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

python implementation of the parquet columnar file format.
https://github.com/jcrobak/parquet-python

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python implementation of the parquet columnar file format.

• Host: GitHub
• URL: https://github.com/jcrobak/parquet-python
• Owner: jcrobak
• License: apache-2.0
• Created: 2013-09-02T19:17:29.000Z (about 11 years ago)
• Default Branch: master
• Last Pushed: 2021-10-26T04:40:11.000Z (about 3 years ago)
• Last Synced: 2024-10-01T18:06:29.949Z (about 1 month ago)
• Language: Python
• Size: 156 KB
• Stars: 335
• Watchers: 11
• Forks: 256
• Open Issues: 15
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
parquet-python

==============

.. image:: https://travis-ci.org/jcrobak/parquet-python.svg?branch=master

    :target: https://travis-ci.org/jcrobak/parquet-python

parquet-python is a pure-python implementation (currently with only

read-support) of the `parquet

format `_. It comes with a

script for reading parquet files and outputting the data to stdout as

JSON or TSV (without the overhead of JVM startup). Performance has not

yet been optimized, but it's useful for debugging and quick viewing of

data in files.

Not all parts of the parquet-format have been implemented yet or tested

e.g. nested data—see Todos below for a full list. With that said,

parquet-python is capable of reading all the data files from the

`parquet-compatability `_

project.

requirements

============

parquet-python has been tested on python 2.7, 3.6, and 3.7. It depends

on ``pythrift2`` and optionally on ``python-snappy`` (for snappy compressed

files, please also install ``parquet-python[snappy]``).

getting started

===============

parquet-python is available via PyPi and can be installed using

`pip install parquet`. The package includes the `parquet`

command for reading python files, e.g. `parquet test.parquet`.

See `parquet --help` for full usage.

Example

-------

parquet-python currently has two programatic interfaces with similar

functionality to Python's csv reader. First, it supports a DictReader

which returns a dictionary per row. Second, it has a reader which

returns a list of values for each row. Both function require a file-like

object and support an optional ``columns`` field to only read the

specified columns.

.. code:: python

    import parquet

    import json

    ## assuming parquet file with two rows and three columns:

    ## foo bar baz

    ## 1   2   3

    ## 4   5   6

    with open("test.parquet", "rb") as fo:

       # prints:

       # {"foo": 1, "bar": 2}

       # {"foo": 4, "bar": 5}

       for row in parquet.DictReader(fo, columns=['foo', 'bar']):

           print(json.dumps(row))

    with open("test.parquet", "rb") as fo:

       # prints:

       # 1,2

       # 4,5

       for row in parquet.reader(fo, columns=['foo', 'bar']):

           print(",".join([str(r) for r in row]))

Todos

=====

-  Support the deprecated bitpacking

-  Fix handling of repetition-levels and definition-levels

-  Tests for nested schemas, null data

-  Support reading of data from HDFS via snakebite and/or webhdfs.

-  Implement writing

-  performance evaluation and optimization (i.e. how does it compare to

   the c++, java implementations)

Contributing

============

Is done via Pull Requests. Please include tests with your changes and

follow `pep8 `_.

To run the tests you must install and execute ``tox`` (``pip install tox``) to

run for all supported versions. If you want to run just for your current

version, execute: ``pip install -r requirements-development.txt`` and then

``nosetests``.