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
Raw data
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"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 <https://github.com/apache/parquet-format>`_, 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 <https://github.com/jd-boyd/python-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",
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