visualisations.ipynb

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 },
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enable debug logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(level=logging.DEBUG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Initialise the Explorer with a connection to the Aircloak server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "DEBUG:root:Connecting to Aircloak: user=daniel-613C7ADF4535BB56DBCD, host=attack.aircloak.com, port=9432, dbname=GiveMeSomeCredit\nDEBUG:root:Sending query: \n        SHOW TABLES\n    \nDEBUG:root:Sending query: \n        SHOW COLUMNS FROM \"loans\"\n    \nColumnInfo(type='integer', isolator='false', id=None)\n"
    }
   ],
   "source": [
    "\n",
    "from explorer.connection import AircloakConnection\n",
    "\n",
    "aircloak = AircloakConnection(dbname='GiveMeSomeCredit')\n",
    "print(aircloak.column_info('loans', 'MonthlyIncome'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "DEBUG:root:Sending query: \n        SELECT\n            min(\"MonthlyIncome\")\n        ,   max(\"MonthlyIncome\")\n        ,   avg(\"MonthlyIncome\")\n        ,   count(*)\n        ,   count_noise(*)\n        FROM \"loans\"\n    \nDEBUG:root:Sending query: \n        SELECT\n            \"MonthlyIncome\"\n        ,   count(*)\n        FROM \"loans\"\n        WHERE \"MonthlyIncome\" IS NOT NULL\n        GROUP BY 1\n    \nDEBUG:root:Estimating bucket size for:\n                range 57538,\n                count 150000,\n                num buckets 100,\n                min bucket count 20\nDEBUG:root:Precision bound: 575.38, Size bound: 7.671733333333333\nDEBUG:root:Options are: [100, 200, 10, 50, 20, 500]\nDEBUG:root:Returning bucket size 500\n"
    }
   ],
   "source": [
    "from explorer.numeric_explorer import NumericColumnExplorer\n",
    "e = NumericColumnExplorer(aircloak_connection=aircloak, table='loans', column='MonthlyIncome')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Explore some buckets: the following will dig three levels deep in the bucket hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "DEBUG:root:Sending query: \n    SELECT\n        bucket(\"MonthlyIncome\" by 50) as \"bucket_50\"\n, bucket(\"MonthlyIncome\" by 100) as \"bucket_100\"\n, bucket(\"MonthlyIncome\" by 500) as \"bucket_500\"\n    ,   count(*)\n    ,   count_noise(*)\n    ,   min(\"MonthlyIncome\")\n    ,   max(\"MonthlyIncome\")\n    ,   avg(\"MonthlyIncome\")\n    FROM \"loans\"\n    WHERE \"MonthlyIncome\" IS NOT NULL\n    GROUP BY GROUPING SETS (1, 2, 3)\n    \nDEBUG:root:Received query results, processing...\nDEBUG:root:... finished processing query results.\n"
    }
   ],
   "source": [
    "e.explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Dig a little deeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "DEBUG:root:Sending query: \n    SELECT\n        bucket(\"MonthlyIncome\" by 10) as \"bucket_10\"\n    ,   count(*)\n    ,   count_noise(*)\n    ,   min(\"MonthlyIncome\")\n    ,   max(\"MonthlyIncome\")\n    ,   avg(\"MonthlyIncome\")\n    FROM \"loans\"\n    WHERE \"MonthlyIncome\" IS NOT NULL\n    GROUP BY GROUPING SETS (1)\n    \nDEBUG:root:Received query results, processing...\nDEBUG:root:... finished processing query results.\n"
    }
   ],
   "source": [
    "e.explore(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Extract to pandas dataframe for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 13304 entries, 500 to 10\nData columns (total 6 columns):\nlower_bound    13304 non-null float64\ncount          13304 non-null float64\ncount_noise    2633 non-null float64\nmin            2122 non-null float64\nmax            2122 non-null float64\navg            2633 non-null float64\ndtypes: float64(6)\nmemory usage: 727.6 KB\n"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(**e.extract_to_dataframe())\n",
    "df = df.set_index(['bucket_size'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot column distribution as a bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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            "gridwidth": 2,
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            "gridwidth": 2,
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            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
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        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "count"
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     },
     "metadata": {},
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   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "bucket_size_to_plot = 500\n",
    "px.bar(df.loc[[bucket_size_to_plot]], x='lower_bound', y='count', hover_data=['lower_bound', 'count', 'min', 'max', 'count_noise'])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get a sum of the counts by bucket size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "bucket_size\n10     118677.0\n50     119646.0\n100    119847.0\n500    120064.0\nName: count, dtype: float64"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['count'].groupby('bucket_size').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}