polars-readstat 0.11.1

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

polars_readstat

Polars IO plugin to read SAS (sas7bdat), Stata (dta), and SPSS (sav) files. It's competitive with pandas and pyreadstat for Stata and SPSS files, and can be much faster for SAS files - 50% faster for full files and 20x faster for reading a small subset of columns from a wide file. See some basic benchmarks below.

Install

pip install polars-readstat

Basic usage

import polars as pl
from polars_readstat import scan_readstat
df_stata = scan_readstat("/path/file.dta")
df_sas = scan_readstat("/path/file.sas7bdat")
df_spss = scan_readstat("/path/file.sav")


# Then do any normal thing you'd do in polars
df_stata = (df_stata.head(1000)
                    .filter(pl.col("a") > 0.5)
                    .select(["b","c"]))
...
df_stata = df_stata.collect()


# For sas7bdat files, there are two "engines"
#   1. readstat:  generally, but not always slower
#   2. cpp:       faster, the default, used to be more likely to have errors, but not the case anymore in my testing
#                 If it's going to throw an error, it usually does so quickly
df = scan_readstat("/path/file.sas7bdat",
                   engine="readstat")

df = scan_readstat("/path/file.sas7bdat",
                   engine="cpp")


# If you want to get the metadata, use the ScanReadstat python class:

from polars_readstat import ScanReadstat
reader = ScanReadstat(path=path)  # You can pass engine for sas7bdat files, as above

# Python dictionary with metadata information, including value labels
metadata = reader.metadata  

# Polars schema
schema = reader.schema  

# LazyFrame
df = reader.df  

# You can also use memory mapping to speed up reads with the readstat engine
#   Some caveats, 
#     1) it can use a LOT of ram)
#     2) It doesn't really speed up reads for sas files (and I haven't tested SPSS)
df = scan_readstat("/path/file.dta",
                   use_mmap=True) # default is False 
                   
# Then do any normal thing you'd do in polars

# That's it

:key: Dependencies

This plugin calls rust bindings to load files in chunks, it is only possible due to the following excellent projects:

• The ReadStat C library developed by Evan Miller
• The readstat-rs rust bindings to that ReadStat C library developed by Curtis Alexander
• The cpp-sas7bdat C++ library by Olivia Quinet
• Polars (obviously) developed by Ritchie Vink and many others

This takes a modified version of the readstat-rs bindings to readstat's C functions. My modifications:

• Swapped out the now unmaintained arrow2 crate for Polars
• Removed the CLI and write capabilities
• Added read support for Stata (dta) and SPSS (sav) files
• Removed some intermediate steps that resulted in processing full vectors of data repeatedly before creating polars dataframe
• Modified the parsing of SAS and Stata data formats (particularly dates and datetimes) to provide a better (?... hopefully) mapping to polars data types
• Modified readstat to read from a shared memory map to improve multithreaded performance (speeds up Stata file reads in my test, but not SAS)

Because of concerns about the performance of readstat reading large SAS files, I have also started integrating a different engine from the cpp-sas7bdat library. To do so, I have modified it as follows:

• Added an Arrow sink to read the sas7bdat file to Arrow arrays using the c++ Arrow library
• Updated the package build to use UV instead of pip for loading conan to manage the C++ packages
• Added rust ffi bindings to the C++ code to zero-copy pass the Arrow array to rust and polars

Other notable features

• Multithreaded using the number of pl.thread_pool_size
• Currently comparable to pandas and pyreadstat or faster (see benchmarks below)

Pending tasks:

• Write support for Stata (dta) and SPSS (sav) files. Readstat itself cannot write SAS (sas7bdat) files that SAS can read, and I'm not fool enough to try to figure that out. Also, any workflow that involves SAS should be one-way (SAS->something else) so you should only read SAS files, never write them.
• Unit tests on the data sets used by pyreadstat to confirm that my output matches theirs

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).

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 engine="cpp" (the default option)	1.31 (1.6×)	0.09 (22.9×)	1.56 (1.9×)	0.09 (23.2×)
polars_readstat engine="readstat"	5.27 (0.4×)	0.69 (3.0×)	7.62 (0.4×)	0.79 (2.6×)
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 engine="readstat" (the only option)	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×)

Details

This was run on my computer, with the following specs (and reading the data from an external SSD):
CPU: AMD Ryzen 7 8845HS w/ Radeon 780M Graphics
Cores: 16
RAM: 14Gi
OS: Linux Mint 22
Last Run: August 31, 2025 Version: 0.7 (with mmap in readstat)

This is not intended to be a scientific benchmark, just a test of loading realistic files. The Stata and SAS files used are different. One is tall and narrow (lots of rows, few columns) and the other is shorter and wider (fewer rows, many more columns).

All reported times are in seconds using python's time.time() (I know...).

File details:

• Stata (dta)
  • 2000 5% sample decennial census file from ipums
  • Schema: Schema({'index': Int32, 'YEAR': Int32, 'SAMPLE': Int32, 'SERIAL': Int32, 'HHWT': Float64, 'CLUSTER': Float64, 'STRATA': Int32, 'GQ': Int32, 'PERNUM': Int32, 'PERWT': Float64, 'RELATE': Int32, 'RELATED': Int32, 'SEX': Int32, 'AGE': Int32, 'MARST': Int32, 'BIRTHYR': Int32})
  • Rows: 10,000,000 (limited to 10 million to fit in laptop memory)
• SAS (sas7bdat)
  • American Community Survey 5-year file for Illinois, available here as the sas_pil.zip file.
  • Schema: Schema({'RT': String, 'SERIALNO': String, 'DIVISION': String, 'SPORDER': Float64, 'PUMA': String, 'REGION': String, 'STATE': String, 'ADJINC': String, 'PWGTP': Float64, 'AGEP': Float64, 'CIT': String, 'CITWP': Float64, 'COW': String, 'DDRS': String, 'DEAR': String, 'DEYE': String, 'DOUT': String, 'DPHY': String, 'DRAT': String, 'DRATX': String, 'DREM': String, 'ENG': String, 'FER': String, 'GCL': String, 'GCM': String, 'GCR': String, 'HINS1': String, 'HINS2': String, 'HINS3': String, 'HINS4': String, 'HINS5': String, 'HINS6': String, 'HINS7': String, 'INTP': Float64, 'JWMNP': Float64, 'JWRIP': Float64, 'JWTRNS': String, 'LANX': String, 'MAR': String, 'MARHD': String, 'MARHM': String, 'MARHT': String, 'MARHW': String, 'MARHYP': Float64, 'MIG': String, 'MIL': String, 'MLPA': String, 'MLPB': String, 'MLPCD': String, 'MLPE': String, 'MLPFG': String, 'MLPH': String, 'MLPIK': String, 'MLPJ': String, 'NWAB': String, 'NWAV': String, 'NWLA': String, 'NWLK': String, 'NWRE': String, 'OIP': Float64, 'PAP': Float64, 'RELSHIPP': String, 'RETP': Float64, 'SCH': String, 'SCHG': String, 'SCHL': String, 'SEMP': Float64, 'SEX': String, 'SSIP': Float64, 'SSP': Float64, 'WAGP': Float64, 'WKHP': Float64, 'WKL': String, 'WKWN': Float64, 'WRK': String, 'YOEP': Float64, 'ANC': String, 'ANC1P': String, 'ANC2P': String, 'DECADE': String, 'DIS': String, 'DRIVESP': String, 'ESP': String, 'ESR': String, 'FOD1P': String, 'FOD2P': String, 'HICOV': String, 'HISP': String, 'INDP': String, 'JWAP': String, 'JWDP': String, 'LANP': String, 'MIGPUMA': String, 'MIGSP': String, 'MSP': String, 'NAICSP': String, 'NATIVITY': String, 'NOP': String, 'OC': String, 'OCCP': String, 'PAOC': String, 'PERNP': Float64, 'PINCP': Float64, 'POBP': String, 'POVPIP': Float64, 'POWPUMA': String, 'POWSP': String, 'PRIVCOV': String, 'PUBCOV': String, 'QTRBIR': String, 'RAC1P': String, 'RAC2P19': String, 'RAC2P23': String, 'RAC3P': String, 'RACAIAN': String, 'RACASN': String, 'RACBLK': String, 'RACNH': String, 'RACNUM': String, 'RACPI': String, 'RACSOR': String, 'RACWHT': String, 'RC': String, 'SCIENGP': String, 'SCIENGRLP': String, 'SFN': String, 'SFR': String, 'SOCP': String, 'VPS': String, 'WAOB': String, 'FAGEP': String, 'FANCP': String, 'FCITP': String, 'FCITWP': String, 'FCOWP': String, 'FDDRSP': String, 'FDEARP': String, 'FDEYEP': String, 'FDISP': String, 'FDOUTP': String, 'FDPHYP': String, 'FDRATP': String, 'FDRATXP': String, 'FDREMP': String, 'FENGP': String, 'FESRP': String, 'FFERP': String, 'FFODP': String, 'FGCLP': String, 'FGCMP': String, 'FGCRP': String, 'FHICOVP': String, 'FHINS1P': String, 'FHINS2P': String, 'FHINS3C': String, 'FHINS3P': String, 'FHINS4C': String, 'FHINS4P': String, 'FHINS5C': String, 'FHINS5P': String, 'FHINS6P': String, 'FHINS7P': String, 'FHISP': String, 'FINDP': String, 'FINTP': String, 'FJWDP': String, 'FJWMNP': String, 'FJWRIP': String, 'FJWTRNSP': String, 'FLANP': String, 'FLANXP': String, 'FMARP': String, 'FMARHDP': String, 'FMARHMP': String, 'FMARHTP': String, 'FMARHWP': String, 'FMARHYP': String, 'FMIGP': String, 'FMIGSP': String, 'FMILPP': String, 'FMILSP': String, 'FOCCP': String, 'FOIP': String, 'FPAP': String, 'FPERNP': String, 'FPINCP': String, 'FPOBP': String, 'FPOWSP': String, 'FPRIVCOVP': String, 'FPUBCOVP': String, 'FRACP': String, 'FRELSHIPP': String, 'FRETP': String, 'FSCHGP': String, 'FSCHLP': String, 'FSCHP': String, 'FSEMP': String, 'FSEXP': String, 'FSSIP': String, 'FSSP': String, 'FWAGP': String, 'FWKHP': String, 'FWKLP': String, 'FWKWNP': String, 'FWRKP': String, 'FYOEP': String, 'PWGTP1': Float64, 'PWGTP2': Float64, 'PWGTP3': Float64, 'PWGTP4': Float64, 'PWGTP5': Float64, 'PWGTP6': Float64, 'PWGTP7': Float64, 'PWGTP8': Float64, 'PWGTP9': Float64, 'PWGTP10': Float64, 'PWGTP11': Float64, 'PWGTP12': Float64, 'PWGTP13': Float64, 'PWGTP14': Float64, 'PWGTP15': Float64, 'PWGTP16': Float64, 'PWGTP17': Float64, 'PWGTP18': Float64, 'PWGTP19': Float64, 'PWGTP20': Float64, 'PWGTP21': Float64, 'PWGTP22': Float64, 'PWGTP23': Float64, 'PWGTP24': Float64, 'PWGTP25': Float64, 'PWGTP26': Float64, 'PWGTP27': Float64, 'PWGTP28': Float64, 'PWGTP29': Float64, 'PWGTP30': Float64, 'PWGTP31': Float64, 'PWGTP32': Float64, 'PWGTP33': Float64, 'PWGTP34': Float64, 'PWGTP35': Float64, 'PWGTP36': Float64, 'PWGTP37': Float64, 'PWGTP38': Float64, 'PWGTP39': Float64, 'PWGTP40': Float64, 'PWGTP41': Float64, 'PWGTP42': Float64, 'PWGTP43': Float64, 'PWGTP44': Float64, 'PWGTP45': Float64, 'PWGTP46': Float64, 'PWGTP47': Float64, 'PWGTP48': Float64, 'PWGTP49': Float64, 'PWGTP50': Float64, 'PWGTP51': Float64, 'PWGTP52': Float64, 'PWGTP53': Float64, 'PWGTP54': Float64, 'PWGTP55': Float64, 'PWGTP56': Float64, 'PWGTP57': Float64, 'PWGTP58': Float64, 'PWGTP59': Float64, 'PWGTP60': Float64, 'PWGTP61': Float64, 'PWGTP62': Float64, 'PWGTP63': Float64, 'PWGTP64': Float64, 'PWGTP65': Float64, 'PWGTP66': Float64, 'PWGTP67': Float64, 'PWGTP68': Float64, 'PWGTP69': Float64, 'PWGTP70': Float64, 'PWGTP71': Float64, 'PWGTP72': Float64, 'PWGTP73': Float64, 'PWGTP74': Float64, 'PWGTP75': Float64, 'PWGTP76': Float64, 'PWGTP77': Float64, 'PWGTP78': Float64, 'PWGTP79': Float64, 'PWGTP80': Float64})
  • Rows: 623,757