{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pandas in /home/deploy/.local/lib/python3.6/site-packages (1.0.0)\n",
      "Requirement already satisfied: matplotlib in /home/deploy/.local/lib/python3.6/site-packages (3.1.3)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/deploy/.local/lib/python3.6/site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/deploy/.local/lib/python3.6/site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /home/deploy/.local/lib/python3.6/site-packages (from pandas) (1.18.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/deploy/.local/lib/python3.6/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/deploy/.local/lib/python3.6/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/deploy/.local/lib/python3.6/site-packages (from matplotlib) (2.4.6)\n",
      "Requirement already satisfied: six>=1.5 in /home/deploy/.local/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas) (1.14.0)\n",
      "Requirement already satisfied: setuptools in /home/deploy/.local/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib) (45.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>forks</th>\n",
       "      <th>stars</th>\n",
       "      <th>watchs</th>\n",
       "      <th>openIssues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gin</th>\n",
       "      <td>4074</td>\n",
       "      <td>35455</td>\n",
       "      <td>1212</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beego</th>\n",
       "      <td>4688</td>\n",
       "      <td>23243</td>\n",
       "      <td>1268</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris</th>\n",
       "      <td>1942</td>\n",
       "      <td>17507</td>\n",
       "      <td>683</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Revel</th>\n",
       "      <td>1357</td>\n",
       "      <td>11575</td>\n",
       "      <td>558</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Echo</th>\n",
       "      <td>1508</td>\n",
       "      <td>16500</td>\n",
       "      <td>551</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buffalo</th>\n",
       "      <td>430</td>\n",
       "      <td>5372</td>\n",
       "      <td>171</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         forks  stars  watchs  openIssues\n",
       "Gin       4074  35455    1212         242\n",
       "Beego     4688  23243    1268         813\n",
       "Iris      1942  17507     683           5\n",
       "Revel     1357  11575     558          87\n",
       "Echo      1508  16500     551          46\n",
       "Buffalo    430   5372     171          70"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计go框架fork次数信息\n",
    "\n",
    "frameworks = {\n",
    "    \"Gin\":\"gin-gonic/gin\",\n",
    "    \"Beego\": \"astaxie/beego\",\n",
    "    \"Iris\": \"kataras/iris\",\n",
    "    \"Revel\": \"revel/revel\",\n",
    "    \"Echo\": \"labstack/echo\",\n",
    "    \"Buffalo\": \"gobuffalo/buffalo\"\n",
    "}\n",
    "\n",
    "\n",
    "stats = {}\n",
    "for name in frameworks.keys():\n",
    "    url = \"https://api.github.com/repos/\" + frameworks[name]\n",
    "    stats[name] = requests.get(url=url).json() # 获取仓库统计信息\n",
    "\n",
    "indexs = []\n",
    "forks = []\n",
    "stars = []\n",
    "watchs = []\n",
    "openIssues = []\n",
    "\n",
    "for name in stats:\n",
    "    indexs += [name]\n",
    "    forks += [stats[name]['forks_count']] # fork次数\n",
    "    stars += [stats[name]['watchers_count']] # star次数\n",
    "    watchs += [stats[name]['subscribers_count']] # watch次数\n",
    "    openIssues += [stats[name]['open_issues_count']] # open_issue次数\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'forks':forks,\n",
    "    'stars':stars,\n",
    "    'watchs':watchs,\n",
    "    'openIssues': openIssues\n",
    "}, index = indexs)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6786d59240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar', figsize=(15, 8))"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}