{ "result": { "2024.11.0": { "name": "fastparquet", "version": "2024.11.0", "metadata_version": "2.4", "summary": "Python support for Parquet file format", "home_page": "https://github.com/dask/fastparquet/", "author": "Martin Durant", "author_email": "mdurant@anaconda.com", "maintainer": "", "maintainer_email": "", "license": "Apache License 2.0", "description": "fastparquet\n===========\n\n.. image:: https://github.com/dask/fastparquet/actions/workflows/main.yaml/badge.svg\n :target: https://github.com/dask/fastparquet/actions/workflows/main.yaml\n\n.. image:: https://readthedocs.org/projects/fastparquet/badge/?version=latest\n :target: https://fastparquet.readthedocs.io/en/latest/\n\nfastparquet is a python implementation of the `parquet\nformat `_, aiming integrate\ninto python-based big data work-flows. It is used implicitly by\nthe projects Dask, Pandas and intake-parquet.\n\nWe offer a high degree of support for the features of the parquet format, and\nvery competitive performance, in a small install size and codebase.\n\nDetails of this project, how to use it and comparisons to other work can be found in the documentation_.\n\n.. _documentation: https://fastparquet.readthedocs.io\n\nRequirements\n------------\n\n(all development is against recent versions in the default anaconda channels\nand/or conda-forge)\n\nRequired:\n\n- numpy\n- pandas\n- cython >= 0.29.23 (if building from pyx files)\n- cramjam\n- fsspec\n\nSupported compression algorithms:\n\n- Available by default:\n\n - gzip\n - snappy\n - brotli\n - lz4\n - zstandard\n\n- Optionally supported\n \n - `lzo `_\n\n\nInstallation\n------------\n\nInstall using conda, to get the latest compiled version::\n\n conda install -c conda-forge fastparquet\n\nor install from PyPI::\n\n pip install fastparquet\n\nYou may wish to install numpy first, to help pip's resolver.\nThis may install an appropriate wheel, or compile from source. For the latter,\nyou will need a suitable C compiler toolchain on your system.\n\nYou can also install latest version from github::\n\n pip install git+https://github.com/dask/fastparquet\n\nin which case you should also have ``cython`` to be able to rebuild the C files.\n\nUsage\n-----\n\nPlease refer to the documentation_.\n\n*Reading*\n\n.. code-block:: python\n\n from fastparquet import ParquetFile\n pf = ParquetFile('myfile.parq')\n df = pf.to_pandas()\n df2 = pf.to_pandas(['col1', 'col2'], categories=['col1'])\n\nYou may specify which columns to load, which of those to keep as categoricals\n(if the data uses dictionary encoding). The file-path can be a single file,\na metadata file pointing to other data files, or a directory (tree) containing\ndata files. The latter is what is typically output by hive/spark.\n\n*Writing*\n\n.. code-block:: python\n\n from fastparquet import write\n write('outfile.parq', df)\n write('outfile2.parq', df, row_group_offsets=[0, 10000, 20000],\n compression='GZIP', file_scheme='hive')\n\nThe default is to produce a single output file with a single row-group\n(i.e., logical segment) and no compression. At the moment, only simple\ndata-types and plain encoding are supported, so expect performance to be\nsimilar to *numpy.savez*.\n\nHistory\n-------\n\nThis project forked in October 2016 from `parquet-python`_, which was not designed\nfor vectorised loading of big data or parallel access.\n\n.. _parquet-python: https://github.com/jcrobak/parquet-python\n\n", "keywords": "", "platform": [], "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: System Administrators", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Programming Language :: Python :: Implementation :: CPython", "Environment :: MetaData :: IBM Python Ecosystem" ], "download_url": "", "supported_platform": [], "comment": "", "provides": [], "requires": [], "obsoletes": [], "project_urls": [], "provides_dist": [], "obsoletes_dist": [], "requires_dist": [ "pandas>=1.5.0", "numpy", "cramjam>=2.3", "fsspec", "packaging", "python-lzo; extra == \"lzo\"" ], "requires_external": [], "requires_python": ">=3.9", "description_content_type": "", "provides_extras": [ "lzo" ], "dynamic": [ "author", "author-email", "classifier", "description", "home-page", "license", "license-file", "provides-extra", "requires-dist", "requires-python", "summary" ], "license_expression": "", "license_file": [ "LICENSE" ], "+links": [ { "rel": "releasefile", "hash_spec": "sha256=da1c40f2a3d1758e76f3aed8449477dd7f50eb711cecbae53dfe35809f3c5dcd", "hashes": { "sha256": "da1c40f2a3d1758e76f3aed8449477dd7f50eb711cecbae53dfe35809f3c5dcd" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/da1/c40f2a3d1758e/fastparquet-2024.11.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", "log": [ { "what": "overwrite", "who": null, "when": [ 2026, 8, 20, 10, 35, 35 ], "count": 1 }, { "what": "upload", "who": "ppc64le", "when": [ 2026, 8, 20, 10, 35, 35 ], "dst": "ppc64le/linux" } ] }, { "rel": "releasefile", "hash_spec": "sha256=5cbd95249ea1991b1db5926d3dfc220150eac335e78dfbca1c919b4b3651963a", "hashes": { "sha256": "5cbd95249ea1991b1db5926d3dfc220150eac335e78dfbca1c919b4b3651963a" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/5cb/d95249ea1991b/fastparquet-2024.11.0-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", "log": [ { "what": "overwrite", "who": null, "when": [ 2026, 8, 20, 10, 35, 35 ], "count": 1 }, { "what": "upload", "who": "ppc64le", "when": [ 2026, 8, 20, 10, 35, 35 ], "dst": "ppc64le/linux" } ] }, { "rel": "releasefile", "hash_spec": "sha256=29b4bfaad3f088f8174ac03ebf73a67272811c6c1bdc1adb7674ff12ba6c50b4", "hashes": { "sha256": "29b4bfaad3f088f8174ac03ebf73a67272811c6c1bdc1adb7674ff12ba6c50b4" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/29b/4bfaad3f088f8/fastparquet-2024.11.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", "log": [ { "what": "overwrite", "who": null, "when": [ 2026, 8, 20, 10, 35, 36 ], "count": 1 }, { "what": "upload", "who": "ppc64le", "when": [ 2026, 8, 20, 10, 35, 36 ], "dst": "ppc64le/linux" } ] }, { "rel": "releasefile", "hash_spec": "sha256=8a2026bc7d65946162479e064d94de08978ccec7a86951bddfc1b68578fc7698", "hashes": { "sha256": "8a2026bc7d65946162479e064d94de08978ccec7a86951bddfc1b68578fc7698" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/8a2/026bc7d659461/fastparquet-2024.11.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", "log": [ { "what": "overwrite", "who": null, "when": [ 2026, 8, 20, 10, 35, 37 ], "count": 1 }, { "what": "upload", "who": "ppc64le", "when": [ 2026, 8, 20, 10, 35, 37 ], "dst": "ppc64le/linux" } ] }, { "rel": "releasefile", "hash_spec": "sha256=4220894ae5b8e110c6d63cce1b86f009675b2a75357b8638240474de076f267c", "hashes": { "sha256": "4220894ae5b8e110c6d63cce1b86f009675b2a75357b8638240474de076f267c" }, "href": "https://wheels.developerfirst.ibm.com/ppc64le/linux/+f/422/0894ae5b8e110/fastparquet-2024.11.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", "log": [ { "what": "overwrite", "who": null, "when": [ 2026, 8, 20, 10, 35, 37 ], "count": 1 }, { "what": "upload", "who": "ppc64le", "when": [ 2026, 8, 20, 10, 35, 37 ], "dst": "ppc64le/linux" } ] } ] } }, "type": "projectconfig" }