fastparquet
Python support for Parquet file format
Decision gist · record as of 2026-08-14
Yes, if you are on pandas 2.x and need a lightweight Parquet reader/writer without pyarrow. No, if you are adopting pandas 3.0 or later—use pyarrow instead, which pandas now depends on explicitly. The project is being retired and will receive no further development; evaluate your pandas version and long-term maintenance needs before committing to it.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy and pandas installed; building from source requires a C compiler toolchain and cython >= 0.29.23.
- Medium install friction due to compiled wheels across multiple platforms and Python versions (3.10–3.14).
- Active maintenance with recent releases, though the project is being retired as of March 2026 following pandas 3.0 changes and pandas' explicit dependency on pyarrow.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 is permissive; you may use, modify, and distribute fastparquet freely in commercial and private projects, provided you include a copy of the license and note any changes.
last release 2026-05-15 (91 days) · last repo commit 2026-06-29 · 901 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 15,251,519 downloads/mo, #1,192 on PyPI
Alternatives
Verify before relying
from fastparquet import ParquetFile, write
import pandas as pd
# Read
pf = ParquetFile('myfile.parq')
df = pf.to_pandas()
# Write
write('outfile.parq', df)- Whether the project retirement announced in March 2026 affects long-term support or security patches for pandas 2.x users.
- Performance characteristics compared to pyarrow for specific workloads or file sizes.
- Full list of supported compression algorithms and any optional dependencies beyond those documented.
What it is and what it does
fastparquet is a Python implementation of the Apache Parquet columnar file format, designed to integrate with pandas and numpy for big data workflows. It reads Parquet files into pandas DataFrames and writes DataFrames back to Parquet, supporting features like column selection, categorical encoding, compression, and row-group partitioning. The library has been used implicitly by Dask, Pandas, and intake-parquet.
The package is now in maintenance mode: as of March 2026, the project is being retired because pandas 3.0 now depends explicitly on pyarrow, eliminating the original rationale for fastparquet's existence. Continued use is anticipated only for those still on pandas 2.x. Installation requires numpy, pandas, and cramjam; optional compression support includes gzip, snappy, brotli, lz4, and zstandard by default.
Use it for
- Read Parquet files produced by Spark or Hive into pandas DataFrames for local analysis.
- Write pandas DataFrames to Parquet format for efficient columnar storage and downstream consumption by big data tools.
- Load specific columns or apply categorical encoding when reading large Parquet files to reduce memory footprint.
- Partition data into row groups and apply compression when writing Parquet files for distributed processing.
- Integrate Parquet I/O into existing pandas-based data pipelines without introducing a pyarrow dependency.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are on pandas 2.x and need a lightweight Parquet reader/writer without pyarrow.
No, if you are adopting pandas 3.0 or later—use pyarrow instead, which pandas now depends on explicitly. The project is being retired and will receive no further development; evaluate your pandas version and long-term maintenance needs before committing to it.
Install
fastparquet on PyPI
Before you install
Medium install friction due to compiled wheels across multiple platforms and Python versions (3.10–3.14). Active maintenance with recent releases, though the project is being retired as of March 2026 following pandas 3.0 changes and pandas' explicit dependency on pyarrow.
Requires numpy and pandas installed; building from source requires a C compiler toolchain and cython >= 0.29.23.
License in practice
Apache License 2.0 is permissive; you may use, modify, and distribute fastparquet freely in commercial and private projects, provided you include a copy of the license and note any changes.
Quickstart
from fastparquet import ParquetFile, write
import pandas as pd
# Read
pf = ParquetFile('myfile.parq')
df = pf.to_pandas()
# Write
write('outfile.parq', df)Verify before relying
- Whether the project retirement announced in March 2026 affects long-term support or security patches for pandas 2.x users.
- Performance characteristics compared to pyarrow for specific workloads or file sizes.
- Full list of supported compression algorithms and any optional dependencies beyond those documented.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagespandasnumpycramjamfsspecpackaging |
| Maintenance | Actively maintained 91 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 15,251,519 / month, #1,192 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 :: System AdministratorsLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPython |
Evidence: fastparquet-2026.5.0-cp310-cp310-macosx_10_9_universal2.whl; fastparquet-2026.5.0-cp310-cp310-macosx_11_0_arm64.whl; fastparquet-2026.5.0-cp310-cp310-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl; fastparquet-2026.5.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; fastparquet-2026.5.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; fastparquet-2026.5.0-cp310-cp310-musllinux_1_2_i686.whl; fastparquet-2026.5.0-cp310-cp310-musllinux_1_2_x86_64.whl; fastparquet-2026.5.0-cp310-cp310-win32.whl; fastparquet-2026.5.0-cp310-cp310-win_amd64.whl; fastparquet-2026.5.0-cp311-cp311-macosx_10_9_universal2.whl; fastparquet-2026.5.0-cp311-cp311-macosx_11_0_arm64.whl; fastparquet-2026.5.0-cp311-cp311-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl; fastparquet-2026.5.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; fastparquet-2026.5.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; fastparquet-2026.5.0-cp311-cp311-musllinux_1_2_i686.whl; fastparquet-2026.5.0-cp311-cp311-musllinux_1_2_x86_64.whl; fastparquet-2026.5.0-cp311-cp311-win32.whl; fastparquet-2026.5.0-cp311-cp311-win_amd64.whl; fastparquet-2026.5.0-cp312-cp312-macosx_10_13_universal2.whl; fastparquet-2026.5.0-cp312-cp312-macosx_11_0_arm64.whl
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See also hepconvert · parquet · parquet-metadata · dask-geopandas · feather-format · pyarrowfs-adlgen2 · pyreadr · datafusion · pgzip · delta-sharing