pandera
A light-weight and flexible data validation and testing tool for statistical data objects.
Decision gist · record as of 2026-08-14
Yes. Pandera is actively maintained, has no known vulnerabilities, installs with low friction, and offers a mature, permissive MIT license. It solves a real problem—catching data quality issues early in pipelines—and supports multiple dataframe libraries. The only prerequisite is Python 3.10 or later.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- A supported dataframe library must be installed separately via extras.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
last release 2026-06-29 (46 days) · last repo commit 2026-08-07 · 4,431 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 9,225,733 downloads/mo, #1,553 on PyPI
Alternatives
Verify before relying
pip install 'pandera[pandas]'
import pandera.pandas as pa
schema = pa.DataFrameSchema({
"column1": pa.Column(int, pa.Check.ge(0)),
"column2": pa.Column(float, pa.Check.lt(10)),
})
schema.validate(df)- Whether the package handles all edge cases in complex, nested validation scenarios
- Performance characteristics when validating very large dataframes
- Compatibility guarantees across major versions of supported dataframe libraries
What it is and what it does
Pandera is a data validation framework that lets you define schemas for dataframe-like objects and validate them at runtime. It supports multiple dataframe libraries and offers two ways to define schemas: an object-based API using DataFrameSchema and a class-based API using DataFrameModel with type annotations. You specify column types, constraints (like minimum/maximum values), and custom validation functions, then call validate() to check whether your data conforms.
The package is designed for data scientists, engineers, and analysts who want to make data pipelines more readable and catch data quality issues early. It integrates with pydantic for type validation and uses typeguard for runtime type checking. The framework has been in active development since 2018 and is maintained as an open-source project, with support for modern Python versions (3.10 through 3.14).
Use it for
- Validate incoming data before processing in a pipeline to catch upstream errors early
- Define and enforce data contracts between pipeline stages with executable schema definitions
- Test data quality in unit tests by asserting that fixtures conform to expected schemas
- Document expected column types and constraints as executable code in data transformation functions
- Catch type mismatches and constraint violations across multiple dataframe libraries with one API
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Pandera is actively maintained, has no known vulnerabilities, installs with low friction, and offers a mature, permissive MIT license. It solves a real problem—catching data quality issues early in pipelines—and supports multiple dataframe libraries. The only prerequisite is Python 3.10 or later.
Install
pandera on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance with a recent release 46 days ago and ongoing commits. Depends on common, stable packages (packaging, pydantic, typeguard, typing_extensions, typing_inspect).
Requires Python 3.10 or later. A supported dataframe library must be installed separately via extras.
License in practice
MIT License permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal obligations.
Quickstart
pip install 'pandera[pandas]'
import pandera.pandas as pa
schema = pa.DataFrameSchema({
"column1": pa.Column(int, pa.Check.ge(0)),
"column2": pa.Column(float, pa.Check.lt(10)),
})
schema.validate(df)Verify before relying
- Whether the package handles all edge cases in complex, nested validation scenarios
- Performance characteristics when validating very large dataframes
- Compatibility guarantees across major versions of supported dataframe libraries
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagespackagingpydantictypeguardtyping_extensionstyping_inspect |
| Maintenance | Actively maintained 46 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 9,225,733 / month, #1,553 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: pandera-0.32.1-py3-none-any.whl
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See also dagster-pandera · schema · cuallee · patito · pandas-schema · pyspark-pandas · quinn · datacompy · geopandas · bigframes