2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('postgresql:///piwheels')\n",
    "conn = engine.connect()\n",
    "meta = MetaData()\n",
    "meta.bind = conn\n",
    "meta.reflect(views=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2021-01-01'\n",
    "start_time = '2021-01-01 00:00:00'\n",
    "end_date = '2021-12-31'\n",
    "end_time = '2022-01-01 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>36724799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   downloads_in_period\n",
       "0             36724799"
      ]
     },
     "execution_count": 4,
     "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "downloads_by_day = pd.read_sql(f\"\"\"\n",
    "    SELECT accessed_at::date, COUNT(*) c\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY accessed_at::date\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average downloads per day:\n",
      "100615\n",
      "Max downloads in a day:\n",
      "314030\n"
     ]
    }
   ],
   "source": [
    "print(\"Average downloads per day:\")\n",
    "print(sum(downloads_by_day.c.values) // len(downloads_by_day.c.values))\n",
    "\n",
    "print(\"Max downloads in a day:\")\n",
    "print(max(downloads_by_day.c.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</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>67695042</td>\n",
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      ],
      "text/plain": [
       "   downloads_to_date\n",
       "0           67695042"
      ]
     },
     "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 < '{end_time}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>3248184</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   project_page_hits\n",
       "0            3248184"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project_json_downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1589791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   project_json_downloads\n",
       "0                 1589791"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS project_json_downloads\n",
    "    FROM project_json_downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</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>1603893</td>\n",
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      ],
      "text/plain": [
       "   web_page_hits\n",
       "0        1603893"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        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>2021-01-01</td>\n",
       "      <td>1960137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>3424032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>3878585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2956219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>2752983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>2762739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2325759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>2523731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2198364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>4263286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>4062858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-12-01</td>\n",
       "      <td>3616106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            m        c\n",
       "0  2021-01-01  1960137\n",
       "1  2021-02-01  3424032\n",
       "2  2021-03-01  3878585\n",
       "3  2021-04-01  2956219\n",
       "4  2021-05-01  2752983\n",
       "5  2021-06-01  2762739\n",
       "6  2021-07-01  2325759\n",
       "7  2021-08-01  2523731\n",
       "8  2021-09-01  2198364\n",
       "9  2021-10-01  4263286\n",
       "10 2021-11-01  4062858\n",
       "11 2021-12-01  3616106"
      ]
     },
     "execution_count": 10,
     "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 '{start_date}' AND '{end_date}'\n",
    "    GROUP BY m\n",
    "    ORDER BY m\n",
    "\"\"\", conn)\n",
    "\n",
    "downloads_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average downloads per month:\n",
      "3060399\n",
      "Max downloads in a month:\n",
      "4263286\n"
     ]
    }
   ],
   "source": [
    "print(\"Average downloads per month:\")\n",
    "print(sum(downloads_by_month.c.values) // len(downloads_by_month.c.values))\n",
    "\n",
    "print(\"Max downloads in a month:\")\n",
    "print(max(downloads_by_month.c.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pnjzn8qKiIkVGRkqS/P39FRoaquPHj6tRo0Yu47KyspSVlSVJyszMVFRUlE12rwkICCCrB5DVM8jqGWT1jPqUtTbUp231VtaL4Tngtkx98cUXaty4sdq2bau8vDyrB0tJSVFKSopzuqCgwGp93hIVFUVWDyCrZ5DVM8jqGd7JWpsXddtxv631KWvtqC/P15iY6v9v3JapXbt2KScnR1u2bFFZWZlOnjyp2bNn6/7773eOiYiIUGFhoSIjI1VZWamSkhKFh4fXTnoAAAAf5rZMpaenKz09XZKUl5end99916VISVJCQoLWrl2rDh06aOPGjYqPj5fD4fBMYgAAAB9ywTftXLFihXJyciRJgwYN0s8//6z77rtP//znP3XzzTfXWkAAAABfdl437YyPj1d8fLwkafjw4c75QUFBGjduXO0mAwAAqAf4OBkAAAALlCkAAAALlCkAAAALfNAxLkjtfRgnH8QJAKjfODIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABgIaCuAwAAgPonNjamFtdmt66DBw/VUo4Lw5EpAAAAC5QpAAAAC5QpAAAAC5QpAAAAC24vQC8rK1NGRoYqKipUWVmp3r17Ky0tzWXM2rVrtXTpUkVEREiSUlNTlZyc7JnEAAAAPsRtmQoMDFRGRoZCQkJUUVGhxx57TN26dVOHDh1cxvXt21ejR4/2WFAAAABf5PY0n8PhUEhIiCSpsrJSlZWVcjgcHg8GAABQH9ToPlNVVVV66KGHdOTIEQ0ZMkTt27c/a8ymTZv09ddfq0WLFho5cqSioqJqPSwAAICvcRhjTE0HnzhxQk8//bRuvfVWtW7d2jn/+PHjCgkJUWBgoFavXq0NGzYoIyPjrO/PyspSVlaWJCkzM1NlZWW1sAmeFxAQoIqKirqOUSPeyhocHOTxx6iJU6e88xziOeAZZPUMsrryldcryf1rFlkvjDd+FwQFVb+953UH9IYNGyo+Pl5bt251KVPh4eHOr5OTk7Vs2bJzfn9KSopSUlKc0wUFBefz8HUmKiqKrGepzTvfXjhv/b/wHPAMsnoGWX/NN16vpJq8ZpH1Qnjj+R4TU/32ur1mqri4WCdOnJB0+p1927ZtU2xsrMuYo0ePOr/OyclRy5YtLzQrAABAveL2yNTRo0c1b948VVVVyRijPn36KCEhQStWrFBcXJwSExO1atUq5eTkyN/fX2FhYRozZow3sgMAANQ5t2WqTZs2mjFjxlnzhw8f7vw6PT1d6enptZsMAACgHjiva6bgWbX3Cdz1+9O3AQCoT/g4GQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAuUKQAAAAsB7gaUlZUpIyNDFRUVqqysVO/evZWWluYypry8XHPnztW+ffsUHh6usWPHKjo62mOhAQAAfIXbI1OBgYHKyMjQzJkzNWPGDG3dulW7d+92GbNmzRo1bNhQc+bM0bBhw7R8+XKPBQYAAPAlbsuUw+FQSEiIJKmyslKVlZVyOBwuY3JycpSUlCRJ6t27t7Zv3y5jTO2nBQAA8DFuT/NJUlVVlR566CEdOXJEQ4YMUfv27V2WFxUVKTIyUpLk7++v0NBQHT9+XI0aNXIZl5WVpaysLElSZmamoqKiamMbPC4gIKDeZK0N9WlbvZW1Pj0HyOoZZPWM+pS1NtSnbSVrzdWoTPn5+WnmzJk6ceKEnn76aX377bdq3br1eT9YSkqKUlJSnNMFBQXnvY66EBUV5aWsMV54DPdqtq31Kas97z0H7JHVM8jqGd7J6huvV1JNXrPIeiG88XyPial+e8/r3XwNGzZUfHy8tm7d6jI/IiJChYWFkk6fCiwpKVF4ePj5JwUAAKhn3B6ZKi4ulr+/vxo2bKiysjJt27ZN11xzjcuYhIQErV27Vh06dNDGjRsVHx9/1nVVQF2Jja2tv57s1nPw4KFaygEA8CVuy9TRo0c1b948VVVVyRijPn36KCEhQStWrFBcXJwSExM1aNAgzZ07V/fdd5/CwsI0duxYL0QHAACoe27LVJs2bTRjxoyz5g8fPtz5dVBQkMaNG1e7yQAAAOoB7oAOAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABgwe0HHQPwntjYmFpak916Dh485HZMfcoKAJ7EkSkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALAe4GFBQUaN68eTp27JgcDodSUlI0dOhQlzF5eXmaMWOGoqOjJUm9evXS9ddf75nEAAAAPsRtmfL399eIESPUtm1bnTx5UpMmTVKXLl3UsmVLl3G///3vNWnSJI8FBQAA8EVuy1STJk3UpEkTSVKDBg0UGxuroqKis8oUAMBObGxMLa7Nbl0HDx6qpRzAxc9tmfql/Px87d+/X+3atTtr2e7duzVx4kQ1adJEI0aMUKtWrWotJAAAgK9yGGNMTQaWlpYqIyND1113nXr16uWyrKSkRH5+fgoJCVFubq5efvllzZ49+6x1ZGVlKSsrS5KUmZmpsrKyWtgEzwsICFBFRYXHHyc4OMjjj1ETp065/38h6/kjq2fUJGtt8MbrgK/sU4n96inu9itZL4w3nq9BQdVvb42OTFVUVGjWrFnq37//WUVKkkJDQ51f9+jRQ4sXL1ZxcbEaNWrkMi4lJUUpKSnO6YKCgpo8fJ2LioryUtbaPMR/4Wq2rWQ9X2T1DG+9jnjndcA39qnEfvUU99tK1gvhjedrTEz12+v21gjGGL3wwguKjY3VVVdddc4xx44d05kDXHv37lVVVZXCw8MvMC4AAED94fbI1K5du7Ru3Tq1bt1aEydOlCTddNNNzhY4ePBgbdy4UR9++KH8/f0VFBSksWPHyuFweDY5AACAD3Bbpjp27KiVK1f+5pjU1FSlpqbWWigAAID6gjugAwAAWKBMAQAAWKBMAQAAWKBMAQAAWKBMAQAAWKBMAQAAWKBMAQAAWDivDzoGgPomNrY2P/LCbl0HDx6qpRwAfAlHpgAAACxQpgAAACxQpgAAACxQpgAAACxQpgAAACxQpgAAACxQpgAAACxQpgAAACxQpgAAACxwB3QAwHnjzvLA/+HIFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAXKFAAAgAW3HydTUFCgefPm6dixY3I4HEpJSdHQoUNdxhhjtGTJEm3ZskXBwcEaM2aM2rZt67HQAAAAvsJtmfL399eIESPUtm1bnTx5UpMmTVKXLl3UsmVL55gtW7boyJEjmj17tvbs2aNFixbpqaee8mhwAAAAX+D2NF+TJk2cR5kaNGig2NhYFRUVuYzJycnRgAED5HA41KFDB504cUJHjx71TGIAAAAf4vbI1C/l5+dr//79ateuncv8oqIiRUVFOacjIyNVVFSkJk2auIzLyspSVlaWJCkzM9Ple3xZQEBAvclaG+rTtpLVM8jqGWT1DLJ6BllrrsZlqrS0VLNmzdKoUaMUGhp6QQ+WkpKilJQU53RBQcEFrcfboqKivJQ1xguP4V7NtpWs54usnuE+q2/klMjqKWT1jIsrq72YmOq3t0bv5quoqNCsWbPUv39/9erV66zlERERLhtSWFioiIiIC4gKAABQv7g9MmWM0QsvvKDY2FhdddVV5xyTmJioDz74QP369dOePXsUGhp61im+uhIbW1vN2W49Bw8eqqUcAADAl7gtU7t27dK6devUunVrTZw4UZJ00003OY9EDR48WN27d1dubq7uv/9+BQUFacyYMZ5NDQAA4CPclqmOHTtq5cqVvznG4XDo9ttvr7VQAAAA9QV3QAcAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALBAmQIAALAQ4G7A/PnzlZubq8aNG2vWrFlnLc/Ly9OMGTMUHR0tSerVq5euv/762k8KAADgg9yWqaSkJKWmpmrevHnVjvn973+vSZMm1WowAACA+sDtab5OnTopLCzMG1kAAADqHbdHpmpi9+7dmjhxopo0aaIRI0aoVatWtbFaAAAAn2ddpi699FLNnz9fISEhys3N1cyZMzV79uxzjs3KylJWVpYkKTMzU1FRUbYPX2/Up20lq2eQ1TPI6hlk9QyyekZdZ7UuU6Ghoc6ve/ToocWLF6u4uFiNGjU6a2xKSopSUlKc0wUFBbYPXwMxXngM92q2rWQ9X2T1jIsrq2/klMjqKWT1jIsrq72YmOq31/rWCMeOHZMxRpK0d+9eVVVVKTw83Ha1AAAA9YLbI1PPPfecduzYoePHj+vPf/6z0tLSVFFRIUkaPHiwNm7cqA8//FD+/v4KCgrS2LFj5XA4PB4cAADAF7gtU2PHjv3N5ampqUpNTa2tPAAAAPUKd0AHAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwQJkCAACwEOBuwPz585Wbm6vGjRtr1qxZZy03xmjJkiXasmWLgoODNWbMGLVt29YjYQEAAHyN2yNTSUlJmjx5crXLt2zZoiNHjmj27Nm68847tWjRoloNCAAA4MvclqlOnTopLCys2uU5OTkaMGCAHA6HOnTooBMnTujo0aO1GhIAAMBXWV8zVVRUpKioKOd0ZGSkioqKbFcLAABQL7i9Zqo2ZWVlKSsrS5KUmZnpUsIudvVpW8nqGWT1DLJ6Blk9g6yeUddZrctURESECgoKnNOFhYWKiIg459iUlBSlpKQ4p3/5fZ4T44XHcK9m20rW80VWz7i4svpGTomsnkJWz7i4stqLial+e61P8yUmJmrdunUyxmj37t0KDQ1VkyZNbFcLAABQL7g9MvXcc89px44dOn78uP785z8rLS1NFRUVkqTBgwere/fuys3N1f3336+goCCNGTPG46EBAAB8hdsyNXbs2N9c7nA4dPvtt9dWHgAAgHqFO6ADAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYoEwBAABYCKjJoK1bt2rJkiWqqqpScnKyrr32Wpfla9eu1dKlSxURESFJSk1NVXJycq2HBQAA8DVuy1RVVZUWL16sRx99VJGRkXr44YeVmJioli1buozr27evRo8e7bGgAAAAvsjtab69e/eqefPmatasmQICAtS3b19t3rzZG9kAAAB8ntsjU0VFRYqMjHROR0ZGas+ePWeN27Rpk77++mu1aNFCI0eOVFRU1FljsrKylJWVJUnKzMw855iLVX3aVrJ6Blk9g6yeQVbPIKtn1HXWGl0z5U5CQoL69eunwMBArV69WvPmzVNGRsZZ41JSUpSSkuKcLigoqI2HdyPGC4/hXs22lazni6yecXFl9Y2cElk9hayecXFltRcTU/32uj3NFxERocLCQud0YWGh80LzM8LDwxUYGChJSk5O1r59+y40KwAAQL3itkzFxcXp8OHDys/PV0VFhTZs2KDExESXMUePHnV+nZOTc9bF6QAAABcrt6f5/P39ddttt2nq1KmqqqrSwIED1apVK61YsUJxcXFKTEzUqlWrlJOTI39/f4WFhWnMmDHeyA4AAFDnanTNVI8ePdSjRw+XecOHD3d+nZ6ervT09NpNBgAAUA9wB3QAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALlCkAAAALATUZtHXrVi1ZskRVVVVKTk7Wtdde67K8vLxcc+fO1b59+xQeHq6xY8cqOjraE3kBAAB8itsjU1VVVVq8eLEmT56sZ599VtnZ2fr+++9dxqxZs0YNGzbUnDlzNGzYMC1fvtxjgQEAAHyJ2zK1d+9eNW/eXM2aNVNAQID69u2rzZs3u4zJyclRUlKSJKl3797avn27jDEeCQwAAOBL3JapoqIiRUZGOqcjIyNVVFRU7Rh/f3+Fhobq+PHjtRwVAADA99TomqnakpWVpaysLElSZmamYmJiPP6YvnOAzP22kvVCkNUzLp6svpNTIqunkNUzLp6snub2yFRERIQKCwud04WFhYqIiKh2TGVlpUpKShQeHn7WulJSUpSZmanMzEzb3F41adKkuo5QY2T1DLJ6Blk9g6yeQVbPqE9Zq+O2TMXFxenw4cPKz89XRUWFNmzYoMTERJcxCQkJWrt2rSRp48aNio+Pl8Ph8EhgAAAAX+L2NJ+/v79uu+02TZ06VVVVVRo4cKBatWqlFStWKC4uTomJiRo0aJDmzp2r++67T2FhYRo7dqwXogMAANS9Gl0z1aNHD/Xo0cNl3vDhw51fBwUFady4cbWbzIekpKTUdYQaI6tnkNUzyOoZZPUMsnpGfcpaHYfhHgYAAAAXjI+TAQAAsECZ+oURI0bUdQS3hg8frokTJzr/5efnVzt2ypQp+u///m8vpvs/aWlpmj17tnO6srJSo0eP9ul3cn7++edKS0vTwYMH6zrKOdXHfSrVj5+rX3OXuS5/tiTff67+0ltvvaVx48ZpwoQJmjhxovbs2VPXkX5TYWGhZsyYofvvv1/33XeflixZooqKimrHv/feezp16pQXE55+LXjllVec0//4xz+0cuVKr2aoqTO/s8aNG6eJEyfq3XffVVVVVV3HqnVevc8U7AUFBWnmzJl1HcOt4OBgfffddyorK1NQUJC2bdt21i013KmsrJS/v7+HEp4tOztbHTt2VHZ2ttLS0mr8fVVVVfLz8/zfJbWxT3FxuNDnqrft3r1bX3zxhaZPn67AwEAVFxf/ZjGpa8YYPf300xo8eLAefPBBVVVV6cUXX9Rrr71WbcF+//331b9/fwUHB3stZ2BgoDZt2qRrr71WjRo18trjXohf/s766aefNHv2bJ08edKnn7cXgjL1K6WlpZoxY4ZOnDihiooK3Xjjjbr88suVn5+vadOm6bLLLtPu3bsVERGhBx98UEFBQXUdWfv27dPf/vY3lZaWqlGjRhozZoyaNGkiSVq3bp1eeOEFVVVV6e6771a7du28lqt79+7Kzc1V7969lZ2drX79+mnnzp2STn9M0ZIlS1ReXq6goCCNGTNGMTExWrt2rTZt2qTS0lJVVVXp8ccf90rW0tJS7dy5UxkZGZo+fbrS0tKUl5enlStXKiQkREeOHFF8fLxuv/12+fn5acSIEfrjH/+or776SqNHj1bHjh29kvNC9mlGRoZuvfVWXXLJJZKkv/71rxo9erRz2hvy8vL07rvvOu8ns3jxYsXFxSkpKUn33HOPrrzySn3xxReqqKjQuHHjFBsb67Vs1fmtzHWpuudqdVlzc3P1yiuvKDg4WJdddpny8/O9dl+fo0ePKjw8XIGBgZLk/MVf3WvWlClT1KZNG+3YsaNOXrO2b9+uoKAgDRw4UJLk5+enkSNH6t5779UNN9yglStX6ssvv5TD4VBycrKMMSoqKtLjjz+uRo0aKSMjwys5/fz8lJKSovfee0833XSTy7L8/Hw9//zzOn78uHPfhoaGasKECZo7d678/PxUWlqqv/zlL5ozZ44CArxXAxo3bqw777xTDz/8sG644QYZY7R8+XLt2LFD5eXlGjJkiP74xz9Kkt5++219+umn8vPzU7du3XTzzTd7LeeF4DTfrwQGBmrChAmaPn26MjIy9Morrzg/Z/Dw4cNKTU3VM888o9DQUG3cuNHr+crKypyn+GbOnKmKigq99NJLGj9+vKZPn66BAwfqtddec44/deqUZs6cqdGjR+v555/3atZ+/fopOztbZWVl+uabb9S+fXvnspiYGD3xxBOaMWOG0tLS9OqrrzqX7d+/X+PHj/dakZKkzZs3q1u3boqJiVF4eLj27dsn6XRBue222/Tss8/qhx9+0Oeffy7p9H5t166dZs6c6bUiJV3YPh04cKDzPnCHDh1SeXm5V4tUTYSHh2v69OkaPHiw3n333bqO49Oqe66eS1lZmRYuXKjJkydr+vTpKi4u9mJSqWvXriosLNQDDzygRYsWaceOHT79mvXdd9/p0ksvdZkXGhqqqKgorVmzRj/++KNmzJihp59+Wv3799fQoUMVERGhjIwMrxWpM4YMGaL169erpKTEZf5LL72kK6+8Uk8//bSuuOIKvfTSSwoNDdUll1yiHTt2SJJyc3PVtWtXrxapM5o1a6aqqir99NNPWrNmjUJDQzVt2jRNmzZNH330kfLz87Vlyxbl5OToqaee0syZM3XNNdd4Pef54sjUrxhj9Nprr+nrr7+Ww+FQUVGRfvrpJ0lSdHS085dQ27Zt9eOPP3o9369P83377bf67rvv9OSTT0o6fcrpzFEpSbriiiskSZ06dVJJSYlOnDihhg0beiVrmzZt9OOPPyo7O1vdu3d3WVZSUqJ58+bpyJEjkk6f0jujS5cuCgsL80rGM7KzszV06FBJUt++fbV+/XolJCSoXbt2atasmSQ5jwL17t1bfn5+6t27t1czShe2T/v06aM333xTt9xyiz7++OM6P7JyLr169ZJ0+ufqTGHFuVX3XD2XQ4cOKTo6WtHR0ZJOvx6c+UgvbwgJCdH06dP19ddfKy8vT88++6z+7d/+zWdfs35LXl6ehgwZ4rz0wNuvUb8WGhqqAQMG6P3333c5Q7Jnzx5NmDBBkjRgwAAtX75c0unnyoYNG9S5c2dlZ2dryJAhdZL7l7788kt9++23zgMTJSUlOnz4sL766islJSU5T53W9b6uCcrUr6xfv17FxcXKzMxUQECA7rnnHpWVlUmS81C1dPow65n5da1ly5aaOnVqjcZ6+870iYmJWrp0qaZMmeLy4dcrVqxQfHy88yL6Xx6F8ua1B5L0888/a/v27fr222/lcDicF0f++t5qvxQYGOiV66TO5Xz3aXBwsLp06aKcnBx99tlndXLBur+/v355F5by8nKX5Wf+Qvbz83Mp1nXJXea6UN1z9fLLL/e5rGf4+fkpPj5e8fHxat26tf7rv/7LZ1+zWrZsqU2bNrnMKykpUUFBgZo2beq1HDU1bNgwPfTQQzX6AykxMVGvvfaafv75Z+3bt0+dO3f2fMBz+OGHH+Tn56fGjRvLGKNbb71V3bp1cxnz5Zdf1kk2G5zm+5WSkhI1btxYAQEB2r59e50cfTofMTExKi4u1u7duyVJFRUV+u6775zLN2zYIEnauXOnQkNDFRoa6tV8AwcO1PXXX6/WrVu7zC8pKXFePH3mFFRd2bhxowYMGKD58+dr3rx5ev755xUdHa2dO3dq7969ys/PV1VVlT777DOvntKrzoXs0+TkZC1ZskRxcXF18ldeVFSUvv/+e5WXl+vEiRP66quvvJ7hfPli5uqeq8aYc2aNiYlRfn6+812/Z14PvOXQoUM6fPiwc/rAgQOKjY312desP/zhDzp16pQ++eQTSaePmr3yyitKSkpS165dtXr1amfZ//nnnyWdPvpWWlrqtYy/FBYWpj59+mjNmjXOeR06dHDuw/Xr1ztfs0JCQhQXF6clS5YoISGhTv4YLC4u1sKFC5WamiqHw6Fu3brpww8/dL4p4dChQyotLVWXLl20du1a57skz+xrX8aRqf9VWVmpwMBAXXHFFZo+fbrGjx+vuLg4n7gQ9rcEBARo/PjxWrJkiUpKSlRZWamhQ4eqVatWkk6fFnzwwQdVWVmpu+++2+v5IiMjnackfumaa67RvHnz9NZbb/3mESBvyM7OPuucfK9evbR69Wq1a9dOixcvdl6A3rNnzzpK+X8uZJ+2bdtWDRo0cF5Y6y1nfq6ioqLUp08fjR8/XtHR0Wddl+JLfDlzdc/V7Ozsc2YNCgrS6NGj9dRTTyk4OFhxcXFezVtaWqqXXnpJJ06ckL+/v5o3b64777xTKSkpPvma5XA4NGHCBC1atEhvvvmmjDHq3r27brrpJvn5+enw4cOaMGGCAgIClJycrNTUVKWkpGjq1KnOa6e87aqrrtIHH3zgnL7ttts0f/58/eMf/3BegH5G37599cwzz2jKlCley3fmOt8z787u37+/rrrqKknSoEGDlJ+fr4ceekjS6TcoTJw4Ud26ddOBAwc0adIkBQQEqHv37kpPT/da5gvBHdD/14EDB/Tiiy9q2rRpdR0FPuLX75Cqz8684+jZZ5/16l+k9fHnqj5m/i2lpaUKCQmRMUaLFy9W8+bNnb/MfM2UKVM0YsQIr5c+wBZHpiR9+OGHWrVqlUaNGlXXUYBa98knn+j111/Xv//7v3u1SNXHn6v6mNmdrKwsffLJJ6qoqNCll17qfOs5gNrDkSkAAAALXIAOAABggTIFAABggTIFAABggTIFAABggTIFAABggTIFAABg4X8Apq2wmKR/lPIAAAAASUVORK5CYII=\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",
    "    'Jan': downloads_by_month.c[0],\n",
    "    'Feb': downloads_by_month.c[1],\n",
    "    'Mar': downloads_by_month.c[2],\n",
    "    'Apr': downloads_by_month.c[3],\n",
    "    'May': downloads_by_month.c[4],\n",
    "    'Jun': downloads_by_month.c[5],\n",
    "    'Jul': downloads_by_month.c[6],\n",
    "    'Aug': downloads_by_month.c[7],\n",
    "    'Sep': downloads_by_month.c[8],\n",
    "    'Oct': downloads_by_month.c[9],\n",
    "    'Nov': downloads_by_month.c[10],\n",
    "    'Dec': downloads_by_month.c[11],\n",
    "}\n",
    "\n",
    "colors = ['blue'] * 12\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('year-downloads-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137525"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_by_month.c[9] // 31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222470704"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS searches_in_period\n",
    "    FROM searches\n",
    "    WHERE accessed_at BETWEEN '{start_time}' AND '{end_time}'\n",
    "\"\"\", conn)\n",
    "\n",
    "searches = df.values[0][0]\n",
    "searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58024 days 19:57:59.802491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_time_saved_in_period\n",
       "0 58024 days 19:57:59.802491"
      ]
     },
     "execution_count": 47,
     "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_period                        \n",
    "    FROM downloads d\n",
    "    JOIN files f USING (filename)\n",
    "    JOIN builds b USING (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": 14,
   "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>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50640689705559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sum\n",
       "0  50640689705559"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT SUM(filesize)\n",
    "    FROM downloads\n",
    "    JOIN files using (filename)\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>requests</td>\n",
       "      <td>684141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>numpy</td>\n",
       "      <td>546668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>524595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pip</td>\n",
       "      <td>460168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>idna</td>\n",
       "      <td>443288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>425864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>chardet</td>\n",
       "      <td>420834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>certifi</td>\n",
       "      <td>407524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>websocket-client</td>\n",
       "      <td>404080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>six</td>\n",
       "      <td>403825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>importlib-metadata</td>\n",
       "      <td>392717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pillow</td>\n",
       "      <td>388883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>typing-extensions</td>\n",
       "      <td>387626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pyyaml</td>\n",
       "      <td>368142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>attrs</td>\n",
       "      <td>348164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cffi</td>\n",
       "      <td>337885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>click</td>\n",
       "      <td>337042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>wheel</td>\n",
       "      <td>329901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>jinja2</td>\n",
       "      <td>316285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>h11</td>\n",
       "      <td>315878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>octoprint-pisupport</td>\n",
       "      <td>309915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>octoprint-firmwarecheck</td>\n",
       "      <td>307221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>markupsafe</td>\n",
       "      <td>277070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>zipp</td>\n",
       "      <td>276298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>python-dateutil</td>\n",
       "      <td>264416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>charset-normalizer</td>\n",
       "      <td>256203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>tornado</td>\n",
       "      <td>243797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>pycparser</td>\n",
       "      <td>239223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>async-timeout</td>\n",
       "      <td>238955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>immutabledict</td>\n",
       "      <td>231729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    package       c\n",
       "0                  requests  684141\n",
       "1                     numpy  546668\n",
       "2                setuptools  524595\n",
       "3                       pip  460168\n",
       "4                      idna  443288\n",
       "5                   urllib3  425864\n",
       "6                   chardet  420834\n",
       "7                   certifi  407524\n",
       "8          websocket-client  404080\n",
       "9                       six  403825\n",
       "10       importlib-metadata  392717\n",
       "11                   pillow  388883\n",
       "12        typing-extensions  387626\n",
       "13                   pyyaml  368142\n",
       "14                    attrs  348164\n",
       "15                     cffi  337885\n",
       "16                    click  337042\n",
       "17                    wheel  329901\n",
       "18                   jinja2  316285\n",
       "19                      h11  315878\n",
       "20      octoprint-pisupport  309915\n",
       "21  octoprint-firmwarecheck  307221\n",
       "22               markupsafe  277070\n",
       "23                     zipp  276298\n",
       "24          python-dateutil  264416\n",
       "25       charset-normalizer  256203\n",
       "26                  tornado  243797\n",
       "27                pycparser  239223\n",
       "28            async-timeout  238955\n",
       "29            immutabledict  231729"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_30_downloads = pd.read_sql(f\"\"\"\n",
    "    SELECT package, COUNT(*) c\n",
    "    FROM downloads\n",
    "    JOIN files USING (filename)\n",
    "    JOIN builds USING (build_id)\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY package\n",
    "    ORDER BY c DESC\n",
    "    limit 30\n",
    "\"\"\", conn)\n",
    "\n",
    "top_30_downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (684,141)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (546,668)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (524,595)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (460,168)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/idna'>idna</a></strong> (443,288)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/urllib3'>urllib3</a></strong> (425,864)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/chardet'>chardet</a></strong> (420,834)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/certifi'>certifi</a></strong> (407,524)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/websocket-client'>websocket-client</a></strong> (404,080)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (403,825)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/importlib-metadata'>importlib-metadata</a></strong> (392,717)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (388,883)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/typing-extensions'>typing-extensions</a></strong> (387,626)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyyaml'>pyyaml</a></strong> (368,142)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/attrs'>attrs</a></strong> (348,164)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cffi'>cffi</a></strong> (337,885)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/click'>click</a></strong> (337,042)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/wheel'>wheel</a></strong> (329,901)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/jinja2'>jinja2</a></strong> (316,285)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/h11'>h11</a></strong> (315,878)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/octoprint-pisupport'>octoprint-pisupport</a></strong> (309,915)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/octoprint-firmwarecheck'>octoprint-firmwarecheck</a></strong> (307,221)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/markupsafe'>markupsafe</a></strong> (277,070)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/zipp'>zipp</a></strong> (276,298)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/python-dateutil'>python-dateutil</a></strong> (264,416)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/charset-normalizer'>charset-normalizer</a></strong> (256,203)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tornado'>tornado</a></strong> (243,797)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pycparser'>pycparser</a></strong> (239,223)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/async-timeout'>async-timeout</a></strong> (238,955)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/immutabledict'>immutabledict</a></strong> (231,729)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for i, row in top_30_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": 17,
   "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>pip</td>\n",
       "      <td>28919044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wheel</td>\n",
       "      <td>18932499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>11807471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pyrogram</td>\n",
       "      <td>4237340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>future</td>\n",
       "      <td>3078697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pyngrok</td>\n",
       "      <td>2663116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>youtube-dl</td>\n",
       "      <td>2565085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>aiohttp</td>\n",
       "      <td>2361757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>six</td>\n",
       "      <td>2219219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>requests</td>\n",
       "      <td>2190259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      package         c\n",
       "0         pip  28919044\n",
       "1       wheel  18932499\n",
       "2  setuptools  11807471\n",
       "3    pyrogram   4237340\n",
       "4      future   3078697\n",
       "5     pyngrok   2663116\n",
       "6  youtube-dl   2565085\n",
       "7     aiohttp   2361757\n",
       "8         six   2219219\n",
       "9    requests   2190259"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_searches = pd.read_sql(f\"\"\"\n",
    "    SELECT package, COUNT(*) c\n",
    "    FROM searches\n",
    "    WHERE accessed_at BETWEEN '{start_time}' AND '{end_time}'\n",
    "    GROUP BY package\n",
    "    ORDER BY c desc\n",
    "    LIMIT 10\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (28,919,044)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/wheel'>wheel</a></strong> (18,932,499)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (11,807,471)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyrogram'>pyrogram</a></strong> (4,237,340)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/future'>future</a></strong> (3,078,697)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyngrok'>pyngrok</a></strong> (2,663,116)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/youtube-dl'>youtube-dl</a></strong> (2,565,085)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/aiohttp'>aiohttp</a></strong> (2,361,757)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (2,219,219)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (2,190,259)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_searches.values:\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": 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>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>5855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>numpy</td>\n",
       "      <td>4713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scipy</td>\n",
       "      <td>3206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>2770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lilurl</td>\n",
       "      <td>2658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>2107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pillow</td>\n",
       "      <td>1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pandas</td>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>scikit-image</td>\n",
       "      <td>1673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 package     c\n",
       "0          opencv-python  5855\n",
       "1  opencv-contrib-python  5200\n",
       "2                  numpy  4713\n",
       "3                  scipy  3206\n",
       "4             tensorflow  2770\n",
       "5                 lilurl  2658\n",
       "6           cryptography  2107\n",
       "7                 pillow  1774\n",
       "8                 pandas  1742\n",
       "9           scikit-image  1673"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "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> (5,855)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-contrib-python'>opencv-contrib-python</a></strong> (5,200)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (4,713)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scipy'>scipy</a></strong> (3,206)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tensorflow'>tensorflow</a></strong> (2,770)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/lilurl'>lilurl</a></strong> (2,658)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cryptography'>cryptography</a></strong> (2,107)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (1,774)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pandas'>pandas</a></strong> (1,742)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scikit-image'>scikit-image</a></strong> (1,673)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_project_pages.values:\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": 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>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>222329071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows</td>\n",
       "      <td>61931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>55894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FreeBSD</td>\n",
       "      <td>22324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Open Surface RT - Linux</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OpenBSD</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CYGWIN_NT-10.0-18363</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CYGWIN_NT-6.1-7601-WOW64</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CYGWIN_NT-10.0-19042</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Salman Rahwidean Janotama</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dewandiaksa SM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ajie Kurniawan S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      os_name          c\n",
       "0                       Linux  222329071\n",
       "1                     Windows      61931\n",
       "2                      Darwin      55894\n",
       "3                     FreeBSD      22324\n",
       "4     Open Surface RT - Linux        818\n",
       "5                     OpenBSD        569\n",
       "6                        None         54\n",
       "7        CYGWIN_NT-10.0-18363         17\n",
       "8    CYGWIN_NT-6.1-7601-WOW64         15\n",
       "9        CYGWIN_NT-10.0-19042          7\n",
       "10  Salman Rahwidean Janotama          2\n",
       "11             Dewandiaksa SM          1\n",
       "12           Ajie Kurniawan S          1"
      ]
     },
     "execution_count": 21,
     "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 BETWEEN '{start_time}' AND '{end_time}'\n",
    "    GROUP BY os_name\n",
    "    ORDER BY c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "os_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993633633667109"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_usage.values[0][1] / searches"
   ]
  },
  {
   "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>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>197004318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debian GNU/Linux</td>\n",
       "      <td>22386703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ubuntu</td>\n",
       "      <td>2337182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alpine Linux</td>\n",
       "      <td>184167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OSMC</td>\n",
       "      <td>151241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distro_name          c\n",
       "0  Raspbian GNU/Linux  197004318\n",
       "1    Debian GNU/Linux   22386703\n",
       "2              Ubuntu    2337182\n",
       "3        Alpine Linux     184167\n",
       "4                OSMC     151241"
      ]
     },
     "execution_count": 23,
     "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 BETWEEN '{start_time}' AND '{end_time}'\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": 44,
   "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>188369728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>16330467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>14478817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  distro_version          c\n",
       "0             10  188369728\n",
       "1             11   16330467\n",
       "2              9   14478817"
      ]
     },
     "execution_count": 44,
     "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 BETWEEN '{start_time}' AND '{end_time}'\n",
    "    AND os_name = 'Linux'\n",
    "    AND distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    AND distro_version in ('9', '10', '11')\n",
    "    GROUP BY distro_version\n",
    "    ORDER BY c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 85.9%\n",
      "11 7.5%\n",
      "9 6.6%\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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version_names = {'11': 'Bullseye', '10': 'Buster', '9': 'Stretch'}\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('year-debian-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>176369282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aarch64</td>\n",
       "      <td>22795713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x86_64</td>\n",
       "      <td>15280495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>armv6l</td>\n",
       "      <td>7675313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      arch          c\n",
       "0   armv7l  176369282\n",
       "1  aarch64   22795713\n",
       "2   x86_64   15280495\n",
       "3   armv6l    7675313"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch = pd.read_sql(f\"\"\"\n",
    "    SELECT arch, COUNT(*) c\n",
    "    FROM searches\n",
    "    WHERE accessed_at BETWEEN '{start_time}' AND '{end_time}'\n",
    "    GROUP BY arch\n",
    "    ORDER BY c desc\n",
    "    LIMIT 4\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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.pie(arch.c, labels=arch.arch)\n",
    "plt.title('Architecture usage')\n",
    "\n",
    "plt.savefig('year-arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "armv7l 85.4%\n",
      "aarch64 10.9%\n",
      "armv6l 3.7%\n"
     ]
    }
   ],
   "source": [
    "arm_arch = pd.read_sql(f\"\"\"\n",
    "    SELECT arch, COUNT(*) c\n",
    "    FROM searches\n",
    "    WHERE accessed_at BETWEEN '{start_time}' AND '{end_time}'\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": 30,
   "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.pie(arm_arch.c, labels=arm_arch.arch)\n",
    "plt.title('Raspbian/Debian Arm architecture usage')\n",
    "\n",
    "plt.savefig('year-debian-arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>146425457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.7</td>\n",
       "      <td>32794807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.9</td>\n",
       "      <td>19351145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.8</td>\n",
       "      <td>14803657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6508632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pyver          c\n",
       "0   3.7  146425457\n",
       "1   2.7   32794807\n",
       "2   3.9   19351145\n",
       "3   3.8   14803657\n",
       "4   3.5    6508632"
      ]
     },
     "execution_count": 31,
     "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 BETWEEN '{start_time}' AND '{end_time}'\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": 32,
   "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.pie(py_vers.c, labels=py_vers.pyver)\n",
    "plt.title('Python version usage')\n",
    "\n",
    "plt.savefig('year-py-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7 66.59%\n",
      "2.7 14.91%\n",
      "3.9 8.80%\n",
      "3.8 6.73%\n",
      "3.5 2.96%\n"
     ]
    }
   ],
   "source": [
    "total = sum(row[1] for row in py_vers.values)\n",
    "\n",
    "for ver, c in py_vers.values:\n",
    "    print(ver, f'{100 * (c / total):.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>49979994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.0.1</td>\n",
       "      <td>35675392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.3.1</td>\n",
       "      <td>23942426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.1.2</td>\n",
       "      <td>17729809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.2.4</td>\n",
       "      <td>15006513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.1.3</td>\n",
       "      <td>11046670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.1.1</td>\n",
       "      <td>9005601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.3.3</td>\n",
       "      <td>7531215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.3.4</td>\n",
       "      <td>7529180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.0.1</td>\n",
       "      <td>6447018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  installer_version         c\n",
       "0              18.1  49979994\n",
       "1            21.0.1  35675392\n",
       "2            21.3.1  23942426\n",
       "3            21.1.2  17729809\n",
       "4            21.2.4  15006513\n",
       "5            21.1.3  11046670\n",
       "6            21.1.1   9005601\n",
       "7            20.3.3   7531215\n",
       "8            20.3.4   7529180\n",
       "9            19.0.1   6447018"
      ]
     },
     "execution_count": 34,
     "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 BETWEEN '{start_time}' AND '{end_time}'\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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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('year-pip-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>102341549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.0.0</td>\n",
       "      <td>11528568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.4.0</td>\n",
       "      <td>9541921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.0.0</td>\n",
       "      <td>9261324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.2.0</td>\n",
       "      <td>8512188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.7.1</td>\n",
       "      <td>6423018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49.2.1</td>\n",
       "      <td>4842216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>56.0.0</td>\n",
       "      <td>4556539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53.0.0</td>\n",
       "      <td>4365953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>51.1.2</td>\n",
       "      <td>4330063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  setuptools_version          c\n",
       "0             40.8.0  102341549\n",
       "1             52.0.0   11528568\n",
       "2             57.4.0    9541921\n",
       "3             57.0.0    9261324\n",
       "4             41.2.0    8512188\n",
       "5             40.7.1    6423018\n",
       "6             49.2.1    4842216\n",
       "7             56.0.0    4556539\n",
       "8             53.0.0    4365953\n",
       "9             51.1.2    4330063"
      ]
     },
     "execution_count": 36,
     "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 BETWEEN '{start_time}' AND '{end_time}'\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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 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('year-setuptools-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "downloads_by_hour = pd.read_sql(f\"\"\"\n",
    "    SELECT date_part('hour', accessed_at) h, COUNT(*) c\n",
    "    FROM downloads\n",
    "    WHERE accessed_at BETWEEN '{start_time}' AND '{end_time}'\n",
    "    GROUP BY h\n",
    "    ORDER BY h\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1009260,\n",
       " 990534,\n",
       " 947563,\n",
       " 912148,\n",
       " 883296,\n",
       " 902344,\n",
       " 1156424,\n",
       " 1418243,\n",
       " 1651663,\n",
       " 1763331,\n",
       " 1808218,\n",
       " 1844730,\n",
       " 1913206,\n",
       " 2028391,\n",
       " 2092611,\n",
       " 2124548,\n",
       " 2040559,\n",
       " 1812723,\n",
       " 1712276,\n",
       " 1722364,\n",
       " 1658843,\n",
       " 1546980,\n",
       " 1564232,\n",
       " 1220312]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(downloads_by_hour['c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = [int(h) for h in downloads_by_hour['h']]\n",
    "\n",
    "plt.bar(range(len(downloads_by_hour)), list(downloads_by_hour['c']), color=['blue'] * 24)\n",
    "plt.title('Downloads by hour')\n",
    "plt.xticks(range(len(downloads_by_hour)), hours)\n",
    "\n",
    "plt.savefig('year-downloads-by-hour.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "downloads_by_year = {\n",
    "    '2018': 5_154_233,\n",
    "    '2019': 9_218_822,\n",
    "    '2020': 16_184_523,\n",
    "    '2021': 36_724_799,\n",
    "}\n",
    "\n",
    "colors = ['blue'] * 3 + ['red']\n",
    "\n",
    "plt.bar(downloads_by_year.keys(), downloads_by_year.values(), color=colors)\n",
    "plt.title('Downloads by year')\n",
    "plt.xticks(range(len(downloads_by_year)), downloads_by_year)\n",
    "\n",
    "plt.savefig('year-downloads-by-year.png')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}