt-4/chatglm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HanLP</td>\n",
       "      <td><a href='https://github.com/hankcs/HanLP'>HanLP</a></td>\n",
       "      <td>2014-10-09T06:36:16Z</td>\n",
       "      <td>8157</td>\n",
       "      <td>28798</td>\n",
       "      <td>hankcs</td>\n",
       "      <td>中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ColossalAI</td>\n",
       "      <td><a href='https://github.com/hpcaitech/ColossalAI'>ColossalAI</a></td>\n",
       "      <td>2021-10-28T16:19:44Z</td>\n",
       "      <td>3299</td>\n",
       "      <td>28573</td>\n",
       "      <td>hpcaitech</td>\n",
       "      <td>Making large AI models cheaper, faster and more accessible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MockingBird</td>\n",
       "      <td><a href='https://github.com/babysor/MockingBird'>MockingBird</a></td>\n",
       "      <td>2021-08-07T03:53:39Z</td>\n",
       "      <td>4232</td>\n",
       "      <td>28216</td>\n",
       "      <td>babysor</td>\n",
       "      <td>🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ray</td>\n",
       "      <td><a href='https://github.com/ray-project/ray'>ray</a></td>\n",
       "      <td>2016-10-25T19:38:30Z</td>\n",
       "      <td>4382</td>\n",
       "      <td>25198</td>\n",
       "      <td>ray-project</td>\n",
       "      <td>Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>poetry</td>\n",
       "      <td><a href='https://github.com/python-poetry/poetry'>poetry</a></td>\n",
       "      <td>2018-02-28T15:23:47Z</td>\n",
       "      <td>1955</td>\n",
       "      <td>24709</td>\n",
       "      <td>python-poetry</td>\n",
       "      <td>Python packaging and dependency management made easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>detectron2</td>\n",
       "      <td><a href='https://github.com/facebookresearch/detectron2'>detectron2</a></td>\n",
       "      <td>2019-09-05T21:30:20Z</td>\n",
       "      <td>6683</td>\n",
       "      <td>24521</td>\n",
       "      <td>facebookresearch</td>\n",
       "      <td>Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>python-fire</td>\n",
       "      <td><a href='https://github.com/google/python-fire'>python-fire</a></td>\n",
       "      <td>2017-02-21T21:35:07Z</td>\n",
       "      <td>1379</td>\n",
       "      <td>24389</td>\n",
       "      <td>google</td>\n",
       "      <td>Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ItChat</td>\n",
       "      <td><a href='https://github.com/littlecodersh/ItChat'>ItChat</a></td>\n",
       "      <td>2016-01-19T07:49:48Z</td>\n",
       "      <td>5449</td>\n",
       "      <td>23728</td>\n",
       "      <td>littlecodersh</td>\n",
       "      <td>A complete and graceful API for Wechat. 微信个人号接口、微信机器人及命令行微信，三十行即可自定义个人号机器人。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30-Days-Of-Python</td>\n",
       "      <td><a href='https://github.com/Asabeneh/30-Days-Of-Python'>30-Days-Of-Python</a></td>\n",
       "      <td>2019-11-19T17:24:16Z</td>\n",
       "      <td>5102</td>\n",
       "      <td>23721</td>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.  These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hosts</td>\n",
       "      <td><a href='https://github.com/StevenBlack/hosts'>hosts</a></td>\n",
       "      <td>2012-04-12T20:22:50Z</td>\n",
       "      <td>1963</td>\n",
       "      <td>22808</td>\n",
       "      <td>StevenBlack</td>\n",
       "      <td>🔒 Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>glances</td>\n",
       "      <td><a href='https://github.com/nicolargo/glances'>glances</a></td>\n",
       "      <td>2011-12-04T08:49:15Z</td>\n",
       "      <td>1372</td>\n",
       "      <td>22659</td>\n",
       "      <td>nicolargo</td>\n",
       "      <td>Glances an Eye on your system. A top/htop alternative for GNU/Linux, BSD, Mac OS and Windows operating systems.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>algorithms</td>\n",
       "      <td><a href='https://github.com/keon/algorithms'>algorithms</a></td>\n",
       "      <td>2016-11-17T22:32:08Z</td>\n",
       "      <td>4469</td>\n",
       "      <td>22469</td>\n",
       "      <td>keon</td>\n",
       "      <td>Minimal examples of data structures and algorithms in Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NLP-progress</td>\n",
       "      <td><a href='https://github.com/sebastianruder/NLP-progress'>NLP-progress</a></td>\n",
       "      <td>2018-06-22T17:43:55Z</td>\n",
       "      <td>3565</td>\n",
       "      <td>21552</td>\n",
       "      <td>sebastianruder</td>\n",
       "      <td>Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>locust</td>\n",
       "      <td><a href='https://github.com/locustio/locust'>locust</a></td>\n",
       "      <td>2011-02-17T11:08:03Z</td>\n",
       "      <td>2675</td>\n",
       "      <td>21147</td>\n",
       "      <td>locustio</td>\n",
       "      <td>Write scalable load tests in plain Python 🚗💨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>macOS-Security-and-Privacy-Guide</td>\n",
       "      <td><a href='https://github.com/drduh/macOS-Security-and-Privacy-Guide'>macOS-Security-and-Privacy-Guide</a></td>\n",
       "      <td>2015-08-31T03:36:35Z</td>\n",
       "      <td>1422</td>\n",
       "      <td>20111</td>\n",
       "      <td>drduh</td>\n",
       "      <td>Guide to securing and improving privacy on macOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>pytorch-CycleGAN-and-pix2pix</td>\n",
       "      <td><a href='https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix'>pytorch-CycleGAN-and-pix2pix</a></td>\n",
       "      <td>2017-04-18T10:33:05Z</td>\n",
       "      <td>5814</td>\n",
       "      <td>19717</td>\n",
       "      <td>junyanz</td>\n",
       "      <td>Image-to-Image Translation in PyTorch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Real-ESRGAN</td>\n",
       "      <td><a href='https://github.com/xinntao/Real-ESRGAN'>Real-ESRGAN</a></td>\n",
       "      <td>2021-07-19T03:23:30Z</td>\n",
       "      <td>2144</td>\n",
       "      <td>19670</td>\n",
       "      <td>xinntao</td>\n",
       "      <td>Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Repo_Name</th>\n",
       "      <th>Repo_Link</th>\n",
       "      <th>Created_at</th>\n",
       "      <th>Forks</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Python-100-Days</td>\n",
       "      <td>&lt;a href='https://github.com/jackfrued/Python-1...</td>\n",
       "      <td>2018-03-01 16:05:52+00:00</td>\n",
       "      <td>48814</td>\n",
       "      <td>134335</td>\n",
       "      <td>jackfrued</td>\n",
       "      <td>Python - 100天从新手到大师</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Auto-GPT</td>\n",
       "      <td>&lt;a href='https://github.com/Significant-Gravit...</td>\n",
       "      <td>2023-03-16 09:21:07+00:00</td>\n",
       "      <td>16075</td>\n",
       "      <td>101742</td>\n",
       "      <td>Significant-Gravitas</td>\n",
       "      <td>An experimental open-source attempt to make GP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cpython</td>\n",
       "      <td>&lt;a href='https://github.com/python/cpython'&gt;cp...</td>\n",
       "      <td>2017-02-10 19:23:51+00:00</td>\n",
       "      <td>26582</td>\n",
       "      <td>52050</td>\n",
       "      <td>python</td>\n",
       "      <td>The Python programming language</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>manim</td>\n",
       "      <td>&lt;a href='https://github.com/3b1b/manim'&gt;manim&lt;/a&gt;</td>\n",
       "      <td>2015-03-22 18:50:58+00:00</td>\n",
       "      <td>5275</td>\n",
       "      <td>50801</td>\n",
       "      <td>3b1b</td>\n",
       "      <td>Animation engine for explanatory math videos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yt-dlp</td>\n",
       "      <td>&lt;a href='https://github.com/yt-dlp/yt-dlp'&gt;yt-...</td>\n",
       "      <td>2020-10-26 04:22:55+00:00</td>\n",
       "      <td>3762</td>\n",
       "      <td>45667</td>\n",
       "      <td>yt-dlp</td>\n",
       "      <td>A youtube-dl fork with additional features and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>big-list-of-naughty-strings</td>\n",
       "      <td>&lt;a href='https://github.com/minimaxir/big-list...</td>\n",
       "      <td>2015-08-08 20:57:20+00:00</td>\n",
       "      <td>2134</td>\n",
       "      <td>44762</td>\n",
       "      <td>minimaxir</td>\n",
       "      <td>The Big List of Naughty Strings is a list of s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>d2l-zh</td>\n",
       "      <td>&lt;a href='https://github.com/d2l-ai/d2l-zh'&gt;d2l...</td>\n",
       "      <td>2017-08-23 04:40:24+00:00</td>\n",
       "      <td>8977</td>\n",
       "      <td>42629</td>\n",
       "      <td>d2l-ai</td>\n",
       "      <td>《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sherlock</td>\n",
       "      <td>&lt;a href='https://github.com/sherlock-project/s...</td>\n",
       "      <td>2018-12-24 14:30:48+00:00</td>\n",
       "      <td>4821</td>\n",
       "      <td>40424</td>\n",
       "      <td>sherlock-project</td>\n",
       "      <td>🔎 Hunt down social media accounts by username ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bert</td>\n",
       "      <td>&lt;a href='https://github.com/google-research/be...</td>\n",
       "      <td>2018-10-25 22:57:34+00:00</td>\n",
       "      <td>9193</td>\n",
       "      <td>33980</td>\n",
       "      <td>google-research</td>\n",
       "      <td>TensorFlow code and pre-trained models for BERT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>whisper</td>\n",
       "      <td>&lt;a href='https://github.com/openai/whisper'&gt;wh...</td>\n",
       "      <td>2022-09-16 20:02:54+00:00</td>\n",
       "      <td>3686</td>\n",
       "      <td>33808</td>\n",
       "      <td>openai</td>\n",
       "      <td>Robust Speech Recognition via Large-Scale Weak...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XX-Net</td>\n",
       "      <td>&lt;a href='https://github.com/XX-net/XX-Net'&gt;XX-...</td>\n",
       "      <td>2015-01-15 09:35:51+00:00</td>\n",
       "      <td>7784</td>\n",
       "      <td>31956</td>\n",
       "      <td>XX-net</td>\n",
       "      <td>A proxy tool to bypass GFW.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gym</td>\n",
       "      <td>&lt;a href='https://github.com/openai/gym'&gt;gym&lt;/a&gt;</td>\n",
       "      <td>2016-04-27 14:59:16+00:00</td>\n",
       "      <td>8364</td>\n",
       "      <td>30914</td>\n",
       "      <td>openai</td>\n",
       "      <td>A toolkit for developing and comparing reinfor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>chatgpt_academic</td>\n",
       "      <td>&lt;a href='https://github.com/binary-husky/chatg...</td>\n",
       "      <td>2023-03-20 09:05:13+00:00</td>\n",
       "      <td>3394</td>\n",
       "      <td>29373</td>\n",
       "      <td>binary-husky</td>\n",
       "      <td>科研工作专用ChatGPT/GLM拓展，特别优化学术Paper润色体验，模块化设计支持自定义...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HanLP</td>\n",
       "      <td>&lt;a href='https://github.com/hankcs/HanLP'&gt;HanL...</td>\n",
       "      <td>2014-10-09 06:36:16+00:00</td>\n",
       "      <td>8157</td>\n",
       "      <td>28798</td>\n",
       "      <td>hankcs</td>\n",
       "      <td>中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ColossalAI</td>\n",
       "      <td>&lt;a href='https://github.com/hpcaitech/Colossal...</td>\n",
       "      <td>2021-10-28 16:19:44+00:00</td>\n",
       "      <td>3299</td>\n",
       "      <td>28573</td>\n",
       "      <td>hpcaitech</td>\n",
       "      <td>Making large AI models cheaper, faster and mor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MockingBird</td>\n",
       "      <td>&lt;a href='https://github.com/babysor/MockingBir...</td>\n",
       "      <td>2021-08-07 03:53:39+00:00</td>\n",
       "      <td>4232</td>\n",
       "      <td>28216</td>\n",
       "      <td>babysor</td>\n",
       "      <td>🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ray</td>\n",
       "      <td>&lt;a href='https://github.com/ray-project/ray'&gt;r...</td>\n",
       "      <td>2016-10-25 19:38:30+00:00</td>\n",
       "      <td>4382</td>\n",
       "      <td>25198</td>\n",
       "      <td>ray-project</td>\n",
       "      <td>Ray is a unified framework for scaling AI and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>poetry</td>\n",
       "      <td>&lt;a href='https://github.com/python-poetry/poet...</td>\n",
       "      <td>2018-02-28 15:23:47+00:00</td>\n",
       "      <td>1955</td>\n",
       "      <td>24709</td>\n",
       "      <td>python-poetry</td>\n",
       "      <td>Python packaging and dependency management mad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>detectron2</td>\n",
       "      <td>&lt;a href='https://github.com/facebookresearch/d...</td>\n",
       "      <td>2019-09-05 21:30:20+00:00</td>\n",
       "      <td>6683</td>\n",
       "      <td>24521</td>\n",
       "      <td>facebookresearch</td>\n",
       "      <td>Detectron2 is a platform for object detection,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>python-fire</td>\n",
       "      <td>&lt;a href='https://github.com/google/python-fire...</td>\n",
       "      <td>2017-02-21 21:35:07+00:00</td>\n",
       "      <td>1379</td>\n",
       "      <td>24389</td>\n",
       "      <td>google</td>\n",
       "      <td>Python Fire is a library for automatically gen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ItChat</td>\n",
       "      <td>&lt;a href='https://github.com/littlecodersh/ItCh...</td>\n",
       "      <td>2016-01-19 07:49:48+00:00</td>\n",
       "      <td>5449</td>\n",
       "      <td>23728</td>\n",
       "      <td>littlecodersh</td>\n",
       "      <td>A complete and graceful API for Wechat. 微信个人号接...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30-Days-Of-Python</td>\n",
       "      <td>&lt;a href='https://github.com/Asabeneh/30-Days-O...</td>\n",
       "      <td>2019-11-19 17:24:16+00:00</td>\n",
       "      <td>5102</td>\n",
       "      <td>23721</td>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>30 days of Python programming challenge is a s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>hosts</td>\n",
       "      <td>&lt;a href='https://github.com/StevenBlack/hosts'...</td>\n",
       "      <td>2012-04-12 20:22:50+00:00</td>\n",
       "      <td>1963</td>\n",
       "      <td>22808</td>\n",
       "      <td>StevenBlack</td>\n",
       "      <td>🔒 Consolidating and extending hosts files from...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>glances</td>\n",
       "      <td>&lt;a href='https://github.com/nicolargo/glances'...</td>\n",
       "      <td>2011-12-04 08:49:15+00:00</td>\n",
       "      <td>1372</td>\n",
       "      <td>22659</td>\n",
       "      <td>nicolargo</td>\n",
       "      <td>Glances an Eye on your system. A top/htop alte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>algorithms</td>\n",
       "      <td>&lt;a href='https://github.com/keon/algorithms'&gt;a...</td>\n",
       "      <td>2016-11-17 22:32:08+00:00</td>\n",
       "      <td>4469</td>\n",
       "      <td>22469</td>\n",
       "      <td>keon</td>\n",
       "      <td>Minimal examples of data structures and algori...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NLP-progress</td>\n",
       "      <td>&lt;a href='https://github.com/sebastianruder/NLP...</td>\n",
       "      <td>2018-06-22 17:43:55+00:00</td>\n",
       "      <td>3565</td>\n",
       "      <td>21552</td>\n",
       "      <td>sebastianruder</td>\n",
       "      <td>Repository to track the progress in Natural La...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>locust</td>\n",
       "      <td>&lt;a href='https://github.com/locustio/locust'&gt;l...</td>\n",
       "      <td>2011-02-17 11:08:03+00:00</td>\n",
       "      <td>2675</td>\n",
       "      <td>21147</td>\n",
       "      <td>locustio</td>\n",
       "      <td>Write scalable load tests in plain Python 🚗💨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>macOS-Security-and-Privacy-Guide</td>\n",
       "      <td>&lt;a href='https://github.com/drduh/macOS-Securi...</td>\n",
       "      <td>2015-08-31 03:36:35+00:00</td>\n",
       "      <td>1422</td>\n",
       "      <td>20111</td>\n",
       "      <td>drduh</td>\n",
       "      <td>Guide to securing and improving privacy on macOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>pytorch-CycleGAN-and-pix2pix</td>\n",
       "      <td>&lt;a href='https://github.com/junyanz/pytorch-Cy...</td>\n",
       "      <td>2017-04-18 10:33:05+00:00</td>\n",
       "      <td>5814</td>\n",
       "      <td>19717</td>\n",
       "      <td>junyanz</td>\n",
       "      <td>Image-to-Image Translation in PyTorch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Real-ESRGAN</td>\n",
       "      <td>&lt;a href='https://github.com/xinntao/Real-ESRGA...</td>\n",
       "      <td>2021-07-19 03:23:30+00:00</td>\n",
       "      <td>2144</td>\n",
       "      <td>19670</td>\n",
       "      <td>xinntao</td>\n",
       "      <td>Real-ESRGAN aims at developing Practical Algor...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Repo_Name   \n",
       "0                    Python-100-Days  \\\n",
       "1                           Auto-GPT   \n",
       "2                            cpython   \n",
       "3                              manim   \n",
       "4                             yt-dlp   \n",
       "5        big-list-of-naughty-strings   \n",
       "6                             d2l-zh   \n",
       "7                           sherlock   \n",
       "8                               bert   \n",
       "9                            whisper   \n",
       "10                            XX-Net   \n",
       "11                               gym   \n",
       "12                  chatgpt_academic   \n",
       "13                             HanLP   \n",
       "14                        ColossalAI   \n",
       "15                       MockingBird   \n",
       "16                               ray   \n",
       "17                            poetry   \n",
       "18                        detectron2   \n",
       "19                       python-fire   \n",
       "20                            ItChat   \n",
       "21                 30-Days-Of-Python   \n",
       "22                             hosts   \n",
       "23                           glances   \n",
       "24                        algorithms   \n",
       "25                      NLP-progress   \n",
       "26                            locust   \n",
       "27  macOS-Security-and-Privacy-Guide   \n",
       "28      pytorch-CycleGAN-and-pix2pix   \n",
       "29                       Real-ESRGAN   \n",
       "\n",
       "                                            Repo_Link   \n",
       "0   <a href='https://github.com/jackfrued/Python-1...  \\\n",
       "1   <a href='https://github.com/Significant-Gravit...   \n",
       "2   <a href='https://github.com/python/cpython'>cp...   \n",
       "3   <a href='https://github.com/3b1b/manim'>manim</a>   \n",
       "4   <a href='https://github.com/yt-dlp/yt-dlp'>yt-...   \n",
       "5   <a href='https://github.com/minimaxir/big-list...   \n",
       "6   <a href='https://github.com/d2l-ai/d2l-zh'>d2l...   \n",
       "7   <a href='https://github.com/sherlock-project/s...   \n",
       "8   <a href='https://github.com/google-research/be...   \n",
       "9   <a href='https://github.com/openai/whisper'>wh...   \n",
       "10  <a href='https://github.com/XX-net/XX-Net'>XX-...   \n",
       "11    <a href='https://github.com/openai/gym'>gym</a>   \n",
       "12  <a href='https://github.com/binary-husky/chatg...   \n",
       "13  <a href='https://github.com/hankcs/HanLP'>HanL...   \n",
       "14  <a href='https://github.com/hpcaitech/Colossal...   \n",
       "15  <a href='https://github.com/babysor/MockingBir...   \n",
       "16  <a href='https://github.com/ray-project/ray'>r...   \n",
       "17  <a href='https://github.com/python-poetry/poet...   \n",
       "18  <a href='https://github.com/facebookresearch/d...   \n",
       "19  <a href='https://github.com/google/python-fire...   \n",
       "20  <a href='https://github.com/littlecodersh/ItCh...   \n",
       "21  <a href='https://github.com/Asabeneh/30-Days-O...   \n",
       "22  <a href='https://github.com/StevenBlack/hosts'...   \n",
       "23  <a href='https://github.com/nicolargo/glances'...   \n",
       "24  <a href='https://github.com/keon/algorithms'>a...   \n",
       "25  <a href='https://github.com/sebastianruder/NLP...   \n",
       "26  <a href='https://github.com/locustio/locust'>l...   \n",
       "27  <a href='https://github.com/drduh/macOS-Securi...   \n",
       "28  <a href='https://github.com/junyanz/pytorch-Cy...   \n",
       "29  <a href='https://github.com/xinntao/Real-ESRGA...   \n",
       "\n",
       "                  Created_at  Forks   Stars                 Owner   \n",
       "0  2018-03-01 16:05:52+00:00  48814  134335             jackfrued  \\\n",
       "1  2023-03-16 09:21:07+00:00  16075  101742  Significant-Gravitas   \n",
       "2  2017-02-10 19:23:51+00:00  26582   52050                python   \n",
       "3  2015-03-22 18:50:58+00:00   5275   50801                  3b1b   \n",
       "4  2020-10-26 04:22:55+00:00   3762   45667                yt-dlp   \n",
       "5  2015-08-08 20:57:20+00:00   2134   44762             minimaxir   \n",
       "6  2017-08-23 04:40:24+00:00   8977   42629                d2l-ai   \n",
       "7  2018-12-24 14:30:48+00:00   4821   40424      sherlock-project   \n",
       "8  2018-10-25 22:57:34+00:00   9193   33980       google-research   \n",
       "9  2022-09-16 20:02:54+00:00   3686   33808                openai   \n",
       "10 2015-01-15 09:35:51+00:00   7784   31956                XX-net   \n",
       "11 2016-04-27 14:59:16+00:00   8364   30914                openai   \n",
       "12 2023-03-20 09:05:13+00:00   3394   29373          binary-husky   \n",
       "13 2014-10-09 06:36:16+00:00   8157   28798                hankcs   \n",
       "14 2021-10-28 16:19:44+00:00   3299   28573             hpcaitech   \n",
       "15 2021-08-07 03:53:39+00:00   4232   28216               babysor   \n",
       "16 2016-10-25 19:38:30+00:00   4382   25198           ray-project   \n",
       "17 2018-02-28 15:23:47+00:00   1955   24709         python-poetry   \n",
       "18 2019-09-05 21:30:20+00:00   6683   24521      facebookresearch   \n",
       "19 2017-02-21 21:35:07+00:00   1379   24389                google   \n",
       "20 2016-01-19 07:49:48+00:00   5449   23728         littlecodersh   \n",
       "21 2019-11-19 17:24:16+00:00   5102   23721              Asabeneh   \n",
       "22 2012-04-12 20:22:50+00:00   1963   22808           StevenBlack   \n",
       "23 2011-12-04 08:49:15+00:00   1372   22659             nicolargo   \n",
       "24 2016-11-17 22:32:08+00:00   4469   22469                  keon   \n",
       "25 2018-06-22 17:43:55+00:00   3565   21552        sebastianruder   \n",
       "26 2011-02-17 11:08:03+00:00   2675   21147              locustio   \n",
       "27 2015-08-31 03:36:35+00:00   1422   20111                 drduh   \n",
       "28 2017-04-18 10:33:05+00:00   5814   19717               junyanz   \n",
       "29 2021-07-19 03:23:30+00:00   2144   19670               xinntao   \n",
       "\n",
       "                                          Description  \n",
       "0                                 Python - 100天从新手到大师  \n",
       "1   An experimental open-source attempt to make GP...  \n",
       "2                     The Python programming language  \n",
       "3        Animation engine for explanatory math videos  \n",
       "4   A youtube-dl fork with additional features and...  \n",
       "5   The Big List of Naughty Strings is a list of s...  \n",
       "6   《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。  \n",
       "7   🔎 Hunt down social media accounts by username ...  \n",
       "8     TensorFlow code and pre-trained models for BERT  \n",
       "9   Robust Speech Recognition via Large-Scale Weak...  \n",
       "10                        A proxy tool to bypass GFW.  \n",
       "11  A toolkit for developing and comparing reinfor...  \n",
       "12  科研工作专用ChatGPT/GLM拓展，特别优化学术Paper润色体验，模块化设计支持自定义...  \n",
       "13  中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指...  \n",
       "14  Making large AI models cheaper, faster and mor...  \n",
       "15  🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 s...  \n",
       "16  Ray is a unified framework for scaling AI and ...  \n",
       "17  Python packaging and dependency management mad...  \n",
       "18  Detectron2 is a platform for object detection,...  \n",
       "19  Python Fire is a library for automatically gen...  \n",
       "20  A complete and graceful API for Wechat. 微信个人号接...  \n",
       "21  30 days of Python programming challenge is a s...  \n",
       "22  🔒 Consolidating and extending hosts files from...  \n",
       "23  Glances an Eye on your system. A top/htop alte...  \n",
       "24  Minimal examples of data structures and algori...  \n",
       "25  Repository to track the progress in Natural La...  \n",
       "26       Write scalable load tests in plain Python 🚗💨  \n",
       "27   Guide to securing and improving privacy on macOS  \n",
       "28              Image-to-Image Translation in PyTorch  \n",
       "29  Real-ESRGAN aims at developing Practical Algor...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "import pandas as pd\n",
    "\n",
    "repo_name, repo_link, stars, owner, desc, fork, Created_at = [], [], [], [], [], [],[]\n",
    "\n",
    "# assuming you already have the 'repository_dict' list\n",
    "for repo in repository_dict:\n",
    "    repo_name.append(repo['name'])\n",
    "    repo_url = repo['html_url']\n",
    "    repo_link.append(f\"<a href='{repo_url}'>{repo['name']}</a>\")\n",
    "    Created_at.append(repo['created_at'])\n",
    "    fork.append(repo['forks_count'])\n",
    "    stars.append(repo['stargazers_count'])\n",
    "    owner.append(repo['owner']['login'])\n",
    "    desc.append(repo['description'])\n",
    "\n",
    "data = {\n",
    "    'Repo_Name': repo_name,\n",
    "    'Repo_Link': repo_link,\n",
    "    'Created_at':Created_at,\n",
    "    'Forks': fork,\n",
    "    'Stars': stars,\n",
    "    'Owner': owner,\n",
    "    'Description': desc\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# render the DataFrame with clickable links\n",
    "display(HTML(df.to_html(render_links=True, escape=False)))\n",
    "\n",
    "df['Created_at']= pd.to_datetime(df['Created_at'])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acec3412",
   "metadata": {},
   "source": [
    "# Data Visualisation for Python Repos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8059c3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0e1a1966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "ax=sns.barplot(x='Repo_Name',y='Forks',data=df,width=0.8)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "plt.title(\"Forks VS Python Repositories\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0d78cddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aarus\\AppData\\Local\\Temp\\ipykernel_15620\\4043951022.py:3: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "ax=sns.barplot(y='Repo_Name',x='Stars',data=df,width=0.8)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "plt.title(\"Stars Vs Repo_Name Data\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229188c4",
   "metadata": {},
   "source": [
    "# Thank You !! \n",
    "###### <3<3<3<3<3<3 #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dee4e1c",
   "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",
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