How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It functions as a structured data library that organizes information into typed columns, utilizing the Apache Arrow memory format to enable zero-copy data sharing and cache-friendly, vectorized operations. The engine is built to handle large-scale tabular datasets, providing both local and distributed analytical runtimes that scale from single-machine environments to multi-node clusters. The project distinguishes itself through a sophisticated lazy query engine that constructs abstract e
Xarray is a Python multidimensional array library and labeled dataset framework. It extends the NumPy data structure by adding labels to arrays, allowing for the organization of complex N-dimensional data using named dimensions and coordinates. The library provides a NetCDF data interface for reading and writing scientific data formats such as NetCDF and Zarr. It enables scientific array computing by maintaining the relationship between data and physical coordinates during mathematical operations. The project covers multidimensional data analysis, geospatial data manipulation, and climate da
Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available h
Pandas integration with sklearn
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
The main features of jmcarpenter2/swifter are: Data Manipulation, Data Processing Libraries.
Open-source alternatives to jmcarpenter2/swifter include: vaexio/vaex — Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle… scikit-learn-contrib/sklearn-pandas — Pandas integration with sklearn. pydata/xarray — Xarray is a Python multidimensional array library and labeled dataset framework. It extends the NumPy data structure… pola-rs/polars — Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It… modin-project/modin — Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that… iamseancheney/python_for_data_analysis_2nd_chinese_version — This project is an educational resource and a collection of instructional materials for performing data manipulation…