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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `popmon` introductory notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains examples of how to generate `popmon` reports from a pandas DataFrame."
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# (optional) Adjust the jupyter notebook style for easier navigation of the reports\n",
    "from IPython.core.display import HTML, display\n",
    "\n",
    "# Wider notebook\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))\n",
    "# Cells are higher by default\n",
    "display(HTML(\"<style>div.output_scroll { height: 44em; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup `popmon` and load our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install popmon (if not installed yet) in the current environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "!\"{sys.executable}\" -m pip install -q popmon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas and popmon, load and example dataset provided by popmon and show the first few results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import popmon\n",
    "from popmon import resources\n",
    "from popmon.config import Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(resources.data(\"test.csv.gz\"), parse_dates=[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reporting given a pandas.DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = df.pm_stability_report(\n",
    "    # Use the 'date' column as our time axis\n",
    "    time_axis=\"date\",\n",
    "    # Create batches for every two weeks of data\n",
    "    time_width=\"2w\",\n",
    "    # Select a subset of features\n",
    "    features=[\"date:age\", \"date:isActive\", \"date:eyeColor\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regenerate the report\n",
    "You can change the report parameters without having to rerun the computational part of the pipeline using the `regenerate` method. For example: a short (limited) report will be generated since `extended_report` flag is set to `False`. If a user wants to configure which statistics she/he wants to see, `show_stats` argument has to be set accordingly.\n",
    "\n",
    "Another option is to change the `plot_hist_n` parameter to control the number of histograms being displayed per feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "report_settings = Settings()\n",
    "report_settings.report.extended_report = False\n",
    "report_settings.report.section.histograms.plot_hist_n = 6\n",
    "\n",
    "report.regenerate(settings=report_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reporting given a histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the user would like to generate the report directly from histograms, then popmon also supports that.\n",
    "First, we generate histograms, (but we could load pre-generated histograms from a pickle or json file as well)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hists = df.pm_make_histograms(\n",
    "    time_axis=\"date\",\n",
    "    time_width=\"2w\",\n",
    "    features=[\"date:age\", \"date:gender\", \"date:isActive\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(hists.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then generate the report based on histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "report = popmon.stability_report(hists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "report"
   ]
  }
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