descripstats 0.1.1 Released - Deepsim Insights

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descripstats 0.1.1 Released: Modernizing Descriptive Statistics for Pandas 2.x+

A Lightweight Python Package for Enhanced Exploratory Data Analysis

Exploratory Data Analysis (EDA) remains one of the most important steps in any data science, machine learning, or data engineering workflow. While pandas provides the widely used DataFrame.describe() method, analysts often need additional statistical measures to better understand their data.

To address this need, I developed descripstats, a lightweight open-source Python package that extends pandas’ descriptive statistics with several useful measures commonly used during data exploration.

Today, I am pleased to announce the release of descripstats 0.1.1, a major update that modernizes the package for compatibility with the latest pandas ecosystem.

Why descripstats?

The standard pandas describe() function provides basic statistics such as:

Count
Mean
Standard deviation
Minimum
Quartiles
Maximum

However, many data professionals frequently need additional metrics, including:

Mean Absolute Deviation (MAD)
Variance
Standard Error of the Mean (SEM)
Sum
Skewness
Kurtosis

Instead of calculating these separately, descripstats integrates them into a single, convenient summary table.

What’s New in Version 0.1.1

Full pandas 2.x+ Compatibility

One of the primary motivations for this release was the removal of DataFrame.mad() in pandas 2.0.

Previous versions of descripstats relied on this method to calculate Mean Absolute Deviation. In version 0.1.1, the implementation has been completely updated to use a NumPy-based calculation, ensuring compatibility with modern pandas releases.

Improved Package Metadata

The project packaging has been refreshed to align with current Python packaging standards, including:

Updated README documentation
Improved package metadata
Better dependency management
Cleaner installation experience

Updated Documentation

The documentation has been revised to provide clearer installation instructions, usage examples, and compatibility information.

Example Usage

Installation:

pip install descripstats

Using the package:

from descripstats import Describe
import pandas as pd

df = pd.read_csv("data.csv")

stats = Describe(df)
print(stats)

The output extends pandas’ default descriptive statistics by adding:

MAD
Variance
SEM
Sum
Skewness
Kurtosis

in a single consolidated report.

Open Source and Community Learning

descripstats was originally developed as a simple utility to support educational projects, tutorials, and practical data analysis tasks. Over time, it became clear that many learners and practitioners encounter the same need for richer descriptive statistics during EDA.

This update demonstrates an important lesson in maintaining open-source software: as the Python ecosystem evolves, libraries must evolve with it. Compatibility updates such as this help ensure that educational resources and production code continue to work reliably with modern dependencies.