{
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# (optional) Adjust the jupyter notebook style for easier navigation of the reports\n",
"from IPython.core.display import display, HTML\n",
"\n",
"# Wider notebook\n",
"display(HTML(\"\"))\n",
"# Cells are higher by default\n",
"display(HTML(\"\"))"
]
},
{
"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",
"!\"{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",
"import popmon\n",
"from popmon import resources"
]
},
{
"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.regenerate(extended_report=False, plot_hist_n=3)"
]
},
{
"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.8"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": []
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}
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"nbformat": 4,
"nbformat_minor": 4
}