readstat-arrow

Read and write SPSS (.sav) and Stata (.dta) files as Apache Arrow tables, using the excellent ReadStat C library:

import readstat_arrow

table, metadata = readstat_arrow.read_sav("survey.sav")  # -> (pyarrow.Table, Metadata)
readstat_arrow.write_dta("survey.dta", table, metadata)  # the same pair back out, as Stata

Motivation

There are several options in this space. readstat-arrow relies on the ReadStat library for reading and writing since it is mature and battle-tested, and on Apache Arrow for in-memory representation in Python since it provides zero-copy interoperability with libraries like Pandas, Polars, and DuckDB.

Much of the inspiration for readstat-arrow comes from the great pyreadstat library.

Status

Early development. The library works and is tested, but the API is not stable: names, signatures and return shapes may change in any release, without a deprecation period. Pin an exact version if you depend on it. There is currently support for reading and writing SPSS and Stata files. More file formats can be added on request.

Examples

Reading files

import readstat_arrow

# SPSS
table, metadata = readstat_arrow.read_sav("survey.sav")
# Stata:
table, metadata = readstat_arrow.read_dta("survey.dta")

Every read_* function returns the same pair: a pyarrow.Table, and a Metadata object.

Metadata is a set of mappings from variable name to one attribute — variable_labels, value_labels, formats, storage_widths, display_widths, measures, alignments, missing_values — plus the file-level file_label, notes and multiple_response_sets. Which columns exist, and in what order, is the Arrow schema's business, not the metadata's. Nothing is required: a name absent from a mapping simply declares nothing.

Metadata(
    file_label="2026 satisfaction survey",
    variable_labels={
        "id": "Respondent id",
        "q1": "How satisfied are you ...?",
        "q2": "How many hours ...?",
    },
    value_labels={
        "q1": [
            {"value": 1, "label": "Very unsatisfied"},
            {"value": 5, "label": "Very satisfied"},
            {"value": 9, "label": "No answer"},
        ]
    },
    formats={"q1": "F1.0", "q2": "F2.0"},
    measures={"q1": "ordinal", "q2": "scale"},
)

Use with Pandas or Polars

import readstat_arrow

table, metadata = readstat_arrow.read_sav("survey.sav")

# pandas:
df = table.to_pandas()
# polars:
import polars as pl
df = pl.from_arrow(table)

Writing files:

import pyarrow as pa

import readstat_arrow
from readstat_arrow import Metadata

table = pa.table({"id": [1, 2, 3], "q1": [1, 5, 4], "q2": [10, 11, 2]})

metadata = Metadata(
    file_label="2026 satisfaction survey",
    variable_labels={
        "id": "Respondent id",
        "q1": "How satisfied are you ...?",
        "q2": "How many hours ...?",
    },
    value_labels={
        "q1": [
            {"value": 1, "label": "Very unsatisfied"},
            {"value": 5, "label": "Very satisfied"},
        ]
    },
    formats={"q1": "F1.0", "q2": "F2.0"},
    measures={"q1": "ordinal", "q2": "scale"},
)

# SPSS:
readstat_arrow.write_sav("survey.sav", table, metadata)
# Stata:
readstat_arrow.write_dta("survey.dta", table, metadata)

Writing in batches

To avoid holding an entire table in memory at once, it is possible to write files in batches

import pyarrow as pa
import readstat_arrow
from readstat_arrow import Metadata

def my_data_source():
    # Let's pretend this comes from some external source
    for _ in range(10):
        yield pa.table(
            {
                "q1": pa.array([1, 2, 3, 4, 5], pa.int8()),
                "q2": pa.array([10, 20, 30, 40, 50], pa.int32()),
                "q3": pa.array([100, 200, 300, 400, 500], pa.int32()),
            }
        )

# we need to know these up front:
schema = pa.schema({"q1": pa.int8(), "q2": pa.int32(), "q3": pa.int32()})
num_rows = 50
metadata = Metadata()

# SPSS:
with readstat_arrow.SavWriter("survey.sav", schema, num_rows, metadata) as writer:
    for table in my_data_source():
        writer.write_table(table)
# Stata:
with readstat_arrow.DtaWriter("survey.dta", schema, num_rows, metadata) as writer:
    for table in my_data_source():
        writer.write_table(table)

Handling missing values

By default every kind of missing — system-missing, a Stata tagged missing, an SPSS value the file declares missing — reads as an Arrow null. With preserve_user_missing=True the user-level ones survive, in the way each format has of saying them.

Stata tags its missings .a to .z, so every numeric column becomes a struct<value, tag> to carry both:

import readstat_arrow

table, metadata = readstat_arrow.read_dta("panel.dta", preserve_user_missing=True)

table.schema.field("income").type  # -> struct<value: double, tag: dictionary<int8, string>>
table.column("income")[0].as_py()  # -> {"value": None, "tag": "a"}, Stata's .a
table.column("income")[1].as_py()  # -> {"value": 42.0, "tag": None}, a real number
table.column("income")[2].as_py()  # -> None, a plain .

SPSS instead declares ordinary values missing, so those values simply stay in the column — the type is unchanged, and Metadata.missing_values says which values were the missing ones:

import readstat_arrow

default, metadata = readstat_arrow.read_sav("survey.sav")
kept, _ = readstat_arrow.read_sav("survey.sav", preserve_user_missing=True)

metadata.missing_values["q1"]  # -> {"values": [9.0]}, declared by MISSING VALUES q1 (9)

default.column("q1")[0].as_py()  # -> None, the 9 collapsed to null
kept.column("q1")[0].as_py()  # -> 9.0, the declared missing value itself

System-missing is null either way, and the writers accept both shapes back.

An SPSS declaration takes one of two shapes in Metadata.missing_values:

Metadata(
    missing_values={
        # up to three discrete values: MISSING VALUES q1 (7, 8, 9)
        "q1": {"values": [7, 8, 9]},
        # an inclusive range: MISSING VALUES q2 (90 THRU 99)
        "q2": {"lo": 90, "hi": 99},
        # a range and one value beside it: MISSING VALUES q3 (LO THRU 0, 999)
        "q3": {"lo": float("-inf"), "hi": 0, "value": 999},
    }
)

-inf and inf are SPSS's LO and HI, and more than three discrete values is an error — SPSS itself allows no more.

Reading the metadata without the data

read_sav_metadata and read_dta_metadata stop before reading the actual data. What comes back is the schema a full read would have given, the row count, and the Metadata object.

import readstat_arrow

schema, num_rows, metadata = readstat_arrow.read_dta_metadata("panel.dta")

schema.names  # -> ["id", "year", "income", ...], the variables in file order
schema.field("income").type  # -> the type a full read would give that column
num_rows  # -> 4_000_000, from the header
metadata.variable_labels["income"]  # -> "Annual income, NOK"

The row count is None where the file does not record one — Stata files always do, some non-SPSS writers of .sav do not. The schema describes a preserve_user_missing=False read, so it does not show the struct<value, tag> columns that option gives a .dta.

Reading only part of a file

Use columns, row_offset and row_limit to read parts of a file:

import readstat_arrow

table, metadata = readstat_arrow.read_dta(
    "panel.dta",
    columns=["id", "income"],  # only these two; they come back in file order
    row_offset=1_000,  # skip the first 1_000 rows
    row_limit=1_000,  # then read at most 1_000
)

row_limit=0 means no limit, and the returned Metadata covers the columns that were read, not the whole file.

Narrowing types to reduce memory usage

In memory-constrained environments, it can be difficult to hold the whole table in memory at once — especially since the type a column is stored as is often wider than its values need. A .sav is the worst of it: every numeric column is a 64-bit double whatever it holds, so a even survey of one-digit codes costs 8 bytes a cell. A .dta has narrow types of its own — byte, int, long, float — but a variable is only as narrow as whoever wrote the file declared it.

scan_and_narrow_types=True reads each column at the width its values actually need instead:

import readstat_arrow

table, metadata = readstat_arrow.read_sav("big.sav", scan_and_narrow_types=True)

table.schema.field("q1").type  # -> DataType(int8), where the file says double

The file is parsed twice — once to measure the values, keeping none of them, then once to read them at the widths that fit — so the trade is time for memory.

Only a type that holds the column exactly is ever chosen: integer types when every value was a whole number, float32 when every value round-trips through it, else float64. The ladder is int8, int16, int32, int64, float32, float64 — int64 saves nothing over the stored double, but it is the cleaner type for a column of whole numbers. Strings are untouched.

Reading in batches

Read a fixed number of rows at a time and hand each one over as a pyarrow.RecordBatch,

import pyarrow.parquet as pq
import readstat_arrow

reader = readstat_arrow.open_sav("big.sav")
with pq.ParquetWriter("big.parquet", reader.schema) as writer:
    reader.read_batches(writer.write_batch)

open_sav and open_dta return a SavStreamingReader / DtaStreamingReader. Opening reads the metadata and nothing else, so schema, num_rows and metadata are all there before the data itself is read:

reader = readstat_arrow.open_sav("panel.sav")
reader.schema  # the schema every batch has
reader.num_rows  # rows the header declares, or None
reader.metadata.variable_labels["income"]

read_batches(callback) then reads the file, calling callback with each batch and returning the rows read.

The writers in readstat-arrow take a batch at a time too, so converting between the two formats can be done on the fly:

reader = readstat_arrow.open_sav("panel.sav")
with readstat_arrow.DtaWriter(
    "survey.dta", reader.schema, reader.num_rows, reader.metadata
) as writer:
    reader.read_batches(writer.write_batch)

A reader takes the same arguments the matching read_* takes: columns, row_offset, row_limit, encoding, preserve_user_missing, and scan_and_narrow_types.

Making a pyarrow.RecordBatchReader

read_batches pushes: it drives the parse and calls you. Some consumers want to pull instead — DuckDB, pyarrow.dataset.write_dataset, anything that takes a pyarrow.RecordBatchReader. Turning one around into the other needs a thread, and can be done like this:

import queue
import threading

import pyarrow as pa


def record_batch_reader(reader, *, batch_rows=65_536, ahead=2):
    """A pyarrow.RecordBatchReader over a readstat-arrow streaming reader."""
    queued: queue.Queue = queue.Queue(maxsize=ahead)
    done = object()

    def run():
        try:
            reader.read_batches(queued.put, batch_rows=batch_rows)
        except BaseException as exc:  # comes back out of the consumer
            queued.put(exc)
        else:
            queued.put(done)

    threading.Thread(target=run, daemon=True).start()

    def batches():
        while True:
            item = queued.get()
            if item is done:
                return
            if isinstance(item, BaseException):
                raise item
            yield item

    return pa.RecordBatchReader.from_batches(reader.schema, batches())

maxsize is the backpressure: the parse runs at most ahead batches in front of whoever is reading and then waits, so the file is never held. What comes back is an ordinary pyarrow.RecordBatchReader, which DuckDB will query in place:

import duckdb
import readstat_arrow

survey = record_batch_reader(readstat_arrow.open_sav("big.sav"))
duckdb.sql("select region, avg(income) from survey group by region").show()

or pyarrow.dataset will write out partitioned:

import pyarrow.dataset as ds

ds.write_dataset(
    record_batch_reader(readstat_arrow.open_dta("panel.dta")),
    "panel/",
    format="parquet",
)

One thing to know: a consumer that stops reading part way leaves the worker thread parked on a full queue until the process ends. Read it to the end, or add a flag the callback checks if that matters.

Read from something other than a path

Every read_* function also takes a binary file object, so a file that arrives over the network or out of an archive never has to be written to disk first.

import io, zipfile
import readstat_arrow

# straight out of a zip archive, without extracting it
with zipfile.ZipFile("survey.zip") as archive, archive.open("survey.sav") as member:
    table, metadata = readstat_arrow.read_sav(member)

# or from bytes you already have in hand
table, metadata = readstat_arrow.read_sav(io.BytesIO(downloaded))

# an open file works too, and is left open where reading stopped
with open("survey.sav", "rb") as file:
    schema, num_rows, metadata = readstat_arrow.read_sav_metadata(file)

The file object must be seekable, and is read from wherever it currently is - so a .sav embedded in a larger stream can be read by seeking to its first byte. The writers have taken a file object all along.

Development

Requires uv and a C compiler.

git clone <repo-url>
cd readstat-arrow
uv sync            # builds the Cython extension into .venv
uv run coverage run -m pytest && uv run coverage report || uv run  coverage html
uv run ruff check . && uv run ruff format --check . && uv run mypy
uv run pre-commit install    # optional: run those same checks on every commit

uv run mypy checks src/ and tests/ in strict mode. The Cython sources in src/readstat_arrow/_cython/ are excluded — they are typed for Cython's C type system, which mypy cannot follow, and Cython checks them at compile time.

ReadStat is vendored as a git submodule at vendor/ReadStat; bump it with git submodule update --remote vendor/ReadStat.

uv sync rebuilds the extension whenever _cython/, setup.py or the ReadStat sources change (see [tool.uv] cache-keys in pyproject.toml).

Layout

pyproject.toml            project metadata, deps, tool config (uv/ruff/mypy/pytest)
setup.py                  Cython extension definition (compiles ReadStat in)
vendor/ReadStat/          git submodule
src/readstat_arrow/
  __init__.py             public API re-exports
  reader.py               read_*, read_*_metadata, open_* (streaming), table assembly, type narrowing
  writer.py               SavWriter / DtaWriter, write_* functions, type planning
  metadata.py             the Metadata dataclass and its per-variable mappings
  errors.py               ReadstatError, ReadstatWarning
  _formats.py             FileFormat literal type
  _dates.py               display-format -> temporal type conversion
  _cython/                everything Cython compiles (private)
    parser.py             pure-Python-mode Cython: ReadStat callbacks -> Arrow buffers
    writer.py             pure-Python-mode Cython: Arrow buffers -> readstat_insert_*
    readstat.pxd          C declarations for readstat.h
tests/                    pytest suite; sample files under tests/data/

Versioning

Releases use CalVer in the form YYYY.MM.DD.INC0 (e.g. 2026.9.1.0, then 2026.9.1.1 for a fix on the same day). There are no compatibility promises encoded in the number. The version is set once in pyproject.toml and exposed as readstat_arrow.__version__.

Licence

MIT. ReadStat is MIT-licensed; the sample files under tests/data/ come from pyreadstat (Apache 2.0) — see tests/data/README.md.