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Population Shift Monitoring

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popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets.

popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two features. popmon can automatically flag and alert on changes observed over time, such as trends, shifts, peaks, outliers, anomalies, changing correlations, etc, using monitoring business rules.

Traffic Light Overview

Histogram inspector


Spark 3.0

With Spark 3.0, based on Scala 2.12, make sure to pick up the correct histogrammar jar files:

spark = SparkSession.builder.config(

For Spark 2.X compiled against scala 2.11, in the string above simply replace 2.12 with 2.11.



The entire popmon documentation including tutorials can be found at read-the-docs.


Tutorial Colab link
Basic tutorial Open in Colab
Detailed example (featuring configuration, Apache Spark and more) Open in Colab
Incremental datasets (online analysis) Open in Colab
Report interpretation (step-by-step guide) Open in Colab

Check it out

The popmon library requires Python 3.6+ and is pip friendly. To get started, simply do:

$ pip install popmon

or check out the code from our GitHub repository:

$ git clone
$ pip install -e popmon

where in this example the code is installed in edit mode (option -e).

You can now use the package in Python with:

import popmon

Congratulations, you are now ready to use the popmon library!

Quick run

As a quick example, you can do:

import pandas as pd
import popmon
from popmon import resources

# open synthetic data
df = pd.read_csv("test.csv.gz"), parse_dates=["date"])

# generate stability report using automatic binning of all encountered features
# (importing popmon automatically adds this functionality to a dataframe)
report = df.pm_stability_report(time_axis="date", features=["date:age", "date:gender"])

# to show the output of the report in a Jupyter notebook you can simply run:

# or save the report to file

To specify your own binning specifications and features you want to report on, you do:

# time-axis specifications alone; all other features are auto-binned.
report = df.pm_stability_report(
    time_axis="date", time_width="1w", time_offset="2020-1-6"

# histogram selections. Here 'date' is the first axis of each histogram.
features = [

# Specify your own binning specifications for individual features or combinations thereof.
# This bin specification uses open-ended ("sparse") histograms; unspecified features get
# auto-binned. The time-axis binning, when specified here, needs to be in nanoseconds.
bin_specs = {
    "longitude": {"bin_width": 5.0, "bin_offset": 0.0},
    "latitude": {"bin_width": 5.0, "bin_offset": 0.0},
    "age": {"bin_width": 10.0, "bin_offset": 0.0},
    "date": {
        "bin_width": pd.Timedelta("4w").value,
        "bin_offset": pd.Timestamp("2015-1-1").value,

# generate stability report
report = df.pm_stability_report(features=features, bin_specs=bin_specs, time_axis=True)

These examples also work with spark dataframes. You can see the output of such example notebook code here. For all available examples, please see the tutorials at read-the-docs.

Pipelines for monitoring dataset shift

Advanced users can leverage popmon's modular data pipeline to customize their workflow. Visualization of the pipeline can be useful when debugging, or for didactic purposes. There is a script included with the package that you can use. The plotting is configurable, and depending on the options you will obtain a result that can be used for understanding the data flow, the high-level components and the (re)use of datasets.

Pipeline Visualization

Example pipeline visualization (click to enlarge)

Reports and integrations

The data shift computations that popmon performs, are by default displayed in a self-contained HTML report. This format is favourable in many real-world environments, where access may be restricted. Moreover, reports can be easily shared with others.

Access to the datastore means that its possible to integrate popmon in almost any workflow. To give an example, one could store the histogram data in a PostgreSQL database and load that from Grafana and benefit from their visualisation and alert handling features (e.g. send an email or slack message upon alert). This may be interesting to teams that are already invested in particular choice of dashboarding tool.

Possible integrations are:

Grafana logo Kibana logo
Grafana Kibana

Resources on how to integrate popmon are available in the examples directory. Contributions of additional or improved integrations are welcome!

Comparison and profile extensions

External libraries or custom functionality can be easily added to Profiles and Comparisons. If you developed an extension that could be generically used, then please consider contributing it to the package.

Popmon currently integrates:

A Python/C++ implementation of Hartigan & Hartigan's dip test for unimodality. The dip test tests for multimodality in a sample by taking the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference. Other than unimodality, it makes no further assumptions about the form of the null distribution.

To enable this extension install diptest using pip install diptest or pip install popmon[diptest].



Title Host Date Speaker
popmon: Analysis Package for Dataset Shift Detection SciPy Conference 2022 July 13, 2022 Simon Brugman
Popmon - population monitoring made easy Big Data Technology Warsaw Summit 2021 February 25, 2021 Simon Brugman
Popmon - population monitoring made easy Data Lunch @ Eneco October 29, 2020 Max Baak, Simon Brugman
Popmon - population monitoring made easy Data Science Summit 2020 October 16, 2020 Max Baak
Population Shift Monitoring Made Easy: the popmon package Online Data Science Meetup @ ING WBAA July 8 2020 Tomas Sostak
Popmon: Population Shift Monitoring Made Easy PyData Fest Amsterdam 2020 June 16, 2020 Tomas Sostak
Popmon: Population Shift Monitoring Made Easy Amundsen Community Meetup June 4, 2020 Max Baak



  • Kedro-popmon is a plugin to integrate popmon reporting with kedro. This plugin allows you to automate the process of popmon feature and output stability monitoring. Package created by Marian Dabrowski and Stephane Collot.

Project contributors

This package was authored by ING Wholesale Banking Advanced Analytics. Special thanks to the following people who have contributed to the development of this package: Ahmet Erdem, Fabian Jansen, Nanne Aben, Mathieu Grimal.

Citing popmon

If popmon has been relevant in your work, and you would like to acknowledge the project in your publication, we suggest citing the following paper:

  • Brugman, S., Sostak, T., Patil, P., Baak, M. popmon: Analysis Package for Dataset Shift Detection. Proceedings of the 21st Python in Science Conference. 161-168 (2022). (link)

In BibTeX format:

@InProceedings{ popmon-proc-scipy-2022,
  author    = { {S}imon {B}rugman and {T}omas {S}ostak and {P}radyot {P}atil and {M}ax {B}aak },
  title     = { popmon: {A}nalysis {P}ackage for {D}ataset {S}hift {D}etection },
  booktitle = { {P}roceedings of the 21st {P}ython in {S}cience {C}onference },
  pages     = { 161 - 168 },
  year      = { 2022 },
  editor    = { {M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe },

Contact and support

Please note that ING WBAA provides support only on a best-effort basis.


Copyright ING WBAA. popmon is completely free, open-source and licensed under the MIT license.

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