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Installing PyArrow — Apache Arrow v21.0.0

PyArrow is regularly built and tested on Windows, macOS and various Linux distributions. We strongly recommend using a 64-bit system.

PyArrow is currently compatible with Python 3.9, 3.10, 3.11, 3.12 and 3.13.

Install the latest version of PyArrow from conda-forge using Conda:

conda install -c conda-forge pyarrow

Install the latest version from PyPI (Windows, Linux, and macOS):

If you encounter any importing issues of the pip wheels on Windows, you may need to install the Visual C++ Redistributable for Visual Studio 2015.

Warning

On Linux, you will need pip >= 19.0 to detect the prebuilt binary packages.

See Python Development.

Optional dependencies

• NumPy 1.16.6 or higher.

• pandas 1.0 or higher,

• cffi.

Additional packages PyArrow is compatible with are fsspec and pytz, dateutil or tzdata package for timezones.

While Arrow uses the OS-provided timezone database on Linux and macOS, it requires a user-provided database on Windows. To download and extract the text version of the IANA timezone database follow the instructions in the C++ Runtime Dependencies or use pyarrow utility function pyarrow.util.download_tzdata_on_windows() that does the same.

By default, the timezone database will be detected at %USERPROFILE%\Downloads\tzdata. If the database has been downloaded in a different location, you will need to set a custom path to the database from Python:

>>> import pyarrow as pa
>>> pa.set_timezone_db_path("custom_path")

You may encounter problems writing datetime data to an ORC file if you install pyarrow with pip. One possible solution to fix this problem:

1. Install tzdata with pip install tzdata

2. Set the environment variable TZDIR = path\to\.venv\Lib\site-packages\tzdata\

You can find where tzdata is installed with the following python command:

>>> import tzdata
>>> print(tzdata.__file__)
path\to\.venv\Lib\site-packages\tzdata\__init__.py

On conda-forge, PyArrow is published as three separate packages, each providing varying levels of functionality. This is in contrast to PyPi, where only a single PyArrow package is provided.

The purpose of this split is to minimize the size of the installed package for most users (pyarrow), provide a smaller, minimal package for specialized use cases (pyarrow-core), while still providing a complete package for users who require it (pyarrow-all). What was historically pyarrow on conda-forge is now pyarrow-all, though most users can continue using pyarrow.

The pyarrow-core package includes the following functionality:

• Data Types and In-Memory Data Model

• Compute Functions (i.e., pyarrow.compute)

• Memory and IO Interfaces

• Streaming, Serialization, and IPC (i.e., pyarrow.ipc)

• Filesystem Interface (i.e., pyarrow.fs. Note: It’s planned to move cloud fileystems (i.e., S3, GCS, etc) into pyarrow in a future release though Local FS will remain in pyarrow-core.)

• File formats: Arrow/Feather, JSON, CSV, ORC (but not Parquet)

The pyarrow package adds the following:

• Acero (i.e., pyarrow.acero)

• Tabular Datasets (i.e., pyarrow.dataset)

• Parquet (i.e., pyarrow.parquet)

• Substrait (i.e., pyarrow.substrait)

Finally, pyarrow-all adds:

• Arrow Flight RPC and Flight SQL (i.e., pyarrow.flight)

• Gandiva (i.e., pyarrow.gandiva)

The following table lists the functionality provided by each package and may be useful when deciding to use one package over another or when Creating A Custom Selection.

If you know which components you need and want to control what’s installed, you can create a custom selection of packages to include only the extra features you need. For example, to install pyarrow-core and add support for reading and writing Parquet, install libparquet alongside pyarrow-core:

conda install -c conda-forge pyarrow-core libparquet

Or if you wish to use pyarrow but need support for Flight RPC:

conda install -c conda-forge pyarrow libarrow-flight

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