pandas-summary
An extension to pandas describe function.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
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 · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
last release 2021-11-25 (1723 days) · last repo commit 2026-08-07 · 534 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 115,361 downloads/mo, #12,254 on PyPI
Alternatives
Verify before relying
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())- 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
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 on this page. Verify before relying on it.
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
pandas-summary on PyPI
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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdatatilenumpypandas |
| Maintenance | Actively maintained 1,723 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 115,361 / month, #12,254 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonTopic :: Scientific/Engineering |
Evidence: pandas_summary-0.2.0-py2.py3-none-any.whl
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