2021q1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2021-01-01'\n",
    "start_time = '2021-01-01 00:00:00'\n",
    "end_date = '2021-03-31'\n",
    "end_time = '2021-04-01 00:00:00'\n",
    "one_year_ago = '2020-04-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>9262754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   downloads_in_period\n",
       "0              9262754"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "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>40096797</td>\n",
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      ],
      "text/plain": [
       "   downloads_to_date\n",
       "0           40096797"
      ]
     },
     "execution_count": 9,
     "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": 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>project_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>722140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   project_page_hits\n",
       "0             722140"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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_json_downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   project_json_downloads\n",
       "0                   84621"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>web_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>399097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   web_page_hits\n",
       "0         399097"
      ]
     },
     "execution_count": 12,
     "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": 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>m</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>1239451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>1271968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>1170659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1102139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1737186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>1653471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>1850781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1240661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>1552508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1960137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>3424032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>3878585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            m        c\n",
       "0  2020-04-01  1239451\n",
       "1  2020-05-01  1271968\n",
       "2  2020-06-01  1170659\n",
       "3  2020-07-01  1102139\n",
       "4  2020-08-01  1737186\n",
       "5  2020-09-01  1653471\n",
       "6  2020-10-01  1850781\n",
       "7  2020-11-01  1240661\n",
       "8  2020-12-01  1552508\n",
       "9  2021-01-01  1960137\n",
       "10 2021-02-01  3424032\n",
       "11 2021-03-01  3878585"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "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",
    "    'Apr': downloads_by_month.c[0],\n",
    "    'May': downloads_by_month.c[1],\n",
    "    'Jun': downloads_by_month.c[2],\n",
    "    'Jul': downloads_by_month.c[3],\n",
    "    'Aug': downloads_by_month.c[4],\n",
    "    'Sep': downloads_by_month.c[5],\n",
    "    'Oct': downloads_by_month.c[6],\n",
    "    'Nov': downloads_by_month.c[7],\n",
    "    'Dec': downloads_by_month.c[8],\n",
    "    'Jan': downloads_by_month.c[9],\n",
    "    'Feb': downloads_by_month.c[10],\n",
    "    'Mar': 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": 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>d</th>\n",
       "      <th>downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>45005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>49371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>49380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>50458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>62765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2021-03-27</td>\n",
       "      <td>100485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>98778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>129207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>142381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>136200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             d  downloads\n",
       "0   2021-01-01      45005\n",
       "1   2021-01-02      49371\n",
       "2   2021-01-03      49380\n",
       "3   2021-01-04      50458\n",
       "4   2021-01-05      62765\n",
       "..         ...        ...\n",
       "85  2021-03-27     100485\n",
       "86  2021-03-28      98778\n",
       "87  2021-03-29     129207\n",
       "88  2021-03-30     142381\n",
       "89  2021-03-31     136200\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[45005,\n",
       " 49371,\n",
       " 49380,\n",
       " 50458,\n",
       " 62765,\n",
       " 57112,\n",
       " 60234,\n",
       " 50191,\n",
       " 52114,\n",
       " 47987,\n",
       " 53149,\n",
       " 60557,\n",
       " 50917,\n",
       " 68074,\n",
       " 57472,\n",
       " 65114,\n",
       " 41365,\n",
       " 72266,\n",
       " 65297,\n",
       " 73069,\n",
       " 78433,\n",
       " 76858,\n",
       " 73350,\n",
       " 78642,\n",
       " 67019,\n",
       " 96992,\n",
       " 63847,\n",
       " 70139,\n",
       " 76728,\n",
       " 73507,\n",
       " 72725,\n",
       " 72051,\n",
       " 96099,\n",
       " 133541,\n",
       " 119176,\n",
       " 114690,\n",
       " 120603,\n",
       " 125135,\n",
       " 126517,\n",
       " 126893,\n",
       " 125369,\n",
       " 121377,\n",
       " 135326,\n",
       " 121576,\n",
       " 124809,\n",
       " 133277,\n",
       " 109041,\n",
       " 113174,\n",
       " 119986,\n",
       " 131926,\n",
       " 99569,\n",
       " 91399,\n",
       " 140993,\n",
       " 121700,\n",
       " 168570,\n",
       " 151396,\n",
       " 149312,\n",
       " 106895,\n",
       " 123632,\n",
       " 118141,\n",
       " 138679,\n",
       " 135292,\n",
       " 123089,\n",
       " 108978,\n",
       " 113933,\n",
       " 107947,\n",
       " 124523,\n",
       " 123324,\n",
       " 131861,\n",
       " 125142,\n",
       " 118475,\n",
       " 103053,\n",
       " 115639,\n",
       " 123075,\n",
       " 142317,\n",
       " 147776,\n",
       " 142371,\n",
       " 125039,\n",
       " 110895,\n",
       " 141654,\n",
       " 123204,\n",
       " 138086,\n",
       " 128641,\n",
       " 135290,\n",
       " 125110,\n",
       " 100485,\n",
       " 98778,\n",
       " 129207,\n",
       " 142381,\n",
       " 136200]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(downloads_by_day['downloads'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 = downloads_by_day.plot.bar(title='Downloads by day')\n",
    "p.figure.savefig('downloads-by-day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59141950"
      ]
     },
     "execution_count": 21,
     "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 >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "\"\"\", conn)\n",
    "\n",
    "searches = df.values[0][0]\n",
    "searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>2021-01-01</td>\n",
       "      <td>9968928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>21435151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>27737871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  searches\n",
       "0 2021-01-01   9968928\n",
       "1 2021-02-01  21435151\n",
       "2 2021-03-01  27737871"
      ]
     },
     "execution_count": 22,
     "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 >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\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>day</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>265163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>262441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>257576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>259336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>330952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2021-03-27</td>\n",
       "      <td>729311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>721691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>819354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>802443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>846591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           day  searches\n",
       "0   2021-01-01    265163\n",
       "1   2021-01-02    262441\n",
       "2   2021-01-03    257576\n",
       "3   2021-01-04    259336\n",
       "4   2021-01-05    330952\n",
       "..         ...       ...\n",
       "85  2021-03-27    729311\n",
       "86  2021-03-28    721691\n",
       "87  2021-03-29    819354\n",
       "88  2021-03-30    802443\n",
       "89  2021-03-31    846591\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vXtTly5fVunVr+3o6duwoZ2dnXbhwQXFxcXrkkUckSW5ubvbDgffee6/9Z/LuvvtunThxQpUqVbLXk65eDZldr0GDBnrttdfUoUMHdejQwdTtJlQBAABTBQUF6bvvvtO3336rKVOmqEWLFqpbt66++uorubi42H/uTpLeeOMNLVy4UE2aNNGyZcu0bds2+7IKFSrkW6t8+fL2287OzsrMzJRhGPZ611q8eLG2b9+u7777TjNnztTGjRtNO1+Lq/8A4DaU1a+TzjzZPMcJyEBJcPr0abm7u+sf//iH+vfvr7179yoxMVG7d++WJGVkZOjw4cOSpEuXLsnf318ZGRlatWpVruvz9PRUtWrV9M0330iS0tLSlJKSkmf92rVr51rPZrPp1KlTatGihUaNGqXk5GRdvnzZtO1mpgoAAJjq119/1cSJE2WxWFSuXDm9++67cnZ21ttvv63k5GRlZmaqb9++qlevnoYOHaqOHTvKarXqvvvu06VLl3Jd58yZMzV8+HC9//77cnFx0YIFC/KsX758ec2bN09vv/22Ll68qKysLPXt21d169bVwIEDlZycLMMw1KdPH/n4+Ji23YQqAChFsvp10pn/u13cl72j5Lr2tXDtIbgbbc9LmzZtFBYWluMxK1euzLGuXr16qVevXjnap0+f7vDYWrVqacWKFQ5jqlGjhpo3b25vi4iIsN++5557tHLlyhzbsXr16gJvR2Fx+A8AAMAEhCoAAAATEKoAAABMQKgCAKCUMQzjVg/htnajzx+hCgCAUsbJyalQJ5bj/8vMzJST043FI67+AwCglHFzc1NqaqosFovS0tJyLHd1dS3S9uKoURS1DcOQk5OT3Nzccl1vfghVAACUMhaLRe7u7vLz81N8fHyO5UXdXhw1iqN2YXH4DwAAwASEKgAAABNw+A8AyoDs3wg8I75pHSgqzFQBAACYgFAFAABgAkIVAACACQhVAAAAJiBUAQAAmIBQBQAAYAJCFQAAgAkIVQAAACYgVAEAAJiAUAUAAGACQhUAAIAJCFUAAAAmIFQBAACYgFAFAABgAkIVAACACQhVAAAAJiBUAQAAmIBQBQAAYAJCFQAAgAkIVQAAACYgVAEAAJiAUAUAAGACQhUAAIAJXPLrMGfOHO3Zs0c+Pj6aOnVqjuWGYWjRokXau3evXF1dNWDAANWqVatIBgsAAFBS5TtT1aZNG7311lt5Lt+7d69Onz6tmTNn6qWXXtKCBQtMHSAAAMDtIN9Q1bBhQ3l6eua5fPfu3WrVqpUsFovq1q2ry5cv6/z586YOEgAAoKSzGIZh5Nfp7Nmzmjx5cq6H/yZNmqTOnTurfv36kqTx48frueeeU1BQUI6+kZGRioyMtD8uPT1dkuTi4qLMzMxca+e1rKjbqU1talO7JNc+82Rz+23/VVE33G7mmK7XXhw1qE3toqxRvnz5XGs59M+3h4nCwsIUFhZmvx8fHy9J8vPzs9++Vl7Lirqd2tSmNrVLeu1seS0rTPvttN3UpvatqF29evVca/3VTV/95+vr6zCQhIQE+fr63uxqAQAAbis3HapCQkK0ZcsWGYah3377TRUqVFClSpXMGBsAAMBtI9/Df9OnT9fBgweVnJys/v37q2vXrvbjiw8//LDuu+8+7dmzR6+//rrKly+vAQMGFPmgAQAoCln9OkmSzkhynv/lrR0Mbjv5hqpBgwZdd7nFYlHfvn3NGg8AAMBtiW9UBwAAMEGxXv0HAMDNyOrXSWf+7zaH51DSEKoAAKVWXudIce4UigKhCgCA2xQzdyUL51QBAACYgFAFAABgAg7/AQBKFM53wu2KUAUAQAHkdf4S5zUhG6EKAHDbKw3Bhhm62x/nVAEAAJiAUAUAAGACDv8BAFDKcCjx1iBUAQAKpTScv3QrEXhKL0IVAAAlHEH29sA5VQAAACYgVAEAAJiAw38AUIYVx/k9nEOEsoJQBQBAESBMlj2EKgCAKTiZGmUd51QBAACYgJkqAEAOHLoCCo+ZKgAAABMwUwUAQBnBDGTRYqYKAADABMxUAQBuCWZNUNowUwUAAGACQhUAAIAJCFUAAAAmIFQBAACYgFAFAABgAq7+AwAAhcZvPebETBUAAIAJCFUAAAAmIFQBAACYgHOqAACAacryN+UTqgAAQJErC2GLUAUAAHCNG7m6kVAFAADyxFcnFBwnqgMAAJiAUAUAAGACDv8BAIBbpjSdwM5MFQAAgAmYqQIAoIwrTbNFtxIzVQAAACZgpgpAicGnZQBFobjeWwoUqqKjo7Vo0SLZbDa1a9dOnTt3dlgeHx+v2bNn6/Lly7LZbOrevbuCg4OLYrwAAAAlUr6hymazaeHChRo9erSsVqtGjhypkJAQ1ahRw97niy++0IMPPqiHH35YJ0+e1LvvvkuoAgAAZUq+51TFxMSoatWq8vf3l4uLi5o3b65du3Y59LFYLEpJSZEkpaSkqFKlSkUzWgAAgBIq35mqxMREWa1W+32r1aojR4449Hn66ac1ceJEffPNN0pLS9OYMWPMHykAAChTivoncsw+18qUE9W3bt2qNm3a6PHHH9dvv/2mWbNmaerUqXJycpwIi4yMVGRkpCRp0qRJ8vPzuzoIFxf77RwDzGNZUbdTm9rULv7aZ/6yrCxt94205/VcFXU7taldEmpL5ry33GjtvOQbqnx9fZWQkGC/n5CQIF9fX4c+mzZt0ltvvSVJqlu3rjIyMpScnCwfHx+HfmFhYQoLC7Pfj4+Ptw82+/a18lpW1O3Upja1b11tSWVyu29kXVLuz1VxtFOb2reytpnvLQWpXb169TyXZ8v3nKqgoCDFxcXp7NmzyszMVFRUlEJCQhz6+Pn56cCBA5KkkydPKiMjQ97e3vkWBwAAKC3ynalydnZWnz59FBERIZvNprZt2yowMFDLly9XUFCQQkJC1LNnT82bN09ff/21JGnAgAGyWCxFPngAAICSokDnVAUHB+f4ioRu3brZb9eoUUMTJkwwd2QAAAC3EX6mBgAAwASEKgAAABMQqgAAAEzADyoDKPH4oWUAtwNCFYDbWlF/4zIAFBShCoADZoUA4MYQqgDgBjFLBuCvCFUAil1xzIaZFXhu5cwds4bA7YWr/wAAAEzATBWAArnerAmHwQCAmSoAAABTEKoAAABMwOE/AEWGw4IAyhJmqgAAAEzATBUA3GJ8dQJQOCV1FpyZKgAAABMwUwUAAEqFWz3rS6gCSrmSOk0OAKUNh/8AAABMQKgCAAAwAaEKAADABJxTBZRRt/qETgAobQhVwG2EHzUGgJKLw38AAAAmYKYKAMThUAA3j5kqAAAAEzBTBaBMYUYKQFFhpgoAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMQqgAAAEzAb/8BgMmy+nXSmf+7ze8LAmUHM1UAAAAmIFQBAACYgFAFAABgAkIVAACACQhVAAAAJuDqPwAoJln9OkmSzoirAoHSqEChKjo6WosWLZLNZlO7du3UuXPnHH2ioqK0YsUKWSwW3XHHHQoPDzd7rAAAACVWvqHKZrNp4cKFGj16tKxWq0aOHKmQkBDVqFHD3icuLk6rV6/WhAkT5OnpqaSkpCIdNAAAQEmT7zlVMTExqlq1qvz9/eXi4qLmzZtr165dDn02btyo9u3by9PTU5Lk4+NTNKMFAAAoofKdqUpMTJTVarXft1qtOnLkiEOfU6dOSZLGjBkjm82mp59+Wvfee6+5IwUAACjBTDlR3WazKS4uTu+8844SExP1zjvv6P3335eHh4dDv8jISEVGRkqSJk2aJD8/v6uDcHGx384xwDyWFXU7taldEmuf+cuya/vntayo26lNbWpTuyzVvp58Q5Wvr68SEhLs9xMSEuTr65ujT506deTi4qIqVaqoWrVqiouLU+3atR36hYWFKSwszH4/Pj7ePtjs29fKa1lRt1Ob2iW5tqQ826+3rKjbqU1talO7tNauXr16nsuz5XtOVVBQkOLi4nT27FllZmYqKipKISEhDn1CQ0P1yy+/SJIuXryouLg4+fv751scAACgtMh3psrZ2Vl9+vRRRESEbDab2rZtq8DAQC1fvlxBQUEKCQlRkyZN9PPPP+uNN96Qk5OTnn/+eXl5eRXH+AEAAEqEAp1TFRwcrODgYIe2bt262W9bLBb16tVLvXr1Mnd0AAAAtwl+pgYAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMQqgAAAExAqAIAADABoQoAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATOByqwcAIKesfp0kSWckOc//8tYOBgBQIMxUAQAAmIBQBQAAYAJCFQAAgAkIVQAAACYgVAEAAJiAUAUAAGACQhUAAIAJCFUAAAAmIFQBAACYgFAFAABgAkIVAACACQhVAAAAJiBUAQAAmIBQBQAAYAJCFQAAgAkIVQAAACYgVAEAAJiAUAUAAGACQhUAAIAJCFUAAAAmIFQBAACYgFAFAABgAkIVAACACQhVAAAAJiBUAQAAmIBQBQAAYIICharo6GiFh4dr4MCBWr16dZ79tm/frq5duyo2Ntas8QElTla/Tsrq10lnnmx+q4cCAChBXPLrYLPZtHDhQo0ePVpWq1UjR45USEiIatSo4dDvypUrWr9+verUqVNkgwVuRla/Tjrzf7ed539529YAAJRM+c5UxcTEqGrVqvL395eLi4uaN2+uXbt25ei3fPlyPfHEEypXrlyRDBQAAKAky3emKjExUVar1X7farXqyJEjDn2OHj2q+Ph4BQcH68sv8/50HhkZqcjISEnSpEmT5Ofnd3UQLi722zkGmMeyom6ndumrfeYvtwvymOzDe2ck+a+KKtB6rrcs1zEVssaN1C7qdmpTm9rULku1ryffUJUfm82mxYsXa8CAAfn2DQsLU1hYmP1+fHy8pKuDzb59rbyWFXU7tUtn7WyFfUxh23NbdiNjMqt2cbVTm9rUpnZprV29evU8l2fLN1T5+voqISHBfj8hIUG+vr72+6mpqTpx4oTGjRsnSbpw4YKmTJmiYcOGKSgoKN8BAAAAlAb5hqqgoCDFxcXp7Nmz8vX1VVRUlF5//XX78goVKmjhwoX2+2PHjlWPHj0IVAAAoEzJN1Q5OzurT58+ioiIkM1mU9u2bRUYGKjly5crKChIISEhxTFOAACAEq1A51QFBwcrODjYoa1bt2659h07duxNDwoAAOB2wzeqAwAAmOCmr/4DkD++FBQASj9mqgAAAExAqAIAADABoQoAAMAEnFOFYpXXuUVZ/TpJuvqzAAVpBwCgpCFU4bZE2AIAlDQc/gMAADABM1Uo85j1AgCYgVCFfBE6AADIH6GqDCqOL6Lkyy4BAGUN51QBAACYgJkq3DAOCwIA8P+ViVDFoSgAAFDUOPwHAABgAkIVAACACQhVAAAAJigT51TlpbC/Q1cS3U5jBQCgNGOmCgAAwARleqaqJGLmCQCA2xOhCqUOX6EBALgVOPwHAABgAmaqbhFmUwAAKF2YqQIAADABM1W3CU5gBwCgZGOmCgAAwASEKgAAABMQqgAAAEzAOVWlGFcYAgBQfJipAgAAMAEzVYXAFXgAACAvtzxUcYgKAACUBrc8VOWFWSEAAHA7KbGhKi95ha1bGcKuV5uZOAAAygZOVAcAADABoQoAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMU6Lf/oqOjtWjRItlsNrVr106dO3d2WL527Vpt3LhRzs7O8vb21iuvvKLKlSsXxXhRhPgRawAAbly+M1U2m00LFy7UW2+9pQ8++EBbt27VyZMnHfrceeedmjRpkt5//301a9ZMS5cuLbIBAwAAlET5hqqYmBhVrVpV/v7+cnFxUfPmzbVr1y6HPvfcc49cXV0lSXXq1FFiYmLRjBYAAKCEyjdUJSYmymq12u9brdbrhqZNmzbp3nvvNWVwAAAAt4sCnVNVUFu2bNHRo0c1duzYXJdHRkYqMjJSkjRp0iT5+fnpzF+W+/n52W+b1W7muqhNbWpTm9rUpnbZrn09+YYqX19fJSQk2O8nJCTI19c3R799+/Zp1apVGjt2rMqVK5frusLCwhQWFma/Hx8f77D82vtmtxdHDWpTm9rUpja1qV36alevXj3P5dnyPfwXFBSkuLg4nT17VpmZmYqKilJISIhDn2PHjmn+/PkaNmyYfHx88i0KAABQ2uQ7U+Xs7Kw+ffooIiJCNptNbdu2VWBgoJYvX66goCCFhIRo6dKlSk1N1bRp0yRdnSYbPnx4kQ8eAACgpCjQOVXBwcEKDg52aOvWrZv99pgxY8wdFQAAwG2Gb1QHAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMQqgAAAExAqAIAADABoQoAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMQqgAAAExAqAIAADABoQoAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMQqgAAAExAqAIAADABoQoAAMAEhCoAAAATEKoAAABMQKgCAAAwAaEKAADABIQqAAAAExCqAAAATECoAgAAMAGhCgAAwASEKgAAABMQqgAAAExAqAIAADCBS0E6RUdHa9GiRbLZbGrXrp06d+7ssDwjI0Mffvihjh49Ki8vLw0aNEhVqlQpivECAACUSPnOVNlsNi1cuFBvvfWWPvjgA23dulUnT5506LNp0yZ5eHho1qxZeuyxx7Rs2bIiGzAAAEBJlG+oiomJUdWqVeXv7y8XFxc1b95cu3btcuize/dutWnTRpLUrFkzHThwQIZhFMmAAQAASqJ8Q1ViYqKsVqv9vtVqVWJiYp59nJ2dVaFCBSUnJ5s8VAAAgJLLYuQzpbR9+3ZFR0erf//+kqQtW7boyJEjevHFF+19hgwZorfeesserAYOHKiIiAh5e3s7rCsyMlKRkZGSpEmTJpm6IQAAALdSvjNVvr6+SkhIsN9PSEiQr69vnn2ysrKUkpIiLy+vHOsKCwvTpEmTcgSqESNG5Fk/r2VF3U5talOb2tSmNrWpXRj5hqqgoCDFxcXp7NmzyszMVFRUlEJCQhz63H///fr+++8lXZ3Zuvvuu2WxWAo1EAAAgNtZvl+p4OzsrD59+igiIkI2m01t27ZVYGCgli9frqCgIIWEhOhvf/ubPvzwQw0cOFCenp4aNGhQMQwdAACg5CjQ91QFBwcrODjYoa1bt2722+XLl9fgwYNveBBhYWGFXlbU7dSmNrWpTW1qU5vahZHvieoAAADIHz9TAwAAYAJCFQAAgAkIVQAAACYo0InqZvvzzz+1a9cu+zez+/r6KiQkRDVq1Mizf2JiourUqSM3Nzd7e3R0tDw9PSVJtWvX1smTJxUdHa3q1avnOLH+ww8/1GuvvebQ9uuvvyomJkZOTk5q06aNKlSooPT0dK1evVpHjx5VjRo15OXlpRYtWsjPz8/hsZmZmdq6dasqVaqkxo0b68cff9Thw4cVEBCgRo0a6aefflJCQoKcnJxUrVo1tWzZUhUqVLjp5w4Fk5SUJB8fnwL3T05OzvW71YCSrKy+ztnugikt2307KfYT1VevXq2tW7eqRYsW9i8RTUxMtLd17tzZof+6deu0YcMGubq66vLly+rdu7eaNm0qSXrppZdUuXJlZWVlqXHjxjpy5IjuvvturVu3Tt7e3qpWrZokyTAM/fLLL3J2dlaDBg00fPhwRUZGasOGDQoNDdWaNWvUpUsXdenSRfPmzZOrq6uaNWum/fv3a+XKlfL29pa/v79atGihBx98UN7e3po5c6aysrKUlpYmDw8Ppaam6oEHHtD69euVkJCg9u3ba+/evbrzzjvl4eGhnTt3qm/fvrr77ruL8+ku9E4ombsjpqSkaNWqVdq1a5eSkpJksVjk4+OjkJAQde7cWR4eHjn6Dxs2TPXq1dN9992nli1b2pfNnj1b5cuXl8ViUbdu3bR+/Xrt2LFD/v7+6t69uypWrCjp6t97xIgRev7559WoUSN5enoqJSVFn376qWJjY5WVlaXBgwcrMDBQsbGx+uCDD2SxWBQfH69WrVrpySefVNWqVR3GFRsbq6VLl6pSpUrq3r275s6da/9dzFq1aunw4cNKSEiQi4uLqlatqnbt2ikrK0s7d+7U+fPnJf3/Dw9/+9vf5OLi+HnGZrPpnXfe0d133617771X9evXty9bvny53NzcZLFY1KFDB0VFRWnHjh0KCAjQU0895fBBIzw8XIMHD9Ydd9wh6Wr4X7NmjWJiYpSRkaH+/fvLz89Pp0+f1ty5c3X8+HFZLBZ16tRJjzzyiMO6JOnMmTP64osv5Ovrq86dO+uTTz7RkSNHVL16ddWuXVsHDx5UYmKi/cNDu3btdPr06Zve7rS0NL333ntq3Lhxvtts9nbntc0BAQHq3r27Dh48qB07djhs90MPPaRff/21wK9zSZowYYKCgoKUkJDg8Fq/cOGCxo8fr4YNGxbJ6zwrK0suLi5q3bq1WrRo4fBaL+zrvFWrVoqPjy9z+3dht7u0/L3L6vva3//+9wL/313soSo8PFxTp07N8eRnZmZq8ODBmjlzpkP7kCFDFBERoTfeeEPjxo3TtGnT1KpVKz366KN67rnntGTJEmVkZOill17S3LlzVaFCBQ0dOlTnzp3T0KFDZbFYZBiGZsyYITc3N/Xv318NGzbUyJEjNXLkSHl7eys8PFwuLi6aOnWqhg8frsmTJ9vrZ9fYv3+/oqKitHv3btWqVUsnTpzQBx98oPLly6t///6aN2+enJycNGTIEEnS1KlTlZaWpnfffVdjx47ViRMnNG7cOHl6ehZ4J8zrDcisnTAwMFBeXl566qmn5O3tXaA3oMLuhKmpqXrooYfUpk0b+7guXLiglStXKiYmRn379nXY5iVLligmJkYDBw7U5s2b5ezsrPDwcJUrV069evXSM888o7S0NP34449q2bKlWrZsqQEDBqh8+fIO4TExMVEWi0W+vr768MMP9dFHH6lixYpq166dRo0apdq1a2vYsGEaN26cnnvuOdWuXVv9+/e3vwFVrFhRLVq0UPPmzeXr66uRI0eqa9euunz5spYtW6ZevXqpWbNmGj16tJKSkjR27Fht27ZNqampatGihSZOnCgPDw/17dvX/vNNCQkJioyM1KVLlzRgwACH7V60aJF27NihZ599Vlu2bFHDhg3Vq1cvSdILL7ygtm3bKj09XadOnVJAQICaN2+u8ePHS7r6lSbZu3FaWpokyc3NTZ9++qkWL16s5ORktW3bVu+//76Cg4P12muv6d1331W7du0UGhqqF198UYZhyDAMNWrUSC1btlRwcLBcXFz0zjvvqEWLFkpJSdEPP/ygNm3a6MEHH9SMGTOUmJiogQMHavv27XJ3d1eDBg00a9YsVa1aVT169Lip7Z42bZoOHjyoZs2aOWzz7t27tW7dOpUvX97+Gjd7uydMmJDrNu/bt0+fffaZwsLC1LhxY4ftnj59uurVq6eXX365QK9zSRozZoweffRR1alTx+G1PmXKFJ04cUIdOnQoktf5qVOn9Oabb6pDhw7atm2bw2v9vffeK9TrfOzYsapWrZoGDRpUpvbvwm53afl7l9X3tTVr1igkJESPPPJIjr/rtYr98J/FYtH58+dVuXJlh/bz588rPj5eb775pkP76dOnNWrUKCUlJalKlSoaO3aspk6dqnPnzkmSnJyc5OrqKn9/f/vhtcmTJ+uVV17RypUr1aNHD915550qX768XF1dVbNmTSUnJ8swDPtvE95xxx06cuSI/XZsbKyCgoJ06tQpWSwWOTk5qUmTJmrSpIkyMzMVHR2tWbNm6dVXX9XMmTOVlpamlJQUeXp6yjAM2Ww2SVJGRoZSU1MlSYsXL5bFYtHYsWMddsLvv/9eERERue6ES5YsUVJSkh544AFt3rxZ27dvV3h4uGbPnq2UlBT5+flp3LhxatmypUaOHKkBAwZo3759OXbCWbNm2XfCxYsXq1KlSho+fLh27NihFStWqE+fPpKkpUuXatCgQfY3oD179mjfvn0OO+GCBQvsO+GYMWPUq1cvjRkzRqNHj9aBAwdy7IQjRoxQSkqKfZslqWLFitqwYYPKlSunJUuWOGxzbGysbDabQkNDFRoaqpUrV2r8+PEaNmyYsrKy7C/qDRs22Gc1e/TooRUrVmj48OGqWbOmJOnVV1+Vp6enPSDHxsbqvffekyR5eHjo7NmzkqT09HTVrl1bkuTj46PMzExNnTpVhw4d0tatWzV8+HDVqFFDFy5c0H333SdJWrZsmZo1aybp6s7u7u4uq9Wqjh07auTIkXrqqafk7Oys9PR01a1b175tVqtVY8aMkZOTk4YPH25vt1gs9p95euyxx9ShQwctWLBA77//vsLDw5Wenq6ePXvKMAy99NJLGjNmjCwWi/7+979ry5Ytmj59uv35ffXVV+Xh4aEpU6ZIkvbv3693331XLi4u8vLy0u+//y5JunjxokJDQ+3jstlsGj9+vHbv3q2NGzdq3rx5uv/++5WYmKiHH37Y/pw//vjjkqQrV67Iw8ND9evXV/369TVq1Ch169ZN7u7uunTp0k1v9+nTp+Xr66sXX3zRYZvr16+vzZs3Kzg4WM8//3yRbLdhGPL391eTJk0ctvlvf/ubFi5cqK5du0qSw3Z7eXnp9OnTBX6dS1ffH5577jlJcnitp6SkyMfHR507dy6S13n16tVlsVjUs2dP9ezZ0+G1npaWpoSEBIWFhRXode7m5qYLFy6Uuf27sNtdWv7eZfV9rUGDBho2bFjJDFW9e/fW+PHjVa1aNXvSjY+P1+nTp1W+fHm99tprDrM2M2bMsB+Wk64m1REjRmju3LnKyMhQWlqaXF1dHX5PMDU1VRUrVtSAAQP06aefysfHx/6bhCNGjJBhGPZwV6lSJfXu3VtDhgzRwIED5eXlpdGjR8tqtcpqteY4l8rFxUUhISF6+umntX79eg0bNkzPPPOMpk2bpipVqujSpUsyDEMfffSRfv31Vz3xxBOSrobD6tWr59gJO3furH//+9+57oR5vQGZtRN27NhR//73v5WVlWXfWfJ7AyrsTli7dm1t3LhRjz76qEOYzD6k+s477zhs8xtvvKErV67Y73fp0kW+vr565513lJWVZW9v3bq1/fbjjz+uyMhI/fe//5XValXXrl1lsViUlJSktWvXyjAMXblyxf53f/jhh/Wf//xHBw4cUJMmTbRo0SI98MADOn/+vBo1aiRJatCggRo0aKA+ffpo3759+vDDD/Xzzz8rJSVFFotFO3fuVGhoqLKysuwhevfu3fZz/Ly8vHTu3DnZbDY5OV29HsRms8nHx0c+Pj72v0G2QYMG2T+NOTs76+WXX9aKFSs0fvx4+6c1i8Wi++67z/4TUH369FF0dLRmzJihpk2bqkOHDrJYLEpJSdGOHTtkGIYyMzPts8LNmjXTt99+qzNnzqhp06b6+uuvFRoaqkuXLqlmzZqqUKGCWrVqpVatWik5OVnbtm3T9u3bderUKaWkpCg9Pd3+gcNmsykjI0OSdPToUXsNLy8vJSQk3PR2//zzz6pUqVKObbZYLLJarXr00Udz3e6dO3fKZrPd1HYPGzZMy5cvl4eHh8M2nz59WtLVfblq1aoO2125cmX9/vvvDv/RXu91LknPPvusw/OU/VqfP3++/YPRta/zjRs33vTr/MCBA/aZvmtf60OGDNH27dvl4eFRoNd55cqVdezYsQJv943u32Zst5n7d2G3u7T8vcvq+1q5cuVy/D3zcku+/NNmsykmJsbhRPXatWtr3rx5atu2rcNx14SEBDk7O+vTTz9VeHi4w3oOHDige+65J8f6L168qAsXLtiDxZ49e/Trr7+qe/fuuY4nLS1NSUlJ8vT01NmzZ2Wz2eTr66uKFSvq1KlTql69eq6P++v4L1++rP3798vPz0+urq76888/FRgYqICAAEnSxIkT1ahRI7Vu3TrHTNUXX3yhKVOm2M8By5b9BvTRRx/Z277//nv7Tjhnzhx99tlneuaZZ+zLw8PDdccdd9h3wqFDhyozM1MdO3aUYRjasGGDZs2aZX8Bv/zyywoMDFTnzp118OBBXb58WQ888ICmT5+uRo0aaeDAgQ5/tyFDhshqtapdu3ZasmSJevfurdDQUA0ePFg2m03Tp0/X7t27tWHDBo0aNUqXLl3SwIED5ePjo6SkJElXw2S1atX05JNPqk6dOg7bvHTpUrm6uurpp592aM/eyebOnZvj+Pjp06e1bNkyDRkyRLt379aqVat09uxZ+yeRbO3bt5e3t7cuXLigDz/8UB4eHoqLi1NWVpb8/PyUlJSkiRMn5jg0LUm///67li1bJovFol69eunbb7/V//73P3l6eqpcuXK6cOGCatSooVdeeUXVq1fX0aNH9dFHHykxMdH+IeHy5cuyWq3q1q1bjgspZs6cKXd3d/Xr18+hfePGjfr444/16aef5rrds2fP1rhx4/TNN99o+/btOnPmjJo0aeLQL/uQ8IULFzRu3Di5u7vrzJkzysjIkNVqVWpqqqZOnZrrhRT79+/XggUL5OTkpJdffllr167VH3/8oYsXL8rZ2VkeHh7KysrSoEGDVKdOHR09elQzZ85UcnKyfeY2JSWl0Nv99ttv67ffftNnn32W6zZPmDBBNpvNYbsbN27s8JujN7rdeW1zSkqKOnTooI0bN8rFxUU2m03h4eGqW7eu4uLiNGvWLKWkpBTodS5J7733ntq3b6/GjRs7tM+YMUNHjhzRhx9+mGPbzXidN23aVPv27cv1VzBye51v2bJFHh4eub7O4+LiNH/+fCUmJpa6/dvM7b6d/t5/fV87f/68AgMDy/z72sWLF/Xll1/q+eefz7Gua/GN6sXk0qVLWr16tXbv3u2wE95///2qUaOG6tatmyO85fUGZOZOuHTpUrVt21bffvttjjegiIgIOTs7Ozy2sOHi4sWLWrt2re655x7VrVvXYQeKjIyUv79/jqs682rfu3evqlSpkuuVoH99jJOTk06fPq2aNWsWukZe7dHR0fLz89P58+dzfUyVKlVybN/XX3+tevXqyd/fX3/++ad+++031ahRw37Y+dorVvNqDw4OVkxMTK7LvLy8ZLFYVLt2bR06dEi//PKLatWq5dCeV40TJ04oOjpaAQEB16195MgR+7pOnDihvXv3qkaNGvL09JSTk1OeV94mJydLunpexeuvv65r5XZFbkHasz+ZZzt//ryGDBmif/3rXwVe16xZsxw+MOTXf9KkSRo2bJicnJxkGIaSk5Ptz1lujzl06JBiYmJUs2ZNh/8Msq84DgwMzPGfRF7L8mo/dOiQDh48qNq1axe4f25jupHH5HbldFpamlavXq1jx47Jw8ND3bt3l9VqVXp6ulatWmVvf/bZZ+Xn5+fQXqNGDTVp0kRBQUE5rsL+67rS0tK0YsUKHTt2TBUrVrSvK6/aBRlTbrVzG29aWprWrFmjo0ePKj09XS+88IICAwMdnsd169YpNDQ0xxGO6y0raHt6erpOnz6tAwcOFGo9ZtTOlpmZqR9//FG+vr666667FB0drcOHD6tatWpyd3eXn5+fw9XwVatWzbU9ICBArVu31s6dO3NcQX/tY7Zs2aLPP/9cjz76qNzc3PKt8cMPP1x3TAEBAWrTpo22b98uX1/fHI9xdXWVh4eHHnzwQYfHhIWF5RrIr0WoKgE2b96stm3bFmpZQdqzd8KaNWsWej03WzvbunXrtGrVKtWpU0fHjx+3X725bt06LVu2TE2aNHFoX79+vZYuXZqjXZL69+8vV1dXBQQE3NS6brS2m5ubqlevXqB1rVixQl9++aUCAgLUuHFjxcTEqGHDhtq0aZMMw5CPj4/DFat5te/fv1/Ozs5KS0vLcZXrtY/Jq0Zh269XO791XXvlrXR1Rjk7bGYfXs6+Irew7ffcc49iYmLs7derUdj2vGpn979e7b9eWbxx40Zt2LBBTZs21VdffaUuXbqoc+fODlcc79u3T+fOnbPPQv91WV6Pubb922+/zVFj48aN+uabb3L0/+uYClM7vxp5XTn9/vvvq169eho6dGiB2vfv36+1a9fqX//6l5ydnW9qXcVRe/z48bJYLAoKClLLli3VrFkzeXt7q1evXnJzc8txtbikPJcVRftfx3Sztf+6ruyr3tPT0+1BOjQ0VJ9//rkMw1BgYKDD1fB5te/fv1+HDh1SUFBQjivor31MYWvk13///v32DyMF3Y79+/dLuno6TX5uyfdUwdHnn3+eZ7DJa1lB2suXL28/BFrY9dxs7WwbN26Us7Ozhg0bprNnz2ratGk6d+6cNm7cqGrVqhW4/dFHH1VycrKmT58uNze3m1pXcdTevn27qlatqnHjxjlcmRoVFSWLxVLg9k6dOumFF17I9SrXwq6rOGrv2LFD586dU79+/exX3sbGxsrNzU2VK1dWx44d7e1Hjx4tdPvjjz+ujz76SO7u7mrXrl2+NQrbnlft2NjYfGtnn+waGRmp0aNHy9vbWzt27NAPP/xgDyNjxoyRt7e3Hn/8cb344osO+0n2srwec217bjUiIyNz7f/XMRWmdn41fvzxR23dulVdunTR0aNH7eduenl52U+aLkh7/fr1tWrVKvvM+M2sqzhq16hRQzabTf/4xz8UFRWl5cuXq1atWvLw8NB7772nmJgYRUVF6fPPP1etWrXUokULVa5cWVOmTLFfSZ69LK/H3Gx79pjMqJ29rryuev/yyy8lSUOHDi1Q+0MPPaTnn39eb7zxhrKysm5qXcVR+6GHHtLQoUNz/J+XG0JVMbn2qsZshmEoISEh1+VxcXHKyMjIscys9uKqne2vV2+eP3/efqFCQdqzr/bMnkW4mXUVR+2kpCRVqlQpx5WpLi4uMgyjwO3ZJ5jmdpVrYddVHLXzuvJ2+vTpWrdu3U23N2zY0LR1mVk7ryuLs4PXte3Zr6XsC1sK8hiz2s2sndeV035+fjp16lSB20+dOiVXV1f7bPfNrKs4amdmZsrV1TXHVeGzZ8/W66+/roULFzq0//jjjzp58mSuV5Ln9Riz2s2snddV7zabTTabTVeuXClQe0ZGhv2E89TU1JtaV3HUzsjIcLiQ4no4/FdM+vXrp1GjRuX4PirDMDRw4EBNnjw5x7IRI0ZIksOVjWa2F0ftGTNmKC4uTgsXLrS3ZX86SEpK0ueff16g9rlz52rLli2aMmWK7rzzzptaV3HUfumll5ScnKzPP//c4UqZESNGKCsrS++9916B2lNSUvTyyy9rwYIFcnV1val1FUftlJQUjRs3TsOGDbNfebt7927NnTtX0tULT8xoN3NdZtR2cnKyBw+LxaIJEyaoUqVKGjBggC5cuGC/kjG7PTU1VS+88IKsVmuBH2NWu5m1ExMTNWTIEHl6esrLy0vHjh2T1WpVxYoV5eXlpZMnTxao3Wq16plnntGmTZv066+/3tS6iqN2UlKSJkyY4PB+IEnDhg3ThAkT5OrqqmsNHTo0x9Vx13uMWe1m1l67dq3Wr18vwzDUsWNH7d69W1WqVNGePXuUkZEhd3f3ArUfOXJEvr6+iouLk81mu6l1FUftI0eOqFmzZjnOb86VgWIxZ84c49ChQ7kue/XVV3NdNmfOHGP8+PFF1l4ctePj440pU6bk2r5z584CtxuGYWzfvt04f/78Ta+rOGqnp6fn+rzGx8cbx48fL3B7UlKSERsbm+uYCruu4qidlJTk0P7TTz8Zy5Yty9HPrPbiqHEjtbOlpqYaZ86cKXD7jTzGrPabWdfly5eNY8eOGbGxsQ77SWHbb+Qxt6L2n3/+mdvTl2f79ZYVdbvZ60pISDASEhIMwzCMS5cuGdu2bTOOHDlS6HYz11UctQuKmSoAAAATON3qAQAAAJQGhCoAAAATEKoAAABMQKgCAAAwAaEKAADABP8PwP2ThMJ2NYsAAAAASUVORK5CYII=\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": 25,
   "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>17011 days 01:40:52.150383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_time_saved_in_quarter\n",
       "0  17011 days 01:40:52.150383"
      ]
     },
     "execution_count": 25,
     "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": 27,
   "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>2020-04-01</td>\n",
       "      <td>7988 days 01:37:30.597478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>9265 days 21:54:39.625636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>9093 days 15:58:46.802963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>8705 days 12:32:39.953176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>11232 days 02:08:49.566210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>9514 days 20:08:16.196525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>8045 days 18:29:43.486867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>6349 days 07:37:54.756243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>5882 days 16:33:21.660789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>3988 days 02:52:29.327372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>5918 days 00:21:26.095352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>7099 days 22:26:56.727659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month        time_saved_in_month\n",
       "0  2020-04-01  7988 days 01:37:30.597478\n",
       "1  2020-05-01  9265 days 21:54:39.625636\n",
       "2  2020-06-01  9093 days 15:58:46.802963\n",
       "3  2020-07-01  8705 days 12:32:39.953176\n",
       "4  2020-08-01 11232 days 02:08:49.566210\n",
       "5  2020-09-01  9514 days 20:08:16.196525\n",
       "6  2020-10-01  8045 days 18:29:43.486867\n",
       "7  2020-11-01  6349 days 07:37:54.756243\n",
       "8  2020-12-01  5882 days 16:33:21.660789\n",
       "9  2021-01-01  3988 days 02:52:29.327372\n",
       "10 2021-02-01  5918 days 00:21:26.095352\n",
       "11 2021-03-01  7099 days 22:26:56.727659"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_saved_by_month = 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)\n",
    "\n",
    "time_saved_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7988"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_saved_by_month.time_saved_in_month[0].days"
   ]
  },
  {
   "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": [
    "time_saved = {\n",
    "    'Apr': time_saved_by_month.time_saved_in_month[0].days,\n",
    "    'May': time_saved_by_month.time_saved_in_month[1].days,\n",
    "    'Jun': time_saved_by_month.time_saved_in_month[2].days,\n",
    "    'Jul': time_saved_by_month.time_saved_in_month[3].days,\n",
    "    'Aug': time_saved_by_month.time_saved_in_month[4].days,\n",
    "    'Sep': time_saved_by_month.time_saved_in_month[5].days,\n",
    "    'Oct': time_saved_by_month.time_saved_in_month[6].days,\n",
    "    'Nov': time_saved_by_month.time_saved_in_month[7].days,\n",
    "    'Dec': time_saved_by_month.time_saved_in_month[8].days,\n",
    "    'Jan': time_saved_by_month.time_saved_in_month[9].days,\n",
    "    'Feb': time_saved_by_month.time_saved_in_month[10].days,\n",
    "    'Mar': time_saved_by_month.time_saved_in_month[11].days,\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": 33,
   "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>183869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>numpy</td>\n",
       "      <td>169759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chardet</td>\n",
       "      <td>152524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>139034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>idna</td>\n",
       "      <td>119761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>115392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pip</td>\n",
       "      <td>108285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>certifi</td>\n",
       "      <td>107014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>typing-extensions</td>\n",
       "      <td>102449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>h11</td>\n",
       "      <td>102242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             package       c\n",
       "0           requests  183869\n",
       "1              numpy  169759\n",
       "2            chardet  152524\n",
       "3         setuptools  139034\n",
       "4               idna  119761\n",
       "5            urllib3  115392\n",
       "6                pip  108285\n",
       "7            certifi  107014\n",
       "8  typing-extensions  102449\n",
       "9                h11  102242"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (183,869)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (169,759)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/chardet'>chardet</a></strong> (152,524)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (139,034)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/idna'>idna</a></strong> (119,761)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/urllib3'>urllib3</a></strong> (115,392)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (108,285)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/certifi'>certifi</a></strong> (107,014)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/typing-extensions'>typing-extensions</a></strong> (102,449)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/h11'>h11</a></strong> (102,242)</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": 35,
   "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>8010081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wheel</td>\n",
       "      <td>5111590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>2735718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pyrogram</td>\n",
       "      <td>972037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aiodns</td>\n",
       "      <td>957426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>aiohttp</td>\n",
       "      <td>943986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>six</td>\n",
       "      <td>764481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>requests</td>\n",
       "      <td>677639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>future</td>\n",
       "      <td>637936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>distro</td>\n",
       "      <td>524132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      package        c\n",
       "0         pip  8010081\n",
       "1       wheel  5111590\n",
       "2  setuptools  2735718\n",
       "3    pyrogram   972037\n",
       "4      aiodns   957426\n",
       "5     aiohttp   943986\n",
       "6         six   764481\n",
       "7    requests   677639\n",
       "8      future   637936\n",
       "9      distro   524132"
      ]
     },
     "execution_count": 35,
     "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 >= '{start_time}' AND accessed_at < '{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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (8,010,081)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/wheel'>wheel</a></strong> (5,111,590)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (2,735,718)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyrogram'>pyrogram</a></strong> (972,037)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/aiodns'>aiodns</a></strong> (957,426)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/aiohttp'>aiohttp</a></strong> (943,986)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (764,481)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (677,639)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/future'>future</a></strong> (637,936)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/distro'>distro</a></strong> (524,132)</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": 37,
   "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>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scipy</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pillow</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pip</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>qhub-ops</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>grpcio</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 package     c\n",
       "0          opencv-python  1189\n",
       "1  opencv-contrib-python  1172\n",
       "2                  numpy  1161\n",
       "3                  scipy   838\n",
       "4             tensorflow   796\n",
       "5           cryptography   588\n",
       "6                 pillow   517\n",
       "7                    pip   451\n",
       "8               qhub-ops   441\n",
       "9                 grpcio   410"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "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> (1,189)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-contrib-python'>opencv-contrib-python</a></strong> (1,172)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (1,161)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scipy'>scipy</a></strong> (838)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tensorflow'>tensorflow</a></strong> (796)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cryptography'>cryptography</a></strong> (588)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (517)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (451)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/qhub-ops'>qhub-ops</a></strong> (441)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/grpcio'>grpcio</a></strong> (410)</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": 39,
   "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>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>uvloop</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>scipy</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>opencv-python</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cffi</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>scikit-image</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pycparser</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         package    c\n",
       "0          numpy  188\n",
       "1         uvloop  126\n",
       "2          scipy   78\n",
       "3  opencv-python   51\n",
       "4     tensorflow   45\n",
       "5   cryptography   36\n",
       "6           cffi   33\n",
       "7   scikit-image   32\n",
       "8        urllib3   28\n",
       "9      pycparser   27"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (188)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/uvloop'>uvloop</a></strong> (126)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scipy'>scipy</a></strong> (78)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python'>opencv-python</a></strong> (51)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tensorflow'>tensorflow</a></strong> (45)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cryptography'>cryptography</a></strong> (36)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cffi'>cffi</a></strong> (33)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scikit-image'>scikit-image</a></strong> (32)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/urllib3'>urllib3</a></strong> (28)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pycparser'>pycparser</a></strong> (27)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_project_json.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": 41,
   "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>74505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       c\n",
       "0  74505"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select count(distinct(package)) c\n",
    "    from project_json_downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "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>user_agent</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mozilla</td>\n",
       "      <td>83771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ccbot</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python-requests</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lanaibotmarch</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aaabot</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>python-urllib</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>curl</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>anybot</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>elisabot</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>wordpress</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>java</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>zoominfobot (zoominfobot at zoominfo dot com)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ltx71 - (http:</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>netestate ne crawler (+http:</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nibble</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>onalyticabot</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>opera</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>yacybot (-global; amd64 linux 5.8.0-44-generic...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           user_agent      c\n",
       "0                                             mozilla  83771\n",
       "1                                               ccbot    489\n",
       "2                                     python-requests    159\n",
       "3                                       lanaibotmarch     75\n",
       "4                                              aaabot     56\n",
       "5                                       python-urllib     43\n",
       "6                                                curl      7\n",
       "7                                              anybot      6\n",
       "8                                            elisabot      4\n",
       "9                                           wordpress      2\n",
       "10                                               java      2\n",
       "11      zoominfobot (zoominfobot at zoominfo dot com)      1\n",
       "12                                     ltx71 - (http:      1\n",
       "13                       netestate ne crawler (+http:      1\n",
       "14                                             nibble      1\n",
       "15                                       onalyticabot      1\n",
       "16                                              opera      1\n",
       "17  yacybot (-global; amd64 linux 5.8.0-44-generic...      1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select user_agent, count(*) c\n",
    "    from project_json_downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by user_agent\n",
    "    order by c desc\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>59111410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows</td>\n",
       "      <td>13624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>12260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FreeBSD</td>\n",
       "      <td>4644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   os_name         c\n",
       "0    Linux  59111410\n",
       "1  Windows     13624\n",
       "2   Darwin     12260\n",
       "3  FreeBSD      4644\n",
       "4     None        12"
      ]
     },
     "execution_count": 43,
     "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 >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by os_name\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "os_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.95% linux\n"
     ]
    }
   ],
   "source": [
    "linux_usage_pc = 100 * (os_usage.values[0][1] / searches)\n",
    "print(f'{linux_usage_pc:2.2f}% linux')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>52228910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debian GNU/Linux</td>\n",
       "      <td>5683290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ubuntu</td>\n",
       "      <td>1098491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alpine Linux</td>\n",
       "      <td>34535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Red Hat Enterprise Linux Server</td>\n",
       "      <td>16910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       distro_name         c\n",
       "0               Raspbian GNU/Linux  52228910\n",
       "1                 Debian GNU/Linux   5683290\n",
       "2                           Ubuntu   1098491\n",
       "3                     Alpine Linux     34535\n",
       "4  Red Hat Enterprise Linux Server     16910"
      ]
     },
     "execution_count": 45,
     "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 >= '{start_time}' AND accessed_at < '{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": 46,
   "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>57912200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          c\n",
       "0  57912200"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debian_usage = pd.read_sql(f\"\"\"\n",
    "    select count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\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": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.92% debian\n"
     ]
    }
   ],
   "source": [
    "debian_usage_pc = 100 * (debian_usage.c[0] / searches)\n",
    "print(f'{debian_usage_pc:2.2f}% debian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.86% ubuntu\n"
     ]
    }
   ],
   "source": [
    "ubuntu_usage = pd.read_sql(f\"\"\"\n",
    "    select count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name = 'Ubuntu'\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "ubuntu_usage_pc = 100 * (ubuntu_usage.c[0] / searches)\n",
    "print(f'{ubuntu_usage_pc:2.2f}% ubuntu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Debian / Raspbian / PiOS': 57912200, 'Others': 1229750}"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "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(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": 51,
   "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>53710485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>4099280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>1877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  distro_version         c\n",
       "0             10  53710485\n",
       "1              9   4099280\n",
       "2              8      1877"
      ]
     },
     "execution_count": 51,
     "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 >= '{start_time}' AND accessed_at < '{end_time}'\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": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 92.9%\n",
      "9 7.1%\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": 53,
   "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 = {'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": 54,
   "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>49915948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x86_64</td>\n",
       "      <td>4831119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>armv6l</td>\n",
       "      <td>2206776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aarch64</td>\n",
       "      <td>2097318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i686</td>\n",
       "      <td>57991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMD64</td>\n",
       "      <td>13621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>armv8l</td>\n",
       "      <td>12336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amd64</td>\n",
       "      <td>4644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>armv5tel</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>arm64</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ppc64le</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s390x</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mips</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>x86</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        arch         c\n",
       "0     armv7l  49915948\n",
       "1     x86_64   4831119\n",
       "2     armv6l   2206776\n",
       "3    aarch64   2097318\n",
       "4       i686     57991\n",
       "5      AMD64     13621\n",
       "6     armv8l     12336\n",
       "7      amd64      4644\n",
       "8   armv5tel      1016\n",
       "9      arm64       854\n",
       "10   ppc64le       158\n",
       "11     s390x       150\n",
       "12      None        12\n",
       "13      mips         4\n",
       "14       x86         3"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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('arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "armv7l 92.1%\n",
      "armv6l 4.1%\n",
      "aarch64 3.8%\n"
     ]
    }
   ],
   "source": [
    "arm_arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ly5ZZ/rqp8PXXX2Pt2rVITk7G/PnzERcXV+kX425t2rSBIAj45JNPkJqaiq1bt2LBggW1em3Vyc7OxsaNGzFu3DgEBgZW+pgyZQr0ej3Wr19f5+epiXbt2mHbtm2Ij4/H2bNn8eKLL1Yqm/t5+eWXYTabMXz4cPz5559ITU3FL7/8gh07djxyFk9PT+h0OuzatQs3btxATk4OAGDBggXYtm0b3njjDZw6dQopKSnYuXMnJk+ejOLiYgDA22+/ja+++goLFy5EUlISEhMT8dlnnyEzMxMA0KJFCxw/fhwpKSnIzMyEwWCAWq1Gz549sWjRIpw+fRrHjx/H888/D6VS+dCsNclUnf79++PLL79EXFwczpw5gwkTJlT6K+/QoUNYuHAhjhw5gqtXr2Lv3r1ISEiwDDhq0gUzZszAjz/+iGXLluHixYtYvXo1Vq9eDeDvEfvcuXORlJSEsWPHIj4+Hqmpqdi3bx9ef/11XLp0qaY/Mv44b7OvpLqpiYwx9ueff1p2BpWVlbEXX3yRubm5MScnJzZmzBi2bNkydvdLefnll1mbNm2YSqVi7u7ubPDgwezMmTOWr1dMO6yYaeHu7s5mzZpV7dTEBy1TdQfo/v37WVBQEFMqlaxt27Zs48aN1e5AetBOnwULFrAnn3zynu9Bs2bNLDuEFQoF8/X1ZYMHD2Zr1669Z8aAXq9ns2bNYs2bN2dyuZw1atSIDRw4kO3du5cx9vfOrtWrV7OIiAimVCpZ8+bN2ffff19pPVWzfvbZZ8zf35+pVCrWs2dPtmPHjko76Sp2gN495YsxxqRSKVu1atU9r4mx8h1WSqXSMvW0qhEjRlh2hN7v/VHd97S651QqlWzFihXVPg9jjF29epUNGDCAaTQay6yISZMmsYiICMsy99vpff78eTZixAjm7OzM1Go1CwoKqjSbRSqVVlq+6g7O6nZAfvvtt6x58+ZMKpVWmpoYGxvL+vXrx3Q6HdNoNKx9+/bs9ddft8yMYYyxNWvWsKCgIKZQKCy/Azk5OYwxxlJSUljv3r2ZVqutlOH8+fOsT58+TKPRsNatW7NNmzbV6P1b00xVZWRksKFDhzInJyfm7+/Pvvjii0q/C2fOnGGDBg1ijRo1YgqFgjVt2pT961//YqWlpYwxVqMuYKz8Pda4cWOmUqnYgAED2FdffcUAVJrZlJCQwIYNG8ZcXV2ZSqVirVq1Yi+88ALLysq6b35bIzDmeFcaat68OaZMmYJ58+bxjnKPkJAQvPbaa5g4cSLvKISI0oIFCxATE2P5S0UsbH6buSMpKyvDiBEjMHz4cN5RCBEFg8GATz75BIMHD4ZWq8W+ffvw0Ucf4ZVXXuEdrd5RmdsQhUKBt99+m3cMQkSj4vw2n3zyCQoKCtCiRQvMnTsXM2fO5B2t3jnkZhZCCBEbm5/NQggh5OGozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRASozAkhRARkvAMQcj/mYj3MudkwFxWWf+gLwfR3f15U+fNiPQSJBIJcCUGphCBXQFAqAbkSgkIBQaG860MBiUoDqYdX+YdnI0i0Ot4vmZBHRmVOuGJGI4wZ6TBeuwLDtaswXrsK4/Xyz83ZmVbNIqi1kHp4QebtA6mPP+SN/SHzbQKZrz9kvv4QFEqr5iGkNgTGGOMdgogfMxpRlnIOhovnYLh2BcZrV8qL+2YGYDbxjvdwggCZjx8U7QLLP9p3gqJlOwgyGg8R20BlThqEKT8XZUkJKD17GmVJCSi7eBastJR3rHolKJSQt2wHRftAKNt3gqJdIGTevrxjEQdFZU7qhVlfiNK/TqDk9FGUJhyD4fJFwAHfWhJ3TyjvjNxVXXtC0bw170jEQVCZk0dmuJoK/YFdKDkeh7KLSYDJDjaXWJmscROowyOh7hEBRfsgCBKaQEYaBpU5qRXD9TQUx+6C/sDu8tE3qTGJqwfUYX2gfiwSquDuEORy3pGIiFCZk4cy3sqA/sBu6GN3w3AxiXccURDUWqi6PgZNj0iouvWEREPTIkndUJmTapmybkN/cA/0sbtQdv6MQ27/thqZHOrHIuE0ZBSUgSG80xA7RWVOLBhjKDkSi4Kf1qH0r2OA2cw7ksORN28N3ZBoaB4fDIlawzsOsSNU5gTmkhIU7fkZhT/9AOO1q7zjEACCRgttv6HQDXkG8ibNecchdoDK3IGZsjNR8PM6FO3cDHN+Hu845D6Uwd2gGxINdXgEBCkdpESqR2XugMouJaNg6/fQ/7ELMBp4xyE1JPXwhnbQSDgNHQWJkzPvOMTGUJk7CMYYSo7+iYKt36P09FHecUgdCFodnIaPgdOI5+jkYMSCytwBlJw+htyvP4Uh5TzvKKQeSXTO0D31LJyGjYFEo+Udh3BGZS5ihvTLyP16KUriD/COQhqQxNkFTs9MhNOToyDIFbzjEE6ozEXIlJeL/LVfoXDHZjrE3oFIvX3hMm4qNI8PgiAIvOMQK6MyFxFmKEPBth+Qv34VWFEh7ziEE3mr9nCd9BpUnbvzjkKsiMpcJPR//Ibcbz+H6eZ13lGIjVCF9YHby7Mh8/TmHYVYAZW5nStNSkDuisXlh9wTUoWg1cF14mvQRj1Fm15EjsrcTplLSpD3zTIU/rKezptCHkoZ1BXur82DzNefdxTSQKjM7VDJX8eRs3QhjBnpvKMQOyIoVXAe+w84DR8DQSrlHYfUMypzO2Iu1pePxn/dSKNx8sgUbTvC/Z/zIW/WincUUo+ozO1EaVICsj5+C6Yb13hHIWIgk8N51AQ4j5pEF8kQCSpzG8dMJuT/+H/I/3GlfVzFntgVebNWcJ+xAIpW7XhHIXVEZW7DjBnpyPr4LZSd+4t3FCJiglIJt1fmQNtvKO8opA6ozG1U0f6dyPnsfbBiPe8oxEHohjwD1xdnQJDRaXbtEZW5jWGMIe/bz1Gw4RveUYgDUrQPgufcDyH18OIdhdQSlbkNMRfrkfXRPJQcieUdhTgwiasHPOb8F6rAUN5RSC1QmdsI441ryFzwBgxXUnhHIQSQSuE66XU4jXiWdxJSQ1TmNqDkzAlkvfdvmPNzeUchpBJNnwFwe/0tSFRq3lHIQ1CZc1a4cwty/vchYDTyjkJIteTNWsFj3seQN27COwp5ACpzTpjJhNwVi1H48zreUQh5KImLG7wWLoOiVXveUch9UJlzYC4sQOYHs1F68gjvKITUmKDVweudpVB2COYdhVSDytzKTHm5uP3mVBhSL/COQkitCSo1POd9DFVIGO8opAoqcysy5eXg9typMFy+yDsKIY9OroDHrPeh6RHJOwm5C5W5lZhys3Fr7j9gvHKJdxRC6k4qhfv0d6B9fBDvJOQOKnMrMOVk4dbcqTBepSInIiIIcJs6C7oh0byTEFCZNzhTdmZ5kael8o5CSINwmTANzs9M4B3D4VGZNyBTdiZuzXkJxvQrvKMQ0qCcRk2E6/hXeMdwaBLeAcTKlHUbt2a/SEVOHELB+lXI+/4r3jEcGpV5AzBm3sKt2S/BeO0q7yiEWE3+2hXllzQkXFCZ1zNTfi5uz/kHjNepyInjyflyEfQH9vCO4ZCozOsRMxqR9f6/qciJ4zKbkfXxWyg5fZR3EodDZV6Pcj57H6V/neAdgxC+jAZkLvwXyi4l807iUKjM60n+ptUo2v0T7xiE2ARWXITM/0yHKes27ygOg8q8HhTH7UfeN5/xjkGITTFl3sTt/0yHuaSYdxSHQGVeR2Up55H18VuA2cw7CiE2x5ByDlkfzgWj348GR2VeB6bsTGQufAOMRh6E3FdJ/AHk/t+nvGOIHpX5I2JlpchcOAOm2zd5RyHE5hVu+wH62F28Y4galfkjYIwha/E7KEtO5B2FELuRvew9GG9c4x1DtKjMH0HBhm9QfGA37xiE2BWmL0LWojfB6Hq3DYLKvJZKz51B3poveccgxC6VnT+DvNWf844hSlTmtWDWFyLrozcBk4l3FELsVsHmNSg+Hsc7huhQmddCzucfwkTb/AipG8aQvfhtmLIzeScRFSrzGir6fTv0+3fwjkGIKJhzs5H1yXzQ5RTqD5V5DRhv3UDO/z7kHYMQUSk9FY+CDd/wjiEaVOYPwRhD9tIFYPoi3lEIEZ28NV+iNCmBdwxRoDJ/iKLtm1B6Kp53DELEyWRC1kfzYC4p4Z3E7lGZP4DxxjXkrorhHYMQUTPdvI78dV/zjmH3qMzvgzGG7CX/ASvW845CiOgVbF4DwzW6Xm5dUJnfR9Gen+lCE4RYi9GAnP8t4p3CrlGZV8NcrEfet3SUGiHWVHryCF0/tA6ozKuRv34VzDlZvGMQ4nBy/+9TmGnT5iOhMq/CePM6Creu5R2DEIdkyryJ/B9W8I5hl6jMq8hdGQNWVso7BiEOq2DbDzBcTeUdw+5Qmd+l9MxJFB+kbXaEcGU00hHXj4DK/A5mNiNn+Se8YxBCAJQmHEPR/p28Y9gVKvM7ivb+AkPKOd4xCCF35K1cCnMpHRlaU1TmuDMVcfUXvGMQQu5iyrqNot+28o5hN6jMcWcqIp1bmRCbU7DpOzCDgXcMu+DwZW7KyaKpiITYKFPmTRTt+Zl3DLvg8GVe+PM6mopIiA3L3/AtmIkuAv0wDl3m5pJiFG7fxDsGIeQBTDevQb+PZrY8jEOXedGubTAX5PGOQQh5iPwNq8DMZt4xbJrDljkzmVBA28oJsQvG9CsoPrCbdwyb5rBlXvznXphuXucdgxBSQ/nrV9EFoB/AYcs8f9N3vCMQQmrBcPkiig//wTuGzXLIMi85fQyGi0m8YxBCain/R7q83P04ZJkXbKZROSH2yHAxCSV/HecdwyY5XJkbrqSg5Pgh3jEIIY+oaOdW3hFsksOVef7mNQDtRCHEbhUf+h0mmlJ8D4cqc7O+EMWxu3jHIITUASsrhf737bxj2ByHKvPiP/fRofuEiEDhb1t4R7A5DlXmRX/QIcGEiIHxyiWUJiXwjmFTHKbMTTlZKD19jHcMQkg9Kfr9V94RbIrDlLn+wG7AbOIdgxBST4oP7KFznd/Fccr8j994RyCE1CNzQR6Kj/3JO4bNcIgyN964hrJzf/GOQQipZ/p9NKulgkOUuZ6u8k2IKBXHH4S5sIB3DJvgEGVeRJtYCBEnQxmK4w/wTmETbKLMP/roI3Tu3BmdO3dGYGAgpFIpsrOzAQA6na5O6y67lAzj1Uv1EZMQYoNKjsfxjmATBFYPJwg2mUyQSqX1kQc///wzPv30U/z+++8Aysu8sLDwkdeXu2oZCjZ+Wy/ZCCG2R+Lihsbf74IgCLyjcFWjkfmIESPQpUsXdOzYEcuXLwdQXrIzZsxAcHAw4uLioNPpMHPmTHTs2BH9+/dHfHw8IiMj0bJlS/z0008AgPDwcCQmJlrWGxkZiWPHKs/9/uGHHzBmzJj6en0ojttXb+sihNgec14ODCnnecfgrkZlvnLlShw/fhzHjh1DTEwMsrKyUFRUhLCwMJw+fRq9evVCUVER+vbti8TERDg5OWHevHnYvXs3tmzZgvnz5wMARo8ejfXr1wMAMjIykJGRga5du1qeR6/XY+fOnXj66afr5cUZb92A8drVelkXIcR2lZygTS01KvOYmBgEBwcjPDwcaWlpuHDhAqRSaaXSVSgUiIqKAgB06tQJERERkMvl6NSpEy5fvgwAGDVqFDZu3AgAWL9+PaKjoys9z88//4yePXvC3d29Pl4bSk4erpf1EEJsG5V5Dcp8//792LNnD+Li4nD69GmEhISgpKQEKpWq0nZyuVxu2WYlkUigVCotnxuNRgCAn58fPDw8kJCQgHXr1mH06NGVnuvHH3+s100spafi621dhBDbVZqUALO+iHcMrh5a5nl5eXBzc4NGo8G5c+dw+HDdRrujR4/GokWLkJeXh6CgoErP88cff2D48OF1Wn8FxhhKTh+tl3URQmyc0YhSB/99f2iZR0VFwWg0IiAgALNnz0Z4eHidnjA6Oho//vgjRo0aVen+LVu2YMCAAdBqtXVafwXDpWSY83LqZV2EENtXfMKxN6vWy9REW1SwdS1yVyzmHYMQYiVSHz80/nob7xjc2MRBQw2h9Owp3hEIIVZkunENhutpvGNwI94yTzzNOwIhxMpKTx3hHYEbUZa54dpVmHOzeMcghFhZ2UXHPXhIlGVemniSdwRCCAeGyxd4R+BGlGVeRtcGJMQhGS5fBDObecfgQpRlbriSwjsCIYQDVloCY4Zj7gQVZ5mnX+YdgRDCieGSY25qEV2Zm7IzwYoe/ZS5hBD7ZkhN5h2BC9GVuSHtMu8IhBCOylJpZC4KhrRU3hEIIRwZqMzFwUjbywlxaKbbN2AuyOcdw+pEV+a085MQUuaA881FV+ZG2mZOiMNzxE0toipzc7EepsybvGMQQjgzOuAJt0RV5rS9nBACAKYcxzs3k6jKnKYlEkIAwJybzTuC1YmqzI03r/OOQAixASYHPGuqqMrcXJDHOwIhxAaYcmhkbtfMhQW8IxBCbAArKgAzlPGOYVUiK3PHO1CAEFI9Rxudi6rM6QRbhJAKjrbdXFRlTiNzQkgFR5vRIrIyp23mhJByjjbXXGRlTiNzQkg5M5W5fWJGI1hJMe8YhBAbYaLNLPaJRuWEkLux0hLeEayKypwQIkrMZOIdwapEVOa085MQchcqcztlNPJOQAixIczsWJ0gnjKXyXknIITYEgcbmct4B6gvglw0L4XUUX7TQJh9AgBB4B2FcGTy84cn7xBWJJoGFGhkTu7QXjuP+La9cKFRV3hJNdCWSVCcY4DJxHhHI1bU3N0JzXmHsCLRlDnkCt4JiI2QmgzosedLdFE7YWffl7DC4AuTiaGruwvaqjVwNkpRkmOA0UDlLmaO9neZaMqcRuakKkVxAYb9+jH6uvlgW58X8VM2w0FTDgBAJhEQ6uaMAK0WbiYZynKNKCs1c05M6pMgcaw6FxhjohiemLIzcX1cFO8YxIZl+rfHhu7PY2+ODOYq73oBQJCbEwJ1OngyOYx5JpQWO9YONLFp3d4Z/Qb5845hNeIp84I8XP9//XjHIHYgvW04vg98GkeyH/zWb++iQ7CzDo2gAMs3o7jIsaa62buATm7o09+XdwyrEU2Zm4v1uBbdh3cMYkfOhQ7Cd02fQFJuzUq6hU6NEFdnNBYUkBQx6POp3G1ZcFcPhPduxDuG1YimzJnRiPTh4bxjEDt0rNezWOPWDVcLDLV6XGONEl3dXNBEqoRMDxTl1u7xpGF16+mF0O5evGNYjWjKHADShnYDxPNyiBWZpHLE9p2MtbK2yCp+tBG3h1KObu6uaC5XQVVSXu6M9qly0/NxHwR2ducdw2rEVeYjHgMc7CKupH6Vapzx2+MvYYPBB0VlddsB6iyTorunK1oq1NDQXHerezyqMdoGuPKOYTWiKvPrzw+CKes27xhEBArdfLE14kX8XKCDoZ4KWC2ToKu7C9qotXAuk5TPdTeK5tfP5kQNb4JmLZ14x7AaUZX5jX8+D8OFs7xjEBEpn844HntzpPdMZ6wrmURAF3cXtNdo4WaSojTHCEMZbZepL8OeaQZffy3vGFYjmoOGAEDq5gHaBUXqk2f6OUxNn4MnazidsTaMZoYjmbk4glwAgEQAglydEeikg4dZRnPd60ipkvKOYFXiOWsiAKmH4+y5Jtbln3wYszbPxPv5+xDg2jBjIDMDTuXkY83V61iafhWfF1zDn9pCFPgK0PgqoNY6VjnVlVZnG0eFv/POO/j444+r/Vpubi6io6PRvn17BAQEIC4urtLXP/nkEwiCgMzMzIc+j7hG5u6OdI40wkP7Ezvw3okdONbrWXzn1g1ptZzOWFtJeYVIyiu03G7lpEGIizN8BQWEQjP0BTTXvTpyucTqI3OTyQSptHbP+frrryMqKgobN25EWVkZ9Hq95WtpaWnYtWsXmjZtWqN1iWxk7s07AnEQXQ+uxeLt/8Y0eSo81NYbE6UU6LEx/QaWpV1FTE46dihycKuRGcrGcmhdbGMkagu0ukf/mYwYMQJdunRBx44dsXz5cgDA1KlT0bVrV3Ts2BFvv/22ZdnmzZtj1qxZCA0NxYYNG7Bz506EhoYiODgY/fr9fUT62bNnERkZiZYtWyImJgYAkJeXh9jYWEyePBkAoFAo4OrqannM9OnTsWjRIgg1PJWzqEbmMm8f3hGIA5GaDOi7+3/oqXHGb33/gfWl3tAbrLsD85q+FNf0tyy3vVQKdHNzQTO5CsoSoCjH4JCHXuicHv0/tpUrV8Ld3R3FxcXo1q0bnn76abz33ntwd3eHyWRCv379kJCQgKCgIACAh4cHTpw4gdu3byM0NBSxsbFo0aIFsrOzLes8d+4c9u3bh4KCArRr1w5Tp05FamoqvLy8MHHiRJw+fRpdunTB0qVLodVqsW3bNvj5+SE4OLjGucU1Mm/kxzsCcUBKfT6G/bIIXx77BCNdiyCX8jtb3+2SMmzPuI3/XU3DkltpWI9MXPY0QOIng85DAYmDnEnQqQ5/pcTExCA4OBjh4eFIS0vDhQsXsH79eoSGhiIkJASJiYk4e/bvWXOjR48GABw+fBh9+vRBixYtAADu7n8fsDRkyBAolUp4enrC29sbN2/ehNFoxIkTJzB16lScPHkSWq0WH3zwAfR6Pd5//30sWLCgVrlFNjL3BSQSwEzTu4j16XIyMHbrfxDlH4D13Z/H3mwpeA+K8wxG7LmRZbmtkUnQzd0VbVQa6Azinevu5Pxo1zfYv38/9uzZg7i4OGg0GkRGRiIpKQkff/wxjh49Cjc3N0yYMAElJSWWx2i1D5/+qFQqLZ9LpVIYjUb4+/vD398fYWFhAIDo6Gh88MEHSElJQWpqqmVUnp6ejtDQUMTHx8PH5/5bH0Q1MhdkMtpuTrjzTE/Cy5vnYEnGZnR3t61fMb3RjD9uZeP/rqZjScZVfF12A4muJTA2lkLnrYBcYVt5H9Wjjszz8vLg5uYGjUaDc+fO4fDhw8jPz4dWq4WLiwtu3ryJHTt2VPvY8PBwxMbGIjU1FQAqbWapjo+PD5o0aYLz588DAPbu3YsOHTqgU6dOuHXrFi5fvozLly/D398fJ06ceGCRAyIbmQOAzMcPpts3eMcgBE3OH8bs84dxrstgrG7SH+dqeHZGazKYGeIycxF311z3zm7O6KjTwd0kgzHPiNIS+/tL19VN+fCFqhEVFYUvv/wSAQEBaNeuHcLDwxEcHIyQkBC0b98eTZo0Qc+ePat9rJeXF5YvX46RI0fCbDbD29sbu3fvfuDzLVu2DM899xzKysrQsmVLrFq16pFyAyI7AhQAsmPeRdFvW3nHIKQSJgjl0xlduyK9gacz1icBQEdXHTo5OcGLyWHON6FEb9sHMkkkwKRpAZBy3HfBg+hG5opW7VHEOwQhVQiModuB7xEqXY8/+r6AtbLWyH7EszNaEwNwJrcQZ3L/nuve+s5cdx8bnevu4qZ0uCIHxFjmbTvwjkDIfZVPZ/wCPTXO2Nn3H9jAYTpjXV0s0ONiwd8HtzTVqhDq6gw/qRKyIqAoj+9fHh5ej7aJxd6JbjMLMxqRHt2HToVL7EKBR2Ns7f0ifinQ1tvZGXnzvjPXvalcBWXxnfO6W/GlhfXyRudujnc0uOjKHABuTh+PsuRE3jEIqbFMG5rOWN9c5DKEebiihVIFdYkE+pyyBp09PGhEEzRt4Tinvq0gyjLP+d+HKPxlA+8YhNRaWrtwrOkYjaPZ9rXppTa0Mgm6ebiitVINJ4MU+hwDTPU4133sC21s5iRb1iTKMi/a+wuyF7/DOwYhjyypy1B816SvTU5nrG9KqYAubq5op9HAxShFae6jn9ddpZJi/NR29ZzQPoiyzA1XU3Fj6jO8YxBSJ0wQcLT3c1jj0sWupjPWlUwQEOzujA5aLdxNMhjyjCir4Vz3pi10GDSiZmcZFBtRljkzm3Ft9ONgepqkSOyfUa5A7OMvYK20lV1MZ6xvAoBANyd00ungxeQwPWCue/ee3gjp7ng7PwGRljkA3JrzD5QmHOMdg5B6U6J1Lb/YdKmX3U1nrG9tnbUIdnaCj6AACswoLiz/T87RLhV3N9GWee7KGBRsWs07BiH1TozTGeuqqU6FMDdXvBHdHjKZOM4vU1uifdV08BARK6es6xi39R18lrwS/dxNcLxjHe91tbAEF1mJwxY5IOIyV3bqUn6SBkJEyistCa9snoNPb2xFNxs7OyMP3Zq68Y7AlWjfAVIXNyjaBvKOQUiDa3ruEOZs/hfeLYxFO1fHm19doUtTV94RuBJtmQOAOqwP7wiEWE2HY7/g/W1vYLY5AX4OdtCMSi5Bp8YuvGNwJe4yD6cyJ45FYAzdY9fg099mYZriCtyseLFpnsKbe0AuFXWdPZSoX728aUvIGjfhHYMQq5MZytB31+f4PHYBntdlQSMX9a86Ils75tzyu4n7JwxA1b037wiEcKMqysWIXz7EFyeXYISrHjIRXtBZKhHQqxWVuejLnLabEwI4Z6bj+a3v4POL36CvyKYzdvZzgYvasfYRVEf0Za7s2BkSJ8feMUJIBa+riZi2eQ4W39iGriKZzhjRxot3BJsgjp/mAwhSGVRdHuMdgxCb0uzcn5grkumMtL28nOjLHKBZLYTcT8V0xll2Op2xrbcOvi5q3jFsgkOUuapLD0Bmf29UQqxBYAxhd6YzvmJn0xkjaROLhUOUuUSjgyokjHcMQmyazFCGfnemM46zg+mMAoDBHXx4x7AZtv3Tqke6qKd4RyDELqiKcvHUnemMw12LbXY6Y2d/V/i50iaWCg5T5qpuvSD1asQ7BiF2wzkzHeO3vn1nOqPZ5qYzDg2kUfndHKbMBakU2gEjeMcgxO6UT2ecjcU3f0IXdynvOADKz8XSr5037xg2xWHKHAB0A0cAUtt4MxJib5olHcSbm2fg3aID3KczPtGuEbQK291Ru3//fnTu3BkdO3ZERESE5X6dTtdgzynaKw3dT+a7M1Ect493DELsGhMExPceizXOobhWaP2LTX/9bBcE+TXMwYAmkwnSOgz6cnNz8dhjj2Hnzp1o2rQpbt26BW/v8r8idDodCgsL6ytqJQ41MgcA3eCneUcgxO6VT2f8Dp/+NgsvK67CTWW9UXIrT22dinzEiBHo0qULOnbsiOXLlwMoL9kZM2YgODgYcXFx0Ol0mDlzJjp27Ij+/fsjPj4ekZGRaNmyJX766ScAQHh4OBITEy3rjYyMxLFjx7B27VqMHDkSTZs2BQBLkTc0hytzZUgYZL7+vGMQIgoyQxn67/oMnx9YiHFO2VaZzjgy2K9Oj1+5ciWOHz+OY8eOISYmBllZWSgqKkJYWBhOnz6NXr16oaioCH379kViYiKcnJwwb9487N69G1u2bMH8+fMBAKNHj8b69esBABkZGcjIyEDXrl2RnJyMnJwcREZGokuXLli92jrXIna4MhcEAVqapkhIvVIV5eCpnz/AFyeXYrhbw01ndFXLMayTb53WERMTg+DgYISHhyMtLQ0XLlyAVCrF00///Ve7QqFAVFQUAKBTp06IiIiAXC5Hp06dcPnyZQDAqFGjsHHjRgDA+vXrER0dDQAwGo04fvw4fv31V/z2229YuHAhkpOT65S5JhyuzAFA+8RwOiKUkAbgnJmG8Vvexmcp3zbIdMZnQvygkj/69uz9+/djz549iIuLw+nTpxESEoKSkhKoVKpK28nlcjkEoTy9RCKBUqm0fG40GgEAfn5+8PDwQEJCAtatW4fRo0cDAPz9/TFw4EBotVp4enqiT58+OH369CNnrimHLHOpiys0PfvyjkGIaHlfOVPv0xmVMglGhdRtE2leXh7c3Nyg0Whw7tw5HD58uE7rGz16NBYtWoS8vDwEBQUBAIYPH46DBw/CaDRCr9fjyJEjCAgIqNPz1IRDljkA6IaN4R2BENGrmM64sOgA2rrU7a/hJwN94apR1GkdUVFRMBqNCAgIwOzZsxEeHl6n9UVHR+PHH3/EqFGjLPcFBAQgKioKQUFB6N69O6ZMmYLAwIa/uLzDTU282+23pqHkRN3+ZyaE1AwTBMT3GYfvnEJwvZbTGaWCgI2Tw+DvpmmgdPbPYUfmAOA85gXeEQhxGAJjCPtjNT79bQ6mKms3nfHxtl5U5A/h0GWu7BAMJZ1NkRCrkhtK8MRvtZvOOLZbUysks28OXeYA4PIsjc4J4aGm0xl7tvRAR19nK6ezPw69zbzCrXmvoPTkEd4xCHFot5oFYl2XsdifLUFFKQkAvnu+G9o1cuIZzS5QmQMou3AWN6ePB+hbQQh3lzv0xpr2I3Ai24Qn2nvj/ScbfiaIGFCZ35H539koPriHdwxCyB2J4U+hw8uvo6lHw51pUEwcfpt5BZfnX6bT4xJiQ8I8ZVTktUBlfofcrym0A4bzjkEIASBodXB+9iXeMewKlfldXJ59EYKKrilICG/OoyZC6uLKO4ZdoTK/i9TdE87P0WiAEJ6kjRrDaTidbqO2qMyrcBo+BvLWDX9SHEJI9VwnTIMgr9s5WBwRlXkVglQK99fepJ2hhHCg6toTmj4DeMewS1Tm1VC0ag+nEc/yjkGIQxG0Ori9+ibvGHaLyvw+nJ97CVKful2eihBSc65TpkPmaZ3rZYoRlfl9SJQquE+byzsGIQ5BFdoDOpoaXCdU5g+gCgmDpt8Q3jEIETVBo4Xba7R5pa6ozB/Cdcp0SFzceMcgRLRcJ70OmZcP7xh2j8r8IaTOrnB94Q3eMQgRJWVwd+gGjeQdQxSozGtA+/ggqLo+xjsGIaIiqDVw/+dbvGOIBpV5Dbm/8R9IPWhPOyH1xWXCq5B5+/KOIRpU5jUkdXGDx+z/ArKaX7eQEFI9ZXB36IZE844hKlTmtaDsEAzXSa/zjkGIXZN6+cBj1nsQhOovE0ceDZV5LTkNHwN1nyd4xyDELgkKJTznfQwpzRCrd1Tmj8D9tbcga9KCdwxC7I7bq3OhaN2edwxRojJ/BBK1Bp5zF0FQa3hHIcRu6IaPgbYvHYTXUKjMH5G8aQu400mBCKkRZacucJ1M+5saEpV5HWgiBkL35GjeMQixaVIvH3jM+QCClGaCNSQq8zpynTIdivadeMcgxCbRDk/roTKvI0Emg+ebH0Hm6887CiE2h3Z4Wg+VeT2QunvC693PIfXw4h2FEJtBOzyti8q8nsh8/OC5YBkkOmfeUQjhThM5CK5TpvOO4VCozOuRonlreP5nKQSVmncUQrhR9+wH9zfegSCherEm+m7XM2X7TvCc9xEgk/OOQojVqbr1gse/34NAF0S3OirzBqAKCYfHzIUAjUyIA1F27g7PNxdBoJPRcUFt00A0vfrDja4hShyEsmMIPN9aDEGu4B3FYVGZNyDdwBFwmTCNdwxCGpSibUd4vrMEEpWKdxSHRmXewJyfmQCnZybwjkFIg5C3aAuvBcsg0Wh5R3F4AmOM8Q7hCPJ++D/kr/mSdwxC6o2sSQt4f7icju60EVTmVlTwywbkfrkIoG85sXPy5q3htfAzSN09eUchd1CZW1nR/p3I/vQdwGjkHYWQR6LsFArPtxZDotXxjkLuQmXOQfHRg8j672yw0hLeUQipFXWv/vD41wKatWKDqMw5KU1OROZ/psOcm807CiE1ohv2/+D6wht0ZKeNojLnyHjjGm6//RqM6Vd4RyHk/iQSuEx8Dc4jx/JOQh6AypwzU0Eeshb+C6WJJ3lHIeQegloDj5nvQh3Wh3cU8hBU5jaAGcqQvWQB9Pt38o5CiIXUyweeb38KRYs2vKOQGqAytyGFOzYhd/lisLJS3lGIg1O0C4TnW59A6ubBOwqpISpzG1N2KRlZH8yG8dpV3lGIg9INHQXXya9DUCh5RyG1QGVug8zFeuR89j5tdiFWJXFxg/s/34a6ey/eUcgjoDK3YYU7tyB3+cdgpbTZhTQsZUgYPN74Dx3RaceozG1c2eWLyPrvLJq+SBqGTAaX51+B08ixEASBdxpSB1TmdsBcUoycz/8L/e/beUchIiLzawqPme9B0SaAdxRSD6jM7Ujh7p+Q+9UnYMVFvKMQO6d9Yhhc/zETErperWhQmdsZU9Zt5Cz/BMUH9/COQuyQoHWC+6tzoen9BO8opJ5Rmdup4mOHkPvlIhgz0nlHIfZAEKDpOwSuE6bRTk6RojK3Y6ysFPnrViF/47eA0cA7DrFR8tYBcPvHTCgDgnhHIQ2IylwEDOmXkfPFhyg9fZR3FGJDJM4ucHn+FWgHjqAzHToAKnMRKdq/E7krPoU5N4t3FMKTRArdoJFwGTcVEidn3mmIlVCZi4y5sAB5q79A4Y7NgNnEOw6xMkXHznD7x7+haNmWdxRiZVTmImW4dhX5676Gft9OKnUHIPXwgsvE16B9fBDvKIQTKnORM1xPQ/66ldDv2w6YqNTFRurlA6enx0E7YDgkShXvOIQjKnMHYcxIR/66lSj6/VcqdRGQ+TeH8zMToImMgiCT8Y5DbACVuYMx3rj2d6kbjbzjkFqStw6A86gJUPd4nGaokEqozB2U8eZ15K9bhaK9P1Op2wFlp1A4j5oEVWg47yjERlGZOzhj5k0U7diCot3bYMq6zTsOqULVvTecR02kA37IQ1GZEwAAMxlRfDgWhTs2ofRUPEBvC24k7p7Q9hsK7RPDIPdryjsOsRNU5uQexox0FO7aBv2+7TDdvsk7jmOQSqHu3hvaJ4ZB1bUnBKmUdyJiZ6jMyX0xsxmlCcdQtPcXFP/5O1hpCe9IoqNoFwhNZBQ0fQZA6urOOw6xY1TmpEbMxXoUH/odxfEHUXLyMFhRIe9IdkvWpAU0kQOhjYiCzNefdxwiElTmpNaYyYjSpASUHDuEkmN/wpB6gXckmyaoNVB26gJV5+5QhYRB3rQl70hEhKjMSZ0ZM2+h5Hh5sZecigfTO/iVkCRSKNp2gKpzGFQhYVC070QH9pAGR2VO6hUzGlF69hRKjh1CadJpGFIvgBXrecdqcLLGTaEKCYMyJAyqoK6QaHW8IxEHQ2VOGhQzm2HMSIMh5TzKUs6jLOUcDCnnYc7P5R3t0UilkPk3g7xZKyiatYa8WSvIW7eHzMuHdzLi4KjMCRfGzJuWgjdcOo+y1AvlBy0ZynhHKycIkHr7lpd189Z//+vXDIJczjsdIfegMic2xVSQB1PWbZhzMmHKyoQpOxOm7Nt3PjItH49a+oLWCVIXN0hcXO/8W/4hdXb9+3MXN8gaN4FEo63nV0dIw6EyJ3bJXFIMZigDDAYwoxHMaACMBjCzGYIgAIIEEARAUv65RKWGxNmVdkQS0aIyJ4QQEaBzaBJCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAhQmRNCiAj8f9xo+vlko5fPAAAAAElFTkSuQmCC\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('debian-arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>36546216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.7</td>\n",
       "      <td>12162558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.8</td>\n",
       "      <td>2718211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1175685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1136274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.6</td>\n",
       "      <td>372050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pyver         c\n",
       "0   3.7  36546216\n",
       "1   2.7  12162558\n",
       "2   3.8   2718211\n",
       "3   3.9   1175685\n",
       "4   3.5   1136274\n",
       "5   3.6    372050"
      ]
     },
     "execution_count": 108,
     "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 >= '{start_time}' AND accessed_at < '{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",
    "    and py_name = 'CPython'\n",
    "    group by pyver\n",
    "    order by c desc\n",
    "    limit 6\n",
    "\"\"\", conn)\n",
    "\n",
    "py_vers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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('py-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7 67.54%\n",
      "2.7 22.48%\n",
      "3.8 5.02%\n",
      "3.9 2.17%\n",
      "3.5 2.10%\n",
      "3.6 0.69%\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": 61,
   "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>21.0.1</td>\n",
       "      <td>24298687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.1</td>\n",
       "      <td>15748760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.3.3</td>\n",
       "      <td>3674342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2437556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.1.1</td>\n",
       "      <td>2323747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.3.1</td>\n",
       "      <td>1942434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.0.1</td>\n",
       "      <td>1360243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0.1</td>\n",
       "      <td>1288761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.3.4</td>\n",
       "      <td>1209638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.2.4</td>\n",
       "      <td>1062385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  installer_version         c\n",
       "0            21.0.1  24298687\n",
       "1              18.1  15748760\n",
       "2            20.3.3   3674342\n",
       "3              21.0   2437556\n",
       "4            20.1.1   2323747\n",
       "5            19.3.1   1942434\n",
       "6            19.0.1   1360243\n",
       "7             9.0.1   1288761\n",
       "8            20.3.4   1209638\n",
       "9            20.2.4   1062385"
      ]
     },
     "execution_count": 61,
     "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 >= '{start_time}' AND accessed_at < '{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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kyaAd4oQQuL/9isZ1r+H86hPw+UJd0rBjGj0e+8lnEHHSaRhi40NdjhQCMmiHKK22mqZ336Dx3TVoZXJLlEHBYMA661giT12G7cjjQl2NNIBk0A4xvvISGl55lqb330R43KEuR+qEaewEos5eif34BSgGQ6jLkYJMBu0Q4S3Mo/6lp3H8b53sHggjxtQMopZfRMTJZ8ibZ0OYDNow59m/m/qX/onz8/Wg66EuR+ojNT6RqDMvIPK05ag2e6jLkQJMBm2Ycu/cSv2LT+Ha/FmoS5ECSI2MJnLxuUQuOQ9DTGyoy5ECRAZtmPHs3Untkw/h/u7rUJciBZFitRG1/GKill8kVxgbAmTQhgmtpoq6Zx6h6YP/yplbw4hhRAqxP70W+9yFoS5F6gcZtIOc8PloePMF6p9/Qq47MIyZc6YT97MbMGdODnUpUh/IoB3EnJs/o/aJP+MrPBjqUqTBQFWJmL+YmJXXYIhLCHU1Ui/IoB2EvEX51D7xZ1ybNoS6FGkQUmwRRP/kUqLOvECukxsmZNAOIrrbRf2/H6dhzfPg84a6HGmQM6ZmEHft77BOmxXqUqRuyKAdJNx7dlD9wG2ym0DqHUUhcukKYldeI7fgGcRk0IaY0DTqX3yK+hf+AZoW6nKkMGUcNY6EX98lb5YNUjJoQ8hblE/1A7fh2b091KVIQ4HRSPR5lxN97qVy/YRBRgZtiDS+/Qq1Tz6EcDlDXYo0xJgnTSH+13diSh8d6lKkZjJoB5hWXUn1Q7+XU2eloFIsVmIu/QWRi89FUZRQlzPsyaAdQM4v/0f1Q79Hr68NdSnSMGGddQwJN96DGhkV6lKGNRm0A0AIQf3zT1D/nyfk9FlpwBnTRpF46wOYRo0NdSnDlgzaINNdLqr/fDvOzz4MdSnSMKbYIki48ffYZs8NdSnDkgzaIPKVl1L5++vlRojS4KAoxFx4FdHnXRbqSoYdGbRB4t65hcp7bkKvrQ51KZLUhu34BcRfdzuqVS6/OFBk0AZB43trqHlktZxGKw1aprETSbztAYxJqaEuZViQQRtAQtOoffJBGtc8H+pSJKlbanQsCb/9I9apM0NdypAngzZAdLeLqntvwrX581CXIkk9ZzSRePMfsB19QqgrGdJk0AaA7nRQeeev5PYyUngyGEj49V3Y5y0KdSVDlgzaftIdjVTcdi2eXdtCXYok9Z2qEveLW4hcuDTUlQxJMmj7QW+op+K2/4dnz85QlyJJ/acoxF55A1Fn/CTUlQw5Mmj7SKuroeKWa/AekGNkpaEl5tJfEH32ylCXMaTIoO0DrbqS8luuxpf/Q6hLkaSgiD7vcmIuuirUZQwZMmh7yVdRSsVvr8ZXnB/qUiQpqKLOupDYy68LdRlDgjHUBYQTX3kp5Tf/DK2sONSlSFLQNbz+L1AVYn/6y1CXEvbUUBcQLvTGBipuv1aGrDSsNLz6HA1vvhDqMsKeDNoeEF4PlXf/WvbJSsNS7RN/xrHhg1CXEdZk0HZDCEH1n+/A/d03oS5FkkJD16m6/zbc278NdSVhSwZtN+r++Vccn7wX6jIkKbS8Hip//2u88re6PpFB24WGt16i4dXnQl2GJA0KemM9Fbf9Aq2qItSlhB0ZtJ1wfvExtY/dH+oyJGlQ0SrKqLj9WnRHY6hLCSsyaDvg/v47qu67BXQ91KVI0qDjPbCXyrtvRHjless9JYP2ML7yEirv/BXC7Q51KZI0aLm3bqLm738KdRlhQwbtIYTPR9UfbpbbgUtSDzS9+zpN7/831GWEBRm0h6h9+v/w7NkR6jIkKWzU/H01Hrn5aLdk0DZzfvUJja//O9RlSFJYEW43VffehN7YEOpSBjUZtPjXMKj+y52hLkOSwpKvpJDqB+XPT1eGfdAKzUfVH3+L3lAX6lIkKWw5v/iYhrdeCnUZg9awD9q6Zx7B873chkaS+qvuyYfwHNgb6jIGpWEdtM5NG2h47V+hLkOShgThcVO1+jfoLmeoSxl0hm3Q+irLqP7z7SDXPZekgPEV5lH7+J9DXcagM2yDtuah36PXy35ZSQq0pndfx7V1c6jLGFSGZdA2ffAWrm++DHUZkjRk1Tx8L8IjZ1e2GHZBq9VWU/uPv4S6DEka0nzF+dS/8GSoyxg0hl3Q1jx6nxzKJUkDoP7VZ/Ee3B/qMgaFYRW03+4t5kBheajLkKThweej+q/3IDfaHkZB6/Jq3PFRHteNu5DXF6/Ca7aFuiRJGvI832+jce0roS4j5BQxTN5u/r7hB576Iq/141FRJq4uXMfEb98NXVGSNAwo9ghSH30FQ8KIUJcSMsOiRVtQ4+BfG/PbPJbf4OU3MfN5/Kx7aIpNDlFlkjT0CUfTsF+7dlgE7Z8+2INHa79bggDW1Zi4ds4qPj/x0oEvTJKGCecXH+Hc+GmoywiZIR+06/eU82VedZevqXH5uF9kce+y+ykfPWWAKpOk4aXumUcQw3R7qCEdtD5N56GP9/X49ZurdX6ZeQn/Pf1GfCZzECuTpOHHm7cPx/+G5z2RIR20a3eUUlzn6tUxbk3nn00jWHXqH9mXe3KQKpOk4an+348hfL5QlzHghmzQarrg6a8O9vn4A/VeVsUt5Mmzfo8jOjGAlUnS8OUrKaTpvTdCXcaAG7JBu25nKYW1/VuuTQBrayxce+xv+WruRYEpTJKGufoXnkR39+43zXA3JINWF4J/9qM1e7hqp48/qlP547L7qczICth5JWk40qoqaBxmuzEMyaD94PtyDlY7An7er6p1rp18GWtPux7NYAr4+SVpuGh45Rl0R2OoyxgwQy5ohRA8+WVe0M7v8uk86UjhN4v/xA9TTwzadSRpKNPr62h4dfjsbjLkgvajPRX8UNkU9Ovsq/NyU+KpPH3mXTiiE4J+PUkaahrW/AetrjbUZQyIIRe0wWzNHk4X8GatleuOu4XNx50/YNeVpKFAOB00vfNqqMsYEEMqaP+3r4I95QPf71Pp8HGvcTr3L7uP6rQJA359SQpXjW+/itCG/rjaIRW0Tx6yOlcofF4t+EXOlaw79ZdoqjGktUhSONCqynF+/lGoywi6IRO0nx+oYldpQ6jLwOnVedyZzi1n3Ede9vGhLkeSBr2G/74Y6hKCbsgE7dNfBm7cbCDsqfNyQ/IZ/OvMO3BFxIW6HEkatDw7tuDZvzvUZQTVkAjaghoH3xbWhrqMdnQBr9XauW7erWw55txQlyNJg1bjEG/VDomgXbujNNQldKnc4eMu8yz+vOxP1KSMD3U5kjToOP73Llp9bajLCJqwD1ohBO/sHNxB22JDNfxi6s95f9Ev0FVDqMuRpEFDeNw0vftGqMsImrAP2i1Fdb1eCjGUHF6dv7tH8rsl95E/+ZhQlyNJg0bj2lcQmhbqMoIi7IN27Y6SUJfQJ9/X+vh16pn8Z+ltuO3RoS5HkkJOqyjFtWlDqMsIirAOWrdP48PdFaEuo880Aa/URfKrE+9k25xloS5HkkLO8en7oS4hKMI6aP+3r5JGd/jPKilt8nKHdQ4PLfsjtUmjQ12OJIWM86tPER53qMsIuLAO2rcH+WiD3vpftcK1069l/YKr5c0yaVgSziacmz8PdRkBF7ZBW93k6XZ323DU6NF42DuG25feR9GEI0NdjiQNOOeGD0JdQsCFbdCu21WGpotQlxE0O2p8XDfyXF5acgseW0Soy5GkAePcOPS6D8I2aN8O09EGvaHpghfqY7h+/t3sOHJpqMuRpAEhnI4h130QlkG7r6KR3SFYDjFUihu93BpxLA8v+wP1iSNDXY4kBZ1ziI0+CMug/WB3eahLCIn11QaunXkd/zv5SoSihLocSQoa56YNQ2qn3LAM2s35NaEuIWTq3RoP+cZz55kPUDx+ZqjLkaSgEE4HriHUfRB2QevyauwsrQ91GSG3rcbHdaPP49Ulv8VrkTfLpKHH+cXQWRA87IJ2a1EdXm3ojjboDZ8u+Hd9LDcsvJtdMxeHuhxJCij3d9+EuoSACbug/bpg+HYbdKagwcstUXN5dNm9NMalhrocSQoIrbIMX0lhqMsIiPAL2vzaUJcwaL1XbeQXs29gw/zLQ12KJAWE67uvQ11CQIRV0Do9sn+2O3UujT9rE7nrrAcoHZsb6nIkqV/c24dG90FYBe3Wolp8Q3g2WCBtqdG4btyFvL54FV6zLdTlSFKfDJV+2rAK2s0FtaEuIax4NMFzjQncuOhe9hyxKNTlSFKvaeUl+MqKQ11Gv4VV0H4zjMfP9kd+g5ebY+bz+LJ7aIpNDnU5ktQrQ6FVGzZB6/D42FnWEOoywtq6ahPXzlnF5ydeGupSJKnH3NvD/4ZY2ATtlqK6Ib1a10Cpcfm4X2Rx77L7KR89JdTlSFK3XLJFO3C+lt0GAbW5WueXmZfw39NvxGcyh7ocSeqUVlqEVhW+W1ZBGAXtt4W1oS5hyHFrOv9sGsGqU//IvtyTQ12OJHXKk7cv1CX0S9gE7Q+VTaEuYcg6UO9lVdxCnjzrbhzRiaEuR5La8eX/EOoS+iUsgra8wU2TZ2ju9z5YCGBtjZlrj/0tX829KNTlSFIb3oIDoS6hX8IiaA9UydbsQKl2+vijOpU/LrufyoysUJcjSQB4ZYs2+GTQDryvqnV+Mfky1p72azSDKdTlSMOcryAv1CX0S1gEbV6VI9QlDEtun86TjmRuXvwnfph6YqjLkYYxvbEerboy1GX0WVgE7YFq2aINpf11Xm5KPJWnz7wLR3RCqMuRhqlw7qcNi6A9WC1btKGmC3iz1sp1x93C5uPOD3U50jAUzv20gz5oXV6NqiZPqMuQmlU6fNxrnM79y+6jKn1iqMuRhhFvvmzRBk1p/dDZCXMo+bxacG32z1h36nVoqjHU5UjDgK9AtmiDpkQG7aDl9Oo87kzjljPuIy/7+FCXIw1xvoqyUJfQZ4M/aOtk0A52e+q83JB8Bv868w5cEXGhLkcaovT62lCX0GeDPmiLZYs2LOgCXqu1c928W9lyzLmhLkcagoSjCeH1hrqMPhn0QVtS5wx1CVIvlDt83GWexZ+X/YmalPGhLkcaYrS68FzFb9AHbbUjPN/BhrsN1fCLqT/n/UW/QFcNoS5HGiJ0GbTB4fbKxWTClcOr83f3SH635D7yJx8T6nKkIUCvl0EbFC6fHuoSpH76vtbHr1PP5D9Lb8Ntjw51OVIY0+pqQ11Cnwz6oHX7ZIt2KNAEvFIXya9OvJNtc5aFuhwpTIXryINBH7SyRTu0lDZ5ucM6h4eW/ZHapNGhLkcKM7KPNkhkH+3Q9L9qhV9Mv5b1C6+WN8ukHpOjDoJEtmiHriaPxsOeMdy+9D6KJhwZ6nKkMKA31Ie6hD4Z1EErhMAjg3bI21Hj47qR5/LSklvw2CJCXY40mGm+UFfQJ4M6aN0+HRHqIqQBoemCF+pjuH7+3ew4cmmoy5EGKaGFZ1fioA5a2W0w/BQ3erk14lgeXraa+sSRoS5HGmx0GbQBJ4d2DV/rq1WunXkd/zv5SoSihLocabDQw7PxNagXEnXLFu2wVu/WeIjxrD/zAa7JfxO71oRPsWJS3CgGFaGroCtoug+BAZ9BRUfFpAoUQFV0FDSEECjoKIpAUQUgQAEUUFFQFFDQEboAFAT4w10o6EIgBCgoCP/T4D8Diup/TGl9wn+MAq3nUhT/iwWgCBCK/1zoqv/lzX1jzZfzt3x0WrvMlOYL6vhr1YX/6uLHKvzn5ce6Du9uE82P/nhO0VqTaPmElB8/t5bztHmAlvpaHlNaXymaHxVKy3UUhOKvq+VjRRH+ryEKh17SfwrR5ko/1glqc90tz9vSRjOC8DOog9Ylh3ZJgMNnxjsvl+KmGCLMbgyqG69mRfcZEDpo6OhGA6qiYELDIzQEOqgCXdNQVQVVMQCqPzwV0DUVn1Dw6Sq63hyQQkERzaEhmoNDCITQQQVdF/60VAFFRVOU5lP6w0Oo/j86gCL8L1MEqiJQFN3/MTqq0hJx/j+q6r8XoaLhfxMQLXGPorREp46iKP6/W+JRoflN4sfz+Y9t0RKXzQ0W5cfAU5uDVRHNbwY0h5kimt84aH2T8J9bAdFSQwsdxf/O0RzezW9gzcHZErRKyxuKoDU2hQBdwf8fpfUyzW9mKjQ/3/LZtxyvRyX199spJAZ10MoRBxLAqXGJmLUtxMY2sndvAuWRUxhrO0h8zEEMES7sxKHU6pirfTS4IyjTLFRrJtyKFZ8lDq/RggcVE16MwoMFNxEGN3aTB6vJidngBoMbXfUgVA8YNAwGMBuMmA1GjBgwGQwYMKJiRPgUhE8HrwBNA58ArwY+HdWro/oEqk9HEyqabsAnVHzC/7cXFa9Q8AgFD+ARCi5U/8dCxYuCV4AHBU2ooBrQVBOoBoRqRCgGUAwI1YBQDAhFbX5Mxd/+88e5UJr/FiqKotD8NoOhufVu8D/rD/rmPzT/3fK4v2ntf6zlt4KWNFSEAOH/WAi9OQn1H9+Y+PENQrSc99A3HeWQj/H/pqG0vAaBouoo4tCP/deP8kWF4Duw/wZ10FpNciD7cDc6woqn1Mt+l4vMKeXMGZ/Ewc//ywvpC8ltykJ1FzAi8gcmJ9Wjj6ujTtFQdTvj9SSSfCOwVxegVtbgq66gwisoFhZKLHHsixhBgzkGYYwE1YrdYCJaMWIVCkbhBc0Fmgsh3Ajc6MKFpruwGtxYjG6sRi9mgxebyUeERSHCKrAYdXSjjlB1hKqhG3Q0RUPDhwcdb3PEqYoZszBhxYyKCYMwomBEFUYMwoBBGDFhxCiMGDSB6vWhah5Un47i01F8GqpXA68Pxef/W2hehK/5b68X4Wv+4/Xg07TWgPcqCh5hwKMoeIXa/LcBr6riNVrwGsx4jWZ8BhNegwmPavL/v2rEqxjwqEZ8igGPYvCfEwMeFLwYmt9AwCsUfHpzdwugNN8KUoSCUVUxKQpmVcWoKpgUBaOqYFQUTIqKQVUwAkZVxYCCQcH/t66gopAkrBwd0u/IvhnUQRttNYW6BCnEFieMwF3sZfsPVZyRWcUL6husmLWCxHce47EpK/AYE5nROIrSuko82l5SIyuYOdpHlLqdEoud79MEJfHVJIhkxpomc4SezDENGnpNFd76QrxlpXhLC6lpclEeN5LyEWMojUym1BxLsbBS7zMRaYgg2WImwWom2mDEJhRcPgXhFPhcHgQeDEYPqtEDihuBC5/uwudz4vE4cXscCM2F1egj0qoSE2Ek2m4gwgIRFg92sxurUcdi8GFSvBhwoys+vIqG16jjMql4VX+4eVBwI/Ag8KLjERoeXUPBiAEjBt2IAROqMGPEjkWxYsGGWbFgxowNMzHChLElyIWKQVdR9eZWudcHmg/F4wVN8we37kFoDoTmQ9d8/gDXfP5APzTYvR6Ez4PweBBeN7rXi/C4D/njwSvAazDhs9jwmWx4zVZ8ZiteoxWf2YLXaMVrMuMzWvAYTPgMFrwGI16DGZ/BiN00Agi/qduDPGgHdXlSkI2KsOIr86EafLjdXnRDApOt8byjvMfi+cv55don+GDiyTzGRBanJJFcP5uyWifvug9SWqYxY5yZIzK8HCdqaVJrKTDuZIP+DQXmPMxJFiaPmskodSYJvpNJcggymxrwNtXirc3HW/QV3pICvMUFuCwRVKZPpmzEGMpi0jhgiaNEsVPsVih3+LAbDGRE2EgxRJFoSiBaNWDHgF0Dm1vgNWh4XDpCCHw+H3VOLw1eD4rqRijNrWXNhcfnxON24HI7cDQ14XK7UBWIiTQTF2VuDehIq0qkVcFuFthMOhaDhln1YlI8GPGg6vUomgN0L16DGY9qxmMw4VGMeFQDTYqKBwVPc2B7EHiEz/9H9+HRPXg0Nz7NiwEDRmHCqtqxqZFYFTtW1Y5FsWFWorFgxYwZE2Z/K1z3d7UYdAMGXcEgFBQNVF2gaDpoOugaovWPjtB9CKGj+3z+/9d8/ud8XoTPjdCa/P/v9mI87AZduFCEaLnvOTgd++eP8Wiyr3Y4unrUSDzFXsxWN7sPvsgvF8cRreznaXcxc+1LyK5IpeKdf3HAksj9k86j1qlzXnoqSqmGLjTsMeVU1u6grqaUk6bHMyXFSZTvAEL3UWmNp8BoJd9bR3HTATTNy2jrZDItU0kVGcR67BibvKBr+NxNeBtr8NaU460owVOcj1ZeAkLgNduoSp9IefJ4ymLSKbUlUKxGUOI1Utzkw+cfJoDdqDLSbiPFaiHBbCJGNWJHxaQp4BF4nTpuV9ubv4qqYzJ7MZi8qAY3KG503Oi6E6/PhdfrwOV24nQ24XA0oR02mN9qNhAXZSY20kR0hJFom0qkVSXCAjaTjtWkY1F9mFUvRtwYhAtVd/lD+pCxC0LQPrAVFY+q4kE9JLB1PEJvDmxva2B7dBduzYXX50SgY8SM3RCN3RCFTY3Aqkb4A1yxYVFsWBQrZizN4W3CJEwYhQGjMKDEJxA5eUbQv/cCbdAH7Sl/20BVkyfUZUgDLM1u4QwtHk0T2CKb2Ln/Vc6dm8S0yK18FpHCN9WbuSTyRqIrvFSs+zcNlZU8uWgVH1YbGBNpZWlcEk0l/u8bW1Q9bn0veQe/J9Jq4KTpsWSPqMfmOQjo+BQjxbZEChTId5VR6SgEBGbFxkTbTMYaJ5KkJxHpNKC6/Dt+6LqG11mPt74Kb3U53vIivEUH26wupalGatIyKU/OpDw+g1L7CEoMURRrJoqatHYTcqwGlZF2Kyk2C4kmMzEGIxEtYextDmNn5yNxDCYvRpMPg9GNovq7MbTm1rLX5+/CcLkcOBxNuFydbxGlKBATYSIuykJMhJGYCENzQCvYLWAzaVgNGmbV19yKdqMKJ4rmRNE7/1n9MbBNzX/8/b5utbmFrSh4hL9LxC00f+taNAe27sGjOZmUchLHT7yqB99Bg8ugD9pzn/qSA1WOUJchDbCfjxqJt9gfavboWnbsfZMZE2JZNmEXTcYInnH+gElYuMJ4HUpDExXr/oU3bx//O/lKHhUTcGs6RyfGMluNorHafx6TxYPRlk9B8Xc0NDSQFGflpNxoJsTVYHEX0NKKcxgiKLDGUKB7yHfk0+Spbq0rwZTGJOsMRipjiPfGYHH4b0610Hwef/dDfRXeKn//r7foIKKDYKsdMYqKtEmUxY+iLCqJEmM0JbqFIqeg3t1xoFoMChl2G6lWC4lmE7EGU2vLWPEKfC5/GHf3U60oOkazD6PJ428tqx4E/ht+Ps2Fx+PA7XbgbA5mn69nawxYTD+2omMijETZ/d0cERYF++GtaMXjb0Vrznat6E7rHrUUw5jwW8940AftZf/+mm3FdaEuQxpAqTYzS0UCPp//WzMitoLtu9/BajbwuwUlIDTW20ewo+ZbxlqnsNSxGOFxUb7uP3jz9lI4cQ73TfoJBQ1eFODM9GQyGo04m/zhpSg69tgKqup2UFJSCMDIpAhOnBrB2KgKTJ7iNvVUWeIoMNnJ99ZR1HgAn+5ufU4RKmNtOYw3TyFVpBHjtmFweFvHooJ/cSTN6/R3P9RW4qkswVtSgK+kwD88rAOOmCTK0idRkTiG0qgUSi2xFAsbxW6odHQdeiZVIcNuJdVqJdFiIs5gxI4Bs66geHoexocyGH0YzV4MBg+KwQOKC1248elOvF7/TT+X+8fWcm9jRVEg+pBWdLTdQJTNQKQFf1+0WWA1+LCkHU3s2Hm9OvdgMOiD9levbmXDD1WhLkMaQFeNGomv+MdNOSPiitn+/QcA3HGmwOgppdYcw78adiHQmR99LtOqxqBrHsrfewHvD7txRcTxj0WrWF/tH1pkN6qcl56GuUxrDXAAW1QjHrGXvIO7WlttE0ZGMS/bykhbCQZPRZvaNAyU2hLIVw0UuCsob8r3T444hE2NZKJ1JmOME0jSRmB3qqju9puMCqHhdTXibajBW1OBt6IYb9FBtMqyLr8+bns0lemTKEsaR3l0GiVW/825IreBcocXrQc/0UZVIc1mId1mJdFsIs5oIqIljL2guTRcjt6FcQtF0f1dGGYPqqH5ph/u5tayP5hdbn83RlNTY49bywBnn302s2bN6n1RITbog/b2tTt5e2dpqMuQBkiS1cwyEtqEoT3uADu+/xSA65fEEO/7HoB3rLHsq9sOwCVRNxBXraJrXireex7PD7sB+GjBz3lMH4enOX1GRlg4MyEZZ3HbvkSj2YvJ3tKt8OOap9PGx3LMRCNp5gJUb227ep0GG0XWOPKFl3xHIQ3uinavAUgyjWKi9QgylNHEe6IwOzT/XfgO6JrvkP7fMrxlRXgL89Abu1+L1WcyU5U2mfKUcZTFZFBqT6BEjaTYZ6Skydf6degJo6KQajeTZrMx4pAwtrSEsVvH7fD1e/kBg0HDaPagmjyozX3LLeOWvc1D5FyuJhzOJs455xyysrL6d8EQGPRBe/+He3jxm8JQlyENkJ+NzkAvatvCscbuZtfurwA4/8Qksm1bASi3JvJi3RYALIqdnxmvx+jw+MP2/Rfw7PcHcsGkOdw38ScUNvzYqpyZEMNcUwwNlW0Dt6Vbobp+F8XF+Yc8DkdOimd2JiSp+Si+hg7rrzXHkG+KpEBrpLDxAB6t4/sLKgbGW3MZb84mRaQS5bJgcHi6HLykeV14HXV4aitb+399RQcRHncXR/1IVw3UpoyjPGUCZfEjKbWPoNQYRZFmptih4fD2PjFVBVKaW8ZJZjNxJhORzS1j1Qe6S8fl0PzTlwPgrBVjSUqxBeRcA2nQB+3jn/3AE5/nhboMaQAkWkyco47Ae9gPvCn6O/bs/RaAOdnxLB6zo/W5N8x2Chr2ADDaks1ZzqUomoau+aj44AU8+3YB4IqI44lFq/iouu2CdYvTRjDOacHR0P7XV1tkI172kZe/C6/3x5A2qArHTY1nxiiNePJQtI7v4OuolNoSKFSN5HuqKGs6iC46HzUQYYhlsnUmow2ZJGoJ2Bygerr+tVoIHZ/b4b8BV1OBt7IYb0khvtKiXi8pWJ+YQUXaRMoSRlMWmUyJKYbi5ptzdZ3cnOsJBUixmUmz20iymIk3GolUDFh0FdULulvH7dDQetDavuDyCURGhd9EpkEftM9/XcCf1+8NdRnSALhiVAaiuH2wqPbN7D+wE4Aou4lVJ/w4QqDQlszrtV+3vvakqHPIrR4L+H8Fr/jwRTx7d7Y+v37B1Tyuj23zK7TVoHJeeiq2SoHX075VZzT5MEfmU1iynbrDtru2mAzMy40nN91FtJbX5fAmt2qhyBpPARr5ziJqXV33xQKkmccxwTKddGUkcZ4oTE0+lB78ri50Da+robn/twxvWTHe4oNo1ZXdHtsRR3QCFWmTKU8ae9jNOYVKhy8gC/QnWc2k260kW8zEGU1EqQasuupvGbt1PE6NS34+GYMh/CYtDPqgXbu9hDve2RXqMqQgizeb+IlxRIdB5zNvoKDgh9aP7zzTi8HzY2C8ZDRS1pTX+vHKqBuIb2656rqPig/ahu3Bycdyf+bZFDW2vUGVajOzfEQK7hJPJzeBdCJiq6ht3EVhUV67ZyNsRk6aHk9OciOR3jzoovUKUG+KosAcTYHuoKAxD1cn3RGHMmIm05bLOFMWyXoKUS4zqrPrLodDaZoXr6MOb11L/28h3sKDCEdjD8/QnsfWMnNuHGWxaZRaEihR7RR7DJQ29ezmXE8kRph55+rjAnOyATbog/arvGr+38tbQl2GFGSXj8qADlqzAE4+pLSsqPXjG5dGEePd0/rxfnsqb9dsav3YrNi40vRrjM0TXXTNR8X6l/Hs2f7jOSPjeHzhzfyvun1ETYuLYr4tjobyzluntogmfOp+Dhzc0aZboUVclJmTpscyOaGueWJE1z9mQkC5LZECg5l8bzWljQfRRPvzdiTakMAk2yxGqeNI9MVhc4Di7d3eWj6P09/9UFeJt7IUb2kB3qJ88PWshs5oBlPzzLlMyuMyKLEl+Cdt+IyUNGm4ezHrc0pqNP+8MPxGHEAYBG1Fo5vT/v5ZqMuQgijGZOQCcxIed8c/dLWetdTU/DjEb+XJSUwwb239WAj4t0Gnxvnj+NfRlizOcp6J0jxOVdd8VH70Cu7d37U594cLr+EJbUyHd+MXpSYy2W2lqb7z0DKYvFgiiygq/Y7a2o63wk6Jt3FSbhSZMVWYPT27setVTBTbEslHJ99VSrWzqPuDDpFhmchEcy6pZBDricDU1HZsb0/4+3+b8DbW+qcflxf7x/+WF9OncV+Hn19RqEkZT0VKpn/SRsQIio3RlGhmipw6TZ62vxEsmJzEvWdM6fd1Q2HQBy3Ayf/3CXWu8Nz9UureT0eloxZ3/mt2af0rOJ0/3r2fl5vAgvTtbV6zMyKND6s3tnnshKizOaJ6XOvHuq5Ruf7ldmF7MOs47h+/vF1XAvjHm56XkUp0FZ2+EfgJImKrqGvaRUHhgU5fNSY1khNybIyJLMfo6fmwxUZjJIWWGPJ1JwVNB3F4ezeJx6xYmWA7gjHGSSTryUQ6TRhcfZva7p9+XOfv/20Zflacj15b3f3BvdCQkOafOZcwmrKoZCZkZXLKvCMCeo2BEhZB+7Pnv+HbwtpQlyEFQbTRwIXWZDyujkNMCEF+5b/RD7kBlBhj5bpj24aZhoFnRSONnraTWy6O+jUJ1T+ua6zrGpUfv4p719Y2r3NEJ/DEyTd12JUA/hER56ak4Cvxdjtu1BrhQDPsJ+/gDjyezsNs8qhojs8yk2ErxuDp+aQcIaDSmkCB0UKBzz9bTeviJlxn4owpTLIewUh1HAm+WKxNbacT95bm8+B1NE8/rixr7X4QrsBMoY/75a1ELlwakHMNtLAI2j++v5tXtvTuVycpPFwyKh1jF61Zg9HHvqL/tHv8rqXOdhMIvo1IY8NhrVqzYuNnpl9jOmRhos7CFuCDRf+PJ3yj8XZyBycrJpJFkfE0lnUfbAajD0tUEcVl31FT03Vr74gJsRw9wUCKqQC1l61Vn2KgxJpIgaqQ7yqnwvHjqIxeEQpjbNlkmqaQSgYxbhtGR++7HNqcUgg0jxNvk3/6sbeyFE9JfpfTjzuT9Kd/YMmZ3udaQiksgvbVLUWsfn93qMuQAizSaGClNaXd8oCHMttc7M57qd3jq5ZGEOXd1+Yxj2rmGW9Fu7v3oyyTWeY8q7W/FprD9n+v4d65pd2587KP5/5xyyjuoCuhxUnJ8UzTImis7f5mkRCCyLhq6h3+boWufuRUBWZnx3PUWEGichBFa+r2/IdzGOwUWuPIF24KmvJp9PT9V3qLYmeSfSZjDBNJ0kcQ4TB0OJ24t/zTj5uXn6wu908/Ls5Hq+i8OyXt+Q8wRMf2+9qhEBZBu6Wwliue/ybUZUgBtnJUGqbirn8Pt0U1snPfa+0ev2zhCMYat7V7/KuIVDZWb2r3+LyoZcyozmzzmK5rVH7yOu4d37Z7vSM6kcdOvolPu8goo6JwTkYK8bVql8sXHsoS4UQY9pOXvwO3u+sZXUaDwvHTEpgx0kusnoeiu3p0jcNVty6KU09R0wG8Wt/O0yLBmM5k6xFkqGOJ90ZjadLbvIn1R7vpx+XFeAvzwGAg/d/vBeQaoRAWQdvg8nLS/30a6jKkALIbVS61pXYbUPaYGnbs+W+7x0+eMYITUtoHrdNo42lnfpsVtlpcFPVrEqvb7kMndI3KT9/Atb3jN/L3F/2Cf/hGddqVAP5REytSUxFlvh7NbgIwGDWsUUUUl2+nugeTCGwWA/OmxTMtzUmULw+lh0O/DudfFCeRAlUlv5NFcXpLESrj7FMZb8whlTSiXdZupxP3lh4TiXrssQE848AKi6AFOP3Rzyhv6Nmcbmnwu2hUGpZuWrMAEbHlbN+9rt3jaYk2rj7qhw6OgE8iktla/XW7x82KlZ+ZbmjTXwstYbsG1/b2xwAcyJnL/WPPoqSLrgSAzCg7p8ck0lTa8xtTQggiY2tocH1PfsH+Hi0vGGU3MX96HNlJDdi9B7udGNEVl8FKoTWeguZFceo7WRSnt+xqNJNsMxhtmMAILRG7Q0X19KPLYcxoyJ4ckNpCIWyC9tpXtvDFgcAOH5FCw2ZUucyeisvRfUBExBWy/fv1HT73+yWNHS7u0mCK4tmmPR2uKzDSMonlrmXt7q4LofvD9rvNHV7LEZPEo/NvYEMPvgWPT4pjpoiksaZ3wWKxOcF8gLz87bhcPfv1PjHGv3j5xPharJ58+nQT7BC1phgKzP5FcQqa8vD4et9H3Jlk02gmWqeToYwhzhOF2eHrdAWzdnKnQnpawGoZaGETtH/9eB/Pbcrv/oXSoHfByFRsJT37trPH/cCO7zd0+Nxvz7Rh93Tcqn3flsD3te1HFQDMjTqLmdUT2j0uhE7lhjdxbWvfx9vi3VN+yZOejNa9wDqjAMszUkipN/ToDeVQqsGHNbqEsortVFb1vIWZlmjjpGlRjI+pxOTu/ygdXSiU2RMpUI0UeKopbcrrclGc3jJgZHzzdOIUPZUodxcrmB1/LERFBuzaAy1sglaueTA0WAwKP4tMa93toDvWmO/ZtWdjh89deUoiI9XvOnyuyhLHf+q301kL78Ko6xlR3X6XZSE0Kj97C9fWjq8JcGDKCdw3eimlTd23WCONBlakp6KWaWi+3v+o2WNqaHLv5mD+3l7tWpCZHsXcbCujIsowerpfvKYnPKqZQmsCBYpGgaOEGldJQM57qChDHJOsMxllGE+iFo/doaBoAhbO969VGabCJmh3lzVw4bOdtzSk8LBiZCoRPWzNAhijtrJ3X8ct01OPGsGxie1viLV4yxrNgbqdHT7XWX8tNIft52txbfmq03M7YpL4+/wb+KyHvVmjI6wsjU/CUdK32VgWmwvMBzhYsB2ns/ONFTuSMzaG4yabSDcXoXoD1/1Wb4qmwBzVq0Vx+mJq4gJOPuLmoJx7oIRN0Lp9GvMe+gQtQAsISwPPpCpcFZWOs6kX06ltGzmQ932HT41Li+Sn0ztfQrPENoJXatsP3WqRYZnA2a6zO5wNJYRG1Rdv4/z2yy7LW3fqL3nK3X1XQos5ibHMMUTTWNW3wFUNGrboEsqqdlBR0fuW6qxJcczJVEk25KP4ut+xoacOXRSnwFtLSeOBHi+K051ZY87j+AlXBORcoRI2QQvw039v5rviwH1zSAPrJyNTiOrlb5s+06edrh2gKHDX4tpOF94GeM1koahxf6fPHx+1lFnVkzp8Tug6VV++jfPbL7qs8YepJ3L/qCU96kpocWZ6EiMbzb170zmMPboWh9ffraD3cj8Zg6pwdE48s8boJJDXvAtt4PgXxUmgQBHkO0up6uWiOIdaOv1uxo04OoDVDbywCtpHN/zAk1/khboMqQ+MqsLV0ek4GnsXLA7xPmXlnafz7840Y/Uc7PT5g/YU3qzpeCRBiwujfsWI6o5X7Re6TtVX7+D85vMuz9EUm8zfT7qBz6t7/uNkM6qsSEvFXKHj8/b9x9BsdaFaDnKw8Dscjt4HptmoMndaAtMz3MToeSgdjEHur0ZjRPOiOC4KmvJxdLD/WscUfn7C61hNUQGvaSCFVdB+XVDDVS90/qugNHidk5FCTB/22Kxx/Zfauo6XHwS4+rQE0tje6fMAzxsUKh0FnT5vwsKVlhsxNXb867wQOlVfrcP5ddfLdQpFYd0p1/GUO61XXVzpdgvLEpNxdbrgeM8oqo49ppTy6u2Ul/dtQ1ObxcBJ0+OZkuIgypcHIvCr5gkBVdZ4CkxW8r11FDfmdTjBBGBE5HguPPrxgNcw0MIqaL2azvz/+xSnN3BDTKTgMyoKV8emd7gvV3dK6l7qckzp0mOSODK245tlLfZEpPFudeejCADSLBM4t5P+WmgO243v4tzc8VCzQ+2fehL3j1pMWS+7BWbERzPPEktDRd/6bw9lj67D6dtD3sHdve5WaBETaeKk3DiyR9Q3L17ez+1uO6FhpMSWQL6qkO8up6Lpx0Vxjhi1nBMmXR2U6w6ksApakBMXwtGyjGTiS/syNEeQV/6vLoc1ZY2O5oKcrhcc0oXCc4qbend5l687LmopR3bSXwvNYbvpPZybup8O3hiXwt9P/DVf9KIrocXpaSMY38mGkb1lsngwWPPIL/qOpqa+Tz5IivNPjJgQV4PF3cfVwXrIabRRYImjQHjIGnMeoxKPDNq1Bora/Ut+9NOf/pSkpCSmTPlxlfMtW7YwZ84cpk+fzqxZs9i4seOWwymnnEJsbCyLFy/uV8FHjY7v1/HSwFIVGOno266lRqOv27GjewoaEKq5mxoEMyLGdHu9DQ1rKE/ovDWpKCoJRy7CftTcbs8VWVPKDa/fxOX2Ugxq795k1hZX8Fh9Ed40FZO5Vz+i7XjdZlx1E0mJPospkxeQnNy32VXlNS5e+Lic37/u5bHN49njycVrTu1XbZ2x+ZxMbCpmvrOWkXHTgnKNgdarf8VLLrmEdevazju/6aabuP3229myZQt33XUXN910U4fH3njjjTz33HN9r7TZnDEyaMPJ0vTkLreC6YrB1P1xmi7wGpO6fV2Wowy7Kabb171U/3c8kZ0Ht6IoxM9aiP2oed2eSxGC097+M3+oepcke/vJEV1xa4Jn8ot5TVRiSTP1e6y+rqs01aRi52RyMs9k/NhsDAZD9wd2oKC8iWc/KOfON+DZ7yaSp01DM4/oX4EdUGImohgsAT9vKPQqaOfOnUt8fNugUxSF+nr/kKu6ujrS0jp+x5w/fz5RUf2/c5g5IpLUaGu/zyMFn6rAKGfXrc0ujzf2LKBrfd0HqFFo5EZ13i3Qwoub18XzCGPnIeQP2wXYZ5/Qo/oyt37A/V/8idnxvW+dljk9PJJfwJeRTUQm9f1reShHQzS6Yxbj084he9LRREb2fWrrnoIG/vFuBbe/YeSl3VkUiqnoptiA1KnED43WLEDv3mY78OCDD7Jo0SJuuOEGdF3n88+7HgYTCMePT+Slb3u2yZ0UOovTkmgq6/ugdVXt2bHFdUaSevAePtVZxdcGO55uxowWe/azKWo7R9VkdfoaRVGIn+mfFur48qNurx1ZU8pNr9/I26dez9OulF5PvPmupoHvahpYmJpIlsdKU13/+2+9HjNezwSSIsczJqOMqtqdlJT2fbzrtv21bNsPimLjqMnpHDUektT8Dhf+6QklPjz3B+tI/zqAgL///e/85S9/oaCggL/85S9cdtllgairS8ePTwj6NaT+UYBx7n7+2tfDoN3bw2mtFt3N1JjsHr32s8b/Uhbf9XkVRSV+xknYjz6pR+dUhOD0tx/g3qr3GNHLroQW75VU8khNEc5UFbO13z++AAjh71awivlkZ55F5rgpGI19b4MJAV/tqub/3qrmjreieL9oClWGLITB1vOT2FJR7Cl9un5H95K2bt3K0UcfzdSpUznjjDNafws/VEFBASeeeCLZ2dnk5OTw0EMPdXj+NWvWMG3atNb7Uhs2dD8SpdejDvLy8li8eDHbt/vHLsbExFBbW4uiKAghiImJ6fCTAPj444+5//77eeutt3pzyXa8ms6Chz9ttx2xNHicnjaC1PK+9QG2iIgrYPv33bcWLSYDty4s6dG6rE3GCJ5x/oCmdx/iRsxcabkJcyfja1sIoVOz5WOaPv+w23O2aIxL5ZETf81X1X0fMpVgMXFucgpaafcbRvaWyeLBaMunoHg7DQ2BmY1pMRmYlxtPbrqLaC0PpYsNJZWRp2MYe26frvPJJ58QGRnJxRdf3JpTRx55JPfffz/z5s3jqaee4sCBA/z+979vc1xJSQklJSXMmDGDhoYGZs6cyRtvvEF2dts358bGRiIiIlAUhW3btnHuuefy/fcdTxNv0e+3xLS0NP73v/8BsH79eiZMaL/8XKCZDKq8KTbITfD2vx9dp2ctVbdXw2vq/oYYQISviayYKd2/EPDh4XXxH0Q3rTtFUYmbfiIRx57co/MCRNaUcNPrN3JpRDmGPt7oqnJ7+Xt+AZ/YGolMDkz/bQuv24yzNpMREUuYMnkRaakj+31Ot1fjvc0V3LemgdUfJvNVzTQazeNBaf+GrCbO6vN1OrqXtGfPHubO9Y8WWbBgAa+++mq741JTU5kxYwYAUVFRZGVlUVTUvislMjISpfnuZFNTU+v/d6VXQbtixQqOPvpodu/eTUZGBk8++SRPPPEEv/71r8nNzeW3v/0tjz/un8WxefNmLr/88tZjjz/+eM455xw+/PBDMjIyePfdd3tz6XbmZib263gpeE5JHdHrRa87ovdiKmi9Ftvj1x7h9aD08Fu/2PMDG6M7XyGshaIoxOWeQMRxC3pchyIEZ6y9n3trPuxzVwLA93WNPFiUz/4ED5GxfRtK1xl/t0IyFv1EsjOXMWH81H51K7Rocvr47xcVrH7DwZ8/HcmWhlyc5jGAAtYRKFHj+n2NQ+Xk5LBmzRoAXn75ZQoKOp8pCP7f3L/99ltmz57d4fOvv/46kydP5vTTT+epp57q9vphN2GhRZPHx2l/+wyHnCU26FyXNJLG6v4HrTFyC3v3dx9yACtOSCLH3vUMsUOts8Wyt7brqbuHOj/qlyRXd9/nLISgZtsnNG3o3UaCDQlpPDzvejb1oysB/CM9zslIJbEXG0b2ltHkxRSZT2Hxdurre7c1endS4m0sPeUYxk7r+RtWRw7v4vz++++59tprqaqqYsmSJfz1r3+lqqqqw2MbGxuZN28et9xyC8uWLevyOp988gl33XUXH3zwQZevC0xveghEmI0sykoOdRnSYRalJgYkZAG8Ws9btPtLezndVfSu//ilhq7H17ZQFIW4aXOJOH5Rr84fVVXMza/fyKURFX3uSgDQBbxYUMK/XGUY0o0Y+nOyTvi8Jpw140mwncGUyaeQnjY6YOcurXZiS5oasPO1mDx5Mu+99x5ff/01K1asYPz48R2+zuv1snz5ci644IJuQxb83RQ//PADlZVdb7AZtkELcFZu+O4hNFRlab24s9wNr7fn22LvyKuHXuy7muSqYlQPxtW28OHhVfFvhKn7X5sVRSFu6vFEzj2lx+eHlq6E+7in9kMS+9GVAFDn9fHYwUI+MNcRkRLY/tsfqTTVJGHW5pE9fjkTMnMxmfrXdZGSkkJKSt9GG3SlvNw//VrXde6++26uuuqqdq8RQnDZZZeRlZXF9ddf3+m59u3b1zpj8ZtvvsHtdpOQ0PVIqLAO2qyUaLKSw3v5tKHk5JSEgLVmATyengdtk8uHZu5dv/1MQ+8G6pd6DvBl5JYezfJvDdt5p/bqGgATv32X+zf+mVl9mOBwuP0NDh4qzOf7ODeR8YHtvz2UszECX0MuY5LPJmfy8cTExPbpPDNnzux3LR3dS3r++eeZOHEikydPJi0tjUsvvRSA4uJiTjvtNAA+++wznnvuOdavX8/06dOZPn06b7/9NgCPPvoojz76KACvvvoqU6ZMYfr06VxzzTW8+OKL3d4QC9s+2havbS3iD+91vaiINDCuSx7V550DOlLlXNOrPsAblkQT6+vd98JLRiNlTXm9OmZF9LWkVPV8VEXNjs9o/PjtXl0DQFcN/Pe0X/NcUyKB2FikPxtG9p4gIq6S2sZdFBbm9egIg8HAb37zm37NVBuswrpFC3BKVjIR5v6N15T674Tk+ICGLIDb3fMWLUBZU++HlM3swxz9l+r/hieq57+Ox+UcS+SJp/f6OqqusfStP3F33Uck2Pp/p18ArxSW8k9HCaQZMRqDudmhQlPNCEzeuWSNO5uJmdMxm7v+mk2ePLnPIfvQQw8xZcoUcnJyePDBB9s9L4Tg2muvJTMzk2nTpvHNN990eJ5TTjmF3NxccnJyuOqqq9C0wLwhhX3Q2uVNsUFhuhLYVoii6LjdvVvp/0B5738oxjWVEGfrXV+/ho9X9X/1qL+2RVz2MUSe2LeV6yZ/8w4PbPoLM+MD06Bo8un8I7+Qt4012FOD1X/7I1eTHW/DNEYlLSdn8lxiYzseA3/UUUf16fzbt2/niSeeYOPGjWzdupW33nqLffv2tXnNO++8w969e9m7dy+PP/44P//5zzs810svvcTWrVvZvn07FRUVvPzyy32q6XBhH7QAy3LTQ13CsHZ8UhyNlYFtzRpMvQ/NHXm9n1OvKDDDltHr40o9eXwZ+W2vVmWNyz6aqJPO6PW1AKIrC/jNGzdxcWQVvVx1sVP5jS7+WpDPthgnkQnBD1zNa8JRM4Y4y+lMmXQaIzPGtfZtJiQk9Hmy065du5g9ezZ2ux2j0ci8efN47bXX2rxmzZo1XHzxxSiKwpw5c6itraWkpP0WSdHR0QD4fD48Hk+PJiP0xJAI2knJUWSlyJtioTLLEPivfU+WSDxcTYMH3dT7GYOTmsqINPd+/YwvG9+hNKF3W3/HZs0hav6SXl8L/F0JZ771R+6u/5j4AHQltNhYVceDZfmUJ+vYIgN33s4pNNUmYvQex+Qxy5k0YQbHHHMsqtq3OJoyZQqffvopVVVVOBwO3n777XYTEoqKihg58sfZbRkZGR3O+gJYtGgRSUlJREVFcfbZZ/eppsMNiaAFOGuaHOoVCseNiAvI1iuH6+nKXYdroveBaUBjemTH4yq783L933H3or8WIHbybKJOXtqn6wFM/vptHtj8IDMC1JXQ4s2icv7RWIyWZsBkGphocDns6M6pzJrZ8QysnsjKymLVqlUsXLiQU045henTp/d5rV2Ad999l5KSEtxuN+vXr+/zeQ41ZIJ2kbwpFhKzjMH5TUI19C1oKxx9G8eb46zE2ofPRcPHq9pzveqvBYiddBRRJ5/Z6+u1iKnI57dv3MRFUdUB60oAcPp0/plfxBtUYUsz93vB8Z6YmB2Lxdq/n93LLruMr7/+mk8++YS4uDgmTpzY5vn09PQ2rdzCwkLS0zvvcrRarSxdurR12m5/DZmgtZuNnJIV+IHOUueOToylMQitWQCUvp03r7Jv46DMuodp0ZP7dGyZ9yCfR37d6120YicdSfTCs/p0TfB3JZz139Xc1fAJcQHsSgAocbr5v/x8NkU6iBoR3P7bqUf0f4GolgkJ+fn5vPbaa5x//vltnl+yZAnPPvssQgi+/PJLYmJiSE1tuxVPY2Nja7+tz+dj7dq1TJ7ct++Jww2ZoAVYPj29F3ODpP6abY4O4tn71qLdebDvGxBOc9diVPu2hu7Gxncp6WV/LUDMhFlEL1zep2u2yN78Fg98/RBHxAX+N7otNfX8pSSf4iQNe1Tg+29Hj4skNq7/29UsX76c7OxszjjjDB555BFiY2PbTDI47bTTGDduHJmZmVxxxRX87W9/az12+vTpgH8lriVLlrSuNZuUlNThDLK+CPsJC4e7ec13fLinItRlDHlHJsSQWxe46baHs8XuY+fuvu3W8fslTSi+vq2h+klEMlurv+7TsSoGrrL+BktD71vjdfu+of7d9kv39YamGnnj9Bt5vjEuIBMcDmcxKPwkPZWISvB6ArMA7pnnjSE51R6Qcw1mQ6pFC3DVceMwDETH0jB3rLX7fbr6QxO9G0N7KIfa9yU0j/A4UDtYH7UndDRe0Z7tdX8tQEzmDKIX9e8Ot0H3sfy/f+Cuxk+Jswa+9enWBM/mF/Oq3rxhZD/TI31UxLAIWRiCQTsmIYLTp8i+2mA6Ij6axrIg9c0207S+n7/SGdHnY6O8DUzs4cLgHSn35vNZH/prAWIyjyD61HP6fO0W2Zv+ywPf/pXpQehKACh3+TeM/MLevw0jZ8wePmtKD7mgBbjimLGYDUPyUxsU5tlig34Nn693028PlV/Vv99oZug6vVkJ7HCbGt+lOKHrDSA7EzNuOjGn9m0Ll0PFluVxy5pVnB9dE7T7FttrG3iwOJ+DiV4iYnq3YE3aSDtpGX1/Qww3QzKNUqKtnD1dzhYLhulxwW/NAni8fe86+L6wbyHXIsFdw9iYznfA7YlX6v+OO6pvq2VFj8sl5rSf9Ov64O9KOPvNP/D7pg1B6Upo8X5pFY/UFOJIVbD0cMPIWXN6v8YEdL6BYnV1NQsWLGDChAksWLCAmpqadsdu2bKFo48+mpycHKZNm8aLL77Y5bVeffVVFEVh8+bNfar1UEMyaAEunTNajqsNgnkRsQNynd4skXi4g6VNCEP/+v5mKv27E66j8ZLvGYS5bwEXPXYaMaef168aWmRvepMHvn2Y3Ljgha1PF/ynoITnPRWY0kx0NckrfVQEqX1szRqNRh544AF27tzJl19+ySOPPMLOnTtZvXo18+fPZ+/evcyfP5/Vq1e3O9Zut/Pss8+yY8cO1q1bx3XXXUdtbW2H12loaOChhx7qdCub3hqyQRtrN3P+rP5vKCf9aGpcFE2lwW/NArjdvR8qdSiXoWebNXYm1VlBeh9ni7Wo9BWywb6pT/21ANFjphKzeEW/amgRW/YDv1tzEyui64I6BLLa498w8n/Whk43jJx9XN//bTrbQHHNmjWsXLkSgJUrV/LGG2+0O3bixImt6ymkpaWRlJRERUXHI5RuvfVWVq1ahdXa/01GYQgHLcAFR44i1ha8xY6Hm5Mi4gbsWi5X31u0ANWe/q8mNtPU/893c9P7FCX0fWxv9OgpxCw+v/sX9oBB93HOm/dwp+NzYoPYlQCwu76JB4vy2ZfgITLux5/BzMkxjEgOzLDAQzdQLCsra52AkJKSQllZWZfHbty4EY/H0+GWNt988w0FBQWcfnrvl7bszJAO2gizkUtmB24/o+EsKyYSxwD0zQKoqo7X27+dGgqq+/+tPdpRSqK9/78VvVr/KK7ovr/hR4/OIfaMC/pdR4spG9/gz98+zLQgdiW0+Lismr9WFtCQCvZII0cd27/fNFo0NjayfPlyHnzwwdYVt1ooitLlqlslJSVcdNFF/POf/2y3kI2u61x//fU88MADAamzxZAOWoCzj0gnKar/M0+GuwXR8QzU1BaDsfcrdx1uT1H/WsQtZlpTu39RN3Q0XvI+jd7H/lqAqFHZxC65sN+1tIgt+4Fb31zFeUHuSoCWDSNLaZpsIKofbzgtOtpAMTk5uXX6bElJCUlJHQd6fX09p59+Ovfccw9z5sxp93xDQwPbt2/nhBNOYMyYMXz55ZcsWbKk3zfEhnzQWowGrjhmbKjLCGuToiNwDlDfLIBq6v++Y/sKGxB9nE57qMzGEqIt/W+FVfmK2GD/qs/9tQBRI7OIXRq4sDVoXs598x7ucH5BTD8XdelOUqSFlbPH9Ps8nW2guGTJEp555hkAnnnmGZYubb86msfj4ayzzuLiiy/udPnDmJgYKisrycvLIy8vjzlz5vDmm28ya9asftU95IMW4IwpqYxNGD5j9gJtYWzCgLVmAQyG/rdodQEeU/8DUlUEMyLG9Ps8AF83fUhhQmO/zhGVkUXs0osDUk+LqV+9zp+3/I2pQexK+H/zxmMLwCigzjZQvPnmm3n//feZMGECH3zwATfffDMAmzdv5vLLLwf8uyd88sknPP30063HbtmyBYDbbruNN998s9/1dWbIrXXQme3FdVz+n2/QhsenGzCZUXZOdEcjAjO1vUciYivZvrv3mxke7henx5MsdvT7PD7FwDNaLQ5vzzeK7IwiVK6y/wZrff9a7Q1Fu6ld8xyBfAfUDCZeXryKl+ui+9XyPty0tBievKD/u9uGs2HRogWYkhbDBUfK4V69dUpc4oCGLNDnJRIPV1QbmF+HjUIjN2pSQM4lFJ2XvP/sV38tQFT6JOKWXkwgF4w1aF7OW3M3t7m+ItoSmK+dyaDw20WB+dqFs2ETtABXHjtOdiH0wrhIG54B7JttIfq4ROLh9pYErvapzirM/ZwE0aLKV8ynEV/2u9UYmT6RuDNXBjRsAXK/fJU/b/s7UwLQlbDyqNGMTxx624f31rAKWrNR5fZTs+TqXj10WvwI9IFuzQK6CExAfp/fAEpg+h0tupupMdkBORfAN43rKUjo/WaSh4tMm0DcWZcEPGzjS/Zx+5urODemoc+jEsYm2Pnp0WMCWVbYGlZBC5CTGs2FR40KdRmD3qhIK57SwLQse0vX+77OwaG8Ph1vAG6Itch1N2BQAzcB5rW6x3AGYLhTZGpmUMLW35Xwe25zb+x1V4KqwO8WZWGSizsBwzBoAa48dizjEmUXQlfOiB+BHozVo3vApwUmaAHqtNiAnSvC10RWP5ZQPJxQdF7yPYluDlDYLvspXS4y0Ee5X7zCn797lOzYnv92cP6sUUxLD+6axeFkWAatydDchRDIXe2GkJERFnxl/R9i1Vc+X+CCtqQ+sFOwj/B6UPq74vUhqr2lfGL/DBGA1mhkyjjiz7o0KGEbX7yXO9bezNkx3Q9PGxNv56rj5Nj1Qw3LoAXITonmYtmF0KEzEpPQtNANg/N4AzOrC2BfSWC7P2K9dWRG5wT0nN82fUxBfP+HjgFEpIwjPkgtW6PXw/lr7uI296ZOuxIMisLtp2ZhMfZ+1EJ/lkA8ePAgM2bMYPr06eTk5LTuFdaZBx54AEVRqKys7HWdfTFsgxb8C4Rnyi6ENtLsFrQQtmYB3O7ABe3Og/UE+tt8ZhB+bF6rezwg/bUAEcljiV9+WVDCFmD6Fy9z/44nyOqgK+GKY8cwJa1vXQb9WQIxNTWVL774gi1btvDVV1+xevVqiouLO7xOQUEB7733HqNGDVxDa1gHrcmgcvtp2bIL4RBLEpPQfKGd1BHIoHW6NXzmvi0y3ZkRripGRQd2bKhQdF70Bqa/FiAiaUxz2AZnam1i4ffcufZmlsf+uDLZrFFxXDpnTJ/P2Z8lEM1mMxaLf8q12+1G72K4zK9+9Sv+9Kc/dbnwTKAN66AFmJwcJVf4apZsM0N5aFuzAC5X/9aiPVyDHvjlHWeqgf9NqMZXyse2DQHpr4Xgh63R6+GCN+7kVu/XjIqx8vvTs1EDVHtflkAsKChg2rRpjBw5klWrVpGWltbuNWvWrCE9PZ3c3NyA1NlTwz5oAS4/ZgyzRsWGuoyQO3NEMr4Qt2ZV1YemaQE9Z2lj4Fdvy3CWkxIR+Bs+Wx3/Iz++NmDni0gaTcI5lwctbAGO+OJlnj3SQGJkYL7OfV0CceTIkWzbto19+/bxzDPPtAtkh8PBvffey1133RWQOntDBi1gVFX+cMYUUqMDs5p6OBphNaNUhL41azQHNmQBfghSn/NMc3B2cX2t7nGcvdzssCv2xFEknHMFGIITttErLifiiMBs+dKfJRBbpKWlMWXKFD799NM2j+/fv58DBw6Qm5vLmDFjKCwsZMaMGZSWlgak9q7IoG0Wazdz35lTsRiH55fkrKQkfN7QL7gTiLVoD7czr5H+7GrbmbFNJcTZ2v962m+K4EXPk+iWQIbtSBLODnzYWmceQ/SKKwJyrv4sgVhYWIjT6e9yqqmpYcOGDUya1LYfferUqZSXl7cugZiRkcE333xDSkpKQOrvyvBMlU5MSo7i1lMmh7qMAZdgMWGoDMFc2w4oauBno9U1edBM8QE/r6LATFtGwM8Lzf211k8D1l8LzWF7zs/AGJhpyYakVOJvuCtgN5X6swTirl27mD17Nrm5ucybN48bbriBqVOnAnD55ZcHZCfb/hg2yyT2xkMf7+Nfm/JDXcaAuXx0BhSFvtsAICK2gu273wn4eW9aGkW0d0/Az6uh8qxootFTFfBzA5wV/TPGVMUG9JyOqkKqXnkCfH3/N1dsESTd/yTmMZkBrGzoki3aDvy/ueM5blxCqMsYEHFmE6aKwdGaBUAJzvoK5U3B6X83oHNEP3fL7crrdU/giAnsgtz2hAwSz7my7y1b1UDCzffKkO0FGbQdMKgK95yRw6Skob+827KUZLyewRO0guAsy5hXGbxf3HIcFViNUcE5uSJ4KcD9tQC2+DQSz/kZGHt/3tgrfoVt1rEBrWeok0HbCbvZyF+W5w7pjR1jTEYsVYMnZAF0Ebh1Dg6182D/tpDpikl4mRYdvL79Gl8ZH9k+CWh/LYAtPp3Ec6/sVdhGLjmPqCXnBbSO4UAGbRdGRFr4y7JpRARgr6PBaFlqMh734ApaTQ9Oi7a8xoVuCt5qUtNctRgDsBlkZ7Y1fUpefHXAz2uLS/WHrcnc/WuPPYnYK67v9nVSezJouzExKYp7zpgy5BYLjzYasFUPvvugWgBX7jqcg+CMewWwaU6mxAZuCcWOvFH3D5oC3F8LPQtbc850Em74PUqQ1k8Y6uRXrQeOHZfAbxdNCsJIzNBZlpaCxzW4WrMA3iAGbaUruAsITfc4UAO0o0OHFMGLnicC3l8LYItNIfHcK1HM7VvlxtHjSLz1gQ6fk3pGBm0PLZmaxq2nZjEU1p+JMKpEtF9pblDweAK7zsGhDgZ5RbwobwOTYgK7hOLh6nwVfGj9X8D7a6HjsDVmjCbpnr9jiOpbt8tDDz3ElClTyMnJ4cEHH2z3vBCCa6+9lszMTKZNm8Y333zT4XluueUWRo4cSWRkeN6glkHbC2dMSeXO07LDvhtheVoKbmfgp7oGgtsTvBbtrvym7l/UTzN0nWDMQjvUdscGDsQHZ9yuNSa5NWyNqRkk3fsohri+DXXcvn07TzzxBBs3bmTr1q289dZb7Nu3r81r3nnnHfbu3cvevXt5/PHH+fnPf97huc444ww2btzYpzoGAxm0vXRKdgr3nJGDMUybtnajSnTN4K3d7Q5ei7awwoEwBLf7IN5dw7iYrKBeA2BN3ZNB6a+F5rC9+DpG3Pt3DAl9X2KyZbaW3W7HaDQyb948XnvttTavWbNmDRdffDGKojBnzhxqa2tb1zU41Jw5c1pX8ApHMmj7YP6kJFYvnYLJMHgDqzPL0lJwDdLWrBAClytwa9F2xGUI3GaNnZnJAPRlKoIXPY8Hpb8Wuw3rKYsxJvUv2FoWdqmqqsLhcPD2229TUFDQ5jVFRUWMHDmy9eOMjAyKior6dd3BSAZtH83LHMH9Z00Lq0VorAaV2NrB++ZgNGldLtgcCFXu4O+okeKqID0q+LOm6nyVfGhdH9j+WrsdZh8FNlu/T5WVlcWqVatYuHAhp5xyCtOnT8cQpBXEBrvwSYlB6JixCfx52TSspvD4Mi5LS8blGJytWQCDKfjrLRRUD8y/1Uxj7IBcZ7vjC36ID9Bdvgg7zDkSbIGbrnzZZZfx9ddf88knnxAXF8fEiRPbPJ+ent6mlVtYWEh6enrArj9YhEdCDGJHjY7noeW52E2D+53aYlBIqB/c/9wGQ/CDdndRcLsmWox2lJJoH9n9CwPgzfonaerFVuAdiomGObPBGtg1IcrLywHIz8/ntdde4/zzz2/z/JIlS3j22WcRQvDll18SExMT1n2xnRncP3lhYsbIOP7vnOlEWoI4hrKfzkpLwdk0eFuzAIohOAvKHGp/UQNCHZgF3mdaBy4wXnA9hm7tY3/tiESYfSRYup8d1lvLly8nOzubM844g0ceeYTY2FgeffTR1l1qTzvtNMaNG0dmZiZXXHEFf/vb31qPnT59euv/33TTTWRkZOBwOMjIyOCOO+4IeK3BJJdJDKCdpfX84uUt1LsGx5KDLUyqwlXR6TgbB1ddh4uILWf77nVBv87vzjJhdQd/GUxdKPxLdVPnKg/6tQBy7EezoH4uSm9+pDPSYUp20HbMlfzkVzeAslOiefrCWYwfZFuYL0tLHvQhC4ASnHUODlfjDtJKW4dRFcER9jEDci2AHY4v2B9f0fMDJoyHaVNkyA4A+RUOsJFxdv55wSwWTg7+MKKeMKoKyU2Dt0vjUEIMTNAW1Q7c1yPLUYbdFDtg1/tv/VM0xnZzv0BVYfo0mCDXkx0oMmiDwGY2cM8ZU7juhEwMIZ7YcGZaMo6GMGjNAvoABe3e4uDNPjucUWjkRk3s/oUB5O+v7aS/1WyG2bMgbejdcBrMZNAG0QVHjuKRc6cTbw/CoPIeMCoKaY7waM1C8JZIPNzugnqEMnD/JlOdVZgN9gG7XoNWzXuW99uPr42JhmPnQFzcgNUi+cmgDbKZI+N47uKjmJoa3f2LA2xJWhKO+vBozQJ4fQMz9MqnCbymgevasehupsZkD9j1AHY5vmJvfNmPD4zM8A/fCsBEBKn3ZNAOgKQoC4+tmMHy3IEbiK0qMNIVmpZ0X/mCuETi4ep8wVsEvCO57noM6sD+e6ytf5r6eIP/htfUHDD0/cf9pz/9KUlJSUyZ8uOau1u3buXoo49m6tSpnHHGGdTX13d47Lp165g0aRKZmZmsXr26w9d88sknzJgxA6PRyCuvvNLnOgcrGbQDxGRQuXnhJG49ZfKATNs9Iy2Jprrwac0CeDwD06IFKK4f2NCL8DnIignuwuCHS4wci3dKpn8IVz9dcsklrFvXdujd5ZdfzurVq/nuu+8466yzuO+++9odp2ka11xzDe+88w47d+7k+eefZ+fOne1eN2rUKJ5++ul2ExqGChm0A2zJ1DSeWDGD1OjgDZpXgDGu8Fuk2T2AQbuvZGD6gw81w+tBUQbmRy535FJWHPU3EiLHBOR8c+fOJT4+vs1je/bsYe7cuQAsWLCAV199td1xGzduJDMzk3HjxmE2mznvvPNYs2ZNu9eNGTOGadOmoQ7RoWZD87Ma5LJSonn24iM5eVJw+gkXp42gqS74s6wCzeUK3hKJh9t1sAGUgZ02HeOtIzM6uAuDW03RLMn9PSdNvhajIfAzvQ6Vk5PTGpovv/xyu5W5YPisztUdGbQhEmsz8YclU/jLsmkBbd0qwHjPwEwxDSyB2z1wfbQuj4bP1Pe1VvtqZhB/5DLicrlozuOMTzomaNc41FNPPcXf/vY3Zs6cSUNDA2ZzcIM9nIXP2J8h6rjxicwcGcejn/3Ai18XovVzRvRpqSNorAi/1qzR5GOgZ4PX63HEUzqg1xzhqmJU9CTy63cH7JwWYyTHZl7OtIzFKAO4+8fkyZN57733AH83wtq1a9u9ZrisztUd2aIdBGxmA786cQJPXzSLycn9mx46wReOrVkwGAf+xl3pAN8QazFTDdwU7YnJJ7DymH+SO/KMAQ1Z+HFlLl3Xufvuu7nqqqvavebII49k7969HDhwAI/HwwsvvMCSJUsGtM7BQAbtIDI5OYqnL5zFr07MxNaHZRdPSU2ksSb8WrMAagiCdn95aFYzy3CWkxIxtl/niLalctYRf+D0abcSYYnv/oB+WrFiBUcffTS7d+8mIyODJ598kueff56JEycyefJk0tLSuPTSSwEoLi7mtNNOA8BoNPLwww+zaNEisrKyOPfcc8nJ8fdT33bbbbz55psAbNq0iYyMDF5++WWuvPLK1tcMFXL1rkGqpM7Jnz7Yw4Yfer4J33VJI2msDs+gjYipZvuetwb0mlF2E6tOKAAG/kfgB3sqa2s29fo4g2pixqizmTPuIoyG8BtZMlzJPtpBKjXGxl+W5/L+92U8sH4vVU1dD0damJJIY2V4hiwA6sDX3uDwopkTMHiCvA95B8Y2lRBnS6PGWdzjY8aPOJa5E68i1p4WxMqkYJBBO8gtmJzMnDHx/O3TH3hjWzE+vePWV7aw0Uj4Bq1g4Me1AjSKBGIY+KBVFJhpy+CDHgRtQsQY5k26mtEJMwegMikYZB9tGIiymli1YBIvXzaH03NSMBx202N+cjyNVeEbshC6oC1rCt3Nw4lNpUSaEzp93mqK4sRJv+DCox+XIRvmZNCGkYxYG3ecls0Llx7FgslJtMTtNCJDWlcg6AO0ctfhDoTohhiAAZ0jIse3e9xmiuXYzMu57Lj/MH3UmagDPLFCCjx5MyyM7S1vZP2WUtgRmpAKJGPkFvbu3zbg102ItvCr4/IG/LotvIqJp32VuHwN2M3xzBp9DtNGLsFkCM9helLHZB9tGJuQFMmEhZlUz3CxZVMV+/fUoeuhrqpvvNrAzQo7VFW9G90Uh+qtCcn1TcLL7PhZKHHZTE0/XY4kGKJk18EQEJ9o5aRT0znvkkyyc+MwGEK7q0Nf+LyhCVqAJjrvJw2qiAzUSVdwxNSbOWLUsoCFbF+XNHS5XBx11FHk5uaSk5PD7bff3uH5h/qShsEgg3YIiYoxc/xJqZx/2QRmzE4kMip8fmFxewZuQZnDVToHbvcDUFDipqFOuR7jzHtQk49DUQP779TXJQ0tFgvr169n69atbNmyhXXr1vHll1+2e91QX9IwGMLnJ1HqMXuEkSOPSWLW0SMoym9i985a8vY14PMN3u54t3vglkg8XF6lYGxKkC9ijkdJmYuaMhfFGtwW9Ny5c8nLy2vz2OFLGi5atIjf//73bV6jKAqRkf4bq16vF6/X2+G03jFjxgAM2SUNg0EG7RCmKAoZoyPJGB2J262xf3c9e3bWUlYSutZjZ0IZtLvymzgxGEGrGFDic1FS5qHETxuwtWg70rKk4ZlnntnpkobgX6h75syZ7Nu3j2uuuYbZs2cPcKVDk3xLGiYsFgPZ0+I487yx/GTleKYfmYA9YnC8zyqKPqBLJB6uuNKJMPZvMZ82IjJQx5yN4agHMOT8EjVhekhDFnq+pKHBYGDLli0UFhayceNGtm/fPsCVDk2D4ydNGlCx8RZmH5fMkcckUZjfxJ4dteTtb0DTQtO1YDCFbixrC6c6AjsNfT+BPQN1xJEoI45CGYRTZHuypOGhYmNjOfHEE1m3bl2bm2pS38igHcZUVWHUmEhGjYnE69Upym+iIK+R/LxGGusHbqaZwRT6vc0qXRGM6lWjU4HoTNSEGSiJM1FsycEqLSDKy8tJSkrqcknDiooKTCYTsbGxOJ1O3n//fVatWhWCaoceOWFB6lBNlZuCvEYK8hopLXYE9UaaPaqeHfveCNr5e+LUo0ZwbGI3EyZsqShx2Six2Sgxk1FMg3NG3ooVK/j444+prKwkOTmZO++8k8bGRh555BEAli1bxh/+8AcURaG4uJjLL7+ct99+m23btrFy5Uo0TUPXdc4991xuu+02wL+k4axZs1iyZAmbNm3irLPOoqamBqvVSkpKCjt27AjlpzzoyaCVuqVpgvJSJyWFTRQVNFFe4gxo8EbEVrJ999sBO19fjE2N5LIj9rZ90JKIEpvV/CcbxRIXmuKksCe7DqRuGQwKqel2UtPtzJg9As2nU17morrSRXWVm9oqN9VVblzOPva1KqFfEKeyUUHEH4EaORIlcixK1BiUAVhQWxoeZNBKvWYwqq3Beyinw0dNtZuaqh//9CyAByZoVVUlNjaW2NhY4uPjGTFiBKmpqaSmphIVFcBRB5J0GBm0UsDY7EZsdiNpGW33xHI6fdRUuamv9eByajgdPpzNf7ucGppBxWKx4PF4+rRBo8FgwGq1YrPZsNvt2O12IiIiiI2NJS4urvVPbGysHGQvhYTso5UGDSH8W4673W40TWvz+OH/r6r+cLZYLBiNsr0gDW4yaCVJkoJM/h4lSUFSUFDAiSeeSHZ2Njk5OTz00EMAVFdXs2DBAiZMmMCCBQuoqel4icZTTjmF2NhYFi9e3Ok15Epa4UEGrSQFidFo5IEHHmDnzp18+eWXPPLII+zcuZPVq1czf/589u7dy/z581m9enWHx994440899xzXV5DrqQVHmTQDlL9aQ0dPHiQGTNmMH36dHJycnj00Uc7vMatt97KtGnTmD59OgsXLqS4uOc7skrdS01NZcaMGQBERUWRlZVFUVERa9asYeXKlQCsXLmSN954o8Pj58+f3+1oiDFjxjBt2jR5k2+Qk/86g1R/WkOpqal88cUXbNmyha+++orVq1d3GKI33ngj27ZtY8uWLSxevJi77rprID61YSkvL49vv/2W2bNnU1ZWRmpqKgApKSmUlZWFuDop2GTQDlL9aQ2ZzWYsFv9q/W63G72T/W2io6Nb/7+pqanDtUel/mtsbGT58uU8+OCDbb7m4F/KUn7dhz45LiYM9KU1VFBQwOmnn86+ffu47777SEvreEWpW265hWeffZaYmBg++uijoH0Ow5XX62X58uVccMEFLFu2DIDk5GRKSkpITU2lpKSEpKSkEFcpBZts0Q5yfW0NjRw5km3btrFv3z6eeeaZTgP5nnvuoaCggAsuuICHH3444PUPZ0IILrvsMrKysrj++utbH1+yZAnPPPMMAM888wxLly4NVYnSQBHSoOXxeMTChQvFAw880PrYxIkTRXFxsRBCiOLiYjFx4sRuz3PppZeKl19+ucvXHDx4UOTk5PSv4EEgPz9fnHDCCSIrK0tkZ2eLBx98UAghxEsvvSSys7OFoihi06ZNnR7/zjvviIkTJ4rx48eLP/zhD/2q5dNPPxWAmDp1qsjNzRW5ubli7dq1orKyUpx00kkiMzNTzJ8/X1RVVQkhhNi0aZO47LLLWo8/7rjjRGJiorBarSI9PV2sW7dOCCHErbfeKtasWSOEEGLjxo0iPT1d2O12ER8fL7Kzs/tVsxQcMmgHKV3XxUUXXSR++ctftnn8hhtuaA2AP/zhD+LGG29sd2xBQYFwOBxCCCGqq6vFhAkTxLZt29q9bs+ePa3//9e//lUsX748gJ9BaBQXF4uvv/5aCCFEfX29mDBhgtixY4fYuXOn+P7778W8efM6DVqfzyfGjRsn9u/fL9xut5g2bZrYsWPHQJYvDVGy62CQ+uyzz3juuedYv34906dPZ/r06bz99tvcfPPNvP/++0yYMIEPPviAm2++GYDNmzdz+eWXA7Br1y5mz55Nbm4u8+bN44YbbmDq1KmAfzfUzZs3A3DzzTczZcoUpk2bxnvvvdc6hCycdXYTMSsri0mTJnV57MaNG8nMzGTcuHGYzWbOO+881qxZMxBlS0OcvBk2SB133HGdLrDy4Ycftnts1qxZ/OMf/wD8u5xu29bxItYtrwF49dVXA1Dp4HXoTcSeKCoqYuTIka0fZ2Rk8NVXXwWrPGkYkS1aaUjq6iaiJA00GbTSkNPRkKqeSE9Pb7MNd2FhIenp6cEoURpmZNBKQ4roZEhVTxx55JHs3buXAwcO4PF4eOGFF1iyZEmQKpWGExm00pDS2U3E119/nYyMDL744gtOP/10Fi1aBEBxcTGnnXYa4J/2/PDDD7No0SKysrI499xzycnJCeWnIw0Rcj1aSZKkIJMtWkmSpCCTQStJkhRkMmglSZKCTAatJElSkMmglSRJCjIZtJIkSUEmg1aSJCnIZNBKkiQFmQxaSZKkIJNBK0mSFGQyaCVJkoJMBq0kSVKQyaCVJEkKMhm0kiRJQSaDVpIkKchk0EqSJAWZDFpJkqQgk0ErSZIUZDJoJUmSgkwGrSRJUpDJoJUkSQoyGbSSJElB9v8BtRxsVemo9qMAAAAASUVORK5CYII=\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('pip-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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>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>32258663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.0.0</td>\n",
       "      <td>3669617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.2.0</td>\n",
       "      <td>2108452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.2.1</td>\n",
       "      <td>1506885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.5.1</td>\n",
       "      <td>1393110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.7.1</td>\n",
       "      <td>1323398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52.0.0</td>\n",
       "      <td>1172152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39.0.1</td>\n",
       "      <td>1140811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.3.2</td>\n",
       "      <td>1098692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53.1.0</td>\n",
       "      <td>986801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  setuptools_version         c\n",
       "0             40.8.0  32258663\n",
       "1             53.0.0   3669617\n",
       "2             41.2.0   2108452\n",
       "3             49.2.1   1506885\n",
       "4             41.5.1   1393110\n",
       "5             40.7.1   1323398\n",
       "6             52.0.0   1172152\n",
       "7             39.0.1   1140811\n",
       "8             50.3.2   1098692\n",
       "9             53.1.0    986801"
      ]
     },
     "execution_count": 63,
     "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 >= '{start_time}' AND accessed_at < '{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": 64,
   "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('setuptools-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import csv\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = [f for f in glob('/home/ben/piwheels-logs/*') if '.' not in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "ONE_TB = 1e12\n",
    "\n",
    "bandwidth = {}\n",
    "bandwidth_totals = defaultdict(int)\n",
    "for month in sorted(months):\n",
    "    m = month.split('/')[-1]\n",
    "    bandwidth[m] = defaultdict(int)\n",
    "    files = glob(month + '/*-sizes.csv')\n",
    "    for file in files:\n",
    "        with open(file) as f:\n",
    "            r = csv.reader(f)\n",
    "            for log_type, count in r:\n",
    "                bandwidth[m][log_type] += int(count)\n",
    "                bandwidth_totals[m] += int(count) / ONE_TB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "201906 1.09 TB\n",
      "201907 2.97 TB\n",
      "201908 2.90 TB\n",
      "201909 3.36 TB\n",
      "201910 3.82 TB\n",
      "201911 3.81 TB\n",
      "201912 3.52 TB\n",
      "202001 3.59 TB\n",
      "202002 3.91 TB\n",
      "202003 4.11 TB\n",
      "202004 3.89 TB\n",
      "202005 4.24 TB\n",
      "202006 3.78 TB\n",
      "202007 3.92 TB\n",
      "202008 6.38 TB\n",
      "202009 6.27 TB\n",
      "202010 6.58 TB\n",
      "202011 4.43 TB\n",
      "202012 3.32 TB\n",
      "202101 3.79 TB\n",
      "202102 5.87 TB\n",
      "202103 7.07 TB\n",
      "202104 0.46 TB\n"
     ]
    }
   ],
   "source": [
    "for month, data in bandwidth_totals.items():\n",
    "    print(month, f'{data:.2f} TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "bandwidth_in_period = {\n",
    "    'pip downloads': bandwidth['202101']['LOGDOWNLOAD'] + bandwidth['202102']['LOGDOWNLOAD'] + bandwidth['202103']['LOGDOWNLOAD'],\n",
    "    'Misc': bandwidth['202101']['OTHER'] + bandwidth['202102']['OTHER'] + bandwidth['202103']['OTHER'],\n",
    "    'Searches': bandwidth['202101']['LOGSEARCH'] + bandwidth['202102']['LOGSEARCH'] + bandwidth['202103']['LOGSEARCH'],\n",
    "    '': (\n",
    "        bandwidth['202101']['LOGPROJECT'] + bandwidth['202102']['LOGPROJECT'] + bandwidth['202103']['LOGPROJECT'] +\n",
    "        bandwidth['202101']['LOGJSON'] + bandwidth['202102']['LOGJSON'] + bandwidth['202103']['LOGJSON'] +\n",
    "        bandwidth['202101']['LOGPAGE'] + bandwidth['202102']['LOGPAGE'] + bandwidth['202103']['LOGPAGE']\n",
    "    ),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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(bandwidth_in_period.values(), labels=bandwidth_in_period.keys())\n",
    "plt.title('Bandwidth breakdown')\n",
    "\n",
    "plt.savefig('bandwidth.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pip downloads 83.86%\n",
      "Misc 13.77%\n",
      "pip searches 2.32%\n",
      " 0.05%\n"
     ]
    }
   ],
   "source": [
    "for k, v in bandwidth_in_period.items():\n",
    "    pc = 100 * (v / sum(bandwidth_in_period.values()))\n",
    "    print(k, f'{pc:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gQEkCAAAwcAY6AABcqcTEBB8tqXHLOXky30c5AAQTjiQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADDgyyQBP+BLDwGg6eFIEgAAgAElCQAAwICSBAAAYMA1SQDq4PopALiII0kAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAy8+jLJkpISvfTSSzpx4oQcDoceeugh3XjjjXZnAwAACBivSlJ2draSk5M1ZcoUVVVVqaKiwu5cAAAAAeXxdFtpaakOHTqk4cOHS5KcTqeio6NtDwYAABBIDsuyrMsNOHbsmJYsWaJ27drpiy++UFJSkiZMmKDIyMg643JycpSTkyNJysjIUGVlpX2pfcTpdKqqqirQMbxCVnv4K2tERLjt6/BGRYXn5yVZG86brL7Ac8seZL1UeESE7evwRqWfzlyFh5tfSzyebquurtbRo0c1ceJEdenSRdnZ2Xr99dc1duzYOuNcLpdcLpf7dlFRUSMj2y82NrZJ5JTIahf/ZfXVj8Y2jnfbStaG8tf+znPLHmS9VHA8s/z33EpIMG+xx9NtrVu3VuvWrdWlSxdJUr9+/XT06FHfpgMAAAgyHkvSNddco9atWys/P1+SlJeXp3bt2tkeDAAAIJC8+nTbxIkTtWDBAlVVVSkuLk4PP/yw3bkAAAACyquS1KFDB2VkZNidBQAAIGh4VZIAAEDjJCQm+mY5jZw//+RJn+T4IeBnSQAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABk5vBk2ePFmRkZEKCQlRaGioMjIy7M4FAAAQUF6VJElKT09Xy5Yt7cwCAAAQNDjdBgAAYOCwLMvyNGjy5Mlq3ry5JOm2226Ty+W6ZExOTo5ycnIkSRkZGaqsrPRxVN9zOp2qqqoKdAyvkNUe/soaERFu+zq8UVHh+XlJ1obzJqsv8Nyyh7+yhkdE2L4Ob1RWVHgc05Sy+kJ4uPm1xKuSdObMGcXExOjs2bP605/+pPvvv1/dunW77Dz5+flXltSPYmNjVVRUFOgYXiGrPfyVNTExwfZ1eOPkSc/PS7I2nDdZfYHnlj38lTUhMdH2dXgj/+RJj2OaUlZfSEgwv5Z4dbotJiZGktSqVSvdeuut+vTTT32XDAAAIAh5LEnl5eUqKytz//++ffvUvn1724MBAAAEksdPt509e1Zz586VJFVXV2vgwIFKTk62OxcAAEBAeSxJbdu2VVZWlj+yAAAABA2+AgAAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwcAY6AAD8ECQmJvhoSY1bzsmT+T7KAVz9OJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIAB35PkB3w/CgAATQ9HkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMPC6JNXU1Gj69OnKyMiwMw8AAEBQ8LokrVu3TomJiXZmAQAACBpelaTTp09r9+7dGjFihN15AAAAgoJXP0vyyiuvaPz48SorK6t3TE5OjnJyciRJGRkZio2N9U1CGzmdziaR01f8ta1N6XFtSll9oSltK1ntwevApZpSVl9oStsa6KweS9KuXbvUqlUrJSUl6cCBA/WOc7lccrlc7ttFRUW+SWij2NhYP+X01W+3NY6//k3897g2HvuACVkb6urL2ni8DlwqOPYA7/aBppTVFxISzFvssSR9/PHH2rlzpz766CNVVlaqrKxMCxYs0COPPOLzkAAAAMHCY0kaN26cxo0bJ0k6cOCA1q5dS0ECAABXPa+uSQKCUWKirw4IN245J0/m+ygHACCYNKgkde/eXd27d7crS4PwBgkAAOzEN24DAAAYUJIAAAAMKEkAAAAGXLiNOrjWC0BTkuCjn8tq7Ctf/smTPsmB4MKRJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGDgDHQAAEBwSUxM8NGSGreckyfzfZQDuDIcSQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGHj8WZLKykqlp6erqqpK1dXV6tevn9LS0vyRDQAAIGA8lqSwsDClp6crMjJSVVVVeuqpp5ScnKwbb7zRH/kAAAACwuPpNofDocjISElSdXW1qqur5XA4bA8GAAAQSA7LsixPg2pqavT444/r1KlTGjlypMaPH3/JmJycHOXk5EiSMjIyVFlZ6fu03xEREW7r8r1VUeF5O8nacGS1B1ntQVZ7eJM1PCLCD0k8q6yo8DiGrA3nTVZfCA837/MeT7dJUkhIiLKyslRSUqK5c+fq+PHjat++fZ0xLpdLLpfLfbuoqKgRcb2RYPPyvePddpK1ochqD7Lag6z28CZrcCQlq13s7xIXJSSYt7hBn26Ljo5W9+7dtWfPHl9kAgAACFoeS1JxcbFKSkokXfyk2759+5SYmGh7MAAAgEDyeLrtm2++0aJFi1RTUyPLspSamqo+ffr4IxsAAEDAeCxJN9xwg+bMmeOPLAAAAEGDb9wGAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABg4PQ0oKirSokWL9O2338rhcMjlcmn06NH+yAYAABAwHktSaGio7rnnHiUlJamsrEwzZsxQjx491K5dO3/kAwAACAiPp9uuvfZaJSUlSZKaNWumxMREnTlzxvZgAAAAgdSga5IKCwt19OhRde7c2a48AAAAQcFhWZblzcDy8nKlp6frl7/8pVJSUi65PycnRzk5OZKkjIwMVVZW+jbp90REhNu6fG9VVHjeTrI2HFntQVZ7kNUe3mQNj4jwQxLPKisqPI4ha8N5k9UXwsPN+7zHa5IkqaqqSvPmzdOgQYOMBUmSXC6XXC6X+3ZRUdEVxGyIBJuX7x3vtpOsDUVWe5DVHmS1hzdZgyMpWe1if5e4KCHBvMUeT7dZlqWXXnpJiYmJuuOOO3weDAAAIBh5PJL08ccfa/PmzWrfvr2mTZsmSbr77rvVu3dv28MBAAAEiseSdNNNN2n16tX+yAIAABA0+MZtAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMDA6WnA4sWLtXv3brVq1Urz5s3zRyYAAICA83gkaejQoZo5c6Y/sgAAAAQNjyWpW7duat68uT+yAAAABA2uSQIAADDweE2St3JycpSTkyNJysjIUGxsrK8WHdSa0naS1R5ktQdZ7UFWe5DVHoHO6rOS5HK55HK53LeLiop8teh6JNi8fO94t51kbSiy2oOs9iCrPbzJGhxJyWoX+7vERQkJ5i3mdBsAAICBxyNJzz//vA4ePKhz587p3//935WWlqbhw4f7IxsAAEDAeCxJf/jDH/wQAwAAILhwug0AAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMHB6M2jPnj3Kzs5WTU2NRowYoTFjxtgcCwAAILA8HkmqqanRsmXLNHPmTM2fP1+5ubn68ssv/ZENAAAgYDyWpE8//VTx8fFq27atnE6n+vfvrx07dvgjGwAAQMB4LElnzpxR69at3bdbt26tM2fO2BoKAAAg0Ly6JskbOTk5ysnJkSRlZGQoISHBV4s2sixbF98AnreTrFeCrPYgqz3Iag8v3keCJKxX73hkbTB7m4RnHo8kxcTE6PTp0+7bp0+fVkxMzCXjXC6XMjIylJGR4duENpoxY0agI3iNrPYgqz3Iag+y2oOs9mhKWevjsSR16tRJBQUFKiwsVFVVld5//33dcsst/sgGAAAQMB5Pt4WGhmrixIl69tlnVVNTo2HDhun666/3RzYAAICA8eqapN69e6t37952Z/E7l8sV6AheI6s9yGoPstqDrPYgqz2aUtb6OCwrSK7OAgAACCL8LAkAAIDBD6Ikffjhh0pLS9PJkycDHcUoLS1NCxYscN+urq7WpEmTgv6Tgvfcc0+gIzSYp8yzZs3SZ5995qc0dQX7fvpdr732mh577DFNnTpV06ZN05EjRwId6bJOnz6tOXPm6JFHHtHvfvc7ZWdnq6qqqt7xb775pioqKvyY8OLrwMqVK92333jjDa1evdqvGbz1q1/9StOmTdNjjz2madOmae3ataqpqQl0LI+awmtW7WNb+19hYWG9YwP5eiU13feuhvDZ9yQFs9zcXN10003Kzc1VWlqa1/PV1NQoJMT+HhkREaETJ06osrJS4eHh2rdvn/FrFnB1u9L91N8++eQT7dq1S5mZmQoLC1NxcfFlC0egWZaluXPn6ic/+YmmT5+umpoaLVmyRKtWrar3TXPdunUaNGiQIiIi/JYzLCxM27dv15gxY9SyZUu/rfdKhIeHKysrS5J09uxZLViwQGVlZUG93zYV331sg50v3ruqq6sVGhpqU8LGu+pLUnl5uQ4fPqz09HRlZmYqLS1NBw4c0OrVqxUZGalTp06pe/fu+s1vfqOQkBDdc889uu2225SXl6dJkybppptu8kvOXr16affu3erXr59yc3M1YMAAHT58WNLFn4bJzs7WhQsXFB4erocfflgJCQlKT0/X/fffrw4dOkiSnnzySU2aNMl92x8OHDigtWvXur8PY9myZerUqZOGDh2qyZMna8iQIdq1a5eqqqr02GOPKTEx0W/Z6nO5zIFS335aX87du3dr5cqVioiIUNeuXVVYWOi37yT55ptv1KJFC4WFhUmS+w39888/14oVK1ReXq6WLVvq4Ycf1rXXXqtZs2bphhtu0MGDB1VTU6OHHnpInTt39ktWSdq/f7/Cw8M1bNgwSVJISIjuu+8+/fa3v9Vdd92l1atXa+/evXI4HBoxYoQsy9KZM2f0xz/+US1btlR6erpfcoaEhMjlcunNN9/U3XffXee+wsJCvfjiizp37pz7sY2KitLUqVP1l7/8RSEhISovL9ejjz6qhQsXyun030t7q1at9MADD+iJJ57QXXfdJcuy9Ne//lUHDx7UhQsXNHLkSN12222SpNdff11btmxRSEiIkpOT9etf/9pvOWuVl5drzpw5KikpUVVVlcaOHatbb71VhYWFmj17trp27apPPvlEMTExmj59usLDw/2e8fvqe25J0ubNm/XSSy8F5LklXdl718aNG7V9+3aVl5erpqZGf/zjH/2auSGu+tNtO3bsUHJyshISEtSiRQt9/vnnki7+402cOFHz58/XV199pQ8//FCSVFFRoc6dOysrK8tvBUmSBgwYoNzcXFVWVuqLL75Qly5d3PclJCTo6aef1pw5c5SWlqa//e1vkqRhw4Zp48aNkqT8/HxduHDBrwXJGy1atFBmZqZ+8pOfaO3atYGOE7Tq209NKisrtXTpUs2cOVOZmZkqLi72Y1KpZ8+eOn36tH7/+9/r5Zdf1sGDB1VVVaXly5drypQpyszM1LBhw7Rq1Sr3PBUVFcrKytKkSZP04osv+jXviRMn1LFjxzrToqKiFBsbq/Xr1+vrr7/WnDlzNHfuXA0aNEijR49WTEyM0tPT/VaQao0cOVJbt25VaWlpnenLly/XkCFDNHfuXA0cOFDLly9XVFSUOnTooIMHD0qSdu/erZ49e/q1INVq27atampqdPbsWa1fv15RUVGaPXu2Zs+erffee0+FhYX66KOPtHPnTj333HPKysrSz3/+c7/nlC4esZs6daoyMzOVnp6ulStXqvbzSwUFBRo1apT+/Oc/KyoqStu2bfN7vsrKSveptqysrKB+bklX9t4lSUePHtWUKVOCuiBJP4AjSbm5uRo9erQkqX///tq6dav69Omjzp07q23btpLkbr79+vVTSEiI+vXr5/ecN9xwg77++mvl5uaqV69ede4rLS3VokWLdOrUKUkXD09KUmpqqtasWaPx48drw4YNAT0SUp+UlBRJUlJSkruI4lL17acm+fn5iouLU1xcnCRp4MCB7p8E8ofIyEhlZmbq0KFDOnDggObPn69//dd/1YkTJ/TMM89IuniquvYv3dqMktStWzeVlpaqpKRE0dHRfstcnwMHDmjkyJHuw/3NmzcPaJ6oqCgNHjxY69atq3ME48iRI5o6daokafDgwfrrX/8q6eK+8v777+vmm29Wbm6uRo4cGZDc37V3714dP37cXTBKS0tVUFCgvLw8DR061H0KM1CPtWVZWrVqlQ4dOiSHw6EzZ87o7NmzkqS4uDj3H5pJSUn6+uuv/Z7v+6fbjh8/HtTPrSt575KkHj16BPz55o2ruiSdP39e+/fv1/Hjx+VwONwXFl7uO5/CwsL8ch2SyS233KJXX31Vs2bN0rlz59zT/+d//kfdu3d3X8RX27wjIiLUo0cP7dy5Ux988EFALpYLDQ3Vd79F4sKFC3Xur/2rNiQkpM4TJJA8Zfa3+vbTW2+9NahyfldISIi6d++u7t27q3379nrnnXfUrl07Pfvss17N73A4bE74/9q1a6ft27fXmVZaWqqioiK1adPGbzm8dfvtt+vxxx/36o+eW265RatWrdL58+f1+eef6+abb7Y/oMFXX32lkJAQtWrVSpZl6f7771dycnKdMXv37g1Itu/bunWriouLlZGRIafTqcmTJ6uyslKS3KeQpYv7eO30QAvW51athr53SfLr9X6NcVWfbtu2bZsGDx6sxYsXa9GiRXrxxRcVFxenw4cP69NPP1VhYaFqamr0wQcf+PXUWn2GDRumO++8U+3bt68zvbS01H0xXO3ptVojRoxQdna2OnXqFJBWHhsbqy+//FIXLlxQSUmJ8vLy/J6hoYItc337qWVZxpwJCQkqLCx0f+rl/fff92ve/Px8FRQUuG8fO3ZMiYmJKi4u1ieffCJJqqqq0okTJ9xjajMePnxYUVFRioqK8lveH//4x6qoqNCmTZskXfxLfOXKlRo6dKh69uypd999113gz58/L+ni0bLy8nK/Zfyu5s2bKzU1VevXr3dPu/HGG92P4datW92vV5GRkerUqZOys7PVp0+fgPyBV1xcrKVLl2rUqFFyOBxKTk7WP//5T/fF/Pn5+SovL1ePHj20ceNG96cGax9rfystLVWrVq3kdDq1f//+gBwtaoiEhISgfW7VupL3rqbiqj6SlJube8l575SUFL377rvq3Lmzli1b5r5wu2/fvgFK+f9at27tPuXyXT//+c+1aNEivfbaa5ccBUtKSlKzZs3cF6X6S3V1tcLCwhQbG6vU1FRNmTJFcXFxl1z7EUyCNXN9+2lubq4xZ3h4uCZNmqTnnntOERER6tSpk1/zlpeXa/ny5SopKVFoaKji4+P1wAMPyOVyKTs7W6Wlpaqurtbo0aPdP2EUHh6u6dOnq7q6Wg899JBf8zocDk2dOlUvv/yy1qxZI8uy1KtXL919990KCQlRQUGBpk6dKqfTqREjRmjUqFFyuVx69tln3dcm+dsdd9yht99+23174sSJWrx4sd544w33hbu1+vfvrz//+c+aNWuW3/LVXjdT+8mkQYMG6Y477pAkDR8+XIWFhXr88cclXbywf9q0aUpOTtaxY8c0Y8YMOZ1O9erVS+PGjfNb5trn/8CBA5WZmakpU6aoU6dOQfFhkstxOp2aMmVKUD63al3Je1dT8YP8xu3vf2qoKav9FM78+fP9+lfksWPHtGTJEs2ePdtv62ysppi5PuXl5YqMjJRlWVq2bJni4+Pdb1LBZtasWbrnnnv8XuaA77qanv/wn6v6SNLVbtOmTfr73/+ue++9168F6Z///KfeeustTZgwwW/rbKymmPlycnJytGnTJlVVValjx47uj1gDuNTV9vyH//wgjyQBAAB4clVfuA0AAHClKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGDwf+KXzRvEgtDaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bandwidth_by_month = {\n",
    "    'Apr': bandwidth_totals['202004'],\n",
    "    'May': bandwidth_totals['202005'],\n",
    "    'Jun': bandwidth_totals['202006'],\n",
    "    'Jul': bandwidth_totals['202007'],\n",
    "    'Aug': bandwidth_totals['202008'],\n",
    "    'Sep': bandwidth_totals['202009'],\n",
    "    'Oct': bandwidth_totals['202010'],\n",
    "    'Nov': bandwidth_totals['202011'],\n",
    "    'Dec': bandwidth_totals['202012'],\n",
    "    'Jan': bandwidth_totals['202101'],\n",
    "    'Feb': bandwidth_totals['202102'],\n",
    "    'Mar': bandwidth_totals['202103'],\n",
    "}\n",
    "\n",
    "colors = ['blue']*9 + ['red']*3\n",
    "plt.bar(range(len(bandwidth_by_month)), bandwidth_by_month.values(), color=colors)\n",
    "plt.title('Bandwidth by month (TB)')\n",
    "plt.xticks(range(len(bandwidth_by_month)), list(bandwidth_by_month.keys()))\n",
    "\n",
    "plt.savefig('bandwidth-by-month.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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}