Python bindings

This is the documentation of the Python API of Apache Arrow. For more details on the Arrow format and other language bindings see the parent documentation.

The Arrow Python bindings (also named “PyArrow”) have first-class integration with NumPy, pandas, and built-in Python objects. They are based on the C++ implementation of Arrow.

Here will we detail the usage of the Python API for Arrow and the leaf libraries that add additional functionality such as reading Apache Parquet files into Arrow structures.

• Installing PyArrow
  • System Compatibility
  • Python Compatibility
  • Using Conda
  • Using Pip
  • Installing from source
  • Installing Nightly Packages
• Memory and IO Interfaces
  • Referencing and Allocating Memory
  • Input and Output
• Data Types and In-Memory Data Model
  • Type Metadata
  • Schemas
  • Arrays
  • Record Batches
  • Tables
  • Custom Schema and Field Metadata
• Streaming, Serialization, and IPC
  • Writing and Reading Streams
  • Arbitrary Object Serialization
  • Serializing pandas Objects
• Filesystem Interface
  • Usage
  • S3
  • Hadoop File System (HDFS)
  • Using fsspec-compatible filesystems
• Filesystem Interface (legacy)
  • Hadoop File System (HDFS)
• The Plasma In-Memory Object Store
  • The Plasma API
  • Using Arrow and Pandas with Plasma
  • Using Plasma with Huge Pages
• NumPy Integration
  • NumPy to Arrow
  • Arrow to NumPy
• Pandas Integration
  • DataFrames
  • Series
  • Handling pandas Indexes
  • Type differences
  • Memory Usage and Zero Copy
• Timestamps
  • Arrow/Pandas Timestamps
  • Timestamp Conversions
• Reading CSV files
  • Usage
  • Customized parsing
  • Customized conversion
  • Incremental reading
  • Character encoding
  • Performance
• Feather File Format
  • Using Compression
  • Writing Version 1 (V1) Files
• Reading JSON files
  • Usage
  • Automatic Type Inference
  • Customized parsing
• Reading and Writing the Apache Parquet Format
  • Obtaining pyarrow with Parquet Support
  • Reading and Writing Single Files
  • Finer-grained Reading and Writing
  • Inspecting the Parquet File Metadata
  • Data Type Handling
  • Compression, Encoding, and File Compatibility
  • Partitioned Datasets (Multiple Files)
  • Writing to Partitioned Datasets
  • Reading from Partitioned Datasets
  • Using with Spark
  • Multithreaded Reads
  • Reading a Parquet File from Azure Blob storage
• Tabular Datasets
  • Reading Datasets
  • Filtering data
  • Reading partitioned data
  • Reading from cloud storage
  • Reading from Minio
  • Manual specification of the Dataset
  • Manual scheduling
• CUDA Integration
  • CUDA Contexts
  • CUDA Buffers
  • Numba Integration
• Extending pyarrow
  • Controlling conversion to pyarrow.Array with the __arrow_array__ protocol
  • Defining extension types (“user-defined types”)
• Using pyarrow from C++ and Cython Code
  • C++ API
  • Cython API
• API Reference
  • Data Types and Schemas
  • Arrays and Scalars
  • Buffers and Memory
  • Streams and File Access
  • Tables and Tensors
  • Serialization and IPC
  • Arrow Flight
  • Tabular File Formats
  • Filesystems
  • Dataset
  • Plasma In-Memory Object Store
  • CUDA Integration
  • Miscellaneous
• Getting Involved
• Benchmarks
  • Running the benchmarks
  • Running for arbitrary Git revisions
  • Compatibility