X_Data Profiling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Data Profiling Playbook\n",
    " The intention of this notebook is to develop a generic approach to profiling a single column.\n",
    " This will take into account:\n",
    " - The primary data type of the column - eg if it's a date, we can generate more specialised analytics\n",
    " - We may also be able to apply more specialised checks, for example based on REGEX to check national insurance numbers etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all of the libraries we need to use...\n",
    "import pandas as pd\n",
    "import azureml.dataprep as dprep\n",
    "import os as os\n",
    "import re as re\n",
    "import collections\n",
    "import seaborn as sns\n",
    "import pandas_profiling as pp\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "from azureml.dataprep import value\n",
    "from azureml.dataprep import col\n",
    "from azureml.dataprep import Dataflow\n",
    "from commonCode import savePackage, openPackage, createFullPackagePath\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Not used fo now, but should be driven by:\n",
    "stageNumber = '1'\n",
    "dataName = 'PEOPLE'\n",
    "qualityFlag = 'A'\n",
    "noMissingFlag = True\n",
    "\n",
    "# For now, I'm cheating, just specifying file.  But will use helper function to build ultimately:\n",
    "dataFlowFile = './packages/PEOPLE/2/PEOPLE_A_package.dprep'\n",
    "indexColumn = 'ID'\n",
    "targetColumn = 'DOB'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFlow = Dataflow.open(dataFlowFile)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFlow = dataFlow.keep_columns([indexColumn, targetColumn])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataProfile = dataFlow.get_profile()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Count</th>\n",
       "      <th>Missing Count</th>\n",
       "      <th>Not Missing Count</th>\n",
       "      <th>Percent missing</th>\n",
       "      <th>Error Count</th>\n",
       "      <th>Empty count</th>\n",
       "      <th>0.1% Quantile</th>\n",
       "      <th>1% Quantile</th>\n",
       "      <th>5% Quantile</th>\n",
       "      <th>25% Quantile</th>\n",
       "      <th>50% Quantile</th>\n",
       "      <th>75% Quantile</th>\n",
       "      <th>95% Quantile</th>\n",
       "      <th>99% Quantile</th>\n",
       "      <th>99.9% Quantile</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DOB</th>\n",
       "      <td>FieldType.DATE</td>\n",
       "      <td>1900-01-01 00:00:00+00:00</td>\n",
       "      <td>2008-10-25 00:00:00+00:00</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>FieldType.INTEGER</td>\n",
       "      <td>17936</td>\n",
       "      <td>36765</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17957.5</td>\n",
       "      <td>19834.1</td>\n",
       "      <td>19741</td>\n",
       "      <td>22621.6</td>\n",
       "      <td>27301.9</td>\n",
       "      <td>31965.1</td>\n",
       "      <td>35744</td>\n",
       "      <td>36546.7</td>\n",
       "      <td>36743.4</td>\n",
       "      <td>27298.8</td>\n",
       "      <td>5400.8</td>\n",
       "      <td>2.91686e+07</td>\n",
       "      <td>0.0072681</td>\n",
       "      <td>-1.19487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "ColumnProfile:\n",
       "    column_name: DOB\n",
       "    type: FieldType.DATE\n",
       "\n",
       "    min: 1900-01-01 00:00:00+00:00\n",
       "    max: 2008-10-25 00:00:00+00:00\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: ID\n",
       "    type: FieldType.INTEGER\n",
       "\n",
       "    min: 17936.0\n",
       "    max: 36765.0\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "    Quantiles:\n",
       "         0.1%: 17957.505076923077\n",
       "           1%: 19834.11288011696\n",
       "           5%: 19741.04861111111\n",
       "          25%: 22621.57794636583\n",
       "          50%: 27301.880255872522\n",
       "          75%: 31965.093154567367\n",
       "          95%: 35743.97104916763\n",
       "          99%: 36546.678301158296\n",
       "        99.9%: 36743.43258133971\n",
       "\n",
       "    mean: 27298.755562519484\n",
       "    std: 5400.796882343092\n",
       "    variance: 29168606.964326866\n",
       "    skewness: 0.007268103979632813\n",
       "    kurtosis: -1.194865681433907"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataProfile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnDataProfile = dataProfile.columns[targetColumn]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Statistics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <td>FieldType.DATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>1900-01-01 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>2008-10-25 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count</th>\n",
       "      <td>19147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing Count</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Missing Count</th>\n",
       "      <td>19146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percent missing</th>\n",
       "      <td>5.22275e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Error Count</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empty count</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9% Quantile</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Deviation</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variance</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skewness</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "ColumnProfile:\n",
       "    column_name: DOB\n",
       "    type: FieldType.DATE\n",
       "\n",
       "    min: 1900-01-01 00:00:00+00:00\n",
       "    max: 2008-10-25 00:00:00+00:00\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnDataProfile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if columnDataProfile.type == 'FieldType.DATE':\n",
    "    print('Date Field Detected!')\n",
    "    # NOTE - what do I need to do to detect date field?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add year\n",
    "columnByExampleBuilder = dataFlow.builders.derive_column_by_example(source_columns = [targetColumn], new_column_name = 'Year')\n",
    "columnByExampleBuilder.add_example(source_data = {targetColumn : '2008-10-25 00:00:00'}, example_value = '2008')\n",
    "columnByExampleBuilder.preview()\n",
    "dataFlow = columnByExampleBuilder.to_dataflow()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add month\n",
    "columnByExampleBuilder = dataFlow.builders.derive_column_by_example(source_columns = [targetColumn], new_column_name = 'Month')\n",
    "columnByExampleBuilder.add_example(source_data = {targetColumn : '2008-10-25 00:00:00'}, example_value = 'October')\n",
    "columnByExampleBuilder.preview()\n",
    "dataFlow = columnByExampleBuilder.to_dataflow()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add day of month\n",
    "columnByExampleBuilder = dataFlow.builders.derive_column_by_example(source_columns = [targetColumn], new_column_name = 'DayOfMonth')\n",
    "columnByExampleBuilder.add_example(source_data = {targetColumn : '2008-10-25 00:00:00'}, example_value = '25')\n",
    "columnByExampleBuilder.preview()\n",
    "dataFlow = columnByExampleBuilder.to_dataflow()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add day of month\n",
    "columnByExampleBuilder = dataFlow.builders.derive_column_by_example(source_columns = [targetColumn], new_column_name = 'DayOfWeek')\n",
    "columnByExampleBuilder.add_example(source_data = {targetColumn : '2008-10-25 00:00:00'}, example_value = 'Saturday')\n",
    "columnByExampleBuilder.preview()\n",
    "dataFlow = columnByExampleBuilder.to_dataflow()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataProfile = dataFlow.get_profile()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Count</th>\n",
       "      <th>Missing Count</th>\n",
       "      <th>Not Missing Count</th>\n",
       "      <th>Percent missing</th>\n",
       "      <th>Error Count</th>\n",
       "      <th>Empty count</th>\n",
       "      <th>0.1% Quantile</th>\n",
       "      <th>1% Quantile</th>\n",
       "      <th>5% Quantile</th>\n",
       "      <th>25% Quantile</th>\n",
       "      <th>50% Quantile</th>\n",
       "      <th>75% Quantile</th>\n",
       "      <th>95% Quantile</th>\n",
       "      <th>99% Quantile</th>\n",
       "      <th>99.9% Quantile</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DOB</th>\n",
       "      <td>FieldType.DATE</td>\n",
       "      <td>1900-01-01 00:00:00+00:00</td>\n",
       "      <td>2008-10-25 00:00:00+00:00</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayOfWeek</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>Friday</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayOfMonth</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>April</td>\n",
       "      <td>September</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>FieldType.STRING</td>\n",
       "      <td>1900</td>\n",
       "      <td>2008</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>FieldType.INTEGER</td>\n",
       "      <td>17936</td>\n",
       "      <td>36765</td>\n",
       "      <td>19147.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17957.5</td>\n",
       "      <td>19834.1</td>\n",
       "      <td>19741</td>\n",
       "      <td>22621.6</td>\n",
       "      <td>27301.9</td>\n",
       "      <td>31965.1</td>\n",
       "      <td>35744</td>\n",
       "      <td>36546.7</td>\n",
       "      <td>36743.4</td>\n",
       "      <td>27298.8</td>\n",
       "      <td>5400.8</td>\n",
       "      <td>2.91686e+07</td>\n",
       "      <td>0.0072681</td>\n",
       "      <td>-1.19487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "ColumnProfile:\n",
       "    column_name: DOB\n",
       "    type: FieldType.DATE\n",
       "\n",
       "    min: 1900-01-01 00:00:00+00:00\n",
       "    max: 2008-10-25 00:00:00+00:00\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: DayOfWeek\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: Friday\n",
       "    max: Wednesday\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: DayOfMonth\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 1\n",
       "    max: 9\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Month\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: April\n",
       "    max: September\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: Year\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 1900\n",
       "    max: 2008\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "ColumnProfile:\n",
       "    column_name: ID\n",
       "    type: FieldType.INTEGER\n",
       "\n",
       "    min: 17936.0\n",
       "    max: 36765.0\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "    Quantiles:\n",
       "         0.1%: 17957.505076923077\n",
       "           1%: 19834.11288011696\n",
       "           5%: 19741.04861111111\n",
       "          25%: 22621.57794636583\n",
       "          50%: 27301.880255872522\n",
       "          75%: 31965.093154567367\n",
       "          95%: 35743.97104916763\n",
       "          99%: 36546.678301158296\n",
       "        99.9%: 36743.43258133971\n",
       "\n",
       "    mean: 27298.755562519484\n",
       "    std: 5400.796882343092\n",
       "    variance: 29168606.964326866\n",
       "    skewness: 0.007268103979632813\n",
       "    kurtosis: -1.194865681433907"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataProfile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataColumns = dataProfile.columns.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['DOB', 'DayOfWeek', 'DayOfMonth', 'Month', 'Year', 'ID'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataColumns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column DOB : value count None\n",
      "Column DayOfWeek : value count 8\n",
      "Column DayOfMonth : value count 32\n",
      "Column Month : value count 13\n",
      "Column Year : value count 108\n",
      "Column ID : value count None\n"
     ]
    }
   ],
   "source": [
    "for c in dataColumns:\n",
    "    valueCounts = dataProfile.columns[c].value_counts\n",
    "    if valueCounts == None:\n",
    "        valueCountString = 'None'\n",
    "    else:\n",
    "        valueCountString = len(valueCounts)\n",
    "    print('Column {0} : value count {1}'.format(c, valueCountString))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotValueCounts(dataProfile,columnName):\n",
    "    valueCounts = dataProfile.columns[columnName].value_counts\n",
    "    if valueCounts != None:\n",
    "        valueCountsDataFrame = pd.DataFrame(columns = ['ColumnName', 'Value', 'Count'])\n",
    "        for i in valueCounts:\n",
    "            valueCountsDataFrame = valueCountsDataFrame.append({ \\\n",
    "                'Column' : c, \\\n",
    "                'Value' : i.value, \\\n",
    "                'Count' : i.count}, \\\n",
    "                ignore_index = True)\n",
    "        if len(valueCountsDataFrame) > 40:\n",
    "            plot = sns.barplot(x='Value', y='Count', data=valueCountsDataFrame.head(20))\n",
    "            plot\n",
    "            plot = sns.barplot(x='Value', y='Count', data=valueCountsDataFrame.tail(20))\n",
    "            plot\n",
    "        else:\n",
    "            plot = sns.barplot(x='Value', y='Count', data=valueCountsDataFrame)\n",
    "            plot\n",
    "    return plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JnNZVLJKk/k2Vk9SSpCnGBCFJajJBSJKaTBCSpCYThCSpyQQhSWoyQUiSmkwQkqQmE4QkqckEIUlqMkFIkppMEJKkJhOEJKnJBCFJajJBSJKaTBCSpCYThCSpyQQhSWoyQUiSmkwQkqQmE4QkqckEIUlqMkFIkpqmVIKIiKMi4paIWBURZ0x2PJK0LZsyCSIipgGfAo4G9gdOioj9JzcqSdp2TZkEARwErMrMWzPzUeALwPxJjkmStlnTJzuAHrOAO3terwb+YMv9+d8Op5XIIbWz+W2sZzixPDqkrxEPbTeEhcpnbn4bQOROQ2hjxyFEAgypne3Y/GWazjOGEAn8fjxtKO3MWj9ts9vY/eH/GEIkMHL/5rez3fbrBpxz1uNexbR7NzuWiYjM4RxENldEnAC8MjP/pL5+C3BQZr5ro3oLgYX15XOBW8Zpeg/gV0MIcRjtGEu37RhLt+1MpViG1c62GsuzMnNkvIamUg9iNbB3z+vZwF0bV8rMc4BzJtpoRCzNzHmbG9ww2jGWbtsxlm7bmUqxDKsdYxnbVDoH8QNgbkTsGxE7ACcCl09yTJK0zZoyPYjMXB8R7wSuAqYB52XmTZMcliRts6ZMggDIzCuAK4bc7ISHo7ZAO8bSbTvG0m07UymWYbVjLGOYMiepJUlTy1Q6ByFJmkoy80n1AM4D1gI39pS9APgu8GPgK8AzavkOwPm1/EfA4bX86cA9wGPAbyiXhJ25qXbqPO8HVlEuq31lT/lVwHrg34EzxopnU+3UZfot8AiwHFja7zLV9xb3xHIWEAOsm/OAR2sby+vj8AHWy0+B/wAeGe9zGmO97Ac8WNfLI8CVtXwGcDWwsj7vVsujLvcq4AbggFr+L/WzfqSWv2GM9bI9sKiWrwDe3xPjdXX9PgKcPuC2tzdwU88yraBcltjvMo3uBz8BfgH8w4CxfLPG8CCwZsD1uzfwHeAhynbzS2DOgO1kXS+/AX7cbzw9y3RPbeeXtc4gsdxD2S9/Dbx7wHXzRTYcZ24C/nzAz6l3m1kySCz1vQW1/kpgwbjH28k+4A+QIA6rG0JvgvgB8LI6/Tbg7+r0acD5dXpPYBmw3cbt1PLDxmhn//qB7QjsC/yMciJ9GmXn/OP6Af6o1u23ncMol/Su2MxlWlE3gBuBr1NuW9JXOzWWpZRftY8XS3N56nvvBN7I4xNEv+tlJnBorbMbZSc7Efh7NiTjM4CP1Olj6nIHcDDwvVr+Rsqv8m8EngmsAa7fRCxvBL5Qp58K3EY52P0ecCvwktrON4C5A6zf2cC9lKTw9Dr9qQGWaXT7/RXweUqC6DeWmbWN1wIXUw7u+w8Qy0zKNvNHdZlW1nYHaefhOv10ypeMvuKpbbwV+Fdgl9rGcuCiAWL5M8q+/fVBYqnlR1B+8HsjsDNle1re5+e0P2WbHanr5YE6X7+xzKBswzMo+9Ot1KSyyePtZB/wB3lQdtjeBPEgG86n7A38pE5/CnhzT70llB/f9bbzU8ovuGOMdt7P479JXgUcUh9XjcYzWq/fdur0ah6fIPpaprpB39wTy0nAZwZZN5RvyqsmEMsml6e+fimPTxADtZMbDtb3A++i9DJm9uzIt9TpzwAn9czTW28OdZuhJKOHNhHLSZRvddOB3ev2MQM4Afhsz/r9a+B9A3xO2wPrgGdRtrmfA58YcJmOrevkrZQEMchnvTPwbcpB6EHKQb6vWOq83+4pXzxIO3X6oc1th7JfLgN2Ar5aP8Of9xtLfX14bWNzlmkOZZvZHbiD0iPpZ5t5MfCNnvJllJ8A9LteTgI+01P+uHqtx9ZyDuJG4NV1+gQ2/ODuR8D8iJgeEfsCB/L4H+NB+ZbxxSxrbFPttG4DMmuM8n7bgdK1nhMRy+qvxftdplm1vYnGMt66mR0RyyPirwdcnpa+24mIaRGxnHJQ3Y4y/LNXZq4BqM97TjSeiDiI0pW/YROxXAL8G+Ub2x3ARzPz3hr7YcCulAP7MXWevtZvZj4GvJ0yjHA3Zcf9YL/LFBHbAR+gDKGMGuSz/jvgY5Qk+BTKt/B+1+/vAvdHxKURcRPwckpvZpDP6SkRsTQifkjprfUdT2Z+lzLMdDflc1pM+aY80DZDSTQvGnDdQPmM96vvf4Ty2ffzOa0CnhcRcyLiOcDzKV80+o2l3/11q0kQbwNOi4hllC7Yo7X8PMpKWEo5x/Adyhhyr10o3c+x2mndRCjHKO+3HYDXUYZWjqZ0Nf+xz2XqN5ax1s27KRvlf6mPxQMsT0vf6yUzf0vpiawEbgf2GaP98eKZDnwOOGWMWA6ijDs/kzLc9ZcR8ezMXEHZuf+Z8o3wR5T11df6jYjtKQniUMrOeg1lOK7fZXoH5SDYuz33G8sLKQeuq4GzgV9m5oMDxDKdsp38DWWM/CbK9txvO1A+38Nrm0kZVumrnYjYjzIk+DPgzZTtZ1M3dRpvm9mJcqA+fcB1A+XLxirKul4AvJc+PqfMvI+yzXyJst1dTVnP/cbS7/46tX4HMajMvBl4BUBE/C7wqlq+HviL0XoR8R3KgWbU8yldvWVjtcPYtwF5QvmA7aytMayNiMuA38nMfpbpvtrehGIZZ93cXev8OiI+D8xrxTLO8jzBIOulHlC/DFxA2ZmOAu6OiJmZuSYiZo6uu3Hi2ZkyrHNyZl5Xy1qxvJFyMvwxYG1E/CswD7g1M8+NiCWUIYd7gdUDrN8XUnbUjwIXUg4GZwywTIdQDqQjta0dKMMz/cTyMsrBbx3l/MNTI+LaAWLZnjKu/rG6TOsoY9+DfE7r6vo9H/h9yjf3fts5GXgOcHZmfr4mjOf0G0vd9j4I/CIzL63vD7JMAGTmXbWHNbPPzwngSsoXib+hDI/uB+zfZyyrKdtMb/m1jGGr6EFExJ71ebTb/en6+qkR5baSEfFHlGz8k55ZX0054TNmO5TxvhMjYsfa9ZsLfJ96exDKig7q7UH6bafGOBrn0ygHnDv7Wabaxfw1ZYeCspMs7nfdRMR0ygms0R3kWMo3937WS1+f0xjrZYSSGFZQvuH+IeU8y+WUb2LU58U97ZwcxcHAA3Xn2YEy3np/Zn5pnFjuAI6obTyNcqC7uXceygHxtcBFA2x7v6ht3paZH6eMa6/od5ky802UXshPgf9a19PH+4zl05ST7Z+hJK6fZubh/cZC2Q/+c88yHUG5uqrfz2k3SmJYUZfn0H7boQy5HUO54OGsug2/jHKepZ9YAjiXsj38nA36XabZNRbq8h1KSYL97JNBSbwr6vp5B+V8WL+f01XAKyJitxrLK2rZpo11gmIqPijDQWsol46tBk6lDIn8tD4+zIYTQHMoJ2hWUHaEZ23UzvqJtFPr/xWly3oLcHRP+bW1naRsoH23Uz/Qx2objwGXDrhMV/W089Ag64ZyNUtvLFcDpw+wXm6nDNVkXT/nD7BeFtT5f1Mfayg7/+6UE3gr6/OMWj8oJ/p+RhnnnVfLv1PbSUp3/g7KFSCt9bIzpSt/E+Xg9N6eGNf2rJt1A67fl/L4SzkfoFx22+8y9e4H99Z5Bo3lhrqsvxlw/Y62M/o53Uv58tVvO3++UTt39BtPTyyjl7k+QtmfBl2m9ZTLtR+lHMz7becaNmwzj1LOoW3ONvMI5Uqovj+n+t7bKMNdq4BTxjve+ktqSVLTVjHEJEkaPhOEJKnJBCFJajJBSJKaTBCSpCYThDSGiLg2Il65UdnpEfGPY8zzUPeRSd0zQUhju4jyA8heJ7Lh9izSVssEIY3tEuDYiBj9Newcyn2alkfEkoi4PiJ+HBHzN54xIg6PiK/2vP6HiHhrnT4wIr4V5eaMV9VbJUhTiglCGkNm3kO5fchRtehEyj+B+Q3wmsw8gHL30o/VWyKMq97+4ZPA8Zl5IOUmbf9z2LFLm2uruFmf1LHRYabF9fltlNsZ/K+IOIxyK4ZZwF48/vbbm/Jcyt1Gr645ZRrlthnSlGKCkMb3L8DHI+IAYKfMvL4OFY0AB2bmYxFxG+X/KfRaz+N76aPvB3BTZh7SbdjS5nGISRpHZj5EuSnjeWw4Ob0LsLYmh5dTbiW+sdspt2TeMSJ2AY6s5bcAIxFxCJQhp4j4T10ugzQIexDSxFxEuSvo6BVNFwJfiYillP+FcPPGM2TmnRFxMeVuqSuBH9byRyPieMrtqHeh7IdnUu4gK00Z3s1VktTkEJMkqckEIUlqMkFIkppMEJKkJhOEJKnJBCFJajJBSJKaTBCSpKb/D+p4F5zePsivAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plotValueCounts(dataProfile, 'Year')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b352a0f160>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plotValueCounts(dataProfile, 'Month')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b354c41080>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plotValueCounts(dataProfile, 'DayOfWeek')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b354cc8198>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataProfileValues = dataProfile.columns.values()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_values([ColumnProfile:\n",
       "    column_name: DOB\n",
       "    type: FieldType.DATE\n",
       "\n",
       "    min: 1900-01-01 00:00:00+00:00\n",
       "    max: 2008-10-25 00:00:00+00:00\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       ", ColumnProfile:\n",
       "    column_name: DayOfWeek\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: Friday\n",
       "    max: Wednesday\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       ", ColumnProfile:\n",
       "    column_name: DayOfMonth\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 1\n",
       "    max: 9\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       ", ColumnProfile:\n",
       "    column_name: Month\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: April\n",
       "    max: September\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       ", ColumnProfile:\n",
       "    column_name: Year\n",
       "    type: FieldType.STRING\n",
       "\n",
       "    min: 1900\n",
       "    max: 2008\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       ", ColumnProfile:\n",
       "    column_name: ID\n",
       "    type: FieldType.INTEGER\n",
       "\n",
       "    min: 17936.0\n",
       "    max: 36765.0\n",
       "    count: 19147.0\n",
       "    missing_count: 1.0\n",
       "    not_missing_count: 19146.0\n",
       "    percent_missing: 5.222750300308142e-05\n",
       "    error_count: 0.0\n",
       "    empty_count: 0.0\n",
       "\n",
       "\n",
       "    Quantiles:\n",
       "         0.1%: 17957.505076923077\n",
       "           1%: 19834.11288011696\n",
       "           5%: 19741.04861111111\n",
       "          25%: 22621.57794636583\n",
       "          50%: 27301.880255872522\n",
       "          75%: 31965.093154567367\n",
       "          95%: 35743.97104916763\n",
       "          99%: 36546.678301158296\n",
       "        99.9%: 36743.43258133971\n",
       "\n",
       "    mean: 27298.755562519484\n",
       "    std: 5400.796882343092\n",
       "    variance: 29168606.964326866\n",
       "    skewness: 0.007268103979632813\n",
       "    kurtosis: -1.194865681433907\n",
       "])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataProfileValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dataFlow.to_pandas_dataframe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values([targetColumn]).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "profileReport = pp.ProfileReport(df, check_correlation = False)\n",
    "profileReport.to_file('./profileReport.html')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = sns.countplot(x=\"Year\", data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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