---
id: pandas-schema
version: "0.3.6"
license: MIT
license_treatment: permissive
maintenance: abandoned
---

# pandas-schema — A validation library for Pandas data frames using user-friendly schemas

License: permissive · Maintenance: abandoned · Downloads: 109.6K/mo

## What it is and what it does

PandasSchema provides a declarative way to validate tabular data loaded into pandas DataFrames. You define a schema by specifying columns and attaching validation rules—such as range checks, pattern matching, whitespace detection, type coercion, and membership in allowed lists—then call validate() to get a list of all data quality errors found, including row and column location.

The package is built on top of pandas and numpy, making validation fast for CSV, TSV, and other tabular formats. It's designed for data pipelines where you need to catch malformed or out-of-spec input before processing. The repository is now archived and unmaintained; the last release was in February 2022.

Use it for:

- Validate incoming CSV files against a known schema before loading into a database or data warehouse
- Check data quality in ETL pipelines by defining column constraints and running them on each batch
- Enforce data type and range requirements on user-uploaded spreadsheets in web applications
- Detect formatting issues like leading/trailing whitespace or invalid patterns in bulk data imports
- Build automated data quality reports that list all validation failures with row and column references

## Worth the install?

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

Validates pandas DataFrames against user-defined schemas, checking column types, value ranges, patterns, and whitespace to catch data quality issues in tabular data.

No. The package is abandoned (last commit 2023-03-24, no activity for over 1638 days) and will receive no bug fixes, security updates, or compatibility patches. While it has low install friction and a permissive license, the lack of maintenance makes it a liability for production use. Consider a maintained alternative for new projects.

## Install

pip install pandas-schema
uv add pandas-schema
poetry add pandas-schema

## Installing pandas-schema

Before you install: Low install friction with three stable runtime dependencies (numpy, pandas, packaging). However, the package is archived and abandoned as of 2023-03-24, with no maintenance for over 1638 days. Use only if you can accept no future updates or bug fixes.

License in practice: MIT license is permissive and imposes no restrictions on use, modification, or distribution in your own projects.

Quickstart:

import pandas as pd
from pandas_schema import Column, Schema
from pandas_schema.validation import InRangeValidation, InListValidation

schema = Schema([
    Column('Age', [InRangeValidation(0, 120)]),
    Column('Sex', [InListValidation(['Male', 'Female'])])
])
errors = schema.validate(pd.read_csv('data.csv'))

Verify before relying:

- Whether the package remains compatible with current pandas and numpy versions despite abandonment
- Whether validation performance scales acceptably for large datasets
- Active community forks or maintained alternatives that may have superseded this project

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: abandoned
- Downloads: 109.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas dataframe validation, csv schema validation, data quality checks, pandas column validation, tabular data verification, csv data validation, pandas data validation rules, data-validation, abandoned

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