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        "author": "Martin Durant",
        "author_email": "mdurant@continuum.io",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython"
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        "description": "fastparquet\n===========\n\n.. image:: https://travis-ci.org/jcrobak/parquet-python.svg?branch=master\n    :target: https://github.com/dask/fastparquet\n\nfastparquet is a python implementation of the `parquet\nformat <https://github.com/apache/parquet-format>`_, aiming integrate\ninto python-based big data work-flows.\n\nNot all parts of the parquet-format have been implemented yet or tested\ne.g. see the Todos linked below. With that said,\nfastparquet is capable of reading all the data files from the\n`parquet-compatability <https://github.com/Parquet/parquet-compatibility>`_\nproject.\n\nIntroduction\n------------\n\nDetails of this project can be found in the documentation_.\n\n.. _documentation: https://fastparquet.readthedocs.io\n\nThe original plan listing expected features can be found in\n`this issue`_.\nPlease feel free to comment on that list as to missing items and priorities,\nor raise new issues with bugs or requests.\n\n.. _this issue: https://github.com/dask/fastparquet/issues/1\n\n\n\nRequirements\n------------\n\n(all development is against recent versions in the default anaconda channels)\n\nRequired:\n\n- numba (requires `LLVM 4.0.x`_)\n- numpy\n- pandas\n- cython\n- six\n\n.. _LLVM 4.0.x: https://github.com/llvm-mirror/llvm \n\nOptional (compression algorithms; gzip is always available):\n\n- snappy (aka python-snappy)\n- lzo\n- brotli\n- lz4\n- zstandard\n\n\nInstallation\n------------\n\nInstall using conda::\n\n   conda install -c conda-forge fastparquet\n\ninstall from pypi::\n\n   pip install fastparquet\n\nor install latest version from github::\n\n   pip install git+https://github.com/dask/fastparquet\n\nFor the pip methods, numba must have been previously installed (using conda).\n\nUsage\n-----\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\nSince early October 2016, this fork of `parquet-python`_ has been\nundergoing considerable redevelopment. The aim is to have a small and simple\nand performant library for reading and writing the parquet format from python.\n\n.. _parquet-python: https://github.com/jcrobak/parquet-python",
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