rdata
Read R datasets from Python.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- 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 · maintenance · safety
MIT (permissive) — MIT license permits use in any project type—commercial, closed-source, or permissive—without GPL constraints that would apply if R libraries were required.
last release 2026-06-11 (64 days) · last repo commit 2026-06-11 · 60 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 136,279 downloads/mo, #11,401 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on this page. Verify before relying on it.
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
rdata on PyPI
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.
Requires Python 3.11 or later.
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)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 the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpyxarraypandastyping_extensions |
| Maintenance | Actively maintained 64 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 136,279 / month, #11,401 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: File FormatsTopic :: Scientific/Engineering :: MathematicsTopic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: rdata-1.1.0-py3-none-any.whl
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See also pyreadr · ucimlrepo · tensorflow-datasets · pantab · ir-datasets · feather-format · tfds-nightly · gspread-dataframe · pandas-read-xml · dataframe-api-compat