bq_column_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BigQuery: column value distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gcp_key = '../../etc/prod-ostro/gcp.json'\n",
    "gcp_project = 'zephyrus-ef4-prod-ostro'\n",
    "bq_dataset = 'ostro'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### misc functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ppsql(sql_string):\n",
    "    print(sql_string.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sql functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_base(table_id, column_name, timestamp='timestamp'):\n",
    "    return \"\"\"\n",
    "    derived_base AS (\n",
    "        SELECT\n",
    "            format_date('%Y%m%d', DATE({timestamp})) as event_day,\n",
    "            extract(hour from {timestamp}) as event_hour,\n",
    "            {column} AS event_column\n",
    "        FROM {table}\n",
    "        WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-09-04 00:00:00')\n",
    "                                 AND TIMESTAMP('2017-09-10 00:00:00')\n",
    "        )\n",
    "    \"\"\".format(table=table_id, column=column_name, timestamp=timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_base_percentile(table_id, column_name, timestamp='timestamp'):\n",
    "    return \"\"\"\n",
    "    percentiles AS (\n",
    "        SELECT\n",
    "            APPROX_QUANTILES({column}, 20) AS ptiles\n",
    "        FROM {table}\n",
    "        ),\n",
    "    \n",
    "    outlier_percentiles AS (\n",
    "        SELECT\n",
    "            ptiles[OFFSET(1)] AS ptile_lower,\n",
    "            ptiles[OFFSET(ARRAY_LENGTH(ptiles)-2)] AS ptile_upper\n",
    "        FROM percentiles\n",
    "        ),\n",
    "    \n",
    "    derived_base AS (\n",
    "        SELECT\n",
    "            format_date('%Y%m%d', DATE({timestamp})) as event_day,\n",
    "            extract(hour from {timestamp}) as event_hour,\n",
    "            {column} AS event_column\n",
    "        FROM {table}\n",
    "        WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-09-04 00:00:00')\n",
    "                                 AND TIMESTAMP('2017-09-10 00:00:00')\n",
    "        AND {column} > (SELECT ptile_lower FROM outlier_percentiles)\n",
    "        AND {column} < (SELECT ptile_upper FROM outlier_percentiles)\n",
    "        )\n",
    "    \"\"\".format(table=table_id, column=column_name, timestamp=timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_bool_convert():\n",
    "    return \"\"\"\n",
    "    derived_number AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            CASE event_column\n",
    "            WHEN true  THEN 0.75\n",
    "            WHEN false THEN 0.25\n",
    "            END AS number_column\n",
    "        FROM derived_base\n",
    "        )\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_time_convert():\n",
    "    return \"\"\"\n",
    "    derived_number AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            UNIX_SECONDS(event_column) AS number_column\n",
    "        FROM derived_base\n",
    "        )\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_number_convert():\n",
    "    return \"\"\"\n",
    "    derived_number AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            event_column AS number_column\n",
    "        FROM derived_base\n",
    "        )\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_string_convert(slice_size=5):\n",
    "    if slice_size >= 0:\n",
    "        substr_parameters = \"event_column,0,{no_characters}\".format(no_characters=slice_size)\n",
    "    else:\n",
    "        substr_parameters = \"reverse(event_column),0,{no_characters}\".format(no_characters=(-1*slice_size))\n",
    "\n",
    "    return \"\"\"\n",
    "    string_substr AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            SUBSTR({parameters}) AS string_column\n",
    "        FROM derived_base\n",
    "        ),\n",
    "\n",
    "    derived_number AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            CAST(CONCAT('0x', SUBSTR(TO_HEX(MD5(string_column)),0,15)) AS INT64) AS number_column\n",
    "        FROM string_substr\n",
    "        )\n",
    "    \"\"\".format(parameters=substr_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_hash_convert():\n",
    "    return \"\"\"\n",
    "    derived_number AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            STRTOL(SUBSTRING(event_column,0,15), 16) AS number_column\n",
    "        FROM derived_base\n",
    "        WHERE regexp_count(event_column, '[ABCDEF0123456789]') = length(event_column)\n",
    "        )\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_stats():\n",
    "    return \"\"\"\n",
    "    derived_stats AS (\n",
    "        SELECT\n",
    "            min(number_column) AS min_value,\n",
    "            max(number_column) AS max_value,\n",
    "            max(number_column) - min(number_column) AS range_value,\n",
    "            avg(number_column) AS mean_value\n",
    "        FROM derived_number\n",
    "        WHERE number_column > 0\n",
    "        ),\n",
    "\n",
    "    derived_norm AS (\n",
    "        SELECT\n",
    "            event_day,\n",
    "            event_hour,\n",
    "            number_column as raw_value,\n",
    "            (CAST(number_column AS float64) - (SELECT min_value from derived_stats))\n",
    "                / (SELECT range_value from derived_stats)\n",
    "            AS normed_value\n",
    "        FROM derived_number\n",
    "        )\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_normed(sql_base):\n",
    "    return \"\"\"\n",
    "    WITH\n",
    "    {base}\n",
    "\n",
    "    SELECT\n",
    "        event_day,\n",
    "        event_hour,\n",
    "        raw_value,\n",
    "        normed_value\n",
    "    FROM derived_norm\n",
    "    \"\"\".format(base=sql_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_column_stats(sql_base):\n",
    "    return \"\"\"\n",
    "    WITH\n",
    "    {base}\n",
    "\n",
    "    SELECT\n",
    "        min_value,\n",
    "        max_value,\n",
    "        range_value\n",
    "    FROM derived_stats\n",
    "    \"\"\".format(base=sql_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_normed_percentiles(sql_base):\n",
    "    queries = []\n",
    "    idx = 0\n",
    "    for tile in np.linspace(0.0, 1.0, num=21):\n",
    "        queries.append(\"\"\"\n",
    "            SELECT\n",
    "            {tile:.2f} AS ptile,\n",
    "            ptiles[OFFSET({index})] as ptile_value\n",
    "            FROM derived_percentiles\n",
    "            \"\"\".format(tile=tile, index=idx))\n",
    "        idx += 1\n",
    "    ptile_sql = ' UNION ALL '.join(queries)\n",
    "    \n",
    "    return \"\"\"\n",
    "    WITH\n",
    "    {base},\n",
    "    derived_percentiles AS (\n",
    "        SELECT\n",
    "            APPROX_QUANTILES(normed_value, 20) AS ptiles\n",
    "        FROM derived_norm\n",
    "        )\n",
    "    \n",
    "    {ptiles}\n",
    "    \"\"\".format(ptiles=ptile_sql, base=sql_base)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_one_per_day_overlay(df):\n",
    "    sns.set(style=\"white\", palette=\"muted\")\n",
    "    f, axes = plt.subplots(1, 1, figsize=(10, 7), sharex=True)\n",
    "    sns.despine(left=True)\n",
    "\n",
    "    b, g, r, p = sns.color_palette(\"muted\", 4)\n",
    "\n",
    "    for d in df['event_day'].unique():\n",
    "        if int(d) % 2 == 0: c=g\n",
    "        else: c=b\n",
    "        sns.distplot(df[(df['event_day'] == d)]['normed_value'], color=c)\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_one_hour(df, bins=20):\n",
    "    df[(df['event_day'] == '20170906') & (df['event_hour'] == 12)]['normed_value'].plot.hist(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_all(df, bins=20):\n",
    "    df['normed_value'].plot.hist(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_raw_all(df, bins=20):\n",
    "    df['raw_value'].plot.hist(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_cumul_all(df):\n",
    "    sns.distplot(df['normed_value'],\n",
    "             hist_kws=dict(cumulative=True),\n",
    "             kde_kws=dict(cumulative=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of string values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = '.'.join([bq_dataset, 'power_out'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sql_prepare = ','.join([create_base(table, 'model'),\n",
    "                        create_string_convert(),\n",
    "                        create_stats()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_gbq(create_normed(sql_prepare),\n",
    "                 dialect = 'standard',\n",
    "                 project_id = gcp_project,\n",
    "                 private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_stats = pd.read_gbq(create_column_stats(sql_prepare),\n",
    "                        dialect = 'standard',\n",
    "                        project_id = gcp_project,\n",
    "                        private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_value</th>\n",
       "      <th>max_value</th>\n",
       "      <th>range_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>738747573228445</td>\n",
       "      <td>1151708196245399645</td>\n",
       "      <td>1150969448672171200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         min_value            max_value          range_value\n",
       "0  738747573228445  1151708196245399645  1150969448672171200"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_day</th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20170909</td>\n",
       "      <td>17</td>\n",
       "      <td>626881450821167126</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20170909</td>\n",
       "      <td>17</td>\n",
       "      <td>626881450821167126</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20170909</td>\n",
       "      <td>17</td>\n",
       "      <td>626881450821167126</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20170909</td>\n",
       "      <td>17</td>\n",
       "      <td>626881450821167126</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20170909</td>\n",
       "      <td>17</td>\n",
       "      <td>626881450821167126</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  event_day  event_hour           raw_value  normed_value\n",
       "0  20170909          17  626881450821167126      0.544013\n",
       "1  20170909          17  626881450821167126      0.544013\n",
       "2  20170909          17  626881450821167126      0.544013\n",
       "3  20170909          17  626881450821167126      0.544013\n",
       "4  20170909          17  626881450821167126      0.544013"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_percentiles = pd.read_gbq(\n",
    "                     create_normed_percentiles(sql_prepare),\n",
    "                     dialect = 'standard',\n",
    "                     project_id = gcp_project,\n",
    "                     private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ptile</th>\n",
       "      <th>ptile_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.117436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.201602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.204471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.283792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.356066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.453089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.453089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.531790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.578086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.678446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.801241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.924492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ptile  ptile_value\n",
       "0    0.00     0.000000\n",
       "14   0.05     0.117436\n",
       "13   0.10     0.201602\n",
       "20   0.15     0.204471\n",
       "2    0.20     0.283792\n",
       "16   0.25     0.356066\n",
       "11   0.30     0.453089\n",
       "12   0.35     0.453089\n",
       "18   0.40     0.531790\n",
       "9    0.45     0.544013\n",
       "8    0.50     0.544013\n",
       "4    0.55     0.544013\n",
       "5    0.60     0.544013\n",
       "7    0.65     0.544013\n",
       "10   0.70     0.544013\n",
       "6    0.75     0.544013\n",
       "19   0.80     0.578086\n",
       "17   0.85     0.678446\n",
       "15   0.90     0.801241\n",
       "3    0.95     0.924492\n",
       "1    1.00     1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_percentiles.sort_values('ptile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.387476e+14</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.359040e+17</td>\n",
       "      <td>0.117436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.327771e+17</td>\n",
       "      <td>0.201602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.360785e+17</td>\n",
       "      <td>0.204471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.273744e+17</td>\n",
       "      <td>0.283792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.105597e+17</td>\n",
       "      <td>0.356066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.222304e+17</td>\n",
       "      <td>0.453089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.35</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.222304e+17</td>\n",
       "      <td>0.453089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6.128132e+17</td>\n",
       "      <td>0.531790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.45</th>\n",
       "      <td>12.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>14.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>15.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.65</th>\n",
       "      <td>16.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>17.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6.268815e+17</td>\n",
       "      <td>0.544013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>19.0</td>\n",
       "      <td>6.660980e+17</td>\n",
       "      <td>0.578086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.85</th>\n",
       "      <td>20.0</td>\n",
       "      <td>7.866503e+17</td>\n",
       "      <td>0.682826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>21.0</td>\n",
       "      <td>9.229427e+17</td>\n",
       "      <td>0.801241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.064801e+18</td>\n",
       "      <td>0.924492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.151708e+18</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      event_hour     raw_value  normed_value\n",
       "0.00         0.0  7.387476e+14      0.000000\n",
       "0.05         1.0  1.359040e+17      0.117436\n",
       "0.10         2.0  2.327771e+17      0.201602\n",
       "0.15         3.0  2.360785e+17      0.204471\n",
       "0.20         5.0  3.273744e+17      0.283792\n",
       "0.25         6.0  4.105597e+17      0.356066\n",
       "0.30         7.0  5.222304e+17      0.453089\n",
       "0.35         9.0  5.222304e+17      0.453089\n",
       "0.40        11.0  6.128132e+17      0.531790\n",
       "0.45        12.0  6.268815e+17      0.544013\n",
       "0.50        13.0  6.268815e+17      0.544013\n",
       "0.55        14.0  6.268815e+17      0.544013\n",
       "0.60        15.0  6.268815e+17      0.544013\n",
       "0.65        16.0  6.268815e+17      0.544013\n",
       "0.70        17.0  6.268815e+17      0.544013\n",
       "0.75        18.0  6.268815e+17      0.544013\n",
       "0.80        19.0  6.660980e+17      0.578086\n",
       "0.85        20.0  7.866503e+17      0.682826\n",
       "0.90        21.0  9.229427e+17      0.801241\n",
       "0.95        22.0  1.064801e+18      0.924492\n",
       "1.00        23.0  1.151708e+18      1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(np.linspace(0.0, 1.0, num=21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5iLvvN7MHgR3uvhH4Z+A7ZlYPdBP+BSAiIgGJaszd3TcBm07b98Vpj0eB/xTb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\nEpDCXUQkASncRUQSkMJdRCQB/X/TLqDLOhw+ygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f782e9399b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cumul_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UJEnd7D/tAqS9ycrzPjPtEqSpck9FktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuJhYq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2107352e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot one hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w7+cBL+iWNwDbx657ifr+y8CxwNV72X4i8A9AgOOATy32NVbyCOJHl+2oqtuAPZftmLYJ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8848d5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_hour(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### one plot per day, overlayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAC+RiAGAACArxGIAQAA4GsEYgAAAPgagRgAAAC+RiAGAACArxGIAQAA4GsEYgAAAPga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\novIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8861df898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_per_day_overlay(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sql_prepare = ','.join([create_base_percentile(table, 'kwh'),\n",
    "                        create_number_convert(),\n",
    "                        create_stats()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_gbq(create_normed(sql_prepare),\n",
    "                 dialect = 'standard',\n",
    "                 project_id = gcp_project,\n",
    "                 private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_day</th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20170910</td>\n",
       "      <td>19</td>\n",
       "      <td>780</td>\n",
       "      <td>0.378285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20170910</td>\n",
       "      <td>19</td>\n",
       "      <td>780</td>\n",
       "      <td>0.378285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20170910</td>\n",
       "      <td>19</td>\n",
       "      <td>780</td>\n",
       "      <td>0.378285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20170910</td>\n",
       "      <td>19</td>\n",
       "      <td>780</td>\n",
       "      <td>0.378285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20170910</td>\n",
       "      <td>19</td>\n",
       "      <td>780</td>\n",
       "      <td>0.378285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  event_day  event_hour  raw_value  normed_value\n",
       "0  20170910          19        780      0.378285\n",
       "1  20170910          19        780      0.378285\n",
       "2  20170910          19        780      0.378285\n",
       "3  20170910          19        780      0.378285\n",
       "4  20170910          19        780      0.378285"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.022310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.046108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0.073376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>0.100149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>13.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.117997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>0.130392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>17.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>0.178483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>19.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>0.264254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>21.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0.410511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>23.0</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>0.997025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_hour  raw_value  normed_value\n",
       "0.0         0.0       17.0      0.000000\n",
       "0.1         2.0       62.0      0.022310\n",
       "0.2         5.0      110.0      0.046108\n",
       "0.3         8.0      165.0      0.073376\n",
       "0.4        11.0      219.0      0.100149\n",
       "0.5        13.0      255.0      0.117997\n",
       "0.6        15.0      280.0      0.130392\n",
       "0.7        17.0      377.0      0.178483\n",
       "0.8        19.0      550.0      0.264254\n",
       "0.9        21.0      845.0      0.410511\n",
       "1.0        23.0     2028.0      0.997025"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(np.linspace(0.0, 1.0, num=11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3tT6fOXOGf//73/zyl7/EZrNRW1vLk08+GdKT677+3bbZbNx0003Exsbyox/9\nCJfLFeBKu48vfS4tLWXSpEkA3HfffZw/fz6kzyxczXd/J42NjVdcLupqFCLf8b9LqbS3t+NwOLDZ\nbJ22sdlslJWVAZeu3Bk3bhwWiyUY5XYLX/r8r3/9i6KiIjZv3hzy58nh6n2OiIjgww8/ZN++fezb\nt4+EhAQ2b97MqFGjglj19fHlz/mhhx7i4MGDAJw4cQKXy0VsbGwwyu0WvvT59ttv58CBAwB8/vnn\nnD9/noEDBwaj3ICw2WyUl5djGAa1tbVERERc1zxQSC974g+XW0pl48aNjBw5kpSUFHJzc1m8eDF2\nu53IyEheffXVYJd9XXzp8yuvvMLZs2d5+umngUv/8F577bUgV26eL33+ofGlzxMmTOC9994jPT2d\nsLAwlixZEtKjbF/6vHTpUpYvX84bb7yBxWJh7dq1If2fwkWLFnHw4EFaWlpITk5m3rx5dHR0ADB1\n6lQmTpxIVVUVdrudPn36sGbNmuv6eVr2RERETNPpLBERMU0hIiIipilERETENIWIiIiYphARERHT\nFCIiImKaQkRERExTiIiIiGn/D3Nz/JOfjzkiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88623a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot one hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7d+9WoxDpWikAANx1y1L47LPPtGjRIrP85ccej0d79uxxLhkAwOduWQolJSW+ygEA6ABs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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8850fcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_hour(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### one plot per day, overlayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//Me7UZ+8Q6uZ9BLnSz0g867Xz5yzLvk6NgLC8hsEY68RZ5fffiFIUwiOkvPn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\nEEIIIYQY1CQhFkIIIYQQg9r/A9wuLMW1GKXjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88607df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_per_day_overlay(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of time values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sql_prepare = ','.join([create_base_percentile(table, 'first_on_at'),\n",
    "                        create_time_convert(),\n",
    "                        create_stats()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_gbq(create_normed(sql_prepare),\n",
    "                 dialect = 'standard',\n",
    "                 project_id = gcp_project,\n",
    "                 private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_day</th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20170909</td>\n",
       "      <td>9</td>\n",
       "      <td>1504261042</td>\n",
       "      <td>0.796229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20170909</td>\n",
       "      <td>9</td>\n",
       "      <td>1504261042</td>\n",
       "      <td>0.796229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20170909</td>\n",
       "      <td>12</td>\n",
       "      <td>1504261042</td>\n",
       "      <td>0.796229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20170909</td>\n",
       "      <td>9</td>\n",
       "      <td>1504261042</td>\n",
       "      <td>0.796229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20170909</td>\n",
       "      <td>13</td>\n",
       "      <td>1504261042</td>\n",
       "      <td>0.796229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  event_day  event_hour   raw_value  normed_value\n",
       "0  20170909           9  1504261042      0.796229\n",
       "1  20170909           9  1504261042      0.796229\n",
       "2  20170909          12  1504261042      0.796229\n",
       "3  20170909           9  1504261042      0.796229\n",
       "4  20170909          13  1504261042      0.796229"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.501031e+09</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.501854e+09</td>\n",
       "      <td>0.202717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.502893e+09</td>\n",
       "      <td>0.458880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.504024e+09</td>\n",
       "      <td>0.737680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>1.504269e+09</td>\n",
       "      <td>0.798154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>13.0</td>\n",
       "      <td>1.504355e+09</td>\n",
       "      <td>0.819431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.504456e+09</td>\n",
       "      <td>0.844373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1.504547e+09</td>\n",
       "      <td>0.866613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.504678e+09</td>\n",
       "      <td>0.899107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.504822e+09</td>\n",
       "      <td>0.934556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.505088e+09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_hour     raw_value  normed_value\n",
       "0.0         0.0  1.501031e+09      0.000000\n",
       "0.1         2.0  1.501854e+09      0.202717\n",
       "0.2         5.0  1.502893e+09      0.458880\n",
       "0.3         8.0  1.504024e+09      0.737680\n",
       "0.4        11.0  1.504269e+09      0.798154\n",
       "0.5        13.0  1.504355e+09      0.819431\n",
       "0.6        15.0  1.504456e+09      0.844373\n",
       "0.7        17.0  1.504547e+09      0.866613\n",
       "0.8        19.0  1.504678e+09      0.899107\n",
       "0.9        21.0  1.504822e+09      0.934556\n",
       "1.0        23.0  1.505088e+09      1.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(np.linspace(0.0, 1.0, num=11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TI5N77rknqIWJiMjQZypECgoK+NnPfsbf//73wPNrr702qIWJiMjQZ3oV3/j4eHp7e5k0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\n4ovEtMzPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88544ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot one hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8c828c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_hour(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### one plot per day, overlayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5iLxzawVrDTdeeYDpJw/H+liTYfvxfWfSxCF9xKVsidwkZG6D1tw7YCzVxpu4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TDGa7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\ncp6Jlx5l6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qZIWjrxSAA0TaFpEqGpVf2Ke3bF35MBeXd8LeAQ4BLQwhctfJrxz7Qoh1NE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\nIURck4BYCCGEEELENQmIhRBCCCFEXPv/mv42idKD8iAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc887189c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_per_day_overlay(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of bool values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sql_prepare = ','.join([create_base(table, 'generating'),\n",
    "                        create_bool_convert(),\n",
    "                        create_stats()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_gbq(create_normed(sql_prepare),\n",
    "                 dialect = 'standard',\n",
    "                 project_id = gcp_project,\n",
    "                 private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_day</th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20170904</td>\n",
       "      <td>17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20170904</td>\n",
       "      <td>17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20170904</td>\n",
       "      <td>17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20170904</td>\n",
       "      <td>17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20170904</td>\n",
       "      <td>17</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  event_day  event_hour  raw_value  normed_value\n",
       "0  20170904          17       0.25           0.0\n",
       "1  20170904          17       0.25           0.0\n",
       "2  20170904          17       0.25           0.0\n",
       "3  20170904          17       0.25           0.0\n",
       "4  20170904          17       0.25           0.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_hour  raw_value  normed_value\n",
       "0.0         0.0       0.25           0.0\n",
       "0.1         2.0       0.25           0.0\n",
       "0.2         5.0       0.25           0.0\n",
       "0.3         7.0       0.25           0.0\n",
       "0.4        11.0       0.25           0.0\n",
       "0.5        13.0       0.25           0.0\n",
       "0.6        15.0       0.25           0.0\n",
       "0.7        17.0       0.25           0.0\n",
       "0.8        19.0       0.25           0.0\n",
       "0.9        21.0       0.25           0.0\n",
       "1.0        23.0       0.75           1.0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(np.linspace(0.0, 1.0, num=11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ffbz22mtkZmYyd+5cFi1aFOoyQ+7HvxebzXbKv+8/R+Eipnj11Vdpampi9uzZ\n4S6lXx07dozVq1fz8MMPh7uUkPH7/fznP//hxRdf5KmnnmLx4sV0dnaGu6x+5XQ6KSgooLa2lvLy\nch566CGOHTsW7rIGFIVLkIJZUsZqtbJ3714Auru7OXjwIMOHDw9pnWYKdhmdd999l+eee44NGzYQ\nFRUVyhJN93NjPnz4MJ999hm33347drudhoYG7rnnngE7qR/s/9d2u50hQ4aQlJTEL3/5S9xud4gr\nNU8wY66srGTKlCkAXH755Rw9enRAX4UIxo9/Lx6P55TLZv0chUuQgllSxm63U1VVBfTcSTRp0iQs\nFks4yjVFMGP+5JNPWLJkCRs2bBjw1+Lh58ccHR3N+++/T3V1NdXV1aSmprJhwwbGjx8fxqr7Lpg/\n4+uuu47du3cD8O233+J2uwNfZzEQBTPmUaNGUV9fD8AXX3zB0aNHGTFiRDjKDRm73c7WrVsxDIOG\nhgaio6PPaK5pUC7/0h9OtqTM008/zbhx48jKyqKwsJDS0lIcDgexsbGsXbs23GWfkWDG/Ic//IHv\nvvuOBx54AOj5S/ncc8+FufK+C2bMg0kw47366qt55513mDp1KhERETz00EMD+ow8mDE/8sgjLFq0\niE2bNmGxWFi9evWA/ociwMKFC9m9ezft7e1kZmYyf/58uru7AZgxYwaTJ0+mpqYGh8PB0KFDWbly\n5Rn1p+VfRETEdLosJiIiplO4iIiI6RQuIiJiOoWLiIiYTuEiIiKmU7iIiIjpFC4iImK6/wfMK8qH\nqft+0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc87fd9aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot one hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc8865455f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_hour(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### one plot per day, overlayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vfU3vf//79Zd/+Zfatm2bHnnkEUnSd77zHe3YsaPhi12JzgnEbKoDAABI1ILnEH/xi1/U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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88455a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_per_day_overlay(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of \"unique\" values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sql_prepare = ','.join([create_base(table, 'mill_signature'),\n",
    "                        create_string_convert(-8),\n",
    "                        create_stats()])\n",
    "\n",
    "df = pd.read_gbq(create_normed(sql_prepare),\n",
    "                 dialect = 'standard',\n",
    "                 project_id = gcp_project,\n",
    "                 private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.875702e+13</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.196453e+17</td>\n",
       "      <td>0.103755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.351351e+17</td>\n",
       "      <td>0.203931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.503671e+17</td>\n",
       "      <td>0.303883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.671947e+17</td>\n",
       "      <td>0.405220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>13.0</td>\n",
       "      <td>5.901342e+17</td>\n",
       "      <td>0.511857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>7.034651e+17</td>\n",
       "      <td>0.610161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8.163105e+17</td>\n",
       "      <td>0.708043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>19.0</td>\n",
       "      <td>9.287558e+17</td>\n",
       "      <td>0.805578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.044619e+18</td>\n",
       "      <td>0.906077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.152900e+18</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_hour     raw_value  normed_value\n",
       "0.0         0.0  2.875702e+13      0.000000\n",
       "0.1         2.0  1.196453e+17      0.103755\n",
       "0.2         5.0  2.351351e+17      0.203931\n",
       "0.3         7.0  3.503671e+17      0.303883\n",
       "0.4        11.0  4.671947e+17      0.405220\n",
       "0.5        13.0  5.901342e+17      0.511857\n",
       "0.6        15.0  7.034651e+17      0.610161\n",
       "0.7        17.0  8.163105e+17      0.708043\n",
       "0.8        19.0  9.287558e+17      0.805578\n",
       "0.9        21.0  1.044619e+18      0.906077\n",
       "1.0        23.0  1.152900e+18      1.000000"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(np.linspace(0.0, 1.0, num=11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nujtWr9Iny8Kf24fY7XYOHjwIQGtrK0ePHmXgwIFdmrMz+XvLlPfee48XX3yR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\n43K3AVt44wQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88585ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot one hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3rz799NNgxwAAY4UFe4dHjx7Vrbfeqvj4eElSZmamKisrNXny5D5f7/P5JEkNDQ2D2t/F\njqbBBQWAEDt58uSg3/ttZ37boZcLevE3NjYqLi7Ovx0bG6ujR49e9fWnTp2SJHm9XsezAcBwklox\n9O9x6tQp3Xrrrb0eC3rxD1RCQoJKSkoUHR0tt9sd6jgAMCL4fD6dOnVKCQkJ33ku6MUfGxvba9qm\nsbFRsbGxV3399ddfr8TExGBEA4BR5coj/W8F/eTu9OnTVV9frxMnTqirq0vl5eXyeDzBjgEAxgr6\nEX9YWJiefvppPfLII/L5fFqwYIGmTJkS7BgAYCyXZVlWqEMAAIKHO3cBwDAUPwAYZtQU/7WWgejq\n6tITTzyhtLQ05efnD+nGiOHiWmP+/e9/r3nz5ik7O1sPPPCAvvrqqxCkDCy7y31UVFTojjvu0Icf\nfhjEdM6wM+a33npL8+bNU2ZmplavXh3khM641rj/97//aenSpcrNzVV2draqq6tDkDJw1q9fr+Tk\nZGVlZfX5vGVZ+vWvf620tDRlZ2fr2LFjg9+ZNQp0d3dbqamp1pdffml1dnZa2dnZ1ieffNLrNX/4\nwx+sjRs3WpZlWXv37rUef/zxUEQNGDtjPnjwoHX+/HnLsiyrpKTEiDFblmWdO3fOWrJkiZWfn28d\nPXo0BEkDx86Yjx8/buXk5Fitra2WZVnW6dOnQxE1oOyMe8OGDVZJSYllWZb1ySefWHPnzg1F1IA5\ncuSIVVdXZ2VmZvb5/HvvvWc9/PDDVk9Pj/XBBx9YCxcuHPS+RsUR/+XLQISHh/uXgbhcVVWV8vLy\nJEkZGRk6ePCgrBF8XtvOmGfNmqUbbrhBkjRjxoxBL3sxXNgZsyRt2bJFy5Yt09ixY0OQMrDsjPnP\nf/6zvF6voqKiJEkTJkwIRdSAsjNul8ul9vZ2SdK5c+cUExMTiqgBc9ddd/n/D/tSWVmp3NxcuVwu\nzZgxQ21tbWpqGtySNKOi+PtaBqKxsfE7r7nlllskXbqkNCIiQi0tLUHNGUh2xny50tJSpaSkBCOa\nY+yM+dixY2poaNA999wT5HTOsDPm+vp6HT9+XIsWLdLPf/5z1dTUBDtmwNkZ92OPPaa//e1vSklJ\nUWFhoTZs2BDsmEF15b9JXFxcv7/z/RkVxY/+7dmzR3V1dXrkkUdCHcVRPT092rRpk9atWxfqKEHl\n8/n0xRdfaNeuXXrppZe0ceNGtbW1hTqW48rLy5WXl6eamhoVFxdr7dq16unpCXWsEWFUFL+dZSBi\nY2P19ddfS5K6u7t17tw5fe973wtqzkCyu/TFgQMHtH37dm3btk3h4eHBjBhw1xpzR0eHPv74Y/3i\nF7+Qx+NRbW2tHn300RF9gtfuz7bH49GYMWMUHx+v2267TfX19UFOGlh2xl1aWqr77rtPkjRz5kx1\ndnaO6L/ir+XKf5OGhoZ+l7vpz6gofjvLQHg8Hu3evVvSpSs+Zs2aJZfLFYq4AWFnzP/5z3/09NNP\na9u2baNi3vdaY46IiNDhw4dVVVWlqqoqzZgxQ9u2bdP06dNDmHpo7Pw/33vvvTpy5Igk6cyZM6qv\nr/cvez5S2Rn3LbfcooMHD0qSPvvsM3V2dmr8+PGhiBsUHo9HZWVlsixLtbW1ioiIGPR5jWG/Oqcd\nV1sGYsuWLUpISFBqaqoWLlyoNWvWKC0tTVFRUXrllVdCHXtI7Iz5xRdf1Pnz5/X4449LuvSLsn37\n9hAnHzw7Yx5t7Ix59uzZ2r9/v+bNmye32621a9eO6L9mJXvjLioq0oYNG/Taa6/J5XJp06ZNI/pg\nbtWqVTpy5IhaWlqUkpKiFStWqLu7W5K0ePFizZkzR9XV1UpLS9MNN9yg559/ftD7YskGADDMqJjq\nAQDYR/EDgGEofgAwDMUPAIah+AHAMBQ/ABiG4gcAw/wf9r1aY0I3OdEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc883989828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_hour(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### one plot per day, overlayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+UoQ8L24wTEmHxNYRhoGJhaNnMTWTmICIgC4t4qv5yFdfqo1xNjKjUcxfItfL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc87d2ca6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_per_day_overlay(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of categorial values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sql_prepare = ','.join([create_base(table, 'country'),\n",
    "                        create_string_convert(),\n",
    "                        create_stats()])\n",
    "\n",
    "df = pd.read_gbq(create_normed(sql_prepare),\n",
    "                 dialect = 'standard',\n",
    "                 project_id = gcp_project,\n",
    "                 private_key = gcp_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_hour</th>\n",
       "      <th>raw_value</th>\n",
       "      <th>normed_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.142066e+16</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.626681e+17</td>\n",
       "      <td>0.215406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.35</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.45</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2.817049e+17</td>\n",
       "      <td>0.232404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>15.0</td>\n",
       "      <td>5.273256e+17</td>\n",
       "      <td>0.451715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.65</th>\n",
       "      <td>16.0</td>\n",
       "      <td>5.273256e+17</td>\n",
       "      <td>0.451715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.485178e+17</td>\n",
       "      <td>0.470638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>18.0</td>\n",
       "      <td>9.970888e+17</td>\n",
       "      <td>0.871160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.062289e+18</td>\n",
       "      <td>0.929377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.85</th>\n",
       "      <td>20.0</td>\n",
       "      <td>1.062289e+18</td>\n",
       "      <td>0.929377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.062289e+18</td>\n",
       "      <td>0.929377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.95</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.080930e+18</td>\n",
       "      <td>0.946021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.141384e+18</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      event_hour     raw_value  normed_value\n",
       "0.00         0.0  2.142066e+16      0.000000\n",
       "0.05         1.0  2.626681e+17      0.215406\n",
       "0.10         2.0  2.817049e+17      0.232404\n",
       "0.15         3.0  2.817049e+17      0.232404\n",
       "0.20         5.0  2.817049e+17      0.232404\n",
       "0.25         6.0  2.817049e+17      0.232404\n",
       "0.30         7.0  2.817049e+17      0.232404\n",
       "0.35         9.0  2.817049e+17      0.232404\n",
       "0.40        11.0  2.817049e+17      0.232404\n",
       "0.45        12.0  2.817049e+17      0.232404\n",
       "0.50        13.0  2.817049e+17      0.232404\n",
       "0.55        14.0  2.817049e+17      0.232404\n",
       "0.60        15.0  5.273256e+17      0.451715\n",
       "0.65        16.0  5.273256e+17      0.451715\n",
       "0.70        17.0  5.485178e+17      0.470638\n",
       "0.75        18.0  9.970888e+17      0.871160\n",
       "0.80        19.0  1.062289e+18      0.929377\n",
       "0.85        20.0  1.062289e+18      0.929377\n",
       "0.90        21.0  1.062289e+18      0.929377\n",
       "0.95        22.0  1.080930e+18      0.946021\n",
       "1.00        23.0  1.141384e+18      1.000000"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quantile(np.linspace(0.0, 1.0, num=21))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4S0REJFABXZ21Z88eVq5cya233srQoUNZvnw5e/bs4YYbbuCGG25ot09oaCibNm3ir3/9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\nXAywDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88596a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot one hour of a given day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc88596a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_hour(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### one plot per day, overlayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BJmWa1w/ULRMEEEUB9cVJALK5resfvmZiV9ymsXpxHwDdqMvAxLUrnCdZbi1t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\n6BxRmOWVKzM9rkqS3rZaafP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wuMH4PI9FRkJsMJzH1r2Rt/oP20XuhCi39m5LpxQTHyhC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc886204ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_one_per_day_overlay(df)"
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}