A Python interface to the Parquet file format.


The Parquet format is a common binary data store, used particularly in the Hadoop/big-data sphere. It provides several advantages relevant to big-data processing:

  • columnar storage, only read the data of interest
  • efficient binary packing
  • choice of compression algorithms and encoding
  • split data into files, allowing for parallel processing
  • range of logical types
  • statistics stored in metadata allow for skipping unneeded chunks
  • data partitioning using the directory structure

Since it was developed as part of the Hadoop ecosystem, Parquet’s reference implementation is written in Java. This package aims to provide a performant library to read and write Parquet files from Python, without any need for a Python-Java bridge. This will make the Parquet format an ideal storage mechanism for Python-based big data workflows.

The tabular nature of Parquet is a good fit for the Pandas data-frame objects, and we exclusively deal with data-frame<->Parquet.


The original outline plan for this project can be found here

Briefly, some features of interest:

  • read and write Parquet files, in single- or multiple-file format. The latter is common found in hive/Spark usage.
  • choice of compression per-column and various optimized encoding schemes; ability to choose row divisions and partitioning on write.
  • acceleration of both reading and writing using numba
  • ability to read and write to arbitrary file-like objects, allowing interoperability with s3fs, hdfs3, adlfs and possibly others.
  • can be called from dask, to enable parallel reading and writing with Parquet files, possibly distributed across a cluster.

Caveats, Known Issues

Not all parts of the Parquet-format have been implemented yet or tested. fastparquet is, however, capable of reading all the data files from the parquet-compatibility project. Some encoding mechanisms in Parquet are rare, and may be implemented on request - please post an issue.

Some deeply-nested columns will not be readable, e.g., lists of lists.

Not all output options will be compatible with every other Parquet framework, which each implement only a subset of the standard, see the usage notes.

A list of current issues can be found here.

Relation to Other Projects

pure-Python Parquet quick-look utility which was the inspiration for fastparquet.

implementation of the Parquet format which can be called from Python using Apache Arrow bindings. Future collaboration with parquet-cpp is possible, in the medium term, and that perhaps their low-level routines will replace some functions in fastparquet or that high-level logic in fastparquet will be migrated to C++.

  • PySpark, a Python API to the Spark

engine, interfaces Python commands with a Java/Scala execution core, and thereby gives Python programmers access to the Parquet format. fastparquet has no defined relationship to PySpark, but can provide an alternative path to providing data to Spark or reading data produced by Spark without invoking a PySpark client or interacting directly with the scheduler.

  • fastparquet lives within the dask ecosystem, and

although it is useful by itself, it is designed to work well with dask for parallel execution, as well as related libraries such as s3fs for pythonic access to Amazon S3.