PyArrow

Language: Python

Data

PyArrow was created by the Apache Arrow project to provide fast, standardized, and language-agnostic data structures. It allows Python applications to work efficiently with large datasets in memory, perform zero-copy reads, and interoperate with systems like Pandas, Parquet, and Spark.

PyArrow is a cross-language development platform for in-memory data, primarily designed for columnar data processing. It provides a Python interface for the Apache Arrow columnar memory format and enables efficient data interchange and analytics.

Installation

pip: pip install pyarrow

conda: conda install -c conda-forge pyarrow

Usage

PyArrow provides tools for handling Arrow arrays, tables, and memory-mapped files. It supports reading and writing Parquet and Feather formats, interacting with Pandas DataFrames efficiently, and integrating with big data frameworks.

Creating an Arrow Table from Pandas

import pyarrow as pa
import pandas as pd
df = pd.DataFrame({'col1': [1,2,3], 'col2': ['a','b','c']})
table = pa.Table.from_pandas(df)
print(table)

Converts a Pandas DataFrame into a PyArrow Table for columnar processing.

Writing to Parquet file

import pyarrow.parquet as pq
pq.write_table(table, 'example.parquet')

Writes a PyArrow Table to a Parquet file on disk.

Reading from Parquet file

table = pq.read_table('example.parquet')
df = table.to_pandas()
print(df)

Reads a Parquet file into a PyArrow Table and converts it back to Pandas for analysis.

Zero-copy data sharing with NumPy

import numpy as np
arr = np.array([1,2,3,4])
arrow = pa.array(arr)
print(arrow_array)

Demonstrates converting a NumPy array to an Arrow array without copying memory.

Feather format for fast I/O

import pyarrow.feather as feather
feather.write_feather(df, 'example.feather')
df2 = feather.read_feather('example.feather')
print(df2)

Reads and writes Feather files for fast, language-agnostic serialization of dataframes.

Using Arrow Memory Pool

import pyarrow as pa
pool = pa.memory_pool()
arr = pa.array([1,2,3], memory_pool=pool)
print(pool.bytes_allocated())

Demonstrates memory allocation tracking using PyArrow’s memory pool for optimized memory management.

Error Handling

ArrowInvalid: Check the data type compatibility when creating Arrow arrays or tables.

ParquetFileError: Ensure that the Parquet file exists and is not corrupted.

MemoryError: Use Arrow memory pools or process data in batches to avoid running out of memory.

Best Practices

Use Arrow Tables for columnar, in-memory analytics for speed and efficiency.

Prefer Feather or Parquet formats for storage and interoperability between Python and other languages.

Use PyArrow with Pandas for zero-copy operations when dealing with large datasets.

Leverage memory pools to reduce memory fragmentation and improve performance.

Combine PyArrow with Dask for parallel processing of large datasets.

Official Docs Github