---
id: rdata
version: "1.1.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# rdata — Read R datasets from Python.

License: permissive · Maintenance: active · Downloads: 136.3K/mo

## What it is and what it does

rdata is a pure Python library that parses and converts R dataset files (.rda and .rds formats) into native Python objects—primarily pandas DataFrames, numpy arrays, and xarray structures—without requiring R or any R runtime. It also reverses the process, allowing you to write Python data back to R-compatible formats. The library separates parsing (which is deterministic) from conversion (which is customizable), so you can define how specific R classes map to your Python types or use sensible defaults that preserve information.

The package is designed for data scientists and engineers who need to exchange datasets with R workflows, integrate R data into Python pipelines, or work in environments where R cannot be installed (such as web applications using Pyodide). It supports all Python versions from 3.9 onward and depends only on numpy, pandas, xarray, and typing_extensions.

Use it for:

- Load R datasets from repositories or collaborators into pandas DataFrames for analysis in Python.
- Export Python data structures to .rda or .rds files for use in R scripts or sharing with R users.
- Integrate R data into web applications or serverless environments where R runtime is unavailable.
- Define custom conversion rules for R classes that don't map directly to standard Python types.
- Build data pipelines that consume R outputs and produce R inputs without spawning external R processes.

## Worth the install?

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

Reads and writes R dataset files (.rda and .rds formats) in pure Python without requiring R or its libraries installed.

Yes. The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and solves a specific problem—R↔Python data interchange—without introducing R as a system dependency. Install it if you need to read or write R datasets; skip it if you work exclusively within Python or have R already available and prefer rpy2.

## Install

pip install rdata
uv add rdata
poetry add rdata

## Installing rdata

Before you install: Low friction installation with a pure Python implementation and no R dependencies. Active maintenance with a recent release 64 days ago and ongoing repository activity.

License in practice: MIT license permits use in any project type—commercial, closed-source, or permissive—without GPL constraints that would apply if R libraries were required.

Quickstart:

pip install rdata

import rdata

# Read an R dataset
converted = rdata.read_rds("data.rds")
print(converted)

# Write a pandas DataFrame to R format
import pandas as pd
df = pd.DataFrame({"col": [1, 2, 3]})
rdata.write_rds("output.rds", df)

Requires Python 3.11 or later.

Verify before relying:

- Whether custom R class conversion covers the full range of R object types encountered in practice.
- Performance characteristics when handling large or deeply nested R data structures.
- Completeness of round-trip fidelity (R → Python → R) for complex objects.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 136.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

read R datasets python, rda rds file parser, R data format conversion, import R data files, R object serialization python, cross-language data interchange, R compatibility layer, data-interchange, r-integration, file-format

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