examples/WIP_historical_profiler_advanced.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HistoricalProfiler - Proof of Concept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import requirements\n",
    "import os\n",
    "import json\n",
    "import dataprofiler as dp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"../dataprofiler/tests/data/csv/ny_weather\"\n",
    "\n",
    "dataObjects = []\n",
    "for i in range(22, 14, -1):\n",
    "    current_data = dp.Data(os.path.join(data_path, \"ny_weather - {}.csv\".format(i)))\n",
    "    dataObjects.append(current_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dataObjects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check Data\n",
    "dataObjects[0].data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make the Profiler Objects\n",
    "profilerObjects = []\n",
    "for dataObj in dataObjects:\n",
    "    profilerObjects.append(dp.Profiler(dataObj))\n",
    "profilerObjects[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create\n",
    "\n",
    "##### Instantiate new HistoricalProfiler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler = dp.HistoricalProfiler(profilerObjects[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Append new Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.append(profilerObjects[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read\n",
    "\n",
    "##### Get Most Recent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_most_recent_profile_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get Oldest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_oldest_profile_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get Report From Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_profile_report_by_index(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get Full Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(historical_profiler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update\n",
    "\n",
    "##### Update Profile Report at Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Previous Report:\")\n",
    "historical_profiler.get_most_recent_profile_report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.update_profile_report_at_index(profilerObjects[2], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Updated Report:\")\n",
    "historical_profiler.get_most_recent_profile_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete\n",
    "\n",
    "##### Delete Report at Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(historical_profiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.delete_profile_report_at_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(historical_profiler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supplementary Reports\n",
    "\n",
    "##### Consecutive Profiles Diff Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_consecutive_diffs_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Numeric Min and Max Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_numeric_min_and_max_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standard Deviation Bounds Dict\n",
    "\n",
    "Here, each numeric key will be replaced with a tuple. The tuple will denote the mean of the former value list stored at this key minus (-), plus (+) the standard deviation of the former value list stored at this key times (*) the number 'n' provided. \n",
    "\n",
    "For example, if I wanted to see get the range of 1 standard deviation away from the mean for each statistic, I would call this function with n=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_numeric_statistic_n_standard_deviations_bounds(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standard Deviation Delta Bounds Dict\n",
    "\n",
    "Here, each numeric key that has a delta value will be replaced with a tuple. The tuple will denote the mean of the former consecutive diff list stored at this key minus (-), plus (+) the standard deviation of the former consecutive diff list stored at this key times (*) the number 'n' provided. \n",
    "\n",
    "For example, if I wanted to see get the range of 1 standard deviation away from the mean for each statistic, I would call this function with n=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_numeric_statistic_delta_n_standard_deviation_bounds(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Replace 'Unchanged' in Consecutive Diff Report with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdr = historical_profiler.get_consecutive_diffs_report()\n",
    "cdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.convert_consecutive_diffs_unchanged_to_zero(cdr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Numeric Statistics Report\n",
    "\n",
    "Returns a report containing only keys in data_stats dictionaries that store numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_profiler.get_numeric_stats_report()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.9 ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b763ba2d8f48700d722f535167776e097060cd6ab642a45ac0705748ebad623e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}