7 Steps to Automating Descriptive Statistics with Python

2026-07-09 · Source: KDnuggets · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

This tutorial, published on July 9, 2026, outlines 7 steps to automate descriptive statistics in Python, moving beyond manual `mean()` and `std()` calls. It demonstrates how to generate publication-ready summary tables efficiently using the Palmer Penguins dataset. The guide covers leveraging Pandas' `describe()` with `include="all"` and `.agg()` for custom summaries, then introduces external libraries. These include `skimpy` for rich console summaries with ASCII histograms, `fg-data-profiling` (renamed from `ydata-profiling` in April 2026) for comprehensive interactive HTML reports, `tableone` for generating stratified "Table 1" research tables with p-values and SMDs, and `Great Tables` for styling any DataFrame into a presentation-ready format. The article concludes by showing how to encapsulate these steps into a reusable function.

Key takeaway

For data scientists and analysts seeking to streamline initial data exploration and reporting, you should integrate specialized Python libraries into your workflow. Automating descriptive statistics with tools like `skimpy` for quick console overviews or `tableone` for formal research tables significantly reduces manual effort. Consider wrapping your preferred summary generation into a reusable function to ensure consistency and efficiency across projects, freeing up time for deeper analysis.

Key insights

Python offers a spectrum of tools, from Pandas to specialized libraries, to automate and polish descriptive statistics reporting.

Principles

• Choose tools based on reporting needs, from quick checks to publication.
• Automate repetitive data summary tasks into reusable functions.
• Human judgment remains crucial for statistical interpretation.

Method

The article outlines a 7-step process: environment setup, baseline with `df.describe()`, advanced Pandas `agg()` and `groupby`, `skimpy` for console summaries, `fg-data-profiling` for interactive reports, `tableone` for research tables, and `Great Tables` for styling.

In practice

• Use `df.describe(include="all")` for mixed column summaries.
• Employ `skimpy` for quick interactive console data overviews.
• Generate `tableone` outputs directly to LaTeX or HTML for reports.

Topics

• Descriptive Statistics
• Python
• Data Profiling
• tableone
• Great Tables
• Data Automation

Code references

• aeturrell/skimpy
• tompollard/tableone

Best for: Data Scientist, Data Analyst, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.