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dataframe: A fast, safe, and intuitive DataFrame library.

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A fast, safe, and intuitive DataFrame library for exploratory data analysis.

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Versions [RSS] 0.1.0.0, 0.1.0.1, 0.1.0.2, 0.1.0.3, 0.2.0.0, 0.2.0.1, 0.2.0.2, 0.3.0.0, 0.3.0.1, 0.3.0.2, 0.3.0.3, 0.3.0.4, 0.3.1.1, 0.3.1.2, 0.3.2.0, 0.3.3.0, 0.3.3.1, 0.3.3.2, 0.3.3.3, 0.3.3.4, 0.3.3.5, 0.3.3.6, 0.3.3.7, 0.3.3.8, 0.3.3.9, 0.3.4.0, 0.3.4.1, 0.3.5.0, 0.4.0.0, 0.4.0.2, 0.4.0.3, 0.4.0.4, 0.4.0.5, 0.4.0.6, 0.4.0.7, 0.4.0.8, 0.4.0.9, 0.4.0.10, 0.4.1.0, 0.5.0.0, 0.5.0.1, 0.6.0.0, 0.7.0.0, 1.0.0.0
Change log CHANGELOG.md
Dependencies aeson (>=0.11.0.0 && <3), array (>=0.5.4.0 && <0.6), attoparsec (>=0.12 && <0.15), base (>=4 && <5), bytestring (>=0.11 && <0.13), bytestring-lexing (>=0.5 && <0.6), cassava (>=0.1 && <1), containers (>=0.6.7 && <0.9), dataframe (>=1 && <1.1), deepseq (>=1 && <2), directory (>=1.3.0.0 && <2), filepath (>=1.4 && <2), Glob (>=0.10 && <1), granite (>=0.4 && <0.5), hashable (>=1.2 && <2), http-conduit (>=2.3 && <3), mmap (>=0.5.8 && <0.6), parallel (>=3.2.2.0 && <5), process (>=1.6 && <1.7), random (>=1.2 && <2), regex-tdfa (>=1.3.0 && <2), scientific (>=0.3.1 && <0.4), snappy-hs (>=0.1 && <0.2), stm (>=2.5 && <3), streamly-bytestring, streamly-core, template-haskell (>=2.0 && <3), text (>=2.0 && <3), these (>=1.1 && <2), time (>=1.12 && <2), unix (>=2 && <3), unordered-containers (>=0.1 && <1), vector (>=0.13 && <0.14), vector-algorithms (>=0.9 && <0.10), zlib (>=0.5 && <1), zstd (>=0.1.2.0 && <0.2) [details]
Tested with ghc ==9.4.8 || ==9.6.7 || ==9.8.4 || ==9.10.3 || ==9.12.2
License MIT
Copyright (c) 2024-2025 Michael Chavinda
Author Michael Chavinda
Maintainer mschavinda@gmail.com
Uploaded by mchav at 2026-03-22T06:34:27Z
Category Data
Bug tracker https://github.com/mchav/dataframe/issues
Source repo head: git clone https://github.com/mchav/dataframe
Reverse Dependencies 4 direct, 0 indirect [details]
Executables lazy-bench, dataframe, synthesis, dataframe-benchmark-example
Downloads 824 total (213 in the last 30 days)
Rating (no votes yet) [estimated by Bayesian average]
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Status Docs available [build log]
Last success reported on 2026-03-22 [all 1 reports]

Readme for dataframe-1.0.0.0

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User guide | Discord

DataFrame

A fast, safe, and intuitive DataFrame library.

Why use this DataFrame library?

  • Encourages concise, declarative, and composable data pipelines.
  • Lets you opt into your preferred level of type safety: keep it lightweight for rapid exploration or lock it down completely for robust production pipelines.
  • Delivers high performance thanks to Haskell’s optimizing compiler and efficient memory model.
  • Designed for interactivity: expressive syntax, helpful error messages, and sensible defaults.
  • Works seamlessly in both command-line and notebook environments—great for exploration and scripting alike.

Features

  • Type-safe column operations with compile-time guarantees
  • Familiar, approachable API designed to feel easy coming from other languages.
  • Interactive REPL for data exploration and plotting.

Quick start

Browse through some examples in binder.

Install

See the Quick Start guide for setup and installation instructions.

Example

dataframe> df = D.fromNamedColumns [("product_id", D.fromList [1,1,2,2,3,3]), ("sales", D.fromList [100,120,50,20,40,30])]
dataframe> df
------------------
product_id | sales
-----------|------
   Int     |  Int 
-----------|------
1          | 100  
1          | 120  
2          | 50   
2          | 20   
3          | 40   
3          | 30   

dataframe> :declareColumns df
"product_id :: Expr Int"
"sales :: Expr Int"
dataframe> df |> D.groupBy [F.name product_id] |> D.aggregate [F.sum sales `as` "total_sales"]
------------------------
product_id | total_sales
-----------|------------
   Int     |     Int    
-----------|------------
1          | 220        
2          | 70         
3          | 70

Documentation