2020q3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine, text, MetaData\n",
    "from sqlalchemy.exc import IntegrityError\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ben/.environments/stats/lib/python3.8/site-packages/sqlalchemy/dialects/postgresql/base.py:3470: SAWarning: Predicate of partial index files_size ignored during reflection\n",
      "  util.warn(\n"
     ]
    }
   ],
   "source": [
    "engine = create_engine('postgres:///piwheels')\n",
    "conn = engine.connect()\n",
    "meta = MetaData()\n",
    "meta.bind = conn\n",
    "meta.reflect(views=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2020-07-01'\n",
    "end_date = '2020-09-30'\n",
    "one_year_ago = '2019-10-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>downloads_in_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4492796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   downloads_in_period\n",
       "0              4492796"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads_in_period\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>downloads_to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26171957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   downloads_to_date\n",
       "0           26171957"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads_to_date\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date < '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>project_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>597675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   project_page_hits\n",
       "0             597675"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS project_page_hits\n",
    "    FROM project_page_hits\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>web_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>332517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   web_page_hits\n",
       "0         332517"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS web_page_hits\n",
    "    FROM web_page_hits\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>m</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>920963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>1014683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>1032964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1079669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>985288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1300742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>1199122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>1271968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>1127875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1102139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1737186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>1653471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            m        c\n",
       "0  2019-10-01   920963\n",
       "1  2019-11-01  1014683\n",
       "2  2019-12-01  1032964\n",
       "3  2020-01-01  1079669\n",
       "4  2020-02-01   985288\n",
       "5  2020-03-01  1300742\n",
       "6  2020-04-01  1199122\n",
       "7  2020-05-01  1271968\n",
       "8  2020-06-01  1127875\n",
       "9  2020-07-01  1102139\n",
       "10 2020-08-01  1737186\n",
       "11 2020-09-01  1653471"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_by_month = pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', accessed_at) m, COUNT(*) AS c\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{one_year_ago}' AND '{end_date}'\n",
    "    GROUP BY m\n",
    "    ORDER BY m\n",
    "\"\"\", conn)\n",
    "\n",
    "downloads_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
    "\n",
    "downloads = {\n",
    "    'Oct': downloads_by_month.c[0],\n",
    "    'Nov': downloads_by_month.c[1],\n",
    "    'Dec': downloads_by_month.c[2],\n",
    "    'Jan': downloads_by_month.c[3],\n",
    "    'Feb': downloads_by_month.c[4],\n",
    "    'Mar': downloads_by_month.c[5],\n",
    "    'Apr': downloads_by_month.c[6],\n",
    "    'May': downloads_by_month.c[7],\n",
    "    'Jun': downloads_by_month.c[8],\n",
    "    'Jul': downloads_by_month.c[9],\n",
    "    'Aug': downloads_by_month.c[10],\n",
    "    'Sep': downloads_by_month.c[11],\n",
    "}\n",
    "\n",
    "colors = ['blue']*9 + ['red']*3\n",
    "plt.bar(range(len(downloads)), downloads.values(), color=colors)\n",
    "plt.title('Downloads by month')\n",
    "plt.xticks(range(len(downloads)), list(downloads.keys()))\n",
    "\n",
    "plt.savefig('downloads-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>d</th>\n",
       "      <th>downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>39625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>37730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>35179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>31345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>45192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-09-26</td>\n",
       "      <td>49693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-09-27</td>\n",
       "      <td>50060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>51771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>51027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>45821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             d  downloads\n",
       "0   2020-07-01      39625\n",
       "1   2020-07-02      37730\n",
       "2   2020-07-03      35179\n",
       "3   2020-07-04      31345\n",
       "4   2020-07-05      45192\n",
       "..         ...        ...\n",
       "87  2020-09-26      49693\n",
       "88  2020-09-27      50060\n",
       "89  2020-09-28      51771\n",
       "90  2020-09-29      51027\n",
       "91  2020-09-30      45821\n",
       "\n",
       "[92 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_by_day = pd.read_sql(f\"\"\"\n",
    "    SELECT accessed_at::date AS d, COUNT(*) AS downloads\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY d\n",
    "    ORDER BY d\n",
    "\"\"\", conn)\n",
    "\n",
    "downloads_by_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l6h06dKBDhw5VaUVERERqkFmzZuHn5+e6uO9y+eOPPxg4cCDJycmXPNaYMWPo06cPt99++2XorGJVCmQiIiJS85QMu/v//n2udS6g7rnoq0ttqdbSVyeJiIjIFfXWW2/Ro0cP+vXrR0ZGBgDbt2/nzjvvJC4ujscee4yjR4+SlZXFbbfdBsCOHTto3Lix6+bx3bp148SJE4wZM4bnn3+evn370rVrV77++uty2zt58iRjx46ld+/e3Hrrraxfvx44PYt2zz330KdPH/r06eO6D6phGEyZMoWbb76Z+++/v8zFh9OnTycmJoa4uDhefvnly/acaIZMRERErpitW7fy1Vdf8a9//Yvi4mJuu+022rZty5gxY3jllVfo2rUrs2bN4o033uCll17i1KlT5OXl8dNPP9G+fXs2bNhAVFQUdrvddSXjoUOH+N///V9++eUXBg8ezJ133llmmx988AEWi4U1a9aQkZHBgw8+yI8//khISAj/+Mc/8PX1Zd++fTz55JP861//YtWqVezdu5e1a9dy5MgRevXqxUMPPeT69qLk5GQsFgu5ubmX7XlRIBMREZErZuPGjdx2222uMNWnTx8KCgrIzc2la9euAPz1r391nVMWFRVFamoqGzZsYNSoUaxbt46SkhI6d+7sGvO2227Dw8ODVq1aceTIkXLbTE1NZfDgwQCEh4fTpEkT9u7dS6NGjZgyZQo7d+7Ew8ODffv2AafvEtGvXz88PT1p2LAh3bt3B6Bu3br4+Pgwfvz4cvdGvVQ6ZCkiIiJuq3PnzmzcuJEDBw7Qp08fduzYwU8//USnTp1c65x5F/wqfAGRy6JFi6hfv75rVqyoqOi863t5efHNN9/Qt29fEhMTefjhhy98h85BgUxEgNMn95YMu5tD93QzuxURqcG6dOnC6tWrOXHiBPn5+Xz77bf4+/sTFBTExo0bAfjiiy9cs2WdO3dm+fLlXHvttXh4eBAcHExSUlKZQFaZTp06sWLFCgD27t3LgQMHCA8P59ixYzRo0AAPDw+++OILSkpKXD1+9dVXlJSUcOjQIVJSUgA4fvw4eXl59O7dm5deeomdO3detudFhyxFRETkimnbti133XUXt9xyCyEhIURERAAwZ84cJk6cyMmTJ2nWrBlz584FTt/H1DAM1yHKTp068eeff1KvXr0qb3PgwIFMmjSJ3r174+npSXx8PD4+PgwcOJDhw4ezbNkyevXqhb+/PwC3334769evJyYmhsaNG3PTTTcBp79OcsiQIa6b5b/44ouX7XlRIBMREamlzrxNxeX8Uu3KjB49mtGjR5cb58wrJM+sp6Wluepjxozhqaeecv08Z86cMmPv2bMHOB3kkpKSAPD19SU+Pr5cH82bNycxMdH185QpU4DTN3adNm1amXVL+/nmm28ubGerSIcsRUREREymQCYiIiJiMgUyEREREZMpkImIiNQSF3JLCLl4F/M8K5CJiIjUEh4eHpXea0suTXFxMR4eFx6vdJWliIhILeHr64uHhwf5+flYLJYyy3x8fDh16lS5x6he9bphGHh4eODr61tuvcookImIiNQSFosFu91e4SG1kJAQsrKyVL9M9QulQ5YiIiIiJlMgExERETGZApmIiIiIyRTIREREREymQCYiIiJiMgUyEREREZMpkImIiIiYTPchE5HzKhl2NwCHAM9FX5nbjIhIDaUZMhERERGTKZCJiIiImEyHLEVERK6gkmF3c+j//1unAUgpzZCJiIiImEyBTERERMRkCmQiIiIiJlMgExERETGZApmIiIiIySq9yvLPP/8kPj7e9fPhw4cZMGAAPXv2JD4+niNHjlC/fn3Gjh2L1WrFMAyWLFnC5s2b8fHxYeTIkTRv3hyAtWvXsnz5cgD69+9PTEwMAPv27WPBggUUFhYSGRnJ4MGDsVgs1bC7IiIiIu6n0hmysLAwZs2axaxZs3jttdfw9vamU6dOJCQk0LZtW+bOnUvbtm1JSEgAYPPmzWRmZjJ37lyGDx/O4sWLAcjPz2fZsmVMnz6d6dOns2zZMvLz8wFYtGgRI0aMYO7cuWRmZpKenl5tOywiIiLibi7okOW2bdto2LAh9evXJzU1lZ49ewLQs2dPUlNTAUhLSyM6OhqLxUKrVq04fvw4OTk5pKen065dO6xWK1arlXbt2pGenk5OTg4nTpygVatWWCwWoqOjXWOJiIiI1AYXFMjWr19P9+7dAcjNzSU4OBiAevXqkZubC4DD4SAkJMT1GLvdjsPhwOFwYLfbXXWbzVZhvXR9ERERkdqiynfqLy4u5t///jcPPfRQuWUWi+WKnPOVmJhIYmIiADNnznQFPy8vrzIhsJTqF1d3x55qW92MbR86Y1lV6gCH7unmWh66IsXU/i+27o491dS6O/ZkRv18nym9BjWnXtmycutWaS1Onxt27bXXUq9ePQCCgoLIyckhODiYnJwc6tatC5ye+crKynI9Ljs7G5vNhs1mY+fOna66w+GgdevW2Gw2srOzy61fkbi4OOLi4lw/l24nJCSkzDZLqX5xdXfsqbbVzd72hdYrWuZuz6k7Pte1re6OPZn5XMCV/9xciW2oXvGysLCwCtcpVeVDlmcergSIiopi3bp1AKxbt46OHTu66snJyRiGwe7du/H39yc4OJiIiAi2bNlCfn4++fn5bNmyhYiICIKDg/Hz82P37t0YhkFycjJRUVFVbUtERETkqlelGbKTJ0+ydetWhg8f7qr169eP+Ph4kpKSXLe9AIiMjGTTpk08/fTTeHt7M3LkSACsViv33nsvkyZNAuC+++7DarUCMHToUBYuXEhhYSERERFERkZe1p0UERERcWdVCmS+vr68//77ZWqBgYG88MIL5da1WCwMHTq0wnFiY2OJjY0tV2/RogWzZ8+uSisicolKht0NnD6PxXPRV+Y2IyIigO7ULyIiImI6BTIRERERkymQiYiIiJhMgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGTKZCJiIiImEyBTERERMRkCmQiIiIiJlMgExERETGZApmIiIiIyRTIREREREzmZXYDIiIiAiXD7ubQ//+356KvTO1FrjzNkImIiIiYTIFMRERExGQKZCIiIiImUyATERERMZlO6hcRuUQlw+4G4BA6GVtELo5myERERERMphkyEbnq6PYAIlLTKJCJiNtS8BKR2kKBTERERNxKbfxjTOeQiYiIiJhMgUxERETEZApkIiIiIibTOWQiIiJyVajJ55ZphkxERETEZFWaITt+/DjvvPMOf/zxBxaLhSeeeIKwsDDi4+M5cuQI9evXZ+zYsVitVgzDYMmSJWzevBkfHx9GjhxJ8+bNAVi7di3Lly8HoH///sTExACwb98+FixYQGFhIZGRkQwePBiLxVI9eywiIiLiZqoUyJYsWUJERATjx4+nuLiYU6dOsWLFCtq2bUu/fv1ISEggISGBRx55hM2bN5OZmcncuXPZs2cPixcvZvr06eTn57Ns2TJmzpwJwMSJE4mKisJqtbJo0SJGjBhBy5YtmTFjBunp6URGRlbrjotI9bnQwwo1+TCEiEhVVHrIsqCggJ9//pnY2FgAvLy8CAgIIDU1lZ49ewLQs2dPUlNTAUhLSyM6OhqLxUKrVq04fvw4OTk5pKen065dO6xWK1arlXbt2pGenk5OTg4nTpygVatWWCwWoqOjXWOJiIiI1AaVzpAdPnyYunXrsnDhQn7//XeaN2/OoEGDyM3NJTg4GIB69eqRm5sLgMPhICQkxPV4u92Ow+HA4XBgt9tddZvNVmG9dH0RERExT8mwuwE4hGaur4RKA1lJSQm//vorQ4YMoWXLlixZsoSEhIQy61gslityzldiYiKJiYkAzJw50xX8vLy8yoTAUqpfXN0de6pt9ercxqEzll1K/WIfU1Gfl7MnM57r8/VzOcavKXV37MmMenW/rw/d0801VuiKlIvu1cz39ZX47Jv5fq9w3cpWsNvt2O12WrZsCUCXLl1ISEggKCiInJwcgoODycnJoW7dusDpma+srCzX47Ozs7HZbNhsNnbu3OmqOxwOWrdujc1mIzs7u9z6FYmLiyMuLs71c+l2QkJCymyzlOoXV3fHnmpb/Upt43LVL+Qx5+vnUnsq/Yseyv9FfyWe63P16W7vr5r8vr5a6qUu9XNzMeNfLe/r82377PrFfPav9Ps9LCyswnVKVXoOWb169bDb7fz5558AbNu2jSZNmhAVFcW6desAWLduHR07dgQgKiqK5ORkDMNg9+7d+Pv7ExwcTEREBFu2bCE/P5/8/Hy2bNlCREQEwcHB+Pn5sXv3bgzDIDk5maioqMraEhERkQtQMuzu0xfQ3NPN7FYA9+vHbFW6ynLIkCHMnTuX4uJiGjRowMiRIzEMg/j4eJKSkly3vQCIjIxk06ZNPP3003h7ezNy5EgArFYr9957L5MmTQLgvvvuw2q1AjB06FAWLlxIYWEhERERusJSREREapUqBbJrrrnGdbuKM73wwgvlahaLhaFDh1Y4TmxsrOtqzTO1aNGC2bNnV6UVERGRWkW3hakd9NVJIiIitZiupnQPCmQiIiJXIc2c1Sz6LksRERERk2mGTERERKpMhzirh2bIREREREymGTIRkSq60JkBd5xJ0HlHIu5JM2QiIiIiJlMgExERETGZDlmKyBWjw2UiIhXTDJmIiIiIyRTIREREREymQ5YiUuPpUKmIuDsFMhERUWgVMZkCmYjIWdzx/mEi7u5cnxt9nqpGgUxE5ArTf1AicjYFMhGRGkiHIEWuLgpkIiJuQjNnIrWXbnshIiIiYjLNkIlIraUZKRFxF5ohExERETGZZshEROSC6aIBkctLM2QiIiIiJlMgExERETGZDlmKiIjb0QUXUtsokImIyDnpXDGRK0OBTETkKna1BybNhImcpkAmIiJSDa72sCxXlk7qFxERETGZZshEREREzuNKzHYqkImISI2lc9TkaqFAJiIiVz0FL6nI1XQeX5UC2ZNPPomvry8eHh54enoyc+ZM8vPziY+P58iRI9SvX5+xY8ditVoxDIMlS5awefNmfHx8GDlyJM2bNwdg7dq1LF++HID+/fsTExMDwL59+1iwYAGFhYVERkYyePBgLBZL9eyxiIiIyGVwOQNflWfIXnzxRerWrev6OSEhgbZt29KvXz8SEhJISEjgkUceYfPmzWRmZjJ37lz27NnD4sWLmT59Ovn5+SxbtoyZM2cCMHHiRKKiorBarSxatIgRI0bQsmVLZsyYQXp6OpGRkZe0YyIiNcWZsz/g/n/pi8iFu+irLFNTU+nZsycAPXv2JDU1FYC0tDSio6OxWCy0atWK48ePk5OTQ3p6Ou3atcNqtWK1WmnXrh3p6enk5ORw4sQJWrVqhcViITo62jWWiIjImUqG3X16VuKebma34lLaT2lwFrkYVZ4hmzZtGgC33HILcXFx5ObmEhwcDEC9evXIzc0FwOFwEBIS4nqc3W7H4XDgcDiw2+2uus1mq7Beur6IiIhIbVGlQPbKK69gs9nIzc3l1VdfJSwsrMxyi8VyRc75SkxMJDExEYCZM2e6gp+Xl1eZEFhK9Yuru2NPta1ends4dMayS6lfzrFUr3rdHXs6+31R0XuxdEbrEBC6IsUt+jxXrxdad7fX4GqvX6ltnOt9WvqYS3mfnm8b51KlQGaz2QAICgqiY8eOZGRkEBQURE5ODsHBweTk5LjOL7PZbGRlZbkem52djc1mw2azsXPnTlfd4XDQunVrbDYb2dnZ5davSFxcHHFxca6fS7cTEhJSZpulVL+4ujv2VNvqV2obl6t+OcdS/fx1d+zp7Pr53nNm9HO++uX8PFV3r7WxXl3bOPPw8rnOybzc76+zJ7POVuk5ZCdPnuTEiROuf2/dupVmzZoRFRXFunXrAFi3bh0dO3YEICoqiuTkZAzDYPfu3fj7+xMcHExERARbtmwhPz+f/Px8tmzZQkREBMHBwfj5+bF7924MwyA5OZmoqKjK2hIRETek86lELk6lM2S5ubm88cYbAJSUlNCjRw8iIiJo0aIF8fHxJCUluW57ARAZGcmmTZt4+umn8fb2ZuTIkQBYrVbuvfdeJk2aBMB9992H1WoFYOjQoSxcuJDCwkIiIiJ0haWIiIjUKpUGstDQUGbNmlWuHhgYyAsvvFCubrFYGDp0aIVjxcbGEhsbW67eokULZs+eXZV+RURERGocfbm4iIiIiMn01UkiIiJVcDV9DY9cfTRDJiIiImIyBTIRERERkymQiYiIiJhMgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGT6auTRERERC6jkmF3A3CIqn/NlgKZiIjUOuf6D/Ni/iMVuRwUyM5DH0wRERG5EnQOmYiIiIjJFMhERERETKZAJiIiImIyBTIRERERkymQiYiIiJhMgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGTKZCJiIiImEyBTERERMRkCmQiIiIiJvMyuwGpXiXD7ubQ//+356KvTO1FREREKqYZMhERERGTVXmGzOl0MnHiRGw2GxMnTuTw4cPMmTOHvLw8mjdvzqhRo/Dy8qKoqIj58+ezb98+AgMDGTNmDA0aNABgxYoVJCUl4eHhweDBg4mIiAAgPT2dJUuW4HQ66d27N/369auOfRUxhWYpRUSkMlWeIVu5ciWNGzd2/fzJJ5/Qt29f5s2bR0BAAElJSQAkJSUREBDAvHnz6Nu3L0uXLgVg//79pKSk8OabbzJlyhTee+89nE4nTqeT9957j8mTJxMfH8/69evZv3//Zd5NEREREfdVpUCWnZ3Npk2b6N27NwCGYbBjxw66dOkCQExMDKmpqQCkpaURExMDQJcuXdi+fTuGYZCamkq3bt2oU6cODRo0oGHDhmRkZJCRkUHDhg0JDQ3Fy8uLbt26ucYSuZqUDLubQ/d0o2TY3Wa3IiIiV5kqHbL84IMPeOSRRzhx4gQAeXl5+Pv74+npCYDNZsPhcADgcDiw2+0AeHp64u/vT15eHg6Hg5YtW7rGPPMxpeuX/nvPnj0V9pGYmEhiYiIAM2fOJCQk5PROeHm5/l1m5y6xfuiMZdUx/pWon28fzjWOu+3D1VI/13Ptbq/BhfZ5vv4v11iqV73ujj3Vhro79lRT6+7Y0+Xct3OpNJD9+9//JigoiObNm7Njx44qDVpd4uLiiIuLc/2clZUFnN7Z0n+f6XLVz9xWdYx/JfqvaB/Ot7677cPVUi9V1feRma/BhfRZWf1yjqX6+evu2FNtq7tjTzW17o49XWw9LCyswuWlKg1ku3btIi0tjc2bN1NYWMiJEyf44IMPKCgooKSkBE9PTxwOBzabDTg985WdnY3dbqekpISCggICAwNd9VJnPubMenZ2tqtemdJDQ4e4ek+W1gnfIiIiUuk5ZA899BDvvPMOCxYsYMyYMdx44408/fTTtGnThg0bNgCwdu1aoqKiALjppptYu3YtABs2bKBNmzZYLBaioqJISUmhqKiIw4cPc/DgQcLDw2nRogUHDx7k8OHDFBcXk5KS4hqrJtH5RXI2vSdERKTURd8Y9uGHH2bOnDl8+umnXHvttcTGxgIQGxvL/PnzGTVqFFarlTFjxgDQtGlTunbtyrhx4/Dw8OCxxx7Dw+N0HhwyZAjTpk3D6XTSq1cvmjZteul7JiIiInKVuKBA1qZNG9q0aQNAaGgoM2bMKLeOt7c348aNq/Dx/fv3p3///uXqHTp0oEOHDhfSioiIiEiNoTv1i4iIiJhM32V5GZ15kQHoJH0RERGpGs2QiYiIiJisRs6Q1YTbYYiIiEjtUSMDWXVT4BMREZHLSYcsRUREREymGTK5KtXkWcqavG8iIlIxBbJaSl/ZJCIi4j4UyKTGUdgUEZGrjQKZyAW6WgKfDn2KiFw9dFK/iIiIiMkUyERERERMpkAmIiIiYjIFMhERERGT1aqT+nWSs4iIiLijWhXI5OqjEC0iIrWBDlmKiIiImEwzZFJrXC33DxMRkdpHgcxNKTyIiIjUHgpkUiXnOpdL53iJiIhcOgUyKUMBS0RE5MrTSf0iIiIiJlMgExERETGZApmIiIiIyRTIREREREymQCYiIiJiMl1lKW5BV3dePD13IiJXPwUykauEgpeISM2lQFZD6M7+IiIiVy8FMjTzIBVTyBURkStFJ/WLiIiImKzSGbLCwkJefPFFiouLKSkpoUuXLgwYMIDDhw8zZ84c8vLyaN68OaNGjcLLy4uioiLmz5/Pvn37CAwMZMyYMTRo0ACAFStWkJSUhIeHB4MHDyYiIgKA9PR0lixZgtPppHfv3vTr168691lERETErVQ6Q1anTh1efPFFZs2axeuvv056ejq7d+/mk08+oW/fvsybN4+AgACSkpIASEpKIiAggHnz5tG3b1+WLl0KwP79+0lJSeHNN99kypQpvPfeezidTpxOJ++99x6TJ08mPj6e9evXs3///urdaxERERE3Umkgs1gs+Pr6AlBSUkJJSQkWi4UdO3bQpUsXAGJiYkhNTQUgLS2NmJgYALp06cL27dsxDIPU1FS6detGnTp1aNCgAQ0bNiQjI4OMjAwaNmxIaGgoXl5edOvWzTWWiIiISG1QpZP6nU4nzz77LJmZmfTp04fQ0FD8/f3x9PQEwGaz4XA4AHA4HNjtdgA8PT3x9/cnLy8Ph8NBy5YtXWOe+ZjS9Uv/vWfPngr7SExMJDExEYCZM2eWWRYSEuL69yE3qFfXNgC8vLzK1dxhn8/u6Vy9VlS/nNutqc+pu9XdsafaUHfHnmpD3R17qql1d+zpcv/fWJEqBTIPDw9mzZrF8ePHeeONN/jzzz+rNPjlFhcXR1xcXIXLsrKy3KpendsICQkxZbsXUz9Xr+fbBzNeg6vpOXXHujv2VFPr7thTbau7Y081te6OPV1sPSwsrMLlpS7oKsuAgADatGnD7t27KSgooKSkBDg9K2az2YDTM1/Z2dnA6UOcBQUFBAYGlqmf+Ziz69nZ2a6xRERERGqDSgPZsWPHOH78OHD6isutW7fSuHFj2rRpw4YNGwBYu3YtUVFRANx0002sXbsWgA0bNtCmTRssFgtRUVGkpKRQVFTE4cOHOXjwIOHh4bRo0YKDBw9y+PBhiouLSUlJcY0lciWUDLubQ/d0c92PTkRE5Eqr9JBlTk4OCxYswOl0YhgGXbt25aabbqJJkybMmTOHTz/9lGuvvZbY2FgAYmNjmT9/PqNGjcJqtTJmzBgAmjZtSteuXRk3bhweHh489thjeHiczoNDhgxh2rRpOJ1OevXqRdOmTatvj69yulmpiIhIzVNpIPvLX/7C66+/Xq4eGhrKjBkzytW9vb0ZN25chWP179+f/v37l6t36NCBDh06VKVfERERkRpHd+oXERERMZkCmYiIiIjJFMhERERETKZAJiIiImIyBTIRERERkymQiYiIiJisSl+dJHKhSm+yegjdL01ERKQyCmRyRSmoiYiIlKdDliIiIiImUyATERERMZkCmYiIiIjJFMhERERETKZAJiIiImIyBTIRERERkymQiYiIiJhMgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGTKZCJiIiImEyBTERERMRkCmQiIiIiJlMgExERETGZApmIiIiIyRTIREREREymQCYiIiJiMgUyEREREZMpkImIiIiYzKuyFbKysliwYAFHjx7FYrEQFxfHHXfcQX5+PvHx8Rw5coT69eszduxYrFYrhmGwZMkSNm/ejI+PDyNHjqR58+YArF27luXLlwPQv39/YmJiANi3bx8LFiygsLCQyMhIBg8ejMViqb69FhEREXEjlc6QeXp68uijjxIfH8+0adNYvXo1+/fvJyEhgbZt2zJ37lzatm1LQkICAJs3byYzM5O5c+cyfPhwFi9eDEB+fj7Lli1j+vTpTJ8+nWXLlpGfnw/AokWLGDFiBHPnziUzM5P09PRq22ERERERd1NpIAsODnbNcPn5+dG4cWMcDgepqan07NkTgJ49e5KamgpAWloa0dHRWCwWWrVqxfHjx8nJySE9PZ127dphtVqxWq20a9eO9PR0cnJyOHHiBK1atcJisRAdHe0aS0RERKQ2uKBzyA4fPsyvv/5KeHg4ubm5BAcHA1CvXj1yc3MBcDgchISEuB5jt9txOBw4HA7sdrurbrPZKqyXri8iIiJSW1R6DlmpkydPMnv2bAYNGoS/v3+ZZRaL5Yqc85WYmEhiYiIAM2fOLLPszBB4yA3q7thTbai7Y081te6OPdWGujv2VBvq7thTTa27Y0+Xc9/OpUqBrLi4mNmzZ3PzzTfTuXNnAIKCgsjJySE4OJicnBzq1q0LnJ75ysrKcj02Ozsbm82GzWZj586drrrD4aB169bYbDays7PLrV+RuLg44uLiKlx25jbdoe6OPdW2ujv2VFPr7thTTa27Y0+1re6OPdXUujv2dLH1sLCwCpeXqvSQpWEYvPPOOzRu3Jg777zTVY+KimLdunUArFu3jo4dO7rqycnJGIbB7t278ff3Jzg4mIiICLZs2UJ+fj75+fls2bKFiIgIgoOD8fPzY/fu3RiGQXJyMlFRUZW1JSIiIlJjVDpDtmvXLpKTk2nWrBnPPPMMAA8++CD9+vUjPj6epKQk120vACIjI9m0aRNPP/003t7ejBw5EgCr1cq9997LpEmTALjvvvuwWq0ADB06lIULF1JYWEhERASRkZHVsrMiIiIi7qjSQHb99dfz2WefVbjshRdeKFezWCwMHTq0wvVjY2OJjY0tV2/RogWzZ8+urBURERGRGkl36hcRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGTKZCJiIiImEyBTERERMRkCmQiIiIiJlMgExERETGZApmIiIiIyRTIREREREymQCYiIiJiMgUyEREREZMpkImIiIiYTIFMRERExGQKZCIiIiImUyATERERMZkCmYiIiIjJFMhERERETKZAJiIiImIyBTIRERERkymQiYiIiJhMgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGTKZCJiIiImEyBTERERMRkXpWtsHDhQjZt2kRQUBCzZ88GID8/n/j4eI4cOUL9+vUZO3YsVqsVwzBYsmQJmzdvxsfHh5EjR9K8eXMA1q5dy/LlywHo378/MTExAOzbt48FCxZQWFhIZGQkgwcPxmKxVNPuioiIiLifSmfIYmJimDx5cplaQkICbdu2Ze7cubRt25aEhAQANm/eTGZmJnPnzmX48OEsXrwYOB3gli1bxvTp05k+fTrLli0jPz8fgEWLFjFixAjmzp1LZmYm6enpl3cPRURERNxcpYGsdevWWK3WMrXU1FR69uwJQM+ePUlNTQUgLS2N6OhoLBYLrVq14vjx4+Tk5JCenk67du2wWq1YrVbatWtHeno6OTk5nDhxglatWmGxWIiOjnaNJSIiIlJbXNQ5ZLm5uQQHBwNQr149cnNzAXA4HISEhLjWs9vtOBwOHA4HdrvdVbfZbBXWS9cXERERqU0qPYesMhaL5Yqd85WYmEhiYiIAM2fOLLPszCB4yA3q7thTbai7Y081te6OPdWGujv2VBvq7thTTa27Y0+Xc9/O5aICWVBQEDk5OQQHB5OTk0PdunWB0zNfWVlZrvWys7Ox2WzYbDZ27tzpqjscDlq3bo3NZiM7O7vc+ucSFxdHXFxchcvO3K471N2xp9pWd8eeamrdHXuqqXV37Km21d2xp5pad8eeLrYeFhZW4fJSF3XIMioqinXr1gGwbt06Onbs6KonJydjGAa7d+/G39+f4OBgIiIi2LJlC/n5+eTn57NlyxYiIiIIDg7Gz8+P3bt3YxgGycnJREVFXUxLIiIiIletSmfI5syZw86dO8nLy+Pxxx9nwIAB9OvXj/j4eJKSkly3vQCIjIxk06ZNPP3003h7ezNy5EgArFYr9957L5MmTQLgvvvuc10oMHToUBYuXEhhYSERERFERkZW176KiIiIuKVKA9mYMWMqrL/wwgvlahaLhaFDh1a4fmxsLLGxseXqLVq0cN3fTERERKQ20p36RUREREymQCYiIiJiMgUyEREREZMpkImIiIiYTIFMRERExGQKZCIiIiImUyATERERMZkCmYiIiIjJFMhERERETKZAJiIiImIyBTIRERERkymQiYiIiJhMgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMkUyERERERMpkAmIiIiYjIFMhERERGTKZCJiIiImEyBTERERMRkCmQiIiIiJlMgExERETGZApmIiIiIyRTIREREREymQCYiIiJiMgUyEREREZMpkImIiIiYTIFMRERExGQKZCIiIiImUyATERERMZmX2Q2USk9PZ8mSJTidTnr37k2/fv3MbklERETkinCLGTKn08l7773H5MmTiY+PZ/369ezfv9/stkRERESuCLcIZBkZGTRs2JDQ0FC8vLzo1q0bqampZrclIiIickW4RSBzOBzY7XbXz3a7HYfDYWJHIiIiIleOxTAMw+wmNmzYQHp6Oo8//jgAycnJ7Nmzh8cee6zMeomJiSQmJgIwc+bMK96niIiISHVwixkym81Gdna26+fs7GxsNlu59eLi4pg5c2a5MDZx4sQKx1X94uru2FNtq7tjTzW17o491dS6O/ZU2+ru2FNNrVe27GxuEchatGjBwYMHOXz4MMXFxaSkpBAVFWV2WyIiIiJXhFvc9sLT05MhQ4Ywbdo0nE4nvXr1omnTpma3JSIiInJFuEUgA+jQoQMdOnS4qMfGxcWpfhnr7thTbau7Y081te6OPdXUujv2VNvq7thTTa1XtuxsbnFSv4iIiEht5hbnkImIiIjUZgpkIiIiIiZTIBMRERExmduc1F9VBw4cIDU11XUnf5vNRlRUFE2aNDnn+g6Hg5YtW+Lr6+uqp6enY7VaAQgPD2f//v2kp6cTFhZW5uKC+fPn89RTT5Ub95dffiEjIwMPDw9iYmLw9/ensLCQhIQE9u3bR2FhIYMHDy53tWhxcTHr168nODiYdu3a8cMPP7Br1y4aN25M27Zt+fe//012djYeHh40atSIHj164O/vf8nPm5xfbm4uQUFBVV4/Ly+PwMDAauxI5NLVxvd1bdvn2ra/NdlVdVJ/QkIC69evp3v37q4bxzocDletX79+ZdZfuXIlq1evxsfHh+PHjzNo0CA6duwIwPDhw6lfvz4lJSW0a9eOPXv2kJOTw7Fjx6hbty6NGjXCMAx27NjBjTfeyM8//8wHH3wAnP7GgNWrV9OpUye+/PJL+vfvT//+/Xn33Xfx8fGhS5cuvPzyy1gsFlq0aEH37t3p2rUrdevWZe7cuZSUlHDq1CkCAgI4efIknTt3ZtWqVWRnZ9OnTx82b97MNddcQ0BAAD/99BNDhw6lTZs2V/KpNvVDXlBQwIoVK0hNTSU3NxeLxUJQUBBRUVH069ePgICAcutPmDCB6667jsjISHr06AHA0aNHefnll2ndujX3338/q1atYuPGjYSGhvLQQw9Rr149AAzDYOLEiTzyyCO0bdsWq9VKQUEBH374IXv37qWkpIRx48bRtGlT9u7dS3x8PBaLhaysLKKjo7nnnnto2LChq5+9e/fyySefEBwczEMPPcTbb7/t+r7W5s2bs2vXLrKzs/Hy8qJhw4b07t2bkpISfvrpJ3JycoD/+0MjNjYWL6+yfzc5nU5efPFF2rRpQ0REBNdffz0Ap06dYtasWbRr147bbruNlJQUNm7cSOPGjbnvvvvK/EECMHr0aMaNG8df/vIX4PQfC19++SUZGRkUFRXx+OOPExISQmZmJm+//Ta///47FouFu+++m9tvv73MeIcOHeKLL77AZrPRr18/PvjgA/bs2UNYWBjh4eHs3LkTh8Ph+kOjd+/eZGZmXtF9NnN/b7nlFq699tpa9b6Ojo4mKyur2va3cePGDBgwwLW/tWGfq/s1vtB9Dg0NpXHjxhw9erRKn+Oa8Fm++eab+eWXX6r8GgNMnz6dyZMnl6uf7aoKZKNHj2b27NnlXuTi4mLGjRvH3Llzy9THjx/PtGnTGDt2LFOnTuXNN98kOjqaO+64g4cffpiPP/6YoqIihg8fzttvv83UqVNp1KgRGRkZjBw5EsMweOuttxgzZgzvvPOOa/xJkyYxadIk6taty+jRo/Hy8mL27Nk8++yzvPbaawBMmDABp9PJo48+SkpKCmlpaTRv3pw//viD+Ph4vL29efzxx3n33Xfx8PBg/PjxAMyePZtTp04xY8YMXnrpJf744w+mTp2K1Wqt8ov/yiuv0KJFC7Kzs93iQ36hv9ROnjzJzTffTExMjKuno0ePsnz5cjIyMhg6dGiZ/f3444/JyMhg1KhRfPfdd3h6ejJ69Ghef/11/vjjD2677TZ++OEHevToQY8ePRg5ciTe3t5lAqfD4cBisWCz2Zg/fz7vvPMO9erVo3fv3kyZMoXw8HAmTJjA1KlTefjhhwkPD+fxxx+npKQELy8v6tWrR/fu3enWrRuzZs1iwIABHD9+nKVLlzJw4EC6dOnCc889R25uLi+99BI//vgjJ0+epHv37rz66qsEBAQwdOhQ13e6Zmdnk5iYSH5+PiNHjiyzv0uWLGHjxo08+OCDJCcn07p1awYOHMibb77Jzp076dKlC3/++SeNGzemW7dupKWlsXLlSry9vV2vL5z+BQjg6+vLhx9+yEcffUReXh69evXijTfeoEOHDjz11FPMmDGD3r1706lTJx577DEMw8AwDNq2bUuPHj3o0KEDr7zyCt27d6egoIDvv/+emJgYunbtyltvvYXD4WDUqFFs2LABPz8/brjhBubNm0fDhg159NFHq2WfX375ZQC8vb3dYn+//PJLsrOziYmJqTXv65deeolGjRoxZsyYatnf1NRUlixZQv369cuMU5P3ubpfY5vNxqRJk6q8zxkZGRw8eJCWLVvy4IMPuj7H69atIycnp9znuCZ8lufMmcN1113HiBEjqvQaw+mvevzv//7vcvWzXVWHLC0WCzk5OeU+gDk5OWRlZfG3v/2tTD0zM5MpU6aQm5tLgwYNeOmll5g9ezZHjhwBwMPDAx8fH0JDQ/H392fGjBmsXLmSTZs24e/vzzXXXIO3tzetW7fGx8eH/Px81wtat25dAP7yl7+wZ88e17/37t1LixYtKC4uxsfHh/bt29O+fXuKi4tJT09n3rx5PPnkk8ydO5dTp05RUFCA1WrFMAycTicARUVFnDx5EoCPPvoIi8XCSy+9VO7FnzZtWoUv/i+//ELz5s3p3Lkz3333HRs2bGD06NEsWLCAgoICQkJCmDp1Kj169GDSpEmMHDmSrVu3lvuQz5s3z/Uh/+ijjwgODubZZ59lypQp/OMf/2DChAl88sknjBkzxvUh37RpE1u3bi3zIV+8eLHrA/78888zcOBAnn/+eZ577jm2b99e7pfaxIkTKSgoKPOXb7169Vi9ejV16tTh448/LrO/e/fuxel00qlTJzp16sTy5ct5+eWXKSgoICgoiH79+rF69WrXDOqjjz7K559/zrPPPkuzZs0AePLJJ7Fara5AvXfvXmbNmgVAQEAAhw8fBqCwsJDw8HAAgoKCKC4uZvbs2fz888+sX7+eZ599llOnTpGdnU1cXBxLly6lS5cuwOlfIn5+ftjtdu68804mTZrEfffdh6enJ4WFhbRq1cq1T3a7neeffx4PDw+effbZMp+B0q8Z69u3L7fddhuLFy/mjTfeIDMzE5vNxmOPPcbw4cN5/vnnsVgsXH/99Xz33Xd06NCBRx55xPW8PvnkkwQEBPD6668DsG3bNmbMmIGXlxeBgYH89ttvABw7doxOnTq5+nI6nbz88sukpaWxZs0a3n33XQzDIDQ0lPbt27N69WruuusuAE6cOEFAQADXX389119/PVOmTOH+++/Hz8+P/Pz8atvnW265heTkZObMmeMW+3vDDTcwcODAcrP4Nfl97evry9GjR6vtc3z77beTkJBAWFgYjz76aK3Y5+p+jZs0acLRo0eJjIwEqHSfCwoKiI+PZ9y4ca4/rOx2O61atWLAgAFMnDiRM+d8asJnOTAwkMzMzCq/xgDHjx8vV6vIVRXIBg0axMsvv0yjRo1cL35WVhaZmZl4e3vz1FNPlZkxeuutt1yHEuF0mp44cSJvv/02RUVFnDp1Ch8fH9d3Y3p4eBAbG8t3333H8uXLCQoKoqSkBDg9tVz65ioNhsHBwQwaNIjx48czatQoAgMDee6557Db7eTm5vLKK6+4evHy8iIqKoq//vWvrFq1igkTJvDAAw/w5ptv0qBBA1fYe+edd/jll1/4r//6L+B0qAwLC7ugF7+oqIiHH34YwC0+5Bf6Sy08PJw1a9Zwxx13lAmhdevWJTQ0lBdffLHM/o4dO5YTJ064fu7fvz82m41Fixa5QmbPnj1dy++66y4SExNZtmwZdrudAQMGYLFYyM3N5euvv8YwDE6cOOF6rW+99Vb+8Y9/sH37dtq3b8+SJUvo3LkzOTk5tG3bFoAbbriBG264gSFDhjB+/Hg2bNhAQEAAFouFn376iU6dOlFSUuIK3Wlpaa5zGAMDAzly5AhOpxMPj9PX2TidToKCgggKCnI976XGjBnj+gvR09OTESNG8Pnnn7NlyxaCg4OxWCxERkZisViA078E7XY7d9xxB2+99RYdO3bktttuw2KxUFBQwMaNGzEMg+LiYtfsc5cuXfj22285dOgQHTt25JtvvqFTp07k5+fTrFkz/P39iY6OJjo6mry8PCZMmMD//M//EBAQQGFhoesPE6fTSVFREQD79u1zjR8YGEh2dna17fOQIUNIT0+vcH9/+uknnE7nFd3fOnXq4OnpyZdffknPnj2r7X29Zs0at3lf169fn19//bVMQLmc+1v6Pnr88cf58MMPa8U+V/drvHXrVubPn8+WLVsoKCiodJ89PDzYuHGjqw6nP8cbNmygTp06vPTSS4SEhJTZ58v5WTbjd1f9+vX57bffqvwaAzzxxBPlahW5qg5ZwukXOyMjo8xJ/eHh4bz77rv06tXLdTwaTk+denp68uGHHzJ69Ogy42zfvp0bb7yx3PjHjh3j6NGjNGvWjE2bNvHLL7/w0EMPnbOfU6dOkZubi9Vq5fDhwzidTmw2GwUFBYSFhVX4mDN7P378ONu2bSMkJAQfHx8OHDhA06ZNady4MQCvvvoqbdu2LfdL/JlnniE0NJRXX3213PgPPvggS5cudf1HB7B27VrXh3zhwoV8+umnPPDAA67lo0eP5i9/+YvrQ/7MM89QXFzMnXfeiWEYrF69mnnz5mGxWFi1apVrhmznzp0cP36czp07M2fOHNq2bcuoUaPKvF7jx4/HbrfTu3dvPv74YwYNGkSnTp0YN24cTqeTOXPmkJaWxurVq5kyZQr5+fmMGjWKoKAgcnNzgdMhtFGjRtxzzz20bNmyzP5+8skn+Pj48Ne//rVM/a233mLPnj3Mnz+/TD0zM5OlS5cyfvx40tLSWLFiBYcPH+bWW28ts16fPn2oW7cuR48eZf78+QQEBHDw4EFKSkoICQkhNzeXV199tdwh9N9++42lS5disVgYOHAg3377LevWrcNqtVKnTh2OHj1KkyZNeOKJJwgLC2Pfvn288847OBwO1x8Ux48fx263c//995f7Bou5c+fi5+fHsGHDytRfeOEFdu/ezaefflpufxcsWMArr7yC0+nkn//8Jxs2bODQoUO0b9++zLqlh66PHj3K1KlT8fPz49ChQxQVFWG32zl58iSzZ88ud6HJtm3bWLx4MR4eHowYMYKvv/6a//znPxw7dgxPT08CAgIoKSlhzJgxtGzZkn379jF37lzy8vJcM8QFBQWXfZ+nTp1aZn/btWvn+kVfnfvr7++P0+lk9OjRtGrVimPHjrFs2TK8vb1JS0urMe/r5ORkAgICKnxfHzx4kEWLFuFwOKp9f4Fat8/Vsb/n2uczf3/l5OTQtGlTnnjiCby8vPjggw/Yvn07wcHBwOnfXW3atKFJkyZ07NiRa665psz4l+uzbNbvroMHDzJv3jxOnDjB0aNHK32NAVeorcxVF8hqm/z8fBISEqr8Sxxg1qxZ9OnTh3bt2pWpX4kP+bRp0/D09CzzuAsNKMeOHePrr7/mxhtvpFWrVmVOwExMTCQ0NLTcVbOXUvfw8CAzM5NmzZpVy/hn1xs0aFBuv7755huuu+46QkNDOXDgALt376ZJkyauQ+NnXwl8ofXIyEj27t3rWvbzzz+zY8cOmjdvTmBgIBaL5bxj/fHHH6Snp9O4ceNzbuPMcf744w82b95MkyZNXH9Jn+tq5ry8POD0uSVPP/00ZzvXlc6V1UtnCeD0aQ3jx4/n/fffr/I48+bNK/PHRWXrz5w5kwkTJmCxWMjLy3M9T+da/+effyYjI4NmzZqV+c+l9Arupk2bXlL9559/ZufOnYSHh1d5/Qvpp7L1z7wC/dSpUyQkJPDrr78SEBDAQw89hN1up7CwkBUrVrjqDz74ICEhIeXqZ65feiV7kyZNaN++PS1atHBt4/PPP+fXX3+lXr16rrHOte1L7enM8b/88svzXl2/cuVKOnXqVG626FLrhYWFZGZmsn379ssy/oU+pri4mB9++AGbzca1115Leno6u3btokmTJvTs2ZOffvqp3B0FGjZsiJ+fHyEhIRdVT05O5rPPPuOOO+7A19e30vW///57du3aRaNGjao0/pnr+/j4EBAQQNeuXSu8I0JWVhYeHh6EhYXRo0cP8vLy2Lhx40XfKUGB7Cr23Xff0atXryrXq/qY0g95s2bNLngbl6O+cuVKVqxYQcuWLfn9999dV8euXLmSpUuX0r59+zL1VatW8cknn1S5fqHjXK71z1X//PPP+eqrr2jcuDHt2rUjIyOD1q1bk5SUhGEYBAUFua4EbtOmzQXXt23bhqenJ6dOnXJdVXyubVxo/VzbrmyclStXuq5mLrV9+3ZXSC09DF56pfOl1s81/oXWL3b8s6/UXrNmDatXr6Zjx4787//+L/3796dfv35lruC+1Pq3335bbvw1a9bwz3/+s9z6F9NPZeOf6wr0N954g+uuu45nnnnmkurbtm3j66+/5v3338fT07NatnGh9TOvru/RowddunShbt26DBw4EF9fX0JDQ8tcdX8l61XpB7igsT744ANKSkooLCx0heJOnTqxbds21x8DZ99R4LPPPsMwDJo2bXpR9dJtXO71Sw9nVrb+qlWrcDgc3HrrrWXuiJCUlITdbqdjx44XfaeEq+ocMinrs88+qzDknKte1cd4e3u7ziW70G1cjvqaNWvw9PRkwoQJHD58mDfffJMjR46wZs0aGjVqVOPqGzZsoGHDhkydOtV1xa+/vz8pKSlYLJZLrt99990MHjy43FXFl3MbF1rfuHEjR44cYdiwYVgsFgzDYO/evfj6+lK/fn3uvPNOV33fvn2XpX6u8S+0fjHj33XXXRw4cMD1Hk9MTOS5556jbt26bNy4ke+//94VaJ5//vnLUq9o/MTExArXv5h+Khv/hx9+YP369fTv3599+/a5zkcNDAx0nYN6KfXrr7+eFStWuGbkq2MbF1pv0qQJTqeTe++9l5SUFP7nf/6H5s2bExAQwKxZs8jIyCAlJYXPPvvMlHpl/XTv3p369evz+uuvs23btkrHKioqYsiQIURFRTF27FjXXQNuvvlmHnnkEcaOHUtJSUmZOwp89dVXADzzzDNuVf/b3/5WpfW//PJLrFYr9957L3feeafrjggbN26kpKSkXP2WW27h9ddfd12AcD4KZG7u7CtHSx08eJCioqJyyw3DIDs7u8LHnesx7lgvdebVsTk5Oa6LOWpSPTc3l+Dg4DJX/MLpC0EMw7jkeuntLs6+qvhybuNC66+99hpPPPEEy5cv59FHH3Vd0TxnzhxWrlxZ4+rnu1K7NLjl5eXVqPq5rkAPCQnhzz//vOT6n3/+iY+Pj2uWvTq2caH1c11dv2DBAp5++mnee+89t67/8MMP7N+/Hw8Pjyrtw9NPP82mTZv46KOPKC4udt01oKioyHWy/cmTJ8vcUcDpdOJ0Ojlx4sRVWT/XHRE8PT1dFwGeWQ8JCXHVK6NDlm5u2LBhTJkypdz9xiZOnAjgukK0lGEYjBo1itdee63Kj3G3+ltvvcXBgwd57733XLXSv7Jyc3P57LPPalR9+PDh5OXl8dlnn5W56nDixImUlJQwa9asS6oXFBQwYsQIFi9ejI+PT7Vs42J6mjp1KhMmTODDDz8kKCiItLQ03n77beD0BTk1rf7kk0+6wovFYuGVV14hODiYkSNHcvToUdcVZjWl7nA4GD9+PFarlcDAQH799Vfsdjv16tUjMDCQ/fv3X1LdbrfzwAMPkJSUxC+//FIt27jQeunV9WefyD5hwgReeeUVfHx83LoOp2eEzr7K+VyP+frrr/nnP/9JSUkJd911F2lpaTRo0IA9e/Zgs9k4ePAgTqeTO++807Vs06ZNFBUV4efnd9XWDcPgpptuct0RoVevXixfvpwVK1bQvXv3MvVjx44xe/Zspk6dWu45LccQt7Zw4ULj559/rrD+8ssvV/iYJ5988oIe4271rKws4/XXX6+w/tNPP9W4emFhYYWvV1ZWlvH7779fcj03N9fYu3dvufrl3MbF9HRm/d///rexdOnScuvV1PqZTp48aRw6dKjG1o8fP278+uuvxt69e42cnBzX8stVv5xjXWr9wIED5Z4HwzCumvrFPCY7O9vIzs42DMMw8vPzjR9//NHYs2fPeZdd7fX//Oc/xo8//mjs37+/zHNxrnpVaYZMRERExGQela8iIiIiItVJgUxERETEZApkIiIiIiZTIBMRERExmQKZiIiIiMn+H9+3+GA20CqfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = downloads_by_day.plot.bar(title='Downloads by day')\n",
    "p.figure.savefig('downloads-by-day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS searches_in_period\n",
    "    FROM searches\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)\n",
    "\n",
    "searches = df.values[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>month</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>11473197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>15040323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>13922071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  searches\n",
       "0 2020-07-01  11473197\n",
       "1 2020-08-01  15040323\n",
       "2 2020-09-01  13922071"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', accessed_at) AS month, COUNT(*) AS searches\n",
    "    FROM searches\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>day</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>422816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>387272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>351793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>335393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>430520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-09-26</td>\n",
       "      <td>408464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-09-27</td>\n",
       "      <td>411345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>448726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>453911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>454438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           day  searches\n",
       "0   2020-07-01    422816\n",
       "1   2020-07-02    387272\n",
       "2   2020-07-03    351793\n",
       "3   2020-07-04    335393\n",
       "4   2020-07-05    430520\n",
       "..         ...       ...\n",
       "87  2020-09-26    408464\n",
       "88  2020-09-27    411345\n",
       "89  2020-09-28    448726\n",
       "90  2020-09-29    453911\n",
       "91  2020-09-30    454438\n",
       "\n",
       "[92 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searches_by_day = pd.read_sql(f\"\"\"\n",
    "    SELECT accessed_at::date AS day, COUNT(*) AS searches\n",
    "    FROM searches\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY day\n",
    "    ORDER BY day\n",
    "\"\"\", conn)\n",
    "\n",
    "searches_by_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = searches_by_day.plot.bar(title='Searches by day')\n",
    "p.figure.savefig('searches-by-day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>total_time_saved_in_quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29458 days 17:51:55.365536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_time_saved_in_quarter\n",
       "0  29458 days 17:51:55.365536"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT JUSTIFY_INTERVAL(SUM(\n",
    "        CASE f.platform_tag\n",
    "            WHEN 'linux_armv7l' THEN 1                           \n",
    "            WHEN 'linux_armv6l' THEN 6                                                       \n",
    "            ELSE 0           \n",
    "        END *\n",
    "        CASE                        \n",
    "            WHEN b.duration > INTERVAL '1 week' THEN INTERVAL '0'\n",
    "            WHEN b.duration > INTERVAL '6.7 seconds' THEN b.duration - INTERVAL '6.7 seconds'\n",
    "            ELSE INTERVAL '0'               \n",
    "        END                  \n",
    "        )) AS total_time_saved_in_quarter                        \n",
    "    FROM downloads d\n",
    "    JOIN files f ON d.filename = f.filename\n",
    "    JOIN builds b ON b.build_id = f.build_id\n",
    "    WHERE f.abi_tag <> 'none'\n",
    "    AND d.accessed_at::date between '{start_date}' and '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>month</th>\n",
       "      <th>time_saved_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>5043 days 19:37:03.022912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>6492 days 09:32:37.692694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>6613 days 23:37:23.203709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>6498 days 01:18:11.967479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>7218 days 00:56:35.774120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>8467 days 12:38:01.956040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>7738 days 10:24:55.584583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>9265 days 22:25:14.640144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>8727 days 01:46:24.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>8705 days 13:02:56.146469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>11233 days 07:21:53.632977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>9514 days 21:27:05.586090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month        time_saved_in_month\n",
       "0  2019-10-01  5043 days 19:37:03.022912\n",
       "1  2019-11-01  6492 days 09:32:37.692694\n",
       "2  2019-12-01  6613 days 23:37:23.203709\n",
       "3  2020-01-01  6498 days 01:18:11.967479\n",
       "4  2020-02-01  7218 days 00:56:35.774120\n",
       "5  2020-03-01  8467 days 12:38:01.956040\n",
       "6  2020-04-01  7738 days 10:24:55.584583\n",
       "7  2020-05-01  9265 days 22:25:14.640144\n",
       "8  2020-06-01  8727 days 01:46:24.947397\n",
       "9  2020-07-01  8705 days 13:02:56.146469\n",
       "10 2020-08-01 11233 days 07:21:53.632977\n",
       "11 2020-09-01  9514 days 21:27:05.586090"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', d.accessed_at) as month, JUSTIFY_INTERVAL(SUM(\n",
    "        CASE f.platform_tag\n",
    "            WHEN 'linux_armv7l' THEN 1\n",
    "            WHEN 'linux_armv6l' THEN 6\n",
    "            ELSE 0\n",
    "        END *\n",
    "        CASE\n",
    "            WHEN b.duration > INTERVAL '1 week' THEN INTERVAL '0'\n",
    "            WHEN b.duration > INTERVAL '6.7 seconds' THEN b.duration - INTERVAL '6.7 seconds'\n",
    "            ELSE INTERVAL '0'\n",
    "        END\n",
    "            )) AS time_saved_in_month\n",
    "    FROM downloads d\n",
    "    JOIN files f ON d.filename = f.filename\n",
    "    JOIN builds b ON b.build_id = f.build_id\n",
    "    WHERE f.abi_tag <> 'none'\n",
    "    AND d.accessed_at::date between '{one_year_ago}' and '{end_date}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_saved = {\n",
    "    'Oct': 5043,\n",
    "    'Nov': 6492,\n",
    "    'Dec': 6613,\n",
    "    'Jan': 6498,\n",
    "    'Feb': 7218,\n",
    "    'Mar': 8467,\n",
    "    'Apr': 7738,\n",
    "    'May': 9265,\n",
    "    'Jun': 8727,\n",
    "    'Jul': 8705,\n",
    "    'Aug': 11233,\n",
    "    'Sep': 9514,\n",
    "}\n",
    "\n",
    "colors = ['blue']*9 + ['red']*3\n",
    "plt.bar(range(len(time_saved)), time_saved.values(), color=colors)\n",
    "plt.title('Time saved by month')\n",
    "plt.xticks(range(len(time_saved)), list(time_saved.keys()))\n",
    "\n",
    "plt.savefig('time-saved-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "ename": "OverflowError",
     "evalue": "Python int too large to convert to C long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Expected unicode, got datetime.timedelta",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-114-db39c78b0956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m pd.read_sql(f\"\"\"\n\u001b[0m\u001b[1;32m      2\u001b[0m     SELECT JUSTIFY_INTERVAL(SUM(\n\u001b[1;32m      3\u001b[0m         \u001b[0mCASE\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplatform_tag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0mWHEN\u001b[0m \u001b[0;34m'linux_armv7l'\u001b[0m \u001b[0mTHEN\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mWHEN\u001b[0m \u001b[0;34m'linux_armv6l'\u001b[0m \u001b[0mTHEN\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m    507\u001b[0m         )\n\u001b[1;32m    508\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m         return pandas_sql.read_query(\n\u001b[0m\u001b[1;32m    510\u001b[0m             \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m             \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1307\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m             frame = _wrap_result(\n\u001b[0m\u001b[1;32m   1310\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m                 \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36m_wrap_result\u001b[0;34m(data, columns, index_col, coerce_float, parse_dates)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrap_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;34m\"\"\"Wrap result set of query in a DataFrame.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0mframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parse_date_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mfrom_records\u001b[0;34m(cls, data, index, exclude, columns, coerce_float, nrows)\u001b[0m\n\u001b[1;32m   1789\u001b[0m             \u001b[0marr_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1790\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1791\u001b[0;31m             \u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1792\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1793\u001b[0m             \u001b[0marr_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mto_arrays\u001b[0;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;31m# last ditch effort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 548\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_list_to_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_list_to_arrays\u001b[0;34m(data, columns, coerce_float, dtype)\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_or_indexify_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 565\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_object_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    566\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mAssertionError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_convert_object_array\u001b[0;34m(content, coerce_float, dtype)\u001b[0m\n\u001b[1;32m    730\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    730\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(arr)\u001b[0m\n\u001b[1;32m    727\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"O\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_convert_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtry_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_cast_to_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mmaybe_cast_to_datetime\u001b[0;34m(value, dtype, errors)\u001b[0m\n\u001b[1;32m   1434\u001b[0m             )\n\u001b[1;32m   1435\u001b[0m         ):\n\u001b[0;32m-> 1436\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_infer_to_datetimelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1438\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mmaybe_infer_to_datetimelike\u001b[0;34m(value, convert_dates)\u001b[0m\n\u001b[1;32m   1281\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtry_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1282\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0minferred_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"timedelta\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1283\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtry_timedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1284\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0minferred_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"nat\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mtry_timedelta\u001b[0;34m(v)\u001b[0m\n\u001b[1;32m   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1270\u001b[0;31m             \u001b[0mtd_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_timedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1271\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1272\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/tools/timedeltas.py\u001b[0m in \u001b[0;36mto_timedelta\u001b[0;34m(arg, unit, errors)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         raise TypeError(\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/tools/timedeltas.py\u001b[0m in \u001b[0;36m_convert_listlike\u001b[0;34m(arg, unit, errors, name)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msequence_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36msequence_to_td64ns\u001b[0;34m(data, copy, unit, errors)\u001b[0m\n\u001b[1;32m    928\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_string_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m         \u001b[0;31m# no need to make a copy, need to convert if string-dtyped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 930\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobjects_to_td64ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    931\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.environments/stats/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py\u001b[0m in \u001b[0;36mobjects_to_td64ns\u001b[0;34m(data, unit, errors)\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_to_timedelta64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"timedelta64[ns]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.array_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.convert_to_timedelta64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/timedeltas.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.timedeltas.delta_to_nanoseconds\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOverflowError\u001b[0m: Python int too large to convert to C long"
     ]
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT JUSTIFY_INTERVAL(SUM(\n",
    "        CASE f.platform_tag\n",
    "            WHEN 'linux_armv7l' THEN 1\n",
    "            WHEN 'linux_armv6l' THEN 6\n",
    "            ELSE 0\n",
    "        END *\n",
    "        CASE\n",
    "            WHEN b.duration > INTERVAL '1 week' THEN INTERVAL '0'\n",
    "            WHEN b.duration > INTERVAL '6.7 seconds' THEN b.duration - INTERVAL '6.7 seconds'\n",
    "            ELSE INTERVAL '0'\n",
    "        END\n",
    "            )) AS time_saved_total\n",
    "    FROM downloads d\n",
    "    JOIN files f ON d.filename = f.filename\n",
    "    JOIN builds b ON b.build_id = f.build_id\n",
    "    WHERE f.abi_tag <> 'none'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>numpy</td>\n",
       "      <td>551732</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dogpile-cache</td>\n",
       "      <td>388880</td>\n",
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       "      <th>2</th>\n",
       "      <td>sentry-sdk</td>\n",
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       "      <th>3</th>\n",
       "      <td>requests</td>\n",
       "      <td>106192</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>octoprint-firmwarecheck</td>\n",
       "      <td>102182</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>filetype</td>\n",
       "      <td>101755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>semantic-version</td>\n",
       "      <td>94750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pyyaml</td>\n",
       "      <td>92459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cython</td>\n",
       "      <td>74871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>markdown</td>\n",
       "      <td>67303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   package       c\n",
       "0                    numpy  551732\n",
       "1            dogpile-cache  388880\n",
       "2               sentry-sdk  125560\n",
       "3                 requests  106192\n",
       "4  octoprint-firmwarecheck  102182\n",
       "5                 filetype  101755\n",
       "6         semantic-version   94750\n",
       "7                   pyyaml   92459\n",
       "8                   cython   74871\n",
       "9                 markdown   67303"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_downloads = pd.read_sql(f\"\"\"\n",
    "    select b.package, count(*) c\n",
    "    from downloads d\n",
    "    join files f on f.filename = d.filename\n",
    "    join builds b on b.build_id = f.build_id\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by b.package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (551,732)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/dogpile-cache'>dogpile-cache</a></strong> (388,880)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/sentry-sdk'>sentry-sdk</a></strong> (125,560)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (106,192)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/octoprint-firmwarecheck'>octoprint-firmwarecheck</a></strong> (102,182)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/filetype'>filetype</a></strong> (101,755)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/semantic-version'>semantic-version</a></strong> (94,750)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyyaml'>pyyaml</a></strong> (92,459)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cython'>cython</a></strong> (74,871)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/markdown'>markdown</a></strong> (67,303)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for i, row in top_10_downloads.iterrows():\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{row['package']}'>{row['package']}</a></strong> ({row['c']:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_searches = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_10_searches = {\n",
    "    'pip':        5540997,\n",
    "    'wheel':      3418343,\n",
    "    'setuptools': 1900293,\n",
    "    'numpy':       899174,\n",
    "    'pyrogram':    700198,\n",
    "    'youtube-dl':  610228,\n",
    "    'requests':    585098,\n",
    "    'certifi':     430260,\n",
    "    'six':         408890,\n",
    "    'urllib3':     400772,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (5,540,997)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/wheel'>wheel</a></strong> (3,418,343)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (1,900,293)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (899,174)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyrogram'>pyrogram</a></strong> (700,198)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/youtube-dl'>youtube-dl</a></strong> (610,228)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (585,098)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/certifi'>certifi</a></strong> (430,260)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (408,890)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/urllib3'>urllib3</a></strong> (400,772)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_searches.items():\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{pkg}'>{pkg}</a></strong> ({c:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (551,732)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/dogpile-cache'>dogpile-cache</a></strong> (388,880)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/sentry-sdk'>sentry-sdk</a></strong> (125,560)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (106,192)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/octoprint-firmwarecheck'>octoprint-firmwarecheck</a></strong> (102,182)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/filetype'>filetype</a></strong> (101,755)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/semantic-version'>semantic-version</a></strong> (94,750)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyyaml'>pyyaml</a></strong> (92,459)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cython'>cython</a></strong> (74,871)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/markdown'>markdown</a></strong> (67,303)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for i, row in top_10_searches.iterrows():\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{row['package']}'>{row['package']}</a></strong> ({row['c']:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>opencv-python</td>\n",
       "      <td>2524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>1259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scipy</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pip</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>opencv-python-headless</td>\n",
       "      <td>430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pillow</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>grpcio</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pandas</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  package     c\n",
       "0           opencv-python  2524\n",
       "1   opencv-contrib-python  1259\n",
       "2                   numpy  1217\n",
       "3                   scipy  1080\n",
       "4              tensorflow  1004\n",
       "5                     pip   645\n",
       "6  opencv-python-headless   430\n",
       "7                  pillow   429\n",
       "8                  grpcio   412\n",
       "9                  pandas   318"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_project_pages = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from project_page_hits\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_project_pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python'>opencv-python</a></strong> (2,524)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-contrib-python'>opencv-contrib-python</a></strong> (1,259)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (1,217)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scipy'>scipy</a></strong> (1,080)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tensorflow'>tensorflow</a></strong> (1,004)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (645)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python-headless'>opencv-python-headless</a></strong> (430)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (429)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/grpcio'>grpcio</a></strong> (412)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pandas'>pandas</a></strong> (318)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for i, row in top_10_project_pages.iterrows():\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{row['package']}'>{row['package']}</a></strong> ({row['c']:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>numpy</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>scipy</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>opencv-python</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>opencv-python-headless</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ighack</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>genox</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>azureml-contrib-functions</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>av</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gpiozero</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     package   c\n",
       "0                      numpy  45\n",
       "1                      scipy  36\n",
       "2              opencv-python   8\n",
       "3     opencv-python-headless   5\n",
       "4      opencv-contrib-python   2\n",
       "5                     ighack   2\n",
       "6                      genox   2\n",
       "7  azureml-contrib-functions   2\n",
       "8                         av   2\n",
       "9                   gpiozero   2"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_project_json = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from project_json_downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_project_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>os_name</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linux</td>\n",
       "      <td>40398043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows</td>\n",
       "      <td>24582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>12835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ibnu Rifqi</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      os_name         c\n",
       "0       Linux  40398043\n",
       "1     Windows     24582\n",
       "2      Darwin     12835\n",
       "3        None       110\n",
       "4  Ibnu Rifqi        21"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_usage = pd.read_sql(f\"\"\"\n",
    "    select os_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by os_name\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "os_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9990714121131554"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_usage.values[0][1] / searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>distro_name</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Raspbian GNU/Linux</td>\n",
       "      <td>38030119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debian GNU/Linux</td>\n",
       "      <td>1973278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ubuntu</td>\n",
       "      <td>222747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red Hat Enterprise Linux Server</td>\n",
       "      <td>58199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arch Linux</td>\n",
       "      <td>56115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       distro_name         c\n",
       "0               Raspbian GNU/Linux  38030119\n",
       "1                 Debian GNU/Linux   1973278\n",
       "2                           Ubuntu    222747\n",
       "3  Red Hat Enterprise Linux Server     58199\n",
       "4                       Arch Linux     56115"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distro_usage = pd.read_sql(f\"\"\"\n",
    "    select distro_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    group by distro_name\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40003397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          c\n",
       "0  40003397"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debian_usage = pd.read_sql(f\"\"\"\n",
    "    select count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "debian_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>222747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        c\n",
       "0  222747"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ubuntu_usage = pd.read_sql(f\"\"\"\n",
    "    select count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name = 'Ubuntu'\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "ubuntu_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Debian / Raspbian / PiOS': 40003397, 'Others': 432194}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debian = debian_usage.values[0][0]\n",
    "others = searches - debian\n",
    "\n",
    "distro_usage = {\n",
    "    'Debian / Raspbian / PiOS': debian,\n",
    "    'Others': others,\n",
    "}\n",
    "\n",
    "distro_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(distro_usage.values(), labels=distro_usage.keys())\n",
    "plt.title('Linux distro usage')\n",
    "\n",
    "plt.savefig('distro-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>distro_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>31467007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>8466021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>3057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  distro_version         c\n",
       "0             10  31467007\n",
       "1              9   8466021\n",
       "2              8      3057"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distro_versions = pd.read_sql(f\"\"\"\n",
    "    select distro_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and distro_version in ('8', '9', '10')\n",
    "    group by distro_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 78.8%\n",
      "9 21.2%\n",
      "8 0.0%\n"
     ]
    }
   ],
   "source": [
    "total = sum(distro_versions.c)\n",
    "\n",
    "for v in distro_versions.values:\n",
    "    print(v[0], f'{(100 * v[1] / total):.1f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version_names = {'10': 'Buster', '9': 'Stretch', '8': 'Jessie'}\n",
    "plt.pie(distro_versions.c, labels=[version_names[v] for v in distro_versions.distro_version])\n",
    "plt.title('Raspbian/Debian version usage')\n",
    "\n",
    "plt.savefig('debian-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>arch</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>armv7l</td>\n",
       "      <td>36888476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>armv6l</td>\n",
       "      <td>1934747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aarch64</td>\n",
       "      <td>764813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x86_64</td>\n",
       "      <td>746010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i686</td>\n",
       "      <td>53381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMD64</td>\n",
       "      <td>24579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>armv8l</td>\n",
       "      <td>23470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>x86</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ppc</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      arch         c\n",
       "0   armv7l  36888476\n",
       "1   armv6l   1934747\n",
       "2  aarch64    764813\n",
       "3   x86_64    746010\n",
       "4     i686     53381\n",
       "5    AMD64     24579\n",
       "6   armv8l     23470\n",
       "7     None       110\n",
       "8      x86         3\n",
       "9      ppc         2"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(arch.c, labels=arch.arch)\n",
    "plt.title('Architecture usage')\n",
    "\n",
    "plt.savefig('arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "armv7l 93.3%\n",
      "armv6l 4.9%\n",
      "aarch64 1.8%\n"
     ]
    }
   ],
   "source": [
    "arm_arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and arch in ('armv6l', 'armv7l', 'aarch64')\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "total = sum(arm_arch.c)\n",
    "\n",
    "for v in arm_arch.values:\n",
    "    print(v[0], f'{(100 * v[1] / total):.1f}%') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(arm_arch.c, labels=arm_arch.arch)\n",
    "plt.title('Raspbian/Debian Arm architecture usage')\n",
    "\n",
    "plt.savefig('debian-arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>pyver</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.7</td>\n",
       "      <td>24021823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.7</td>\n",
       "      <td>10863588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3449617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.8</td>\n",
       "      <td>1605475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.6</td>\n",
       "      <td>480179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pyver         c\n",
       "0   3.7  24021823\n",
       "1   2.7  10863588\n",
       "2   3.5   3449617\n",
       "3   3.8   1605475\n",
       "4   3.6    480179"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_vers = pd.read_sql(f\"\"\"\n",
    "    select substring(py_version from 1 for 3) pyver, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and py_name = 'CPython'\n",
    "    group by pyver\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "py_vers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(py_vers.c, labels=py_vers.pyver)\n",
    "plt.title('Python version usage')\n",
    "\n",
    "plt.savefig('py-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>installer_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.1</td>\n",
       "      <td>11365236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.2.2</td>\n",
       "      <td>5806465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.1.1</td>\n",
       "      <td>5019396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.2.3</td>\n",
       "      <td>4131963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.3.1</td>\n",
       "      <td>3411900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.0.1</td>\n",
       "      <td>2943069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.2</td>\n",
       "      <td>2385413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.2.1</td>\n",
       "      <td>1508477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19.2.3</td>\n",
       "      <td>1059250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.0.2</td>\n",
       "      <td>720176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  installer_version         c\n",
       "0              18.1  11365236\n",
       "1            20.2.2   5806465\n",
       "2            20.1.1   5019396\n",
       "3            20.2.3   4131963\n",
       "4            19.3.1   3411900\n",
       "5             9.0.1   2943069\n",
       "6              20.2   2385413\n",
       "7            20.2.1   1508477\n",
       "8            19.2.3   1059250\n",
       "9            20.0.2    720176"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_versions = pd.read_sql(f\"\"\"\n",
    "    select installer_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and installer_name = 'pip'\n",
    "    and installer_version is not null\n",
    "    group by installer_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "pip_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(v[1] for v in pip_versions.values)\n",
    "labels = [v[0] if v[1] / total > 0.01 else '' for v in pip_versions.values]\n",
    "\n",
    "plt.pie(pip_versions.c, labels=labels)\n",
    "plt.title('pip version usage')\n",
    "\n",
    "plt.savefig('pip-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>setuptools_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.8.0</td>\n",
       "      <td>18857712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.5.1</td>\n",
       "      <td>3159608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.3.0</td>\n",
       "      <td>1712218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.2.0</td>\n",
       "      <td>1611254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.2.0</td>\n",
       "      <td>1419185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44.1.1</td>\n",
       "      <td>1363174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49.6.0</td>\n",
       "      <td>1282906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44.0.0</td>\n",
       "      <td>1218595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.7.1</td>\n",
       "      <td>550424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>46.1.3</td>\n",
       "      <td>540116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  setuptools_version         c\n",
       "0             40.8.0  18857712\n",
       "1             41.5.1   3159608\n",
       "2             50.3.0   1712218\n",
       "3             41.2.0   1611254\n",
       "4             49.2.0   1419185\n",
       "5             44.1.1   1363174\n",
       "6             49.6.0   1282906\n",
       "7             44.0.0   1218595\n",
       "8             40.7.1    550424\n",
       "9             46.1.3    540116"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setuptools_versions = pd.read_sql(f\"\"\"\n",
    "    select setuptools_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and installer_name = 'pip'\n",
    "    and setuptools_version is not null\n",
    "    group by setuptools_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "setuptools_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(v[1] for v in setuptools_versions.values)\n",
    "labels = [v[0] if v[1] / total > 0.03 else '' for v in setuptools_versions.values]\n",
    "\n",
    "plt.pie(setuptools_versions.c, labels=labels)\n",
    "plt.title('setuptools version usage')\n",
    "\n",
    "plt.savefig('setuptools-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}