fastparquet

fastparquet

.. image:: https://github.com/dask/fastparquet/actions/workflows/main.yaml/badge.svg :target: https://github.com/dask/fastparquet/actions/workflows/main.yaml

.. image:: https://readthedocs.org/projects/fastparquet/badge/?version=latest :target: https://fastparquet.readthedocs.io/en/latest/

fastparquet is a python implementation of the parquet format <https://github.com/apache/parquet-format>_, aiming integrate into python-based big data work-flows. It is used implicitly by the projects Dask, Pandas and intake-parquet.

We offer a high degree of support for the features of the parquet format, and very competitive performance, in a small install size and codebase.

Details of this project, how to use it and comparisons to other work can be found in the documentation_.

.. _documentation: https://fastparquet.readthedocs.io

Requirements

(all development is against recent versions in the default anaconda channels and/or conda-forge)

Required:

• numpy
• pandas
• cython >= 0.29.23 (if building from pyx files)
• cramjam
• fsspec

Supported compression algorithms:

• Available by default:

  • gzip
  • snappy
  • brotli
  • lz4
  • zstandard

• Optionally supported

  • lzo <https://github.com/jd-boyd/python-lzo>_

Installation

Install using conda, to get the latest compiled version::

conda install -c conda-forge fastparquet

or install from PyPI::

pip install fastparquet

You may wish to install numpy first, to help pip's resolver. This may install an appropriate wheel, or compile from source. For the latter, you will need a suitable C compiler toolchain on your system.

You can also install latest version from github::

pip install git+https://github.com/dask/fastparquet

in which case you should also have cython to be able to rebuild the C files.

Usage

Please refer to the documentation_.

Reading

.. code-block:: python

from fastparquet import ParquetFile
pf = ParquetFile('myfile.parq')
df = pf.to_pandas()
df2 = pf.to_pandas(['col1', 'col2'], categories=['col1'])

You may specify which columns to load, which of those to keep as categoricals (if the data uses dictionary encoding). The file-path can be a single file, a metadata file pointing to other data files, or a directory (tree) containing data files. The latter is what is typically output by hive/spark.

Writing

.. code-block:: python

from fastparquet import write
write('outfile.parq', df)
write('outfile2.parq', df, row_group_offsets=[0, 10000, 20000],
      compression='GZIP', file_scheme='hive')

The default is to produce a single output file with a single row-group (i.e., logical segment) and no compression. At the moment, only simple data-types and plain encoding are supported, so expect performance to be similar to numpy.savez.

History

This project forked in October 2016 from parquet-python_, which was not designed for vectorised loading of big data or parallel access.

.. _parquet-python: https://github.com/jcrobak/parquet-python