Installing PyArrow — Apache Arrow v20.0.0 (original) (raw)

System Compatibility#

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

Python Compatibility#

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

Using Conda#

Install the latest version of PyArrow fromconda-forge using Conda:

conda install -c conda-forge pyarrow

Note

While the pyarrow conda-forge package is the right choice for most users, both a minimal and maximal variant of the package exist, either of which may be better for your use case. SeeDifferences between conda-forge packages.

Using Pip#

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.

Installing nightly packages or from source#

See Python Development.

Dependencies#

Optional dependencies

• NumPy 1.16.6 or higher.
• pandas 1.0 or higher,
• cffi.

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

tzdata on Windows#

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 functionpyarrow.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")

Differences between conda-forge packages#

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 onconda-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 whenCreating A Custom Selection.

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