13 open-source projects similar to meh/cesso, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Cesso alternative.
GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to
xsv is a suite of high-performance command-line utilities written in Rust for the analysis, manipulation, and statistical processing of large delimited datasets. It provides a toolkit for processing comma-separated value files through a command line interface. The project provides capabilities for statistical analysis, including the computation of column statistics, value frequencies, and descriptive metrics. It also includes data manipulation utilities for joining, slicing, sampling, and reformatting records. The toolkit covers a broad range of data operations including column selection, da
(DEPRECATED) -- moved under: https://github.com/FasterXML/jackson-dataformats-text
Fast, lightweight, and RFC 4180 compliant CSV library for Java. Zero dependencies, ~90 KiB. Trusted by Apache NiFi, JUnit, and Neo4j.
uniVocity-parsers is a suite of extremely fast and reliable parsers for Java. It provides a consistent interface for handling different file formats, and a solid framework for the development of new parsers.
This project is a comprehensive functional programming curriculum and learning resource for Haskell. It provides sequenced educational paths and technical reference guides designed to take developers from beginner to advanced levels of proficiency. The project distinguishes itself through a deep focus on theoretical and technical foundations, offering detailed studies on type theory, category theory, and runtime internals. It includes a dedicated performance handbook for optimizing execution speed and memory management, as well as an ecosystem guide for managing development tools and editor c