---
id: pandas-summary
version: "0.2.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# pandas-summary — An extension to pandas describe function.

License: permissive · Maintenance: active · Downloads: 115.4K/mo

## What it is and what it does

pandas-summary is a lightweight extension to pandas' built-in describe() function that generates more detailed statistical summaries of DataFrames. It wraps pandas DataFrames to provide enhanced profiling and exploratory data analysis capabilities, useful when you need richer insights into your data's distribution, missing values, and other characteristics beyond what describe() offers by default.

The package depends on numpy and pandas for core functionality, plus datatile for additional data processing. With low install friction and a permissive MIT license, it's straightforward to add to existing data analysis workflows. However, given that the latest release was in 2021, you should verify compatibility with your current pandas version before relying on it for production work.

Use it for:

- Generate quick statistical overviews of CSV or database exports before deeper analysis
- Identify missing values and data quality issues in exploratory data analysis workflows
- Compare summary statistics across multiple datasets during data cleaning phases
- Create baseline profiling reports for machine learning feature engineering
- Automate data inspection steps in Jupyter notebooks for rapid prototyping

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Extends pandas' describe() function to provide richer statistical summaries and data profiling for exploratory data analysis on tabular datasets.

Yes, if you're doing exploratory data analysis with pandas and want richer describe() output without heavy dependencies. The low install friction and permissive license make it a low-risk addition. However, verify compatibility with your pandas version first—the 2021 release date means it may lag behind current pandas APIs. Not essential if pandas' native describe() meets your needs.

## Install

pip install pandas-summary
uv add pandas-summary
poetry add pandas-summary

## Installing pandas-summary

Before you install: Low install friction with only three runtime dependencies (numpy, pandas, datatile). Package is actively maintained with recent commits, though the latest release was in 2021.

License in practice: MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.

Quickstart:

pip install pandas-summary

import pandas as pd
from pandas_summary import DataFrameSummary

df = pd.read_csv('data.csv')
summary = DataFrameSummary(df)
print(summary.summary())

Verify before relying:

- Whether the package works with modern pandas versions (latest release was 2021)
- What specific enhancements describe() receives beyond standard pandas functionality
- Whether datatile dependency is actively maintained and compatible with current ecosystems

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 115.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas data profiling, pandas describe extension, exploratory data analysis, statistical summary pandas, data quality assessment, pandas data exploration, quick data overview, data-profiling, eda

[View on SkillFed](https://skillfed.io/packages/pandas-summary) · [View on PyPI](https://pypi.org/project/pandas-summary/)