final_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Final Distribution"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from config import PROJECT_ID, INITIAL_TS, SNAPSHOT_TS, \\\n",
    "    CITIZENS_AUDIENCE, ETH_ANALYSIS_DATASET_NAME, ETH_ANALYSIS_DISTRIBUTION_TABLE_NAME, \\\n",
    "    CYBERPUNKS_AUDIENCE, \\\n",
    "    HACKERS_AUDIENCE, GAS_ANALYSIS_DATASET_NAME, GAS_ANALYSIS_DISTRIBUTION_TABLE_NAME, \\\n",
    "    LEADERS_AUDIENCE, LEADERS_SEGMENT, ERC20_TOP_DISTRIBUTION_TABLE_NAME, \\\n",
    "    MASTERS_AUDIENCE, ERC721_ANALYSIS_DATASET_NAME, ERC721_ANALYSIS_DISTRIBUTION_TABLE_NAME, \\\n",
    "    INVESTORS_AUDIENCE, ERC20_ANALYSIS_DATASET_NAME, ERC20_ANALYSIS_DISTRIBUTION_TABLE_NAME, \\\n",
    "    HEROES_AUDIENCE, GENESIS_AND_ETH2_DATASET_NAME, GENESIS_AND_ETH2_DISTRIBUTION_TABLE_NAME, \\\n",
    "    ASTRONAUTS_AUDIENCE, ASTRONAUTS_DATASET_NAME, ASTRONAUTS_DISTRIBUTION_TABLE_NAME, \\\n",
    "    MANUAL_ADDRESSES_AUDIENCE, MANUAL_ADDRESSES_SEGMENT, MANUAL_ADDRESSES_DATASET_NAME, \\\n",
    "    MANUAL_ADDRESSES_DISTRIBUTION_TABLE_NAME, \\\n",
    "    FINAL_DATASET_NAME, DISTRIBUTION_TABLE_NAME, FINAL_DISTRIBUTION_TABLE_NAME, STABLECOINS_LOVERS_TABLE_NAME, \\\n",
    "    SERVICE_DATASET_NAME, CONTRACT_ADDRESSES_TABLE, GIFT_PER_ADDRESS_PIVOT_TABLE_NAME, \\\n",
    "    FINAL_DISTRIBUTION_PIVOT_TABLE_NAME, FINAL_DISTRIBUTION_PIVOT_BY_AUDIENCES_TABLE_NAME, \\\n",
    "    FINAL_DISTRIBUTION_PIVOT_BY_GRADES_TABLE_NAME\n",
    "from src.utils_bigquery import create_table, drop_table, get_df\n",
    "from src.utils_charts import heatmap_from_df\n",
    "\n",
    "\n",
    "CREATE_CONTRACT_TABLE = False\n",
    "DROP_TABLES = True\n",
    "CREATE_TABLES = True"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Set cyberGift for the Audiences\n",
    "**Total Supply:** 1 PBOOT = 1e15 BOOT\n",
    "\n",
    "\n",
    "**1% of Total Supply:** 10 TBOOT = 1e13 BOOT\n",
    "\n",
    "\n",
    "Distribution between **audiences**\n",
    "\n",
    "\n",
    "<table style=\"text-align: left\">\n",
    "    <thead style=\"text-align: center\">\n",
    "        <tr>\n",
    "            <th>Audience</th>\n",
    "            <th>Share of Total Supply, %</th>\n",
    "        </tr>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Average Citizens </td>\n",
    "            <td style=\"text-align: center\"> 25 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Masters of the Great Web </td>\n",
    "            <td style=\"text-align: center\"> 13 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Astronauts </td>\n",
    "            <td style=\"text-align: center\"> 10 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Extraordinary Hackers </td>\n",
    "            <td style=\"text-align: center\"> 7 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Heroes of the Great Web </td>\n",
    "            <td style=\"text-align: center\"> 5 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Passionate Investors </td>\n",
    "            <td style=\"text-align: center\"> 5 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Key Opinion Leaders </td>\n",
    "            <td style=\"text-align: center\"> 2.9 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Cyberpunks </td>\n",
    "            <td style=\"text-align: center\"> 2 </td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <td style=\"text-align: left\"> Leeches </td>\n",
    "            <td style=\"text-align: center\"> 0.1 </td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "gift_per_audience = {\n",
    "    CITIZENS_AUDIENCE : 25e13,\n",
    "    MASTERS_AUDIENCE: 13e13,\n",
    "    ASTRONAUTS_AUDIENCE: 10e13,\n",
    "    HACKERS_AUDIENCE: 7e13,\n",
    "    HEROES_AUDIENCE: 5e13,\n",
    "    INVESTORS_AUDIENCE: 5e13,\n",
    "    LEADERS_AUDIENCE: 2.9e13,\n",
    "    CYBERPUNKS_AUDIENCE: 2e13,\n",
    "    MANUAL_ADDRESSES_AUDIENCE: 0.1e13\n",
    "}\n",
    "ZERO_ADDRESSES = ('0x0000000000000000000000000000000000000000',\n",
    "                  '0x000000000000000000000000000000000000dead',\n",
    "                  '0x0000000000000000000000000000000000000ded',\n",
    "                  '0x000000000000000000000000000000000000d3ad',\n",
    "                  '0x000000000000000000000000000000000000dddd',\n",
    "                  '0x00000000000000000000000000000000deadbeef',\n",
    "                  '0xdead000000000000000000000000000000000000',\n",
    "                  '0xdeadbeefdeadbeefdeadbeefdeadbeefdeadbeef',\n",
    "                  '0xdeaddeaddeaddeaddeaddeaddeaddeaddeaddead',\n",
    "                  '0x0000000000000000000000000000000000000001',\n",
    "                  '0x0000000000000000000000000000000000000002',\n",
    "                  '0x0000000000000000000000000000000000000003',\n",
    "                  '0x0000000000000000000000000000000000000004',\n",
    "                  '0x0000000000000000000000000000000000000005',\n",
    "                  '0x0000000000000000000000000000000000000006',\n",
    "                  '0x0000000000000000000000000000000000000008',\n",
    "                  '0x0000000000000000000000000000000000000009',\n",
    "                  '0x000000000000000000000000000000000000000a',\n",
    "                  '0x000000000000000000000000000000000000000d',\n",
    "                  '0x0000000000000000000000000000000000000064',\n",
    "                  '0x0000000000000000000000000000000000000069',\n",
    "                  '0x00000000000000000000000000000000000000ff',\n",
    "                  '0x0000000000000000000000000000000000001388',\n",
    "                  '0x0000000000000000000000000000000000007f60',\n",
    "                  '0x000000000000000000000000000000000000a2e0',\n",
    "                  '0x000000000000000000000000000000000000f351',\n",
    "                  '0x00000000000000000000000000000000000faced',\n",
    "                  '0x0000000000000000000000000000000000080085')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create Contracts Table"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "query_1 = f'''\n",
    "    SELECT\n",
    "        to_address as address\n",
    "    FROM `bigquery-public-data.crypto_ethereum.traces`\n",
    "    WHERE block_timestamp >= '{INITIAL_TS}'\n",
    "      AND block_timestamp <= '{SNAPSHOT_TS}'\n",
    "      AND trace_type ='create'\n",
    "      AND status = 1\n",
    "'''\n",
    "\n",
    "if CREATE_CONTRACT_TABLE:\n",
    "    if DROP_TABLES:\n",
    "        drop_table(table_name=CONTRACT_ADDRESSES_TABLE,\n",
    "                   dataset_name=SERVICE_DATASET_NAME)\n",
    "    if CREATE_TABLES:\n",
    "        create_table(query=query_1,\n",
    "                     table_name=CONTRACT_ADDRESSES_TABLE,\n",
    "                     dataset_name=SERVICE_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create Distribution Table without Gift Size"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table final:distribution_without_gift_size has been deleted.\n",
      "Table final:distribution_without_gift_size has been created and filled 4,827,745 rows.\n"
     ]
    }
   ],
   "source": [
    "query_1 = f'''\n",
    "    SELECT\n",
    "        audience,\n",
    "        segment,\n",
    "        address,\n",
    "        grade\n",
    "    FROM (\n",
    "        SELECT\n",
    "            audience,\n",
    "            segment,\n",
    "            address,\n",
    "            grade,\n",
    "            ROW_NUMBER() OVER (PARTITION BY audience, address ORDER BY grade DESC) AS rank\n",
    "        FROM (\n",
    "            SELECT\n",
    "                '{HEROES_AUDIENCE}' AS audience,\n",
    "                segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{GENESIS_AND_ETH2_DATASET_NAME}.{GENESIS_AND_ETH2_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                IF(segment = 'Contract Creator by Factory', '{MASTERS_AUDIENCE}', '{HACKERS_AUDIENCE}') AS audience,\n",
    "                segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{GAS_ANALYSIS_DATASET_NAME}.{GAS_ANALYSIS_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                '{CITIZENS_AUDIENCE}' AS audience,\n",
    "                segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{ETH_ANALYSIS_DATASET_NAME}.{ETH_ANALYSIS_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                audience,\n",
    "                segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{ERC721_ANALYSIS_DATASET_NAME}.{ERC721_ANALYSIS_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                audience,\n",
    "                segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{ERC20_ANALYSIS_DATASET_NAME}.{ERC20_ANALYSIS_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                '{LEADERS_AUDIENCE}' AS audience,\n",
    "                '{LEADERS_SEGMENT}' AS segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{ERC20_ANALYSIS_DATASET_NAME}.{ERC20_TOP_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                '{MANUAL_ADDRESSES_AUDIENCE}' AS audience,\n",
    "                '{MANUAL_ADDRESSES_SEGMENT}' AS segment,\n",
    "                address,\n",
    "                3 AS grade\n",
    "            FROM `{PROJECT_ID}.{MANUAL_ADDRESSES_DATASET_NAME}.{MANUAL_ADDRESSES_DISTRIBUTION_TABLE_NAME}`\n",
    "\n",
    "            UNION ALL\n",
    "\n",
    "            SELECT\n",
    "                '{ASTRONAUTS_AUDIENCE}' AS audience,\n",
    "                segment,\n",
    "                address,\n",
    "                grade\n",
    "            FROM `{PROJECT_ID}.{ASTRONAUTS_DATASET_NAME}.{ASTRONAUTS_DISTRIBUTION_TABLE_NAME}`\n",
    "            WHERE grade IS NOT NULL\n",
    "            )\n",
    "        WHERE address NOT IN (SELECT address FROM `{PROJECT_ID}.{SERVICE_DATASET_NAME}.{CONTRACT_ADDRESSES_TABLE}`)\n",
    "          AND address NOT IN (SELECT address FROM `{PROJECT_ID}.{ERC20_ANALYSIS_DATASET_NAME}.{STABLECOINS_LOVERS_TABLE_NAME}`)\n",
    "          AND address NOT IN {ZERO_ADDRESSES})\n",
    "    WHERE rank = 1\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=DISTRIBUTION_TABLE_NAME,\n",
    "               dataset_name=FINAL_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_1,\n",
    "                 table_name=DISTRIBUTION_TABLE_NAME,\n",
    "                 dataset_name=FINAL_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create Gift per Address Pivot Table"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table final:gift_per_address_pivot has been deleted.\n",
      "Table final:gift_per_address_pivot has been created and filled 24 rows.\n"
     ]
    }
   ],
   "source": [
    "query_2 = f'''\n",
    "    WITH gift_data AS (\n",
    "        SELECT\n",
    "            audience,\n",
    "            [STRUCT(1 AS grade, grade_1 AS number_of_addresses, ROUND(IF(grade_1 > 0, gift_amount_per_audience / grade_1 / number_of_grades, 0), 2) as gift_per_address),\n",
    "             STRUCT(2 AS grade, grade_2 AS number_of_addresses, ROUND(IF(grade_2 > 0, gift_amount_per_audience / grade_2 / number_of_grades, 0), 2) as gift_per_address),\n",
    "             STRUCT(3 AS grade, grade_3 AS number_of_addresses, ROUND(IF(grade_3 > 0, gift_amount_per_audience / grade_3 / number_of_grades, 0), 2) as gift_per_address)] as gift_data_by_audience,\n",
    "            total_addresses AS number_of_addresses_in_audience,\n",
    "            gift_amount_per_audience\n",
    "        FROM (\n",
    "            SELECT\n",
    "                audience,\n",
    "                SUM(IF(grade=1, address_count, 0)) AS grade_1,\n",
    "                SUM(IF(grade=2, address_count, 0)) AS grade_2,\n",
    "                SUM(IF(grade=3, address_count, 0)) AS grade_3,\n",
    "                SUM(address_count) AS total_addresses,\n",
    "                COUNTIF(address_count > 0) AS number_of_grades\n",
    "            FROM (\n",
    "                SELECT\n",
    "                    audience,\n",
    "                    grade,\n",
    "                    count(distinct address) as address_count\n",
    "                FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{DISTRIBUTION_TABLE_NAME}`\n",
    "                WHERE grade IN (1, 2, 3)\n",
    "                  AND audience != '{MANUAL_ADDRESSES_AUDIENCE}'\n",
    "                GROUP BY audience, grade)\n",
    "            GROUP BY audience)\n",
    "        LEFT JOIN (\n",
    "            SELECT\n",
    "                gift.audience as audience,\n",
    "                gift.amount_per_audience as gift_amount_per_audience\n",
    "            FROM UNNEST([{''.join(f\"STRUCT('{k}' AS audience, {v} AS amount_per_audience), \" for k,v in gift_per_audience.items())[:-2]}]) AS gift\n",
    "            )\n",
    "        USING (audience))\n",
    "    SELECT\n",
    "        audience,\n",
    "        number_of_addresses_in_audience,\n",
    "        gift_data_by_audience.number_of_addresses as number_of_addresses,\n",
    "        gift_data_by_audience.grade as grade,\n",
    "        gift_data_by_audience.gift_per_address as gift_per_address\n",
    "    FROM gift_data\n",
    "    CROSS JOIN UNNEST(gift_data.gift_data_by_audience) AS gift_data_by_audience\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=GIFT_PER_ADDRESS_PIVOT_TABLE_NAME,\n",
    "               dataset_name=FINAL_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_2,\n",
    "                table_name=GIFT_PER_ADDRESS_PIVOT_TABLE_NAME,\n",
    "                dataset_name=FINAL_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create Final Distribution Table"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table final:final_distribution has been deleted.\n",
      "Table final:final_distribution has been created and filled 4,827,747 rows.\n"
     ]
    }
   ],
   "source": [
    "query_3 = f'''\n",
    "    SELECT\n",
    "        audience,\n",
    "        segment,\n",
    "        address,\n",
    "        CAST(grade as INT64) as grade,\n",
    "        gift\n",
    "    FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{DISTRIBUTION_TABLE_NAME}`\n",
    "    LEFT JOIN (\n",
    "        SELECT\n",
    "            audience,\n",
    "            grade AS grade,\n",
    "            gift_per_address / 1e6 AS gift\n",
    "        FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{GIFT_PER_ADDRESS_PIVOT_TABLE_NAME}`\n",
    "        )\n",
    "    USING (audience, grade)\n",
    "    WHERE audience != '{MANUAL_ADDRESSES_AUDIENCE}'\n",
    "\n",
    "    UNION ALL\n",
    "\n",
    "    SELECT\n",
    "        '{MANUAL_ADDRESSES_AUDIENCE}' AS audience,\n",
    "        '{MANUAL_ADDRESSES_AUDIENCE}' AS segment,\n",
    "        address,\n",
    "        3 AS grade,\n",
    "        amount / 1e6 AS gift\n",
    "    FROM `{PROJECT_ID}.{MANUAL_ADDRESSES_DATASET_NAME}.{MANUAL_ADDRESSES_DISTRIBUTION_TABLE_NAME}`\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=FINAL_DISTRIBUTION_TABLE_NAME,\n",
    "               dataset_name=FINAL_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_3,\n",
    "                 table_name=FINAL_DISTRIBUTION_TABLE_NAME,\n",
    "                 dataset_name=FINAL_DATASET_NAME,\n",
    "                 clustering_fields=['audience', 'segment', 'grade'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create Final Distribution Pivot Tables for Dashboard Speeding up"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table service:final_distribution_pivot_by_segments_and_grades has been deleted.\n",
      "Table service:final_distribution_pivot_by_segments_and_grades has been created and filled 41 rows.\n"
     ]
    }
   ],
   "source": [
    "query_4 = f'''\n",
    "    SELECT\n",
    "        audience,\n",
    "        segment,\n",
    "        grade,\n",
    "        count(DISTINCT address) as number_of_addresses,\n",
    "        sum(gift) as gift\n",
    "    FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{FINAL_DISTRIBUTION_TABLE_NAME}`\n",
    "    GROUP BY audience, segment, grade\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=FINAL_DISTRIBUTION_PIVOT_TABLE_NAME,\n",
    "               dataset_name=SERVICE_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_4,\n",
    "                table_name=FINAL_DISTRIBUTION_PIVOT_TABLE_NAME,\n",
    "                dataset_name=SERVICE_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table service:final_distribution_pivot_by_audiences has been deleted.\n",
      "Table service:final_distribution_pivot_by_audiences has been created and filled 10 rows.\n"
     ]
    }
   ],
   "source": [
    "query_5 = f'''\n",
    "    SELECT\n",
    "        audience,\n",
    "        count(DISTINCT address) as number_of_addresses\n",
    "    FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{FINAL_DISTRIBUTION_TABLE_NAME}`\n",
    "    GROUP BY audience\n",
    "\n",
    "    UNION ALL\n",
    "\n",
    "    SELECT\n",
    "        'Total' as audience,\n",
    "        count(DISTINCT address) as number_of_addresses\n",
    "    FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{FINAL_DISTRIBUTION_TABLE_NAME}`\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=FINAL_DISTRIBUTION_PIVOT_BY_AUDIENCES_TABLE_NAME,\n",
    "               dataset_name=SERVICE_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_5,\n",
    "                table_name=FINAL_DISTRIBUTION_PIVOT_BY_AUDIENCES_TABLE_NAME,\n",
    "                dataset_name=SERVICE_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table service:final_distribution_pivot_by_grades has been deleted.\n",
      "Table service:final_distribution_pivot_by_grades has been created and filled 3 rows.\n"
     ]
    }
   ],
   "source": [
    "query_6 = f'''\n",
    "    SELECT\n",
    "        grade,\n",
    "        count(DISTINCT address) as number_of_addresses\n",
    "    FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{FINAL_DISTRIBUTION_TABLE_NAME}`\n",
    "    GROUP BY grade\n",
    "'''\n",
    "\n",
    "if DROP_TABLES:\n",
    "    drop_table(table_name=FINAL_DISTRIBUTION_PIVOT_BY_GRADES_TABLE_NAME,\n",
    "               dataset_name=SERVICE_DATASET_NAME)\n",
    "\n",
    "if CREATE_TABLES:\n",
    "    create_table(query=query_6,\n",
    "                table_name=FINAL_DISTRIBUTION_PIVOT_BY_GRADES_TABLE_NAME,\n",
    "                dataset_name=SERVICE_DATASET_NAME)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Distribution Heatmap"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "query_7 = f'''\n",
    "    SELECT\n",
    "        ANY_VALUE(audiences) AS audiences,\n",
    "        count(address) AS number_of_addresses\n",
    "    FROM (\n",
    "        SELECT\n",
    "            address,\n",
    "            ARRAY_AGG(DISTINCT audience) as audiences\n",
    "        FROM `{PROJECT_ID}.{FINAL_DATASET_NAME}.{FINAL_DISTRIBUTION_TABLE_NAME}`\n",
    "        WHERE audience NOT IN ('{ASTRONAUTS_AUDIENCE}')\n",
    "        GROUP BY address)\n",
    "    GROUP BY TO_JSON_STRING(audiences)\n",
    "'''\n",
    "\n",
    "final_distribution_df = get_df(query_7)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x864 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_from_df(final_distribution_df,\n",
    "                title='Distribution of Gift Participants by Audiences\\n',\n",
    "                fig_size=12)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}