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Pandas integration with sklearn
The main features of scikit-learn-contrib/sklearn-pandas are: Data Manipulation, Data Manipulation Libraries, Data Processing Libraries.
Open-source alternatives to scikit-learn-contrib/sklearn-pandas include: modin-project/modin — Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that… pola-rs/polars — Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It… vaexio/vaex — Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle… jmcarpenter2/swifter — A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner. pydata/xarray — Xarray is a Python multidimensional array library and labeled dataset framework. It extends the NumPy data structure… iamseancheney/python_for_data_analysis_2nd_chinese_version — This project is an educational resource and a collection of instructional materials for performing data manipulation…
Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle billion-row tabular datasets in Python. It functions as a lazy evaluation framework that defers computations and transformations until results are required, enabling the processing of datasets that exceed available system RAM by mapping files directly from disk. The project distinguishes itself as a tool for big data visualization and exploration, specifically integrated for use within interactive notebooks. It provides specialized capabilities for machine learning feature engin
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
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
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner