---
id: dataframe-api-compat
version: "0.2.7"
license: unclear
license_treatment: permissive
maintenance: dormant
---

# dataframe-api-compat — Implementation of the DataFrame Standard for pandas and Polars

License: permissive · Maintenance: dormant · Downloads: 122.1K/mo

## What it is and what it does

Dataframe API Compat is a thin abstraction layer that sits between your code and dataframe libraries like pandas and Polars, allowing you to write dataframe operations using the Python Dataframe API Standard. Instead of learning library-specific syntax, you write against a common interface that any compliant dataframe library can implement. The package itself is intentionally minimal—under 50 kB, pure Python, with only packaging as a dependency—making it easy to add to any project without bloat.

The main use case is writing library-agnostic dataframe code: you can prototype with pandas, switch to Polars for performance, or support multiple backends without rewriting your logic. It's designed for teams or libraries that want to avoid lock-in to a single dataframe implementation while still using standard, predictable operations.

Use it for:

- Write dataframe transformation code that works with both pandas and Polars without conditional imports or adapter code.
- Build a library or tool that accepts dataframes from multiple sources and processes them with a single code path.
- Prototype analysis with pandas, then swap to Polars for production performance without refactoring your dataframe calls.
- Support multiple dataframe backends in a data pipeline without maintaining separate code branches for each.

## Worth the install?

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

Provides a lightweight compatibility layer that lets you write dataframe code once and run it against pandas, Polars, or any other library implementing the Python Dataframe API Standard.

Yes, if you need to write dataframe code that works across multiple libraries or want to avoid lock-in to a single implementation. The low install friction and permissive license make it a safe addition. However, maintenance is dormant (no commits since April 2024), so verify that the standard implementation covers your actual use cases and that the libraries you target are fully compliant before committing to it in production.

## Install

pip install dataframe-api-compat
uv add dataframe-api-compat
poetry add dataframe-api-compat

## Installing dataframe-api-compat

Before you install: Low friction: pure Python wheel under 50 kB with only packaging as a runtime dependency. Dormant maintenance since April 2024 (840 days), though the repository remains active and unarchived.

License in practice: MIT license (permissive) means you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart:

pip install dataframe-api-compat

import dataframe_api_compat
# Use standard DataFrame API methods across pandas, Polars, or other compliant libraries

Requires Python 3.8 or later; the underlying dataframe libraries (pandas, Polars, etc.) must implement the Python Dataframe API Standard.

Verify before relying:

- How complete is the standard implementation coverage across different dataframe libraries in practice.
- Whether dormant status (no commits since April 2024) signals incomplete standard support or stable maturity.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: dormant
- Downloads: 122.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

dataframe api standard compatibility, pandas polars interop layer, write once run anywhere dataframes, dataframe abstraction layer, cross-dataframe library support, dataframe api wrapper, unified dataframe interface, dataframe-abstraction, cross-library-compat

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