2020q2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/piwheels/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/dialects/postgresql/base.py:3470: SAWarning: Predicate of partial index files_size ignored during reflection\n",
      "  % idx_name\n"
     ]
    }
   ],
   "source": [
    "engine = create_engine('postgres:///piwheels')\n",
    "conn = engine.connect()\n",
    "meta = MetaData()\n",
    "meta.bind = conn\n",
    "meta.reflect(views=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2020-04-01'\n",
    "end_date = '2020-06-30'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3631509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   downloads\n",
       "0    3631509"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>1239168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>1271663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>1120678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           m        c\n",
       "0 2020-04-01  1239168\n",
       "1 2020-05-01  1271663\n",
       "2 2020-06-01  1120678"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_by_month = pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', accessed_at) m, COUNT(*) AS c\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY m\n",
    "    ORDER BY m\n",
    "\"\"\", conn)\n",
    "\n",
    "downloads_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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",
    "    'Jul': 814607,\n",
    "    'Aug': 804750,\n",
    "    'Sep': 795325,\n",
    "    'Oct': 913798,\n",
    "    'Nov': 1014577,\n",
    "    'Dec': 1017563,\n",
    "    'Jan': 1060112,\n",
    "    'Feb': 980532,\n",
    "    'Mar': 1276799,\n",
    "    'Apr': downloads_by_month.c[0],\n",
    "    'May': downloads_by_month.c[1],\n",
    "    'Jun': downloads_by_month.c[2],\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": 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>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32033978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   searches\n",
       "0  32033978"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS searches\n",
    "    FROM searches\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{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>month</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>10428233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>10841076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>10764669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  searches\n",
       "0 2020-04-01  10428233\n",
       "1 2020-05-01  10841076\n",
       "2 2020-06-01  10764669"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', accessed_at) AS month, COUNT(*) AS searches\n",
    "    FROM searches\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>345489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>319725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>334385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>331875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>340062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>341257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>313979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>346689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>340698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>376936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           day  searches\n",
       "0   2020-04-01    345489\n",
       "1   2020-04-02    319725\n",
       "2   2020-04-03    334385\n",
       "3   2020-04-04    331875\n",
       "4   2020-04-05    340062\n",
       "..         ...       ...\n",
       "86  2020-06-26    341257\n",
       "87  2020-06-27    313979\n",
       "88  2020-06-28    346689\n",
       "89  2020-06-29    340698\n",
       "90  2020-06-30    376936\n",
       "\n",
       "[91 rows x 2 columns]"
      ]
     },
     "execution_count": 107,
     "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",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>345489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>319725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>334385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>331875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>340062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>341257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>313979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>346689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>340698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>376936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           day  searches\n",
       "0   2020-04-01    345489\n",
       "1   2020-04-02    319725\n",
       "2   2020-04-03    334385\n",
       "3   2020-04-04    331875\n",
       "4   2020-04-05    340062\n",
       "..         ...       ...\n",
       "86  2020-06-26    341257\n",
       "87  2020-06-27    313979\n",
       "88  2020-06-28    346689\n",
       "89  2020-06-29    340698\n",
       "90  2020-06-30    376936\n",
       "\n",
       "[91 rows x 2 columns]"
      ]
     },
     "execution_count": 115,
     "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",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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 = df.plot.bar(title='Searches by day')\n",
    "p.figure.savefig('searches-by-day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>25996 days 22:33:44.131687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_time_saved_in_quarter\n",
       "0  25996 days 22:33:44.131687"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>7979 days 11:21:10.828580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>9256 days 05:45:50.646303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>8751 days 05:26:42.656804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month       time_saved_in_month\n",
       "0 2020-04-01 7979 days 11:21:10.828580\n",
       "1 2020-05-01 9256 days 05:45:50.646303\n",
       "2 2020-06-01 8751 days 05:26:42.656804"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', d.accessed_at) as month, JUSTIFY_INTERVAL(SUM(\n",
    "        CASE f.platform_tag\n",
    "            WHEN 'linux_armv7l' THEN 1\n",
    "            WHEN 'linux_armv6l' THEN 6\n",
    "            ELSE 0\n",
    "        END *\n",
    "        CASE\n",
    "            WHEN b.duration > INTERVAL '1 week' THEN INTERVAL '0'\n",
    "            WHEN b.duration > INTERVAL '6.7 seconds' THEN b.duration - INTERVAL '6.7 seconds'\n",
    "            ELSE INTERVAL '0'\n",
    "        END\n",
    "            )) AS time_saved_in_month\n",
    "    FROM downloads d\n",
    "    JOIN files f ON d.filename = f.filename\n",
    "    JOIN builds b ON b.build_id = f.build_id\n",
    "    WHERE f.abi_tag <> 'none'\n",
    "    AND d.accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_saved = {\n",
    "    'Jul': 92737,\n",
    "    'Aug': 60992,\n",
    "    'Sep': 97272,\n",
    "    'Oct': 118188,\n",
    "    'Nov': 153604,\n",
    "    'Dec': 153948,\n",
    "    'Jan': 150659,\n",
    "    'Feb': 169774,\n",
    "    'Mar': 196052,\n",
    "    'Apr': 191496,\n",
    "    'May': 222144,\n",
    "    'Jun': 210024,\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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flask-Login</td>\n",
       "      <td>130830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PyYAML</td>\n",
       "      <td>94638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Markdown</td>\n",
       "      <td>93945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>semantic-version</td>\n",
       "      <td>93309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sentry-sdk</td>\n",
       "      <td>93164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cffi</td>\n",
       "      <td>86801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pycparser</td>\n",
       "      <td>81878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>numpy</td>\n",
       "      <td>81279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>zope.component</td>\n",
       "      <td>74446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>57608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            package       c\n",
       "0       Flask-Login  130830\n",
       "1            PyYAML   94638\n",
       "2          Markdown   93945\n",
       "3  semantic-version   93309\n",
       "4        sentry-sdk   93164\n",
       "5              cffi   86801\n",
       "6         pycparser   81878\n",
       "7             numpy   81279\n",
       "8    zope.component   74446\n",
       "9      cryptography   57608"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pip</td>\n",
       "      <td>2583198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>896893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wheel</td>\n",
       "      <td>759387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>requests</td>\n",
       "      <td>541003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>youtube-dl</td>\n",
       "      <td>502525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>certifi</td>\n",
       "      <td>494112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>476375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>six</td>\n",
       "      <td>431755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>idna</td>\n",
       "      <td>362577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pytz</td>\n",
       "      <td>350693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      package        c\n",
       "0         pip  2583198\n",
       "1  setuptools   896893\n",
       "2       wheel   759387\n",
       "3    requests   541003\n",
       "4  youtube-dl   502525\n",
       "5     certifi   494112\n",
       "6     urllib3   476375\n",
       "7         six   431755\n",
       "8        idna   362577\n",
       "9        pytz   350693"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rpi-gpio</td>\n",
       "      <td>98507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>picamera</td>\n",
       "      <td>42893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gpiozero</td>\n",
       "      <td>24431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pigpio</td>\n",
       "      <td>20439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sense-hat</td>\n",
       "      <td>10410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>guizero</td>\n",
       "      <td>6729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sense-emu</td>\n",
       "      <td>5392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pyjokes</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>piwheels</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lars</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     package      c\n",
       "0   rpi-gpio  98507\n",
       "1   picamera  42893\n",
       "2   gpiozero  24431\n",
       "3     pigpio  20439\n",
       "4  sense-hat  10410\n",
       "5    guizero   6729\n",
       "6  sense-emu   5392\n",
       "7    pyjokes    625\n",
       "8   piwheels    105\n",
       "9       lars      5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and package in ('gpiozero', 'rpi-gpio', 'picamera', 'pigpio', 'pyjokes', 'guizero', 'piwheels', 'lars', 'sense-hat', 'sense-emu')\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)"
   ]
  },
  {
   "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>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>31964825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows</td>\n",
       "      <td>41410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>27651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   os_name         c\n",
       "0    Linux  31964825\n",
       "1  Windows     41410\n",
       "2   Darwin     27651"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select os_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by os_name\n",
    "    order by c desc\n",
    "    limit 3\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>30315489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debian GNU/Linux</td>\n",
       "      <td>1140956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ubuntu</td>\n",
       "      <td>400922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distro_name         c\n",
       "0  Raspbian GNU/Linux  30315489\n",
       "1    Debian GNU/Linux   1140956\n",
       "2              Ubuntu    400922"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql(f\"\"\"\n",
    "    select distro_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    group by distro_name\n",
    "    order by c desc\n",
    "    limit 3\n",
    "\"\"\", conn)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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(df.c, labels=df.distro_name)\n",
    "plt.title('Linux distro usage')\n",
    "\n",
    "plt.savefig('distro-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>23457804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>7849582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>3755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  distro_version         c\n",
       "0             10  23457804\n",
       "1              9   7849582\n",
       "2              8      3755"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql(f\"\"\"\n",
    "    select distro_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and distro_version in ('8', '9', '10')\n",
    "    group by distro_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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(df.c, labels=[version_names[v] for v in df.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": 125,
   "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>28746040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>armv6l</td>\n",
       "      <td>2064715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x86_64</td>\n",
       "      <td>687139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aarch64</td>\n",
       "      <td>416249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i686</td>\n",
       "      <td>78074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMD64</td>\n",
       "      <td>41408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>armv8l</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arm</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>x86</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      arch         c\n",
       "0   armv7l  28746040\n",
       "1   armv6l   2064715\n",
       "2   x86_64    687139\n",
       "3  aarch64    416249\n",
       "4     i686     78074\n",
       "5    AMD64     41408\n",
       "6   armv8l       256\n",
       "7     None        91\n",
       "8      arm         4\n",
       "9      x86         2"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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": 127,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-127-5f5cf74c1816>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mgroup\u001b[0m \u001b[0mby\u001b[0m \u001b[0march\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0morder\u001b[0m \u001b[0mby\u001b[0m \u001b[0mc\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \"\"\", conn)\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0march\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0mcoerce_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_float\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m             \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_dates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m             \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m         )\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1218\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1219\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1085\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1086\u001b[0m         \u001b[0;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1087\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1088\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1089\u001b[0m     def read_table(\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, object_, *multiparams, **params)\u001b[0m\n\u001b[1;32m   1004\u001b[0m         \"\"\"\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobject_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute_on_connection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_text\u001b[0;34m(self, statement, multiparams, params)\u001b[0m\n\u001b[1;32m   1179\u001b[0m             \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m             \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m             \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1182\u001b[0m         )\n\u001b[1;32m   1183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_events\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1316\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1317\u001b[0m             self._handle_dbapi_exception(\n\u001b[0;32m-> 1318\u001b[0;31m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1319\u001b[0m             )\n\u001b[1;32m   1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1513\u001b[0m                 )\n\u001b[1;32m   1514\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1515\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1516\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1517\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# credit to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, *args)\u001b[0m\n\u001b[1;32m   1276\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1277\u001b[0m                     self.dialect.do_execute(\n\u001b[0;32m-> 1278\u001b[0;31m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1279\u001b[0m                     )\n\u001b[1;32m   1280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.virtualenvs/stats/lib/python3.6/encodings/utf_8.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(input, errors)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mencode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutf_8_encode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'strict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutf_8_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "arm_arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and arch in ('armv6l', 'armv7l', 'aarch64')\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 121,
   "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>16373393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.7</td>\n",
       "      <td>11211993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>2778136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.8</td>\n",
       "      <td>1049887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.6</td>\n",
       "      <td>605774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pyver         c\n",
       "0   3.7  16373393\n",
       "1   2.7  11211993\n",
       "2   3.5   2778136\n",
       "3   3.8   1049887\n",
       "4   3.6    605774"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_vers = pd.read_sql(f\"\"\"\n",
    "    select substring(py_version from 1 for 3) pyver, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and py_name = 'CPython'\n",
    "    group by pyver\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "py_vers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2be0931198>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jessie_searches = pd.read_sql(f\"\"\"\n",
    "    select accessed_at::date as day, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and distro_version = '8'\n",
    "    group by day\n",
    "    order by day\n",
    "\"\"\", conn)\n",
    "\n",
    "jessie_searches.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2be2ad2908>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0RET3drLbiRwAZlLeLorCYXgQSKeBqmpgbFT/rhU5BlBERGGRntFl5jY7kQOYGz5MtBzr+O6cDcBsGpiaLOx6fIABFBFRWFjBkN1O5ADzoMgWq4WBWrdRf4LHeAygiIhCwwqGbFXhlS28DdFy+nuA0hjUmk79MRPJGUAREYWG1dfJxg6U4g4UZUH6e4HGFqC6Vn+CrQwYQBERhYYZDClbOVAx/SebaZId/b1AU2smgGIvKAZQREThwRwo8oAYBjBwEqqpFagyd6A4zoUBFBFRaFjBkJ0+UHHmQJFNpwZ1u4umNr27GYvzCA8MoIiIwiOV/Q6UMICilVgVeE2t+uPqWiaRgwEUEVFoSFZVeOwDRfZYLQxgBVBVNRC2MWAARUQUGllU4TEHimzr7wGipUBdg/6YO1AAGEAREYVHZgeKARS5x2phoCI6ZFDVNcyBAgMoIqLwyKIKT0WjQEmUARStzGphYKmuZSdyMIAiIgqPVBIoKYGKltr7/licARQtS0SAgV6oxnkBVFUtkEpBijx/jgEUEVFYpJL28p8sDKBoJaeGgFQKaJ6/A1Wj/yzyYzwGUEREYZFK2usBZYnF2Imclnd6CwMAKjPOpbgTyRlAERGFRXJ6bkSLHfEy9oGiZUl/j/7LgiM8cweqyPOgGEAREYWEpJL2ekBZeIRHKxno1cUGqxvnPsd5eAAYQBERhUdyOoccqOJOBKblSV8v0NgMFSmZ+ySP8AAwgCIiCg/uQJHb+nsXHt8BQFk5EI0yibzQCyAiIpcks6vCU/EyBlC0pEwLg6aFAZRSSrcyYA4UERGFQioJldURXowBVEDI0cOQkeH8PujIsD4Wbm4782vVNRAe4RERUSikcsiBYhuDQDC++CnID7+T3we1WhicfoQHmPPwuANFRERhwByoUJL0DDA+pmfS5fNxB8zHazozgFJVtcA4d6CIiCgMssyBQqwMmElBDMO7NZFz42P6z0Rffh+3vxcoKQFWN535NQ4UZgBFRBQGkk4Ds2kgnkUAZX3vTMqbRZE7rJ2eoQHI7Gz+HrevB1jdDFVScubXqmuB6SnIzEz+1uMz0ZW+IZFI4Mtf/jJOnToFpRS6urpw4403Ynx8HPfeey8GBgbQ2NiIj370o6iqqsrHmomI6HTWUVy2OVCAThTO5uiP8ssKoGZngeEE0NCcl4eVgd5Fj+8ALJyHV9+Ql/X4zYoBVElJCd7//vejs7MTU1NTuOOOO7BlyxY8/vjj2Lx5M26++WY8+OCDePDBB3HbbbflY81ERHS6TACVZQ7U/NuSP83PNUr05SWAEhGgvxdq/QWLfl1V1UIA3cqgSAOoFY/w6urq0NnZCQAoLy9He3s7hoaGsHv3blxzzTUAgGuuuQa7d+/2dqVERLQ0q6N4LjtQDKB8TeYFUJKvPKixU8D0FNC0SAsDgN3IkWUOVH9/P44ePYr169djZGQEdXV1AIBVq1ZhZKS4k8mIiArKDIJUFkdxytqtYgDlb1YApSL5SyS3WhiscIRXzPPwVjzCs0xPT+Oee+7BBz7wAVRUVCz4mlJKdyZdxM6dO7Fz504AwN13342GBm+3+qLRqOePQd7iNQw+XsP8SyV6MQygpqERcZs/+1RjI4YB1JaXITbvNrx+/jKansF0RRVUZRViY6dQa+PaOL2GU/vGMQqgbsMFiC5yP0Y8hgEAlTKLyiL9XbEVQKXTadxzzz24+uqrcdlllwEAamtrMTw8jLq6OgwPD6OmpmbR23Z1daGrqyvzcSKRcGHZS2toaPD8MchbvIbBx2uYfzLQDwAYTSahbP7sZUof+43090M1z92G189fjIF+SGUVpL4R093HMWPj2ji9hsaRw0AkguFI6aK/T2IYQCSCiZM9mArx70pb2xJHmLBxhCci+NrXvob29nbcdNNNmc9v374dTzzxBADgiSeewCWXXOLCUomIKCfJHHKg4syBCgIZHwWqaqAamvN7hLe6CSq6+D6LikSAquLuBbXiDtShQ4fw5JNPYs2aNfjYxz4GAHjve9+Lm2++Gffeey8effTRTBsDIiIqDLGCoKw6kZdlbrt4Egb5wsQYULNKV9+NDEOSSahs+n3lQPqXaWFgqa4t6nl4KwZQGzduxHe/+91Fv3bnnXe6viAiIspBLjtQrMILhvFRqLaz5toXDPYBbWs8e7hMC4PO85b/xqoa3cagSLETORFRGKTMbuK59IGygi/yJ+sIr7FFf+z1Md74KDA1sXQLA5OqrmUbAyIiCjj2gQolSSV1gFtVk9mB8rwX1EotDCxFPg+PARQRURhkOpHHbN9ERaNASZQBlJ9Zg4SranQeVCzm+Q6UmAHUijlQVbXA5Liew1iEGEAREYVBchqIxZfsybekWJwBlJ+ZTTRVVY2+tqubIQN52IFSkZVHxljdyCfGvF2PTzGAIiIKg1Qyt4HADKD8zepCXmX2WsxHK4P+HmB1I1S0dNlvU/MHChchBlBERGGQTGaX/2SJxfRtyZcyc/DMYEU1NAODfbpSzqvHtNPCAJg3D48BFBERBZSkcg2g4nM9pMh/FtuBmpoEJse9e8z+3pUTyAGdA4WFw46LCQMoIqIwSE3nFkDFy3iE52fjo4BSQEUVAHMHCgAGTnrycDI+qoOzRjs7UDzCIyKioGMOVDiNjwIVVVAlJfpjr3tBWS0MmpfvAQUAqKrWwV2R9oJiAEVEFAY550DF53pIkf+Mj80d3wGe94Ky3cIAgIqUAJVVRduNnAEUEVEYpJJzw4GzoLgD5Wt6kHB15mNVXgFUVnu7A6XUyi0MLFW1EB7hERFRYCWndTCUrTgDKF8bG124AwUADR72gurvAeoboUptNmStruERHhERBRhzoMJpfBTqtABKedgLynYLA0t1LZPIiYgowHKtwovF2QfKp0QkM0h4gYZmYKgfYhjuP+hAL5SdCjyTqqqda7VQZBhAEREFnBgGkEoBsRx3oGZS3rwYkzPJaSA9M9cuwNLQDKTTwKkhVx9OJsZ10npWO1A1wPhYUf7+MIAiIgq6mZT+M9c+UPPvg/zj9CaapkwvqITLvaAyLQyyPMITA5jwsLGnTzGAIiIKOiuHKYcqvEzQxTwo/5k3SHgBsxeU260MpL/HvH8bPaAs1tqKsJUBAygioqBLmn2ccs2Bmn8f5B/WDlRl9cLP1zfqVgNuJ5IPmD2gGm22MACgingeHgMoIqKgy+xA5ZgDNf8+yDdkqSO80lJg1Wr3A6i+XqC+Ibt2GJkAqvgSyRlAEREFnVlFl0sfKMUAyr+sAOr0JHIAaGhyvReUDPTam4E3n7m2YmymyQCKiCjorODHyREeAyj/GR8DVAQorzzjS6qhxf0dqP5eqGwq8ADmQBERUYCl3MiBYgDlO+YYFxVZ5KW6oRkYGYLMzLjyUDI5ofOYsgygVLRUB3g8wiMKDkkmIcODhV4GUeE5yYGKcwfKr2SxJpqWhmZABBjsd+fBzARy1ZRFBZ6luoZJ5ERBIj96AMb//2e6Wy9RERMXqvCEAZT/jI8tGCQ8n9u9oMTsAZXtDhQAoLp2LuG9iDCAosCSE8eAkeGifOdDtICjKryyhfdB/rHcDpTbvaCsAMq836xUcQeKKFisJw7rHz5RsUoyiTyUFhkknFFbB0RL3Usk7+sBVtVD5RCEq+pa5kARBYXMO/sXBlBU7NyowmMjTV9ZcpCwSUUiwOom13agZKAXyCX/CTDn4Y0WXToFAygKprFTcy8a1vgBomKVnAaiUaiSkqxvqqJRoKSEO1B+MzUJzM4ufYQH6I7hbvWCyqWFgaWqFphNA1MT7qwlIBhAUTDNf9LgDhQVu1RyLpcpF7EyBlB+s0QX8vlUQ7MrR3gyPQmMnsotgRwo2m7kDKAokDLb1nUNPMIjSk3nlkBuicUZQPmNNUh4sS7kloZmYHJc93Byot9BCwPMW2ORJZIzgAoQGR7E7J//NuT4kUIvpfDMAEpdsBXo7y26s3eiBVKp3PKfLLFYoBtpyonX9XPjgDsl/b5gdwcKcL4L5aSFATC3A1Vk3cgZQAXJ8SPA6CnIK/sLvZLCS/QBNauAjrP1ufv4WKFXRFQwkpyea4iZi1g80H2g5MhB/dz48p5CL8U1Sw0SXqDBbDngsBeUOGlhAOgcKADCIzzyKxkyO86ePFHYhfiADJwEGprntpyZSE7FLJV0uAMV8CM8awfmtYOFXYebbAVQegfKcSVefw9QWwdVVp7b7XmER75nle33MoBCok9vX5tbzjLAPCgqYslpZ0nk8YAnkSfM58Yjhwq8EBeNjwIlUWCZoEZVVuk5dA4DKOnvzf34DoCKxfXvEHegyLeGEvrPk92FXUeByewsMJzQ29cNzXpaeR8DKCpiruxABbcPVGYHpq87PCNFxseAqhoopZb/voYmSMLhPLz+k7m3MLBU1TAHivxLrKGRI0OQqcnCLqaQhgYAwwAamvQk8NWNbGVAxS2VhHKQA6WCfoQ3cBJoadd/D8kulIyNLjkHb4HGFv3/n+vjJKeBkSGg0WEAVV0L4REe+dbgwNx5eDHvQlkVeFYFSlMrj/CouBVxDpRMT+mRJ9uvAiIRyGvhCKCWnYM3j2poBgb7c69EtloYNOfYhdxShONcGEAFhMzMACNDUBdcpD8u4kTyzHa9GUCpplY9x4kKTmZmYPzdZzBz9JVCL6W4JB32gYoHN4DK5P+0rQU6ztEVeWEwbnMHqqEZmEnpweq5sApwHB7hKR7hkW8Nm/lPGzbrsQtFHEAh0QdEIkB9o/64sVU3k5tgK4OCe+MI5PmnMfEf/1zolRQNESnqHaj5O9Jq3Qbg6CsQY7bAi3LBcoOE53HaC0oO7tPBd+tZOd0+w9yBKqaefAyggsLMf1JNrUBjS1HvQCHRB9Q3ZuZ+ZbaemQdVcNJ9DACQfPbJousJUzDptM4JdBxApSCG4d668kSsHkgNzUDnRiA5BXQfL+yiHBJjFpgYt3WEZ/WCkhx6QYlhQPY8C2zaBlUay/r2C1TX6J2wIhpKzQAqIGRoQP9ldRPQ0gEUcSsDqwdUhtXKgAFU4XUf07uD6RnIs48VejXFwdo5cthIE4B+AQyaRD8QLweqqqHWbQQASND7QU1OAGLYDKCa9J+57EAdexU4NQR10Y7sb3u6zDy84jnGYwAVFIMDgFJA3Wqolg49vmQ2BNvUuUj0Qc3vmNvQrH82zIMqOOk5DqxZh+i5F0CeeqSotvMLxnrH72iYsBlABfAYTxJ9QGOzLvdvaNYv5EHPg7LTRNOkSmNAbX1OAZTseRaIRKC2bM/6tmeso8oa51I8O88MoIJiqF93io2W6h2o2bQrU7iDRpLT+h3O6qbM51RpDKhrAFiJV3gnXodqX4PyrncCPcdDU1Lua1bQ4+QIz0pAD2AAhUTfXEGJUsC6jcGvxDNHU9nJgQKQcy8oeXEXcN6FUJU2ktVXUoTdyBlABYQMDmSCBmX1OynGPCjrSWL+ER4ANLfxCK/AZPSUfvJsPxtlV3cB8TLIMzsLvazwMxtgKidVeFbwFbD8FREBBk7OJVIDUJ0bgf6eYOfgWbs41fYCKJVDLyg52Q30vuHO8R2QOcIL9M89SwyggmJoAMqqOmvpAFCkrQzMREl1WgClGls5D6/QenTirmpfg0h5JdT2KyHPPQmZdqfpq3Qfg7HrcVfuK1Rc2IFSQT3CGxvRa54fQK3boP8S4N1PW4OE52toBoYHIem0/cfY+ywAQF10WbbLW5wV7OWhlYEMnITxna8X/E0zA6gAEMPQ3bfNAEpVVgE1q4oykTzTA+r0qeFNrcD4GGRiPP+LIgBzFXhoWwsAUFfdACSnIbufdn7fhgHjH+6B3P+FrF4kikLSxSTyoAVQmRYG854P1p4LlJQEux9ULgGUmK8TNsmLu4A1nVCrG3NY4CLi5UC0NC9HeHLoJcijD+lUlgJiABUEo6d0qfK8vB+0dBTpDlSffrK3Kj5MqtlsAsc8qMLpPqYb/9XW6Y/XbQRaz3LlGE9+8Qxw4nVgdrYoc/+W5UYOVEADqNOb6gLQI206zgl2Jd74KFAas31N53pB2TvGk9Fh4Mgh947vYOaf5asb+ZFDQEUl0Nzu/WMtgwFUEFg9oOrn3imolo6iHOciZsLoGQM2G6cL0fYAACAASURBVHUvqEJv6RYz6TkOtK3NXBulFNRVbwVeOwhx0JdHZmch3/+2njoPFGfu3zLEjSo8a/cqGawAKpP309C04NOqcwPw+ivBrVQ2x7isOEjYkukFZe/NhezdDYhAbXPp+M5SXZOXeXjy2kGgcwNUpLAhDAOoAJAhswv5/K3WlnZgYqyoEvYA6CfM0xPIAaDR/BzzoApCRIDuY1DtaxZ8Xl3+FqAkCnn6kdzve9djQF831G/9vv6YAdRCLu5AScB2oDDYD1TXnplAv26jToi3jpUDRsbH7I1xsdTVAyVR27uz8uIu/TzafnZuC1xKVa3nbQxkckInv3du9PRx7GAAFQRDZuXZ/NJ9M5G8mN6NiwiQ6F/YA8qkYnHdyoA7UIUxNABMT53xhKyqa4GLLoXselTPc8ySzMxAfvgAsHY91OXX6X43RfQ7b4tZhedoFl6Qj/AWez7o1Inkgc2DsjlI2KIiJUB9w1yV8jJkehI4sBfqoh32d7jsrqO6xvscqKOH9e6ZVSxQQAyggmCwH6iohCqvmPuc2cqgqN6Nj4/pMQ2nbddnNLXyCK9QzHf6p+9AAUDkqhv0tTOrfrIhTz8MDPYjcvNt+sm+pV2XX9OcpBs7UAHtA3VaC4OMhmZdaBPUflBj9ubgLdDQrKc0rGT/i0B6xr3qu/nykAMlrx3UjZPPYQBFNshQYm5wrmV1o04yLKYAaokWBhbV1ModqAKZq8A7M4DCBVuB+kYYT2V3jCfJJORH3wXOvQDYtA2A2QOt9wQ7nM+XSurxOdFo7vcRwB0omZ3VO5+LPB8opYDOjUWzAwWYvaBsHOHJnmf18eD683Nd3dKqaoDkFMTDkUBy5CDQtmbhhkKBMIAKgsH+hRV4MLdsm9sgRdTKYLGKmwWaWoGxEX1GTvnVfQyob4CqqDrjSypSAnVlF3BgD2TQfrdkefxHwMgwIje/f+6ooaUDmBwvqm7HK0olgVjc0XGMikaBkpJgNdIcTughyku9oVq3QY+8CtjviszO6t/xHHagMD4KmZ5a+r7Taci+3VBbLs0MY3eVx/PwxDCAI4czR7SFxgAqCAYHFlTgWXQlXvEEUFghgFJNViuD7KeSkzPSfTzT/2kx6sou/X02WxrI1CTkJ/+hp8Sft2nufoow929FyWln+U+WWDxQO1BzPaCWeD6wkoyD1lBzQo9xsduFPCPTymCZXahX9gOTE+5X35lUJoDy6Bjv5AlgakIXCfgAAyifk8kJ/QuzWLOzlg4g0e/pdqmvJPp0aW/ZElu3TWxlUAiSTgMn31g0/8miVjcCF1wEeWYnxFi5tFwe+T4wMYbIzbct/EJrEXfhX4q5A+VYwAKoTL7PUjvSZ6/XDTWD1g8q2yaaJju9oOTFXUAsBpy/LdfVLc/jeXjWtfRDBR7AAMr/rM6y9YskTre06+6zRRIwyFItDCxWNQ5bGeTXQK9u9LpCSXTk6huAoQTw8p5lv0/GRyGPPAhs2wF19rkLv1jXoF8AeplIbpFkcQZQSPTr3K9FducBszK34xxI0HagzAAq+yTy5XtBiYge33LBNt1s1AtV5jw8r8a5HDkEVFQBzW3e3H+WGED53aAOoBZrt69ai+w4I9G35HY9YA5TXVVfNAGlbyxTgbfA1kuBqpoVk8nlv/4TSE4j8q7bzviaikSA5nZIHwOoDBd3oCRIjTQTfUBdw7K5PGrdRuDo4WA11MxxBwpV1XqcylKtDI4fAYYSrnYfP4PHR3h+aaBp8ccqaEli9YBa7F2W2ca+GBLJxZjVuxeNy+xAAUBTG4/w8ky6jwEqkhlyvRQVLdWNNfc+Bxk9tfh9nRqCPPYQ1KVvXjIgK7rcv5WkijMHSgYX7wG1QOcG/f/U/Xpe1uSGrAcJm5RSQEPT0jtQe3YBKgK15RKnS1xaRaUuRvDgCE8mx3UDTR/0f7IwgPK7wQFdnlyz6owvqXiZbp5WDC8mw0N6cORyR3iwWhnwCC+fpPsY0Nyqj0xWoK56KzCb1t3FF7uvH38XSKehfvW9S99JSweQ6AtE7p+I2Mr5cqRIc6CW7AE1jzKTjSVI/aCs3ZtsOpFbGpqXLKKRF3cB516gm116RCmlAz8vupEfOawfwyf5TwADKP8bGgDqG5fesmzpKI7Ggiv0gMpoagVGT+luu5QfK1Tgzafa1gDrNkKeeuSMXk6S6IM8+TDUlV1QTcvkOLR2ACJAn/8DZfnZT2F87Hdz6sJuWzJ55iiTXMTLAhNASTKph6yv9HywukkPtw5SP6jxMSBeBlUay/qmVi+oM/5tDZzUo5Y8qr5boMqbeXhyxGqgeZ7r950rBlA+J4P9SyZJAnNDhcPeWHDFHlCmTCuDfrYyyAdJJYGB3pXzn+ZRV9+gd01Pq46Shx4AFKBu+s3lb29NYA/CzuvBl/QLvZfHyqmkTqx3SMXic2Nh/G7Q5vOBUkDnhmBV4uXQRDOjoVn/PpwWwMgePQVAbc1DAFVd680R3muHfNNA08IAyu+GBhZNIM9o6dDjTU4N5W9NhZDo03k2ywSTADKtDDDAPKi86H1Dz6XKYiipetOVQFm5HtNikpMnID97DOqaty/a82yB5uCMMZIes0O7l2tNTs+NYnEiSEd4K/SAmk+t2wgMnFwy785vxEEApZboBSV7dgEdZy86R9RtyoNxLmIYwNHDmSNZv2AA5WOSngFGhhdvYWBSLQF6N+5Eog+oWw0VLV3++8wnCAnA8U4YZEa4ZLMDVVYOdcnVkN1PQ6b0Uav84DtAaSnUje9Z+fbxuD6a8XkrAzFmgZ439N+9/PdZhDlQMmAGCCsVlSCADTXHR3PLfwLmWhnMy4OSsRHglQPeVt/NV1UDuN3GoNdsoOmj/CcAWHF40le+8hW88MILqK2txT333AMAGB8fx7333ouBgQE0Njbiox/9KKqqzhzhQA4NJXSux+qlA6j5jQXV+VvztLD8W7EHlEmVleucB1bi5Uf3cSBaCjS2ZnUzdfUNkKcehux+EjhnA2T3U1Bvfw9UTZ29O2hp9/8OVH8vkDZznzxaqxiz+jFcyYEKTgCFRJ8O+KrPLK45w9p1uqHmkYPeDNB12/goVK59jqxB6/N2oGTfbkCM/OQ/AfoIb3ICkk7rEUEusGYa+qkCD7CxA3XttdfiE5/4xILPPfjgg9i8eTO++MUvYvPmzXjwwQc9W2BRM5toqvqGpb+nth4oK9cRepgl+m1t1wMAGlshPMLLC+l+HWg7K/u5WmefC7SvhTy9E8b3vwWUV0K97ddt39xqZSCGkd3j5lP3cf1nda13rUasgMe1HaiUv3+mJkn0AaubbM3/U7E4cFZncCrxnBzhxct0ADNv5qS8uEunPpzV6dYKl2dV+blZiffaQaCyOnN87xcrBlAXXHDBGbtLu3fvxjXXXAMAuOaaa7B7925vVpeDIPzjt0vMJprL7UAppcxKvPAGUJJKAiNDtrbrAUA1t3IHKl+6j0PZrMCbTymlk8mPHgb2Pgd1w81QlVnsYrd06ODh1GDWj50vuj+Wgtp6KdDnUaGH1fjSjc7SVhAWgPYQSNjoATWP6twAvP6K7xtqSnoGmJ7KPYkcABqaM0U3kkwCB/ZAbdvhaNh0NjLz8Fw8xpMjh3QDzTz9P9iV0/7ayMgI6ur0VvuqVaswMuKPadfGQw9gaO9uyCc+57sfdE4G+3XZZt0yO1DQ78bl0Et5WlQBWIFkFjtQOPVTSHLanfLuFRjf/zbk2cft36ChGZE//d9QEQ+moeeRTIzrACaL/Kf51I5rIf9+P1BWDtX1zuxu29oBAYCT3SsXFhSIdB/TL/Jr1gFPP6LfBKxa7e6DuL0DNf8+fUpEgMTJBUOmV9S5AXj0IeDE6/pIz6/GzUHCDgIo1dgyN77m5ReBVEoH8flS5W43cpkwG2he+mZX7s9Njg8olVLLBis7d+7Ezp16Avvdd9+NhoblgwEnJlvaMfb9b2P19ASiZ53t2ePky8jEKFKrVqOxdfn8kol152F812OoryxHpLwyT6vzTjQaXfB7kjx2GKcArFq3ATEbvz/T6zdgBMCqmWmUti/fHdspMQwMPP5jlNStRmnnyv1JZgf6MPPyHtTNJBHNMfDwi1T/CQwDqD1/C+KnXZfTr+GiGhow9eG/QKS2HvGO7H4WsyVbkABQOX4KFR4+pziR6DuB6DnnomLDBfrnNDmG2Hp3czhmJkYwBKCmoRFlDn8OU/UNGAVQX1lh7/oViDE6goHpKVSu7USlzTXOXnIFEv9wDyr7TqDiTf7Ng8pcz7b2nK/n+FlnY+L5Z1CigNiBPUhWVaPh8mtcy0daSXpqLQYBVMNw/DsJAMnjr+IUgNqLLzvjeabQcvqJ1tbWYnh4GHV1dRgeHkZNzdLRcldXF7q6ujIfJxKJXB7SFll/AQBg6LGfIHLjb3j2OPky23sCqFu94s9MqvVu4ODLL0GtXZ+PpXmqoaFhwf+zceQVAMBIaRzKxu+PlOujoFOHX4aqspFk6oAcfw0yPgrjN38PMzvesvL3v/4K8PIeDO/fAxX3Tz+TXBgv7wMAjFavOuO6nH4Nl7RFv5iNZfm8ICJAeSXGXz2ISQ+fU3IlMykYPSdgbLscMxW6ourUoZcRaTvb3cfp09VWY8kUxh3+HAyz2edQ30k0NrV6+lzthLyunw8my6swZXONoqJAbR3G9z2PyUuv8XJ5jsgbuqp1TFTO19OorAGMWSR7TmD6uaegNm/H4Kn8tXCQtE6jGe3pdvw7CQDGi88BKoLR+iZbz/9ua2tbOqE/pzYG27dvxxNPPAEAeOKJJ3DJJR7O1smCWrUa0XMvyDQNC7zBfqjlKvAs5gyy0M7ES/QBpTHAboWWWREmfd7nQclBHUSojVvs3aD1LEApiJVgHGTdx4HyyhWPmL2gc//a/duFv/cNQAzdod0q9PAiTzFpNr50oQ9UZhRP0ufNNBP2WxhYMg01/d7KINdBwvNYxTbTj/8XMDGWv+o7S2WVTj1xKQdKjhwE2tdAlfnvDeeKAdR9992HT37yk+jp6cGHPvQhPProo7j55puxb98+fOQjH8FLL72Em2++OR9rtaXssjfr6dvD/k0utUMMwxzjYuPFqalVN5kM6YT6TMWNzQncqrxCV6LkoRJPDuwDWjqgbOa2qHiZTvIM0HDTpUj36/qJrUD5hqql3bfVp1aArKyfj1eFHikz4buYcqAG7HUhP10QGmrmOkh4AfPnMvlf/6nfeG662IWV2aciJbpizoVu5JkGmj7r/2RZ8QjvT//0Txf9/J133un6YtwQv/TNGP/m1yB7n4W69sZCLyd3YyNAOr18DyiTKi3V78Z8+mLimM0eUAs0tUI8rsSTdBp4ZT/U5ddld8P2s+dK3ANKRHQF3varCreIlg7g549Bpif99+60+5geAm52xlctHZDD7hd6iDV6xc0qPJ8HUEj0AVXVWV9z1blRFx4cOQjkq6lktqwAqjLHRpqALqqIRHSguOWSvBTSnKG6FuJGEnnvG8DUJOCz/k+W0HUiL+lYCzS3Q14M+DGe2cdDLdOFfIEwtzLIpgeUSTW1ed/K4PXDQHIa6nybx3cm1b4G6O+BBKFcfCkjQ8DkeM4VeG5Q5tE1fHiMJ93H9M6klbjb0g4MJSDTU+4+kJtVePFgBFB6RzrLN1TAXENNP/eDGh/TPdEcJHyrkpLMsXrBGodWu9ON3Jph6NcdqNAFUEopqIsuBQ69BJmcKPRycjbXA8peibZq6QD6enRn4hCRiXHdwj+LfAcA+lhzOKH7oHhEDu7TZ/0bNmd3w/a1gGEEe8fwhE52zWYGnuvmdeH3nZ5jC/pjKXOtcHvEkJWv5MYugxmEeflvxhWJvpxmumUaah7x8WDhMQdjXOZraJ7rQVYIVS7NwztyUP88cu3M7rHQBVAA9Myf2TTkl78o9FJyZ3Yht93jpqVdj3SwAq+wyGJo6AJNZuuHxMnlv88BObAPOKsTKsvtdtWuX1gzg2YDKLP2tgK2YmhsASIR383Ek8kJPYapY16D0RaPgr0i6wMlxqzenc/2+cCk1m3UDTXTaZdX5g4ng4TnU5dcjfK3vxuqxtsq5CUfv7rGnRyo1w4B5/ivgaYllAEUOs8DalYBQa7GG+zXW7kV9vo6Zd7h+vHduBNWAJT1EZ4ZQHl0jCfJJHDkoP3qu/ma2oCSaLDzoLqPA7V1+omyQJQ5g893O1BmcLmgQ3ujWejh9lqt3aLSmPP7CkAAhVNDwGw65wAKnRt04r1fizhcCqAi1/wKav7gz1xYUI6qa4GJMUcnIjIxBpw8oYNenwplAKUiJVBbL4W89DzE7G0SNDI0YPv4DkBoWxlYIwmsKeO2mQGU9Lt8ZGJ57WUgnc46/wmAzm9oadd5MgEl3cf0UWShtbT7rvo006JiXn5YptDD7XytVBKIxWxXqC7LaoXg5wAq1x1pk/VibOXW+M74KJQLAVTBVdUCIsDEeO73ceQwAHMMj0+FMoACALVth54pdGhfoZeSm8H+rEZUqKoafVbst3fjTiX6gIoq2ztxFlVRpd/JebUDdXAfUFICmM1bs6Xaz9aVWgEkxizQm9sMPLfp3L9uf+X+dR8D4uVnVtB6UeiRmnalBxRgBvYlJb7uAyU59IBaoL5R9+XyayL5+OjcMN4gs/4fHBzjyZGDetf2nJUnPBRKaAMobNwCxMuDW403NACVzQ4UEMpKPMmlhYHFw1YGcmCfPpsvK8/tDtrXAEMDkKlJdxeWD4k+fQzih1E0rR263Ueif+XvzRO9O3dmfyxPCj2SSXfynyyxuL93oAb6dOFGjvMPlVLAug2+TCSXZFL/7J20MPCJzEBhB4nk8tpBoH1t7s+xeRDaAEqVxoALt0H2PqebcQWITE0CkxO2ekDNp1o6fFnS7UjCQcJoU6snO1AyOQ4cey23/CeTlUiOngDmQfmhAs+kmtv1X3zyxkFEdAXeYsebLe3ATMrVQg9JTbtTgWfxewCV6APqVuv8txypzo1Aog8yOuziwlww4UITTb+wdqBybGUgxqxuoOnT/k+W0AZQgFmNNzIEmLOTAsPsAZX1u6yWDmBsZK6bbcCJYQCDfVC5btc3telWBm73Wzr8S0CMnPKfMqxKvAIls0qiD3Lstdxum6nAO8vFFeWoRQdQvtl5HT2le/ksEkDNFXq4+CYnlSqqHShJ9OW+I23KvCj77RjPfN4OTQ4UAMn1CK/nDZ2C49P+T5ZwB1Cbt+vGaXt2FXop2TFbGKgsAyg/NxbMyciwPp5xcIQHEd3J3EVy8CUgFgPOcfDuqL5R58kUqBLP+MfPw/jcJ3LrFtx9HGhsKUyH49Ooqhpd8eOX33kzIFaLtXfwopVBctqdLuSWWBzi4wAKiT6obAtKTrd2PRCNQl7Z786a3OLGGBe/sP4fcjzCs45Y/VyBB4Q9gKqsAs67MHB5UHNNNLM7wkOr+W7cZ1VJOTMDn5wrbjxqZSAH9gLrN+nKqhypSARoO6sglXjS+wbw6gFgegry3/+R/e39UoFnaWn3TfVppgKv4+wzvqYLPWrcPW5MeZAD5dNGmjKTAk4NOt+BKo0B687PDAL3i8ybmRAkkatoFKiozD2J/LVD+t+K9RzuU6EOoACzGu/kCd88wdoy2K/naGXbBG11s75dkP5flzHXwsDBER7cbWUgo8NAz3FH+U8W1XE20H1M583kkTz9iK622rwd8tiPIKeG7N92Zgbo6/ZFBZ5F5/755He++xhQXTuXRHs6tws9ku5V4QHw9xGeldrgMIACALVxM/DGUX+lO4yP6T/DsAMF6GO8HH++cuQg0OnfBpqW8AdQZit7CVJTzaEBoK4h694uqqQEaGrzTz6IUwmz4ibbnTiTqqzSFS0u7kDJAf2u1VH+k6VtjX6CGcvfdHhJz0B+9iiw9TJE3vtBYHYW8uPv2r+DvhN6DI0fKvAsLR3A+Kg7w0sdWml3TrV2uPsGJ5XUI0rc4ucAymEPqPnUxq36L4fcH/Ccs/FR/XxXUVXolbijuianHCjdQLPb1/2fLOEPoOobgbXrIXuDE0DpJpq5BQ0IUyVeog+orddb7rlyu5XBoZf01vSaTsd3lanUymce1N7ngPFRRK56K1RjC9SVb4U8+fDcbt8KxEcVeJa5OXOFfeMghqF3J5c73mxu14UeE2PuPGgq6WoOlIqX+TaActwDar6zz9Vtbvx0jDc+qnvelZQUeiXuqK7N7QjviE7u93v+E1AEARRgTqQ+ciiro4qCGuzPvgeUSbV0AAO9kHQwO7DPJwkHPaBMqtHdVgZyYC9w3maoiAtPcgWoxDOefkRPat90EQBAveMWQCnIQw/Yu4OeY3oMTbOPchMyydkFfuMw2K+Dj+V2oNwu9PAiByrl00aaA31AtBSoqXN8VyoaBc7blNlR9gWXxrj4harO7QhPXjMbaJ59rgercldxBFDbdgAikH3PFXopK5L0jK4+y7FRHFra9RGLy5VnBZHod75d39wKDCVcGekjAyd1FZAL+U8A9KDP6tq87UDJ4ACw/0WoK7syAaCqb4C69kbIzx6zdfQr3ceBlnZHfXhct7pRv7AW+uh6uQo8i1Xo4UIAJSLmDlRx5EDpFgZN7oytAfS/475uyFDClftzSibG9DSJsKiq0UfrWeZ4ypFDQIe/G2haiiKAQtsaoLElGNV4w4O69D7XvB/rHW7AE8llZkZX3Djdrm9qBcTI5E84WtNBF/OfLG1r8laJJ8/sBACoK69f8Hn19ncDsRjkB99Z+U66l2gSWUAqUgI0txV8B2qxGXhnsAo93Aj2ZlL6uaKYcqBcyH+yWG+EfHOMNxauHShU1wKzs7optE1izAJHDgfi+A4okgBKKaWP8Q7u9f/oDLPSJNseUBl+ayyYq6EB/eLgxhEe4M4x3sGXdGVkq3sNJFXH2UDPcc+75YsxqwOo87eesaunalZBXf9OyO6nIG8cXfo+pib17+dyOyyF0tJe+DcN3ceA1U1QZRVLfourhR5WuwE3q/DicSCV8uf0Bjd6QM3Xcbbe8fFLABWWQcKWXObh9RwHkv5voGkpigAKMLuSp9OQX75Q6KUsa64HVI45UOUVwKr6wh9nOOWwB1SGS60MRARyaB/Uxi3ulta2rdGl6IMez3J7ea+er3jVDYt+Wd3wa0B5JYzvf2vp+zDHzvhtBwowd14TJ105qs2V7f5YbrVdsHaKYg6KLE5n7Wb5bBdKJseByXF3d6AiEWDDZsjBfXlvJXI6EQlfDpTZjTybcS7ympVA7v8KPKCIAiis36h/Of3ezsDsQo66HHegALPXTLAr8Rz3gLJUVeuqOac7UL1v6Nw0l/KfLPmaiSdPPwJUVeud2MXWUVkF9bZfA/Y+p5M4F7sP66jRhwEUWjrM3D9vhkevRNJmfywbPxtd6HESkk47e1Ar2dvtHCgAkvRZIrmLLQzmUxu3AsMJT2ZmZiU5DaRnQtFEMyOXgcKvHdSv040+KlJZRtEEUCpSArX1UshLz/u7Qm2w3yzdd9Dl2nyHW+h3VY4k+nSuyKp6R3ejlAIanbcyyPR/cjmAso7D5MTr7t7vPDI2AtnzLNSO65b9vVLXvxOorl16F6rnuH6xzrXFhofm5swVaOe1r0fne9gJLlvb9fc6LfQwd4lc7wMF/wZQbu5AAfPyoA7sdfV+sxamMS4WMxjMpheUHDkErNvo+waalqIJoACzGm9qQg+D9SkZGgDqG5zdSUsHMDWpB5sGVaIPqG9ypV2Aamp1vDMhB/cBDc1QjS7mYMA8cl3d5OkOlPz8MWA2DXXVW5dfS1k51NvfAxzYu+gLinQfA9rWuFYF5apmM/evQHlQVgCsbDQYnWtl4HCtSS93oHx2hOdmD6j5mtt0W49C50GFaZCwJbMDZS+AkvFRvYsbgAaaFh8+E3ro/K16WKafq/EGB6AcvsNXZql0kPOg3Ji6ntHcBiT6c955FGMWOPyS+7tPFg8r8UREH991brD34n7t24FVq2E8+M0zdzB9WIFnUWXl+oWwUEfXPcf1eBwrOFqOW4UemRwolxtpAhC/9YIa6AMqKqFc7tKtlILauBlyaF9hE+dDuAOlSmN6YLrdXlABaqBpiRZ6AfmkYnFg0zbInmch7/2g795Ji2HoHKhti+ep2GY1Fuw9AbVhswsrW0h+8TMYP/wOIh/7tB6X4oVEH9Ta9e7cV6PVyqA/8+KVleNHdCmuRwGU6lgLeXkPJJ3WDf7cdOQQ0PsG1G//sb21lMagbvpNyDe/Aux7Hth6CQBARk/pd5J+GuFyupb2glWfSvcxoKnNVn8sVWYWejjdLbN2iVzsRJ7ZgZqeApYY51cIrr6hOt3GrcDPH9NVlGed481jrEBCGEABAGpqIY/9GLM/++nK35ueASLBaKBpKaoACtDVePLiLuDYa8A5PrtQYyP6lyjXFgaWVav1tr4HLybSfRzGN+4DktOQvc9BXXGd649hTE4AE2OubderplYIoI/xcgigPMt/srStBWbTOo/G5QBFnnoYiJdDXXK17duoK7sg//2fMB78JiKb36TfaJg7ZH4aInw61dIB2fUYRCT/ORTdx6CyeeJ3YaiweLAD5dcjPCT6gDb32ofMpzZugUDnQakCBVBh3IECgMh7PgA5vN/+DTrOzuyCBkHxBVBbtkMiEZ1U67cAyuoB5fQILxIxGwu6G0DJ1CSMr35aB2dl5ZA9uwAPAqjZPt1ywLWKmyZd0SF9PVA5bMjJwX0696fW+QiJxaj2tfoJvOeYrWM2u2RqErL7KahL35xVV18VjUK9872Qf7oX8oufQV1y1dwRY4d/Ayi0mrl/I8OOiw+yIdNT+gX+yi7bt1EtHZBnn3AW7Fk5UG72gfJhErkYhh5vteUST+5f1TcAze363/kNN3vyGCsaG9O7L+VL9xALInXxFVAXX1HoZXjGX2dYeaCqaoBzN+kXf78ZctYDaj7l8lBhEYFxUNqPqQAAHMRJREFU/xeAgV5EPvgXUBdfDux/wZN3qlYA5dqWfXWtfmLKoRJPZmaAV/d7t/sE6CPXSAQ44W4elOx+CkglV0weX4y67M1A61mQH3wLMjurc3yqaoDqVa6u0U2quUC5f71v6MfPJj+spUMXtIw5KPTwYgcq7r8ACqPDuuu6V0d4ANTGzcDh/c5bS+RqfBSorPZdWgktryivltq2Q3d/7nPWXNFtmSaa9S6Uibd0AIP9rgU48vD3gBd+DvXu34HacKHuJ5RKAQf2uHL/87kdQGVaGeRSiXf0EJBKuTu+5TSqtFS/A+5xOYB6+hHdJiGHqhYVKUHkXe8DTnZDdj2eaRLp6/LizFDh/AZQmRYUWeweZgo9eh28ybESvcPexsCjHlDzqfO36g7Yx1717DGWIyFrolksijOAMpsJit+aag72A+UVUBWVzu/Lqgbqc2Fo6cF9kP/4Z+BNV0C91dziPu9CoLzSk5/hbH+v3jGqdG+wpmpqzW0H6sA+PRn8vAtdW8tiVNuaTJ6RG+TE68DRw1BXvzX3oOfiy4E16yA//A7Qfdy3FXgZdVbuX54r8XqO627g2YwZcSPYSyWBkqi7hQc+DKBca6q7nPP02X7B+kGNj4ZrkHCRKM4AanUTcNY5vjvG0z2gnB/fAXPvcB0nqg4Pwvj6Z4HmNkQ+8JHMi7GKlkJt3g7Z+5wu83fRbF8PsLrZ3d2OplYg0Zf1Fr0c3AesXed6+fQZOtbq9bn0wiVPPwJEo1CXvSXn+1BKIXLzbTqwT075uwIP5k5jS0fee0FJ9zGgNcv+WG4UeiST7lbgAb4MoOaaaHrXwFVV1+jXhEL1g+IOVCAVZQAFmLPxXjsIOfpKoZcyZ3DAvS7PTW2AUropYo79TSQ9A+Pv/gZIpRD5o788Y0iq2naZ/of/6uKjP3I129fj/rvNpjY96uPVl23fRKangKOHvM1/Mqm2tXp4cs8bju9LZmYgux6HumiHfmFw4sKLgfXnz63R51RLe/5zoHLoj6ULPRy2XUgl3T2+g35jhJKSuQR1PxjoA1bV675CHlIbt+jXBAdzAOXoKzD+9R+yf84N2yDhIlG8AdQV1wE1q2Dc/TEY//4Nf5TtDvVDubUDFYtDXfJmyNOPwPjsX0J6s39hlu/+E/DaQUQ+cDtU6yIlxBdeDESjru7kiQhm+3vdn3l1/lagtg7GvXfav96vvgzMznqa/5RhvgC7kQcle3YBE2NQV2efPH46pRQiv/VB4OIrgLXrHN+f51o6gKGBvO2gyNiI7vifw/Gmaulw1gsqOe1uBZ4lFvfVDpSnPaDmUedv1W1klpgFuRIRgfHNr0B2/gA49FJWt+MOVDAVbwDV0IzIp75s9rz5HoxP3V7QeUgyNambNbpQgWdRv/9nUL/7J0DPGzD+z5/AeOgB2924jV2PQx77EdRb3wW1/arF77+sAti4VTcmdWvu3ugp/c7a5ZENqr5BX+8rrrd9veXAPj2Pb90Frq5lUY3NOo/GhUo8eephvZO5casLCwPU2nUo+fAdnu8AuCEzE8+F3D9bzBE8OeWHtbabwV5ub97Egx0oAL4LoJDo8zSBPOPcC4CSktxfB174OXD8NUApfYRu19SE3h1nABU4RRtAAXoCfeS3/xiRP///AKVgfP5/wbj/i5CJ8fwvJtPCwL1zfqUUIldcj8hffRlq2+WQ738bxl99FLLCOyw5cRTyL38LnLcJ6td/Z/nH2HaZHorq1iw3c8CqF0+YqrIKkd+53fb1loP7gM6NUG7nmSy2tkgJ0LrG8Q6UDJwEDuyFuqqrOEui53XhzwexAt5c8sOaO/SxbX+O1cApD3KgAF8FUJKeAYYHs0vQz5EqqwDOPjenPCgxZvUQ7pYOqGveDnnh55CJMXs3DmkTzWJQhM+wZ1IbtyBy1xehfuXdkJ8/CuPOP4I8/7R7uyp2WE00XTrCm0/V1CHywY8h8sefBKYmYfzNx2E88Pc6x+c0MjkO46t3A+VVut/TChU+autl+h3Xi+4c4+Wj4sbO9ZaJMeCNI3nJf8qsq20N0O0sEJVndgJKQV1xvUurCpimVl01mbcdqGO6WrQ2+8adjgs9PN2B8kFKA6DfWIqRlyM8wMyDev1VyOREVreTZ58Eet9A5F23Ql3zNiA9A9n1hL0bj5mDhJ3mK1LeMYAyqVgckXf/DiL/7z16mOrffQbGV/4aMjyYl8fP9IBy8QjvdGrrpYh86m+hrn075Kc/hHHXH0N++Yu5NRgGjG98ARjsR+QP/8JW521VWwd0bnCvnYEVQK329glzxet96CVAJD/5T5aOtcDI0NxcrCyJMQt55qfApos9CcSDQJXGdLVWnloZ6P5Ya3KrGLUKPXLdLUtO60o+t8Xi/hkmnIceUPOp87fqgO0V++NHJJ3WrT7OOge4+AqojnOAteshT/23vTfh4+ZOFXegAocB1GnUmnWIfOJzUO/5XeDlF2Hc9T9gPP4T7yd1Dw3ofJsab8aFWFR5BSK3fgiRj98NxOIwvvApGP9wD2RsFPJf/wHseRbqN/4fqHPt5/2oiy4Djr2q2zA4lehDpG51Xo7NgGWu94G9+sUpj4MtM1Vuue5C7X8RODWIiAvJ44GWp1YGIgL0HM+5OlHF4vrI3sEOlPJqB2raHwFUXnpAzde5ASiNZZUHJc/sBAZOInLzbZljc3X1Dbqv2+srN+YM7SDhIsAAahGqpASRt/0aInd9SZ+Jf+urMD77CYh5zOaJwX6griFveStq/QWI3PkFqJt+C/L8MzD+14chD35Lz0277qbs7svFxqSS6EOJObsuXxZc77XrId/6KuTxnwDnbtJl3fnisBLPeOphPbbGo5lhQaFa2oG+7jy86Uno2XtOGoy2dEByPW5MJr3bgfJJDhQSfUBJFKjLz2xDVRoD1p9vOw9KZlKQh/4VWLcR2Lx97n4ufbP+OT798Mp3MsEAKqgYQC1DNbUi8tH/A/WBPwG6X4fxpb/yLDfAzSaadqnSUkTedSsi/+teoKUdOOscqPf/j6yPI1RLh34hcBhAycgwcPQQomvXO7qfXKmmVkT+7K+gPvARoLYe6pKr87uAVfVARVVOlXjSewLY8xzUFdfnN+jzo5YOPTvNjR3R5ZiBrpMO7dbMymyDPTl5Qlesrlqd82MvuSY/BVADfcDqRl1kkSdq4xag+xhkdOU5hfL4T/Su7823LXjeVOUVUNuvgjz35Mo/y/FRHSRmMfCb/IEB1AqUUohceT0iH/wLPT/vm1/2Jrl8cEB3SC8A1b4WJXd8BpFPfh4qx3/EattlwOFfOqpglB//G5BOo+Jd7835PpzS17sLJZ+7H5Errsv7Y6M9t0o8+cG3gVgMqlDT5H1EWWOMPG6omanAa3PQob21XSeDZ5lrKd83r/e1b8/9sZcSL/NNDlS+ekDNp87X7T9khV5OMj0F+cm/A+dvXbTYRF31VmB6CvL8M8s/4PgYUFXj7zmTtCgGUDapCy+G+tX36sGqj//E1fuW9AwwMuRpArkdTv4Bq4t2ALOzkJeez+n2MjgAefK/oK64HtG2RZp2FgnVvhboPp5VkC7Hj0Cefxrq+ndC1azycHUB0ZqnocI9x/Sxe2XuY35yCfY8v94+yoHKWw+o+das07M4V+oT99MfAmMjetzRYtafD7S0r3iMJ2OcgxdUDKCyoG68Bdi8HfKv/7BiL6WsDA/qfjBBrpw6+1ygtj7nruTy0AMAAHXTb7m5quBpX6sb6w0nbN/E+P63gPJKqBt+zcOFBUhVjW4t0OttJZ5VgedIDkOFPb/ePjnCk+lJfbyVhx5Q86mSEuC8C5fNg5KJcch/fw/YeilU54bF70cpqKtuAF49sPwkCHYhDywGUFlQkQgiv/dnQH0DjK/9ja0zclvMXI1CHeG5QUUiUFsvBX75AmQmldVtpa8H8rOfQr35V6AKvAtXaNlW4slrB4F9u6He9muOdkLCRA8VdjhnbgUyOwv0nnA+H7C6Vue92VxrXq53LA6kkt4n4a8k3xV486iNW4CBk0sWDsnD3wOmJhB51/uWv5/L36K7my/XmZxz8AKLAVSWVGUVIh+6A5gYg/H1z+onUocy/0gDHjyobZfp3jRZjkKQH3wbiJZC3fgbHq0sQLKsxDMe/CZQXQt1/Tu9XFXg6ORsD4/w+nv13DQnFXiYF+zZbLuQl+tttRCZsTf2yTN57gE1XyYPapFdKBk9BfnpD6EuuRrqrHOWv5+aVcDWSyE/f2zpMVrjowCbaAYSA6gcqDWdULd9GDj0EuR7/+L8Dq0mmnUNzu+rkDZsAcrKs6rGkxOvQ3Y/BXX9TbYad4adqqzSlVU2KvHkwF7g4D6oG9+Tc/J/aLV2AKOnIJMejWVyoQLPYlXirSRv19vqLeVBIrkMD0IO77f336Ff6hsVIIBC2xq9O7jIm0H5yb8DqRTUr9ordolcdQMwNgLs3X3mfRmzwMQ4j/ACavk5HbSkyBXXwzhyCPLf/wnpPA/q4ityv7PBfqC2LhDDWpejSkuhNm/Xw4Vv+7Ct0mPj+98Cysqh3vbreVhhQNioxBMRvRtR1wB1jQeVWAGn2tdCAMizT0C95R2u37+cOKZHxljDi51o6QB+9lPI5ARUReXij5fH661q6yAAjG99FZFb/xDKhea+kk7r58qH/lXv3NlVVVOQBGulFNTGLZCDL0FEMgU2MpSAPP4TqCveMlcAsJJNFwF1DTCefhglbzrtdWJyQnc+ZwAVSAygHFC/+QeQ40dgfOMLiLStsf8P6jSF6AHlma2XArufAo4c1lUoy5Cjh3Xn83fdClXJKhSLal8LefRHEGN26SB03/PAkUNQ7/+jwAfenrhgG3DhxZDv/iPk7HOhzjnP1buXnmNAU6srncBVazsE0PP7llpnPq/3th2ovPUPMPHdb8A4sA/qlt+DuuK6nKt05egrMP75S8CJ14E3XYHIm9+mg087VjcVrrz//K36uezkCaBVVwbLj/5Vj3jKothFRUqgrrwe8qPvQgYHFuZ5sgt5oPEIzwFVWorIH34ciJbC+Ordiw7ntWVwIDSzy9Tm7UBJ1FY1nvHgN3X/k65fzcPKAqR9rX6X3n9y0S+LYeifXWML1BVdeV5cMKhIBJHf/3Ogth7G1+6GjI24+wDdx51X4FmsSrwl8qDyfb1VpARVv/G7iNz5RaBtDeT+L8C4907IwOK/j0uR5DSM7/4jjE9/DBgfReSPPoGSD90BdcE2qPO32vsvz1MJ5rN6O1l5UNLfC3lmJ9Sbb8g6L0tdqa+b/OynC79gBlB8AxlMDKAcUqsbEfmD/wn0noD8899m3WRTDENX4QW4Am8+VVEJbNgMefHZZX8WcuiXwMt7oN7+bqiyijyu0P8yeTXdix/jyS9+Bpw4CvWr74WKchN5KaqyGpEP/yUwOgLj7z+n801cIKkk0N/rSv4TAF2mX1Kid6AWe7wCXW/V2oHIx/4a6n0fAo4ehvG//xjGw9+zVTgj+1/Uw8of+T7U1Tcg8qkvQ23bkYdVu0c1tgCrmzJz8eSH3wFKSnQ7m2zvq6EZOH8r5OlHFv4eWjtQTCIPJAZQLlAXXAR18/sgu5+CPPpQdjceH9G7DQGvwJtPbbsM6O9ZsgpK53P8C7CqHuraG/O8ugBoOQtQSvcZOo3MzkJ+8C2g9Sw9b4uWpdau0wHAgb26e7cbek8AYrgWQKloFGhsXbTtQqGvt4pEELn2RkQ+9WXg/Isg//YNGJ/+GOT4kUW/X8ZHYfzTfTDuuwuIliLysb9G5P1/tGRul9+pjVuAQ7/UxS5mPp1aldtcPnXVDfrN8oG5yj4Z4xFekDGAcon6v+3dfXBU13nH8e+5kgwWGKGVQILlpUhgbECDYaQgCASoRNxgJ1EcV1McnNq4det6sI2bxKqamskkJB6wKsWOiCYZAqRMM3kttVtaxxoH4oKxZWTVDdSAqIsjS7LQSoAEGKK9p39cUE1sLK20q927/n1mNHB3tXufmQd2n3vuOc/5o897y1V/+gNs85HBvzDk/x5Qv8/Mv7y58GvXuI33m0Zo/m/MbeWx2U3e58yoUd4X6gdMJLcH90L72zhlXxjR/cH8zFm6CrPsk9g9Px1yo9f36i9sh9sD6r1yp3iF2e+fK0HybQLZOA/+Leb+r0DXKdxNj+L+Yqc3Gsfli6JXfo37+IPYV/ZhVpfjbPw25sZ5cYs5Km6eD+d7cb//JIwa7X3OD5G5ZRGMvQH74ns6k/f2eH+qgPIlFVBRYhwHZ90jkDURt24z9nQI67oD/3Re7gGVJHOgAExmFvzBrA9sZ9C/mihrordXlHyw4LT33cKzfb/zbiNMnwkLFscpMH8ya+6H6TNxf1CDfad1eG/29klITYMozs8xk4LQ0XbV7bFEy7cxBqdoKc7Xt2IWr8T+289xv/Yw9tAB3O98A/v9JyFrIs5X/x7nc2uTYnGDmV3g/aX1LcyqsmE1vDRpaZjild4q5SsjT71nIe26/28dIb6iCRRRZNLH4jxQgfutL+N++d7IXpxEt/DAu9qyu3dhu0NeQXVF40vw1gnMvQ9jUtPiF2CCM8Hp2KZXsL+71P9FZF98HkIdOGsf0MajETJp13n/N7++Afe738L5my2YUaMjfh97shnbeAAmTfG2/IiW3CkQ7vOaR+ZM9s6VoPk2Y27A3PMw9mPLcXdtxa17Aq4b5a3WK7k9qUZGzfiAtwKv5zRm1WeH/35LV2Hrn8Ee/JX3fpe3cUmk/MrgqYCKMjNlBs6j38AeaRr8i7JzMOnJtQ2HWVDsFVD/+XL/PCfrhr2+T7lTMMUr4htggjPB6Vjrerd1puVhL17E/utPYOYcmLsw3uH5ksmaiPNnf4371New/1AL9z066C8ue/Ei9pl/xD7/zzBuPM4X10U3ttwpXiuD9hbImeyLfJs5t+BsfBr70guYuQu8SddJyLn3YQiHMdcPf7GLCU6HvNnYF3+JLf0MtlcbCfuZCqgYMPk3YfJvincY8TVpKkyc7N3Gu1JAvfxraPstzl98JamuUmPiypYub5/ETMvD7t0DZ7pw7v+SrlaHwcxbiPnMGm9Cef5Ng2qyaY804e7aCqfaMZ+4FfP5P43+BU9u0DtXewtm/sd8k28zahRmRXI3co12DzGzdBX2h9+B/zmqjYR9TnOgJCaMMd6kyTf+C3v+nNeJ+NkfwdQZMJyu7R8VEyZBaiq8fRJ74Tz2338Gcxb4f1JuAjCry6GgEPvjbd7mvNdgz/XgXu6BhHFwvvRNnLsfjMlosUkfC+PGe+1QlO+kZoqWwqjR3gbD2kjY11RAScyYBYsg3If9zSHs/no41Y5Tthbj6J/dQExqKuROxba+ha1/Bnp7cMrWxjuspGAcB+e+RyGQ7TXZPNt91fPWWtyG/8D9u7/CvvQrzKfu9FaUzY5xMZM7BdveonwnOTM6HVO0DNvwIpzp1giUj+mbTGInbzbckOH1x/qXH0P+TVBQGO+ofMMEp8H/Hsc+vxtuKcbMmBXvkJKGGTMW5y8r4Fwv7vee7F/9Zrs6cWs3Yb+3GQITcL5ajXPHF0ek3YbJnQKtbynfHwFm6Sq4+K73owLKtzQHSmLGOCnearzLfU+c+zYk9HyOhBOcDi/vA2Nwyr4Q72iSjpmWh1n7AHb7t7G/+CF2Qg725zvBDWP+eB2m5NPRXWk3kElBuHBe+f4oyJvtzRNt+626kPuYCiiJqf4C6ub5/XtLyeCY4HQsYIo+Eb1tQ+QqzpIS3BNHsb/8J++Bm+d785zisKLsyko85Tv5GWO85q4/2aYRKB9TASWxdfMtmGWfxJRow+CI3TgP8/ESzKfvinckSc38yZ+DY2DGjZjFfxi/UdKZc5TvjxDz8RKvQWes59ZJzBgb6e63w9TaOswuwAPIzs6ms7MzpueQ2FIO/U859Dflz/+Uw+iYPHnyNZ/TJHIRERGRCKmAEhEREYnQsOZANTU1sX37dlzXpaSkhLKysmjFJSIiIpKwhjwC5bou27Zto7Kykurqavbv309LS0s0YxMRERFJSEMuoJqbm8nNzSUnJ4fU1FSWLFlCQ0NDNGMTERERSUhDvoXX1dVFVlZW/3FWVhbHjx9/3+/V19dTX18PwBNPPEF2dvZQTzkoqampMT+HxJZy6H/Kob8pf/6nHMZezPtAlZaWUlpa2n8c62WVWrrpf8qh/ymH/qb8+Z9yGB0xaWMQCAQIhUL9x6FQiEAgMNS3ExEREfGNIRdQ+fn5tLW10dHRQV9fHwcOHKCwUBvFioiISPIb8i28lJQU1q1bx6ZNm3Bdl5UrVzJ16tRoxiYiIiKSkIY1B2rhwoUsXLgwWrGIiIiI+II6kYuIiIhESAWUiIiISISMtdbGOwgRERERP0m6EaiKiop4hyDDpBz6n3Lob8qf/ymHsZd0BZSIiIhIrKmAEhEREYlQ0hVQ7902RvxJOfQ/5dDflD//Uw5jT5PIRURERCKUdCNQIiIiIrE2rE7kiaapqYnt27fjui4lJSWUlZXFOyQZwNatW2lsbCQjI4OqqioAent7qa6u5tSpU0yYMIENGzYwduzYOEcqH6Szs5Pa2lpOnz6NMYbS0lJWr16tHPrIpUuX2LhxI319fYTDYYqLiykvL6ejo4Oamhp6enrIy8tj/fr1pKYm1VdGUnFdl4qKCgKBABUVFcrfCEiaESjXddm2bRuVlZVUV1ezf/9+Wlpa4h2WDGDFihVUVlZe9dju3bspKCjgqaeeoqCggN27d8cpOhlISkoKd999N9XV1WzatInnnnuOlpYW5dBH0tLS2LhxI1u2bGHz5s00NTVx7Ngxdu3axW233cbTTz/NmDFjeOGFF+IdqnyIPXv2EAwG+4+Vv9hLmgKqubmZ3NxccnJySE1NZcmSJTQ0NMQ7LBnAnDlz3jcy0dDQwPLlywFYvny58pjAMjMzycvLA+D6668nGAzS1dWlHPqIMYbRo0cDEA6HCYfDGGM4fPgwxcXFgHehoxwmrlAoRGNjIyUlJQBYa5W/EZA043ldXV1kZWX1H2dlZXH8+PE4RiRDdebMGTIzMwEYP348Z86ciXNEMhgdHR28+eabzJw5Uzn0Gdd1eeyxx2hvb+fWW28lJyeH9PR0UlJSAAgEAnR1dcU5SrmWHTt2sHbtWi5cuABAT0+P8jcCkmYESpKTMQZjTLzDkAG8++67VFVVcc8995Cenn7Vc8ph4nMchy1btlBXV8eJEydobW2Nd0gySIcOHSIjI6N/JFhGTtKMQAUCAUKhUP9xKBQiEAjEMSIZqoyMDLq7u8nMzKS7u5tx48bFOyT5EH19fVRVVbFs2TIWLVoEKId+NWbMGObOncuxY8c4f/484XCYlJQUurq69HmaoI4ePcqrr77Ka6+9xqVLl7hw4QI7duxQ/kZA0oxA5efn09bWRkdHB319fRw4cIDCwsJ4hyVDUFhYyL59+wDYt28fRUVFcY5IrsVaS11dHcFgkNtvv73/ceXQP86ePcu5c+cAb0Xe66+/TjAYZO7cuRw8eBCAvXv36vM0Qd11113U1dVRW1vLI488wrx583jooYeUvxGQVI00Gxsb2blzJ67rsnLlSu644454hyQDqKmp4ciRI/T09JCRkUF5eTlFRUVUV1fT2dmpJfAJ7o033uDxxx9n2rRp/bfp1qxZw6xZs5RDnzh58iS1tbW4rou1lsWLF3PnnXfyzjvvUFNTQ29vLzNmzGD9+vWkpaXFO1z5EIcPH+bZZ5+loqJC+RsBSVVAiYiIiIyEpLmFJyIiIjJSVECJiIiIREgFlIiIiEiEVECJiIiIREgFlIiIiEiEVECJiIiIREgFlIiIiEiEVECJiIiIROj/AP+S1A056SI+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jessie_downloads = pd.read_sql(f\"\"\"\n",
    "    select accessed_at::date as day, count(*) c\n",
    "    from downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and distro_version = '8'\n",
    "    group by day\n",
    "    order by day\n",
    "\"\"\", conn)\n",
    "\n",
    "jessie_downloads.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>installer_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.1</td>\n",
       "      <td>10317956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0.1</td>\n",
       "      <td>5406456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.1.1</td>\n",
       "      <td>4741246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0.2</td>\n",
       "      <td>3885306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.3.1</td>\n",
       "      <td>2746288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.1</td>\n",
       "      <td>2501631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.2.3</td>\n",
       "      <td>915949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.0.1</td>\n",
       "      <td>404222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19.1.1</td>\n",
       "      <td>293714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0.3</td>\n",
       "      <td>213349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  installer_version         c\n",
       "0              18.1  10317956\n",
       "1             9.0.1   5406456\n",
       "2            20.1.1   4741246\n",
       "3            20.0.2   3885306\n",
       "4            19.3.1   2746288\n",
       "5              20.1   2501631\n",
       "6            19.2.3    915949\n",
       "7            19.0.1    404222\n",
       "8            19.1.1    293714\n",
       "9             9.0.3    213349"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_versions = pd.read_sql(f\"\"\"\n",
    "    select installer_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and installer_name = 'pip'\n",
    "    and installer_version is not null\n",
    "    group by installer_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "pip_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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(pip_versions.c, labels=pip_versions.installer_version)\n",
    "plt.title('pip version usage')\n",
    "\n",
    "plt.savefig('pip-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>12619926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.1.3</td>\n",
       "      <td>2791126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.5.1</td>\n",
       "      <td>1620448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.2.0</td>\n",
       "      <td>1170652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.4.0</td>\n",
       "      <td>1011978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44.1.0</td>\n",
       "      <td>926089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47.1.1</td>\n",
       "      <td>923071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42.0.2</td>\n",
       "      <td>890662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>47.3.1</td>\n",
       "      <td>597911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33.1.1</td>\n",
       "      <td>474641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  setuptools_version         c\n",
       "0             40.8.0  12619926\n",
       "1             46.1.3   2791126\n",
       "2             41.5.1   1620448\n",
       "3             41.2.0   1170652\n",
       "4             46.4.0   1011978\n",
       "5             44.1.0    926089\n",
       "6             47.1.1    923071\n",
       "7             42.0.2    890662\n",
       "8             47.3.1    597911\n",
       "9             33.1.1    474641"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setuptools_versions = pd.read_sql(f\"\"\"\n",
    "    select setuptools_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    and installer_name = 'pip'\n",
    "    and setuptools_version is not null\n",
    "    group by setuptools_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "setuptools_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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(setuptools_versions.c, labels=setuptools_versions.setuptools_version)\n",
    "plt.title('setuptools version usage')\n",
    "\n",
    "plt.savefig('setuptools-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}