2020.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ben/.environments/stats/lib/python3.8/site-packages/sqlalchemy/dialects/postgresql/base.py:3470: SAWarning: Predicate of partial index files_size ignored during reflection\n",
      "  util.warn(\n"
     ]
    }
   ],
   "source": [
    "engine = create_engine('postgres:///piwheels')\n",
    "conn = engine.connect()\n",
    "meta = MetaData()\n",
    "meta.bind = conn\n",
    "meta.reflect(views=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2020-01-01'\n",
    "start_time = '2020-01-01 00:00:00'\n",
    "end_date = '2020-12-31'\n",
    "end_time = '2021-01-01 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>downloads_in_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16184523</td>\n",
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      ],
      "text/plain": [
       "   downloads_in_period\n",
       "0             16184523"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads_in_period\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>daily_avg_downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44220.008197</td>\n",
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      ],
      "text/plain": [
       "   daily_avg_downloads\n",
       "0         44220.008197"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT AVG(downloads) AS daily_avg_downloads\n",
    "    FROM (\n",
    "        SELECT COUNT(*) AS downloads\n",
    "        FROM downloads\n",
    "        WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "        GROUP BY accessed_at::date\n",
    "    ) downloads_BY_DAY\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>downloads_to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21920948</td>\n",
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      ],
      "text/plain": [
       "   downloads_to_date\n",
       "0           21920948"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads_to_date\n",
    "    FROM downloads\n",
    "    WHERE accessed_at < '{end_time}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2121207</td>\n",
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      ],
      "text/plain": [
       "   project_page_hits\n",
       "0            2121207"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS project_page_hits\n",
    "    FROM project_page_hits\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project_json_downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51149</td>\n",
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      ],
      "text/plain": [
       "   project_json_downloads\n",
       "0                   51149"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS project_json_downloads\n",
    "    FROM project_json_downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002602</td>\n",
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      "text/plain": [
       "   web_page_hits\n",
       "0        2002602"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS web_page_hits\n",
    "    FROM web_page_hits\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1079669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>985288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1300742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>1239451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>1271968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>1170659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>1102139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1737186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>1653471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>1850781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1240661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>1552508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            m        c\n",
       "0  2020-01-01  1079669\n",
       "1  2020-02-01   985288\n",
       "2  2020-03-01  1300742\n",
       "3  2020-04-01  1239451\n",
       "4  2020-05-01  1271968\n",
       "5  2020-06-01  1170659\n",
       "6  2020-07-01  1102139\n",
       "7  2020-08-01  1737186\n",
       "8  2020-09-01  1653471\n",
       "9  2020-10-01  1850781\n",
       "10 2020-11-01  1240661\n",
       "11 2020-12-01  1552508"
      ]
     },
     "execution_count": 12,
     "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": 15,
   "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",
    "    'Jan': downloads_by_month.c[0],\n",
    "    'Feb': downloads_by_month.c[1],\n",
    "    'Mar': downloads_by_month.c[2],\n",
    "    'Apr': downloads_by_month.c[3],\n",
    "    'May': downloads_by_month.c[4],\n",
    "    'Jun': downloads_by_month.c[5],\n",
    "    'Jul': downloads_by_month.c[6],\n",
    "    'Aug': downloads_by_month.c[7],\n",
    "    'Sep': downloads_by_month.c[8],\n",
    "    'Oct': downloads_by_month.c[9],\n",
    "    'Nov': downloads_by_month.c[10],\n",
    "    'Dec': downloads_by_month.c[11],\n",
    "}\n",
    "\n",
    "colors = ['blue'] * 12\n",
    "plt.bar(range(len(downloads)), downloads.values(), color=colors)\n",
    "plt.title('Downloads by month')\n",
    "plt.xticks(range(len(downloads)), list(downloads.keys()))\n",
    "\n",
    "plt.savefig('downloads-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141009039"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS searches_in_period\n",
    "    FROM searches\n",
    "    WHERE accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "\"\"\", conn)\n",
    "\n",
    "searches = df.values[0][0]\n",
    "searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_time_saved_in_quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>98291 days 16:37:23.059288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_time_saved_in_quarter\n",
       "0  98291 days 16:37:23.059288"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52452160990233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sum\n",
       "0  52452160990233"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select sum(filesize)\n",
    "    from downloads d\n",
    "    join files f using (filename)\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1004056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dogpile-cache</td>\n",
       "      <td>389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>webrtcvad-wheels</td>\n",
       "      <td>356793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pyyaml</td>\n",
       "      <td>351503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sentry-sdk</td>\n",
       "      <td>350834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>semantic-version</td>\n",
       "      <td>314402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cffi</td>\n",
       "      <td>301478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>markdown</td>\n",
       "      <td>284277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>flask-login</td>\n",
       "      <td>266479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pycparser</td>\n",
       "      <td>259366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>requests</td>\n",
       "      <td>210274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>markupsafe</td>\n",
       "      <td>206343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>198981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>paho-mqtt</td>\n",
       "      <td>198747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>octoprint-firmwarecheck</td>\n",
       "      <td>184992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>filetype</td>\n",
       "      <td>181539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>opencv-python</td>\n",
       "      <td>179079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pyusb</td>\n",
       "      <td>170359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>zope-component</td>\n",
       "      <td>165536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>adafruit-pureio</td>\n",
       "      <td>162949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>docopt</td>\n",
       "      <td>161961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>pillow</td>\n",
       "      <td>160590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>rpi-gpio</td>\n",
       "      <td>150513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>pynacl</td>\n",
       "      <td>148723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>future</td>\n",
       "      <td>145075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>adafruit-platformdetect</td>\n",
       "      <td>143978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>tornado</td>\n",
       "      <td>141605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>adafruit-blinka</td>\n",
       "      <td>140604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>bcrypt</td>\n",
       "      <td>138836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sysv-ipc</td>\n",
       "      <td>136934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    package        c\n",
       "0                     numpy  1004056\n",
       "1             dogpile-cache   389999\n",
       "2          webrtcvad-wheels   356793\n",
       "3                    pyyaml   351503\n",
       "4                sentry-sdk   350834\n",
       "5          semantic-version   314402\n",
       "6                      cffi   301478\n",
       "7                  markdown   284277\n",
       "8               flask-login   266479\n",
       "9                 pycparser   259366\n",
       "10                 requests   210274\n",
       "11               markupsafe   206343\n",
       "12             cryptography   198981\n",
       "13                paho-mqtt   198747\n",
       "14  octoprint-firmwarecheck   184992\n",
       "15                 filetype   181539\n",
       "16            opencv-python   179079\n",
       "17                    pyusb   170359\n",
       "18           zope-component   165536\n",
       "19          adafruit-pureio   162949\n",
       "20                   docopt   161961\n",
       "21                   pillow   160590\n",
       "22                 rpi-gpio   150513\n",
       "23                   pynacl   148723\n",
       "24                   future   145075\n",
       "25  adafruit-platformdetect   143978\n",
       "26                  tornado   141605\n",
       "27          adafruit-blinka   140604\n",
       "28                   bcrypt   138836\n",
       "29                 sysv-ipc   136934"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_30_downloads = pd.read_sql(f\"\"\"\n",
    "    select b.package, count(*) c\n",
    "    from downloads d\n",
    "    join files f on f.filename = d.filename\n",
    "    join builds b on b.build_id = f.build_id\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by b.package\n",
    "    order by c desc\n",
    "    limit 30;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_30_downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (1,004,056)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/dogpile-cache'>dogpile-cache</a></strong> (389,999)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/webrtcvad-wheels'>webrtcvad-wheels</a></strong> (356,793)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyyaml'>pyyaml</a></strong> (351,503)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/sentry-sdk'>sentry-sdk</a></strong> (350,834)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/semantic-version'>semantic-version</a></strong> (314,402)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cffi'>cffi</a></strong> (301,478)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/markdown'>markdown</a></strong> (284,277)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/flask-login'>flask-login</a></strong> (266,479)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pycparser'>pycparser</a></strong> (259,366)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (210,274)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/markupsafe'>markupsafe</a></strong> (206,343)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cryptography'>cryptography</a></strong> (198,981)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/paho-mqtt'>paho-mqtt</a></strong> (198,747)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/octoprint-firmwarecheck'>octoprint-firmwarecheck</a></strong> (184,992)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/filetype'>filetype</a></strong> (181,539)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python'>opencv-python</a></strong> (179,079)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyusb'>pyusb</a></strong> (170,359)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/zope-component'>zope-component</a></strong> (165,536)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/adafruit-pureio'>adafruit-pureio</a></strong> (162,949)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/docopt'>docopt</a></strong> (161,961)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (160,590)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/rpi-gpio'>rpi-gpio</a></strong> (150,513)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pynacl'>pynacl</a></strong> (148,723)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/future'>future</a></strong> (145,075)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/adafruit-platformdetect'>adafruit-platformdetect</a></strong> (143,978)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tornado'>tornado</a></strong> (141,605)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/adafruit-blinka'>adafruit-blinka</a></strong> (140,604)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/bcrypt'>bcrypt</a></strong> (138,836)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/sysv-ipc'>sysv-ipc</a></strong> (136,934)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for i, row in top_30_downloads.iterrows():\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{row['package']}'>{row['package']}</a></strong> ({row['c']:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pip</td>\n",
       "      <td>15765332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wheel</td>\n",
       "      <td>6921171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>4534167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>youtube-dl</td>\n",
       "      <td>3619434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pyrogram</td>\n",
       "      <td>2473570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>requests</td>\n",
       "      <td>2181233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1901582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>certifi</td>\n",
       "      <td>1827707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>1693529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>six</td>\n",
       "      <td>1592086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      package         c\n",
       "0         pip  15765332\n",
       "1       wheel   6921171\n",
       "2  setuptools   4534167\n",
       "3  youtube-dl   3619434\n",
       "4    pyrogram   2473570\n",
       "5    requests   2181233\n",
       "6       numpy   1901582\n",
       "7     certifi   1827707\n",
       "8     urllib3   1693529\n",
       "9         six   1592086"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_searches = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (15,765,332)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/wheel'>wheel</a></strong> (6,921,171)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (4,534,167)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/youtube-dl'>youtube-dl</a></strong> (3,619,434)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyrogram'>pyrogram</a></strong> (2,473,570)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (2,181,233)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (1,901,582)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/certifi'>certifi</a></strong> (1,827,707)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/urllib3'>urllib3</a></strong> (1,693,529)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (1,592,086)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_searches.values:\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{pkg}'>{pkg}</a></strong> ({c:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>opencv-python</td>\n",
       "      <td>8572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>4727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>numpy</td>\n",
       "      <td>4627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scipy</td>\n",
       "      <td>3973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>3892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pillow</td>\n",
       "      <td>1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pip</td>\n",
       "      <td>1792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>grpcio</td>\n",
       "      <td>1594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>opencv-python-headless</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pandas</td>\n",
       "      <td>1187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  package     c\n",
       "0           opencv-python  8572\n",
       "1   opencv-contrib-python  4727\n",
       "2                   numpy  4627\n",
       "3                   scipy  3973\n",
       "4              tensorflow  3892\n",
       "5                  pillow  1858\n",
       "6                     pip  1792\n",
       "7                  grpcio  1594\n",
       "8  opencv-python-headless  1263\n",
       "9                  pandas  1187"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_project_pages = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from project_page_hits\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_project_pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python'>opencv-python</a></strong> (8,572)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-contrib-python'>opencv-contrib-python</a></strong> (4,727)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (4,627)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scipy'>scipy</a></strong> (3,973)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tensorflow'>tensorflow</a></strong> (3,892)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (1,858)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (1,792)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/grpcio'>grpcio</a></strong> (1,594)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python-headless'>opencv-python-headless</a></strong> (1,263)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pandas'>pandas</a></strong> (1,187)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_project_pages.values:\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{pkg}'>{pkg}</a></strong> ({c:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>140815096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows</td>\n",
       "      <td>113763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>78550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FreeBSD</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ibnu Rifqi</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CYGWIN_NT-10.0-18363</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OpenBSD</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MINGW64_NT-10.0-18363</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>L</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CYGWIN_NT-10.0-19041</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  os_name          c\n",
       "0                   Linux  140815096\n",
       "1                 Windows     113763\n",
       "2                  Darwin      78550\n",
       "3                 FreeBSD       1165\n",
       "4                    None        422\n",
       "5              Ibnu Rifqi         21\n",
       "6    CYGWIN_NT-10.0-18363         10\n",
       "7                 OpenBSD          5\n",
       "8   MINGW64_NT-10.0-18363          3\n",
       "9                       L          2\n",
       "10   CYGWIN_NT-10.0-19041          2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_usage = pd.read_sql(f\"\"\"\n",
    "    select os_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by os_name\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "os_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9986246059020373"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_usage.values[0][1] / searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distro_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>132440012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debian GNU/Linux</td>\n",
       "      <td>6483610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ubuntu</td>\n",
       "      <td>1229348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Red Hat Enterprise Linux Server</td>\n",
       "      <td>275270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alpine Linux</td>\n",
       "      <td>129498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       distro_name          c\n",
       "0               Raspbian GNU/Linux  132440012\n",
       "1                 Debian GNU/Linux    6483610\n",
       "2                           Ubuntu    1229348\n",
       "3  Red Hat Enterprise Linux Server     275270\n",
       "4                     Alpine Linux     129498"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distro_usage = pd.read_sql(f\"\"\"\n",
    "    select distro_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    group by distro_name\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distro_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>105691933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>32726316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>15497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  distro_version          c\n",
       "0             10  105691933\n",
       "1              9   32726316\n",
       "2              8      15497"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distro_versions = pd.read_sql(f\"\"\"\n",
    "    select distro_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and distro_version in ('8', '9', '10')\n",
    "    group by distro_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 76.3%\n",
      "9 23.6%\n",
      "8 0.0%\n"
     ]
    }
   ],
   "source": [
    "total = sum(distro_versions.c)\n",
    "\n",
    "for v in distro_versions.values:\n",
    "    print(v[0], f'{(100 * v[1] / total):.1f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version_names = {'10': 'Buster', '9': 'Stretch', '8': 'Jessie'}\n",
    "plt.pie(distro_versions.c, labels=[version_names[v] for v in distro_versions.distro_version])\n",
    "plt.title('Raspbian/Debian version usage')\n",
    "\n",
    "plt.savefig('debian-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>126976015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>armv6l</td>\n",
       "      <td>7697603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aarch64</td>\n",
       "      <td>2986842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x86_64</td>\n",
       "      <td>2949746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i686</td>\n",
       "      <td>228012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AMD64</td>\n",
       "      <td>113758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>armv8l</td>\n",
       "      <td>54431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amd64</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ppc64le</td>\n",
       "      <td>611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>s390x</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>arm64</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>arm</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>x86</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ppc</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       arch          c\n",
       "0    armv7l  126976015\n",
       "1    armv6l    7697603\n",
       "2   aarch64    2986842\n",
       "3    x86_64    2949746\n",
       "4      i686     228012\n",
       "5     AMD64     113758\n",
       "6    armv8l      54431\n",
       "7     amd64       1170\n",
       "8   ppc64le        611\n",
       "9      None        422\n",
       "10    s390x        368\n",
       "11    arm64         36\n",
       "12      arm         18\n",
       "13      x86          5\n",
       "14      ppc          2"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "armv7l 92.4%\n",
      "armv6l 5.6%\n",
      "aarch64 2.0%\n"
     ]
    }
   ],
   "source": [
    "arm_arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and arch in ('armv6l', 'armv7l', 'aarch64')\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "total = sum(arm_arch.c)\n",
    "\n",
    "for v in arm_arch.values:\n",
    "    print(v[0], f'{(100 * v[1] / total):.1f}%') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(arm_arch.c, labels=arm_arch.arch)\n",
    "plt.title('Raspbian/Debian Arm architecture usage')\n",
    "\n",
    "plt.savefig('debian-arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>76938781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.7</td>\n",
       "      <td>43907496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>10568656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.8</td>\n",
       "      <td>6960455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.6</td>\n",
       "      <td>2115622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pyver         c\n",
       "0   3.7  76938781\n",
       "1   2.7  43907496\n",
       "2   3.5  10568656\n",
       "3   3.8   6960455\n",
       "4   3.6   2115622"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_vers = pd.read_sql(f\"\"\"\n",
    "    select substring(py_version from 1 for 3) pyver, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and 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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7 54.76%\n",
      "2.7 31.25%\n",
      "3.5 7.52%\n",
      "3.8 4.95%\n",
      "3.6 1.51%\n"
     ]
    }
   ],
   "source": [
    "total = sum(row[1] for row in py_vers.values)\n",
    "\n",
    "for ver, c in py_vers.values:\n",
    "    print(ver, f'{100 * (c / total):.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>42194370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0.1</td>\n",
       "      <td>18219576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.3.1</td>\n",
       "      <td>13519046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.0.2</td>\n",
       "      <td>11553138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.1.1</td>\n",
       "      <td>10599592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20.2.4</td>\n",
       "      <td>8834934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.2.3</td>\n",
       "      <td>7712525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.2.2</td>\n",
       "      <td>6830430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.2</td>\n",
       "      <td>3116065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.2.3</td>\n",
       "      <td>3097924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  installer_version         c\n",
       "0              18.1  42194370\n",
       "1             9.0.1  18219576\n",
       "2            19.3.1  13519046\n",
       "3            20.0.2  11553138\n",
       "4            20.1.1  10599592\n",
       "5            20.2.4   8834934\n",
       "6            20.2.3   7712525\n",
       "7            20.2.2   6830430\n",
       "8              20.2   3116065\n",
       "9            19.2.3   3097924"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_versions = pd.read_sql(f\"\"\"\n",
    "    select installer_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and installer_name = 'pip'\n",
    "    and installer_version is not null\n",
    "    group by installer_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "pip_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(v[1] for v in pip_versions.values)\n",
    "labels = [v[0] if v[1] / total > 0.01 else '' for v in pip_versions.values]\n",
    "\n",
    "plt.pie(pip_versions.c, labels=labels)\n",
    "plt.title('pip version usage')\n",
    "\n",
    "plt.savefig('pip-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>58882722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.5.1</td>\n",
       "      <td>8303561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.2.0</td>\n",
       "      <td>6372365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.1.3</td>\n",
       "      <td>4139548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.3.2</td>\n",
       "      <td>3664654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.3.0</td>\n",
       "      <td>3196434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>44.1.1</td>\n",
       "      <td>3037389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44.0.0</td>\n",
       "      <td>2917250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>49.2.0</td>\n",
       "      <td>2276389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42.0.2</td>\n",
       "      <td>2181558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  setuptools_version         c\n",
       "0             40.8.0  58882722\n",
       "1             41.5.1   8303561\n",
       "2             41.2.0   6372365\n",
       "3             46.1.3   4139548\n",
       "4             50.3.2   3664654\n",
       "5             50.3.0   3196434\n",
       "6             44.1.1   3037389\n",
       "7             44.0.0   2917250\n",
       "8             49.2.0   2276389\n",
       "9             42.0.2   2181558"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setuptools_versions = pd.read_sql(f\"\"\"\n",
    "    select setuptools_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and installer_name = 'pip'\n",
    "    and setuptools_version is not null\n",
    "    group by setuptools_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "setuptools_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(v[1] for v in setuptools_versions.values)\n",
    "labels = [v[0] if v[1] / total > 0.03 else '' for v in setuptools_versions.values]\n",
    "\n",
    "plt.pie(setuptools_versions.c, labels=labels)\n",
    "plt.title('setuptools version usage')\n",
    "\n",
    "plt.savefig('setuptools-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "downloads_by_hour = pd.read_sql(f\"\"\"\n",
    "    select date_part('hour', accessed_at) h, count(*) c\n",
    "    from downloads\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by h\n",
    "    order by h\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[511159,\n",
       " 514747,\n",
       " 467567,\n",
       " 440892,\n",
       " 440012,\n",
       " 449358,\n",
       " 487656,\n",
       " 555893,\n",
       " 625293,\n",
       " 684754,\n",
       " 710860,\n",
       " 728097,\n",
       " 759932,\n",
       " 815418,\n",
       " 863938,\n",
       " 893043,\n",
       " 879065,\n",
       " 850085,\n",
       " 849308,\n",
       " 836928,\n",
       " 820064,\n",
       " 752722,\n",
       " 668981,\n",
       " 578751]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(downloads_by_hour['c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = [int(h) for h in downloads_by_hour['h']]\n",
    "\n",
    "plt.bar(range(len(downloads_by_hour)), list(downloads_by_hour['c']), color=['blue'] * 24)\n",
    "plt.title('Downloads by hour')\n",
    "plt.xticks(range(len(downloads_by_hour)), hours)\n",
    "\n",
    "plt.savefig('downloads-by-hour.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}