{"id":43909,"url":"https://github.com/jcmkk3/awesome-dataframes","name":"awesome-dataframes","description":"An awesome list of dataframe libraries","projects_count":74,"last_synced_at":"2026-06-08T08:00:48.592Z","repository":{"id":55655710,"uuid":"321180429","full_name":"jcmkk3/awesome-dataframes","owner":"jcmkk3","description":"An awesome list of dataframe libraries","archived":false,"fork":false,"pushed_at":"2025-04-04T15:06:03.000Z","size":123,"stargazers_count":124,"open_issues_count":5,"forks_count":13,"subscribers_count":7,"default_branch":"main","last_synced_at":"2026-05-06T04:04:41.665Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jcmkk3.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-12-13T23:17:57.000Z","updated_at":"2026-03-26T17:02:31.000Z","dependencies_parsed_at":"2025-01-31T06:02:49.432Z","dependency_job_id":"0b99f5a4-77ed-4280-84d5-655d7f5f6abc","html_url":"https://github.com/jcmkk3/awesome-dataframes","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jcmkk3/awesome-dataframes","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcmkk3%2Fawesome-dataframes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcmkk3%2Fawesome-dataframes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcmkk3%2Fawesome-dataframes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcmkk3%2Fawesome-dataframes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jcmkk3","download_url":"https://codeload.github.com/jcmkk3/awesome-dataframes/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcmkk3%2Fawesome-dataframes/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34053435,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-08T02:00:07.615Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-13T21:18:46.631Z","updated_at":"2026-06-08T08:00:48.592Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Other Lists","Libraries","Other","Papers"],"sub_categories":[],"readme":"# Awesome Dataframes\n\nAn awesome list of dataframe (and dataframe-like) libraries. This list focuses on libraries and tools intended for local (on your personal computer) manipulation of tabular data.\n\n## Libraries\n\nPython\n- [pandas](https://github.com/pandas-dev/pandas) - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.\n- [Polars](https://github.com/pola-rs/polars) - Fast multi-threaded DataFrame library in Rust and Python.\n- [Modin](https://github.com/modin-project/modin) - Speed up your Pandas workflows by changing a single line of code.\n- [Ibis](https://github.com/ibis-project/ibis) - A pandas-like deferred expression system, with first-class SQL support.\n- [Narwhals](https://github.com/narwhals-dev/narwhals) - Lightweight and extensible compatibility layer between dataframe libraries!\n- [agate](https://github.com/wireservice/agate) - agate is a Python data analysis library that is optimized for humans instead of machines. It is an alternative to numpy and pandas that solves real-world problems with readable code.\n- [Lemuras](https://github.com/AivanF/Lemuras) - A small *pure* Python library to deal with big tables.\n- [datatable](https://github.com/h2oai/datatable) - A Python package for manipulating 2-dimensional tabular data structures.\n- [Prosto](https://github.com/prostodata/prosto) - A Python data processing toolkit to programmatically author and execute complex data processing workflows. Conceptually, it is an alternative to purely set-oriented approaches to data processing like map-reduce, relational algebra, SQL or data-frame-based tools like pandas.\n- [siuba](https://github.com/machow/siuba) - Python library for using dplyr like syntax with pandas and SQL.\n- [Vaex](https://github.com/vaexio/vaex) - A high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets.\n- [dfply](https://github.com/kieferk/dfply) - dplyr-style piping operations for pandas dataframes.\n- [kadro](https://github.com/koaning/kadro) - A friendly pandas wrapper with a more composable grammar support.\n- [dexplo](https://github.com/dexplo/dexplo) - Data exploration library with a pandas-like API.\n- [pands_cub](https://github.com/tdpetrou/pandas_cub) - A detailed project that teaches you how to build your own Python data analysis library, pandas_cub, from scratch.\n- [fletcher](https://github.com/xhochy/fletcher) - Pandas ExtensionDType/Array backed by Apache Arrow.\n- [tidypandas](https://github.com/talegari/tidypandas) - A grammar of data manipulation for pandas inspired by tidyverse.\n- [redframes](https://github.com/maxhumber/redframes) - [re]ctangular[d]ata[frames]\n- [static-frame](https://github.com/InvestmentSystems/static-frame) - Immutable and grow-only Pandas-like DataFrames with a more explicit and consistent interface\n\nR\n- [dplyr](https://github.com/tidyverse/dplyr) - A grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges.\n- [data.table](https://github.com/Rdatatable/data.table) - Provides a high-performance version of base R's `data.frame` with syntax and feature enhancements for ease of use, convenience and programming speed.\n- [dance](https://github.com/romainfrancois/dance) - Dancing 💃 with the stats, aka `tibble()` dancing 🕺. dance is a sort of reinvention of dplyr classic verbs, with a more modern stack underneath, i.e. it leverages a lot from vctrs and rlang.\n\nJavaScript\n- [Arquero](https://github.com/uwdata/arquero) - A JavaScript library for query processing and transformation of array-backed data tables. Following the relational algebra and inspired by the design of dplyr, Arquero provides a fluent API for manipulating column-oriented data frames.\n- [dataflow-api](https://github.com/vega/dataflow-api) - JavaScript API for dataflow processing using the vega-dataflow reactive engine. Perform common database operations (sorting, filtering, aggregation, window calculations) over JavaScript objects.\n- [datalib](https://github.com/vega/datalib) - A JavaScript data utility library. It provides facilities for data loading, type inference, common statistics, and string templates.\n- [Tidy.js](https://github.com/pbeshai/tidy) - Tidy up your data with JavaScript, inspired by dplyr and the tidyverse.\n- [Data-Forge](https://github.com/data-forge/data-forge-ts) - The JavaScript data transformation and analysis toolkit inspired by Pandas and LINQ.\n- [zebras](https://github.com/nickslevine/zebras) - A data manipulation and analysis library written in JavaScript offering the convenience of pandas or R.\n- [dataframe-js](https://github.com/Gmousse/dataframe-js) - A javascript library providing a new data structure for datascientists and developers.\n- [Simple Data Analysis (SDA)](https://github.com/nshiab/simple-data-analysis.js) - Easy-to-use JavaScript library for most common data analysis tasks.\n\nJulia\n- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) - Tools for working with tabular data in Julia.\n- [DataKnots.jl](https://github.com/rbt-lang/DataKnots.jl) - A Julia library for querying data with an extensible, practical and coherent algebra of query combinators.\n- [Volcanito.jl](https://github.com/johnmyleswhite/Volcanito.jl) - Backend agnostic for tabular data operations in Julia.\n- [Query.jl](https://github.com/queryverse/Query.jl) - A package for querying julia data sources. It can filter, project, join and group data from any iterable data source, including all the sources supported in IterableTables.jl.\n- [TidierData.jl](https://github.com/TidierOrg/TidierData.jl) - 100% Julia implementation of the dplyr and tidyr R packages.\n\nClojure\n- [tech.ml.dataset](https://github.com/techascent/tech.ml.dataset) - A Clojure high performance data processing system.\n- [tablecloth](https://github.com/scicloj/tablecloth) - Dataset manipulation library build on the top of tech.ml.dataset.\n\nCommon Lisp\n- [Data Frame](https://github.com/Lisp-Stat/data-frame) - Data frames for Common Lisp\n\nC++\n- [DataFrame](https://github.com/hosseinmoein/DataFrame) - A C++ statistical library that provides an interface similar to Pandas package in Python.\n\nElixir\n- [explorer](https://github.com/elixir-nx/explorer) - Explorer brings series (one-dimensional) and dataframes (two-dimensional) for fast data exploration to Elixir.\n\nElm\n- [tidy](https://github.com/gicentre/tidy) - Leaning heavily on the principles of the tidyverse, and especially tidy data, this package makes it easy to reshape and tidy tabular data for easier data analysis and visualization.\n\nGo\n- [column](https://github.com/kelindar/column) - High-performance, columnar, in-memory store with bitmap indexing in Go.\n- [gambas](https://github.com/jpoly1219/gambas) - Data analysis tool for Go. Similar to the famous Python library pandas.\n\nHaskell\n- [DataFrame](https://github.com/mchav/dataframe) - An intuitive, dynamically-typed DataFrame library.\n\nJava\n- [Tablesaw](https://github.com/jtablesaw/tablesaw) - Java dataframe and visualization library.\n\nKotlin\n- [Kotlin Dataframe](https://github.com/Kotlin/dataframe) - Structured data processing in Kotlin.\n- [krangl](https://github.com/holgerbrandl/krangl) - A {K}otlin library for data w{rangl}ing.\n\nLil\n- [Query Language](https://beyondloom.com/decker/lil.html#lilthequerylanguage) - Query language embedded into Lil.\n\nLua\n- [Assistant](https://github.com/coalio/Assistant) - A data science library providing flexible dataframes for Lua 5.1+\n\nQ\n- [qSQL](https://code.kx.com/q/basics/qsql/) - Query language embedded into Q.\n\nRaku\n- [Data::Reshapers](https://github.com/antononcube/Raku-Data-Reshapers) - Raku package with data reshaping functions for different data structures.\n\nRuby\n- [RedAmber](https://github.com/red-data-tools/red_amber) - A dataframe library for Rubyists.\n- [rover](https://github.com/ankane/rover) - Simple, powerful data frames for Ruby.\n- [daru](https://github.com/SciRuby/daru) - daru (Data Analysis in RUby) is a library for storage, analysis, manipulation and visualization of data in Ruby.\n- [polars-ruby](https://github.com/ankane/polars-ruby) - Blazingly fast DataFrames for Ruby.\n\nRust\n- [polars](https://github.com/ritchie46/polars) - A blazingly fast DataFrames library implemented in Rust.\n- [datafusion](https://arrow.apache.org/datafusion/user-guide/dataframe.html) - DataFrame API in Apache Arrow DataFusion\n\nWolfram\n- [Dataset](https://reference.wolfram.com/language/ref/Dataset.html) - Represents a structured dataset based on a hierarchy of lists and associations.\n\nDatabase\n- [SQLite](https://sqlite.org/index.html) - A C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine\n- [DuckDB](https://github.com/cwida/duckdb) - An embeddable SQL OLAP Database Management System.\n\nCLI\n- [VisiData](https://github.com/saulpw/visidata) - A terminal spreadsheet multitool for discovering and arranging data.\n\nGUI\n- [Power Query](https://docs.microsoft.com/en-us/powerquery-m) - A core capability of Power Query is to filter and combine, that is, to mash-up data from one or more of a rich collection of supported data sources.\n\n## Other\n- [prql](https://github.com/prql/prql) - A modern language for transforming data — a simple, powerful, pipelined SQL replacement.\n- [Malloy](https://github.com/looker-open-source/malloy) - An experimental language for describing data relationships and transformations.\n- [Arrow](https://github.com/apache/arrow) - A cross-language development platform for in-memory data.\n- [Substrait](https://github.com/substrait-io/substrait) - A cross platform way to express data transformation, relational algebra, standardized record expression and plans.\n- [Consortium for Python Data APIs](https://data-apis.org/)\n\n## Papers\n- [Query Combinators](https://github.com/rbt-lang/rbt-paper)\n- [Split Apply Combine](https://www.jstatsoft.org/article/view/v040i01)\n- [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf)\n\n## Other Lists\n- [Structured Text Tools](https://github.com/dbohdan/structured-text-tools)\n- [Awesome Big Data](https://github.com/onurakpolat/awesome-bigdata)\n- [Awesome dataviz](https://github.com/fasouto/awesome-dataviz)\n- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/jcmkk3%2Fawesome-dataframes/projects"}