DataFrame Libraries

High-performance libraries providing in‐memory dataframes for efficient data manipulation, analysis, and processing. This collection covers projects that offer a full API for working with tabular data with vectorized operations and out‐of‐core capabilities.

14 projects

15,289 contributors

$54M

Most contributors

Polars

Polars is a high-performance DataFrame library implemented in Rust, offering fast data manipulation and analysis capabilities with a Python API. It features a query optimizer, parallel execution, and efficient memory usage through Arrow columnar format.

Contributors

5,563

Organizations

1,125

Software value

$22M

Dask

Dask is a flexible parallel computing library for analytics that provides dynamic task scheduling optimized for computation and integrates with Python data science libraries like NumPy, Pandas and Scikit-learn. It enables parallel and distributed computing through intuitive APIs and scales Python code from multi-core machines to clusters.

Contributors

3,564

Organizations

900

Software value

$6.8M

xarray

Xarray is a Python library that introduces labeled arrays and datasets, extending NumPy's capabilities by adding coordinates, dimensions, and attributes to N-dimensional arrays. It enables working with multi-dimensional data by providing data structures and operations for labeled arrays, making it particularly useful for scientific computing and analysis of structured data like climate and weather data.

Contributors

2,960

Organizations

699

Software value

$6.5M