polars-readstat 0.12.2

Read SAS (sas7bdat), Stata (dta), and SPSS (sav) files with polars

polars_readstat

Polars plugin for SAS (.sas7bdat), Stata (.dta), and SPSS (.sav/.zsav) files.

The Python package wraps the Rust core in polars_readstat_rs and exposes a simple Polars-first API. I have tried to make sure there are no errors or regressions in this release (tested against 178 test files from pandas, pyreadstat, etc.). If I missed something, you can find info on the readme for the prior version and install v0.11.1 from pypi.

Why use this?

• In project benchmarks, the new Rust-backed engine is typically faster than pandas/pyreadstat on large SAS/Stata files, especially for subset/filter workloads.
• It avoids the older C/C++ toolchain complexity and ships as standard Python wheels.
• API is Polars-first (scan_readstat, read_readstat, write_readstat).

Install

pip install polars-readstat

Core API

1) Lazy scan

import polars as pl
from polars_readstat import scan_readstat

lf = scan_readstat("/path/file.sas7bdat", preserve_order=True)
df = lf.select(["SERIALNO", "AGEP"]).filter(pl.col("AGEP") >= 18).collect()

2) Eager read

from polars_readstat import read_readstat

df = read_readstat("/path/file.dta")

3) Metadata + schema

from polars_readstat import ScanReadstat

reader = ScanReadstat(path="/path/file.sav")
schema = reader.schema
metadata = reader.metadata

4) Write (Stata/SPSS)

from polars_readstat import write_readstat

write_readstat(df, "/path/out.dta", threads=8)
write_readstat(df, "/path/out.sav")

write_readstat supports Stata (dta) and SPSS (sav). SAS writing is not supported.

Tests run

We’ve tried to test this thoroughly:

• Cross-library comparisons on the pyreadstat and pandas test data, checking results against polars-readstat==0.11.1, pyreadstat, and pandas.
• Stata/SPSS read/write roundtrip tests.
• Large-file read/write benchmark runs on real-world data (results below).

If you want to run the same checks locally, helper scripts and tests are in scripts/ and tests/.

Benchmark

For each file, I compared 4 different scenarios: 1) load the full file, 2) load a subset of columns (Subset:True), 3) filter to a subet of rows (Filter: True), 4) load a subset of columns and filter to a subset of rows (Subset:True, Filter: True).

Benchmark context:

• Machine: AMD Ryzen 7 8845HS (16 cores), 14 GiB RAM, Linux Mint 22
• Storage: external SSD
• Last run: August 31, 2025
• Version tested: polars-readstat 0.12 (new Rust engine) against polars-readstat 0.11.1 (prior C++ and C engines) and pandas and pyreadstat
• Method: wall-clock timings via Python time.time()

Compared to Pandas and Pyreadstat (using read_file_multiprocessing for parallel processing in Pyreadstat)

SAS

all times in seconds (speedup relative to pandas in parenthesis below each)

Library	Full File	Subset: True	Filter: True	Subset: True, Filter: True
polars_readstat New rust engine	0.90 (1.7×)	0.07 (29.4×)	1.23 (2.5×)	0.07 (29.9×)
polars_readstat engine="cpp" (fastest for 0.11.1)	1.31 (1.6×)	0.09 (22.9×)	1.56 (1.9×)	0.09 (23.2×)
pandas	2.07	2.06	3.03	2.09
pyreadstat	10.75 (0.2×)	0.46 (4.5×)	11.93 (0.3×)	0.50 (4.2×)

Stata

all times in seconds (speedup relative to pandas in parenthesis below each)

Library	Full File	Subset: True	Filter: True	Subset: True, Filter: True
polars_readstat New rust engine	0.17 (6.7×)	0.12 (9.8×)	0.24 (4.1×)	0.11 (8.7×)
polars_readstat engine="readstat" (the only option for 0.11.1)	1.80 (0.6×)	0.27 (4.4×)	1.31 (0.8×)	0.29 (3.3×)
pandas	1.14	1.18	0.99	0.96
pyreadstat	7.46 (0.2×)	2.18 (0.5×)	7.66 (0.1×)	2.24 (0.4×)

Detailed benchmark notes and dataset descriptions are in BENCHMARKS.md.