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dataframe: An intuitive, dynamically-typed DataFrame library.

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An intuitive, dynamically-typed DataFrame library for exploratory data analysis.

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  • DataFrame
    • DataFrame.Lazy

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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
Change log CHANGELOG.md
Dependencies array (>=0.5 && <0.6), attoparsec (>=0.12 && <=0.14.4), base (>=4.17.2.0 && <4.22), bytestring (>=0.11 && <=0.12.2.0), containers (>=0.6.7 && <0.8), directory (>=1.3.0.0 && <=1.3.9.0), filepath (>=1.0.0.0 && <=1.5.4.0), hashable (>=1.2 && <=1.5.0.0), snappy (>=0.2.0.0 && <=0.2.0.4), statistics (>=0.16.2.1 && <=0.16.3.0), text (>=2.0 && <=2.1.2), time (>=1.12 && <=1.14), vector (>=0.13 && <0.14), vector-algorithms (>=0.9 && <0.10), zstd (>=0.1.2.0 && <=0.1.3.0) [details]
Tested with ghc ==9.8.3 || ==9.6.6 || ==9.4.8 || ==9.10.1 || ==9.12.1 || ==9.12.2
License GPL-3.0-or-later
Copyright (c) 2024-2024 Michael Chavinda
Author Michael Chavinda
Maintainer mschavinda@gmail.com
Category Data
Bug tracker https://github.com/mchav/dataframe/issues
Source repo head: git clone https://github.com/mchav/dataframe
Uploaded by mchav at 2025-07-13T06:59:31Z
Distributions LTSHaskell:0.2.0.2, Stackage:0.2.0.2
Executables dataframe
Downloads 41 total (11 in the last 30 days)
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Status Docs not available [build log]
All reported builds failed as of 2025-07-13 [all 2 reports]

Readme for dataframe-0.2.0.2

[back to package description]

DataFrame

An intuitive, dynamically-typed DataFrame library.

A tool for exploratory data analysis.

Installing

CLI

  • Install Haskell (ghc + cabal) via ghcup selecting all the default options.
  • To install dataframe run cabal update && cabal install dataframe
  • Open a Haskell repl with dataframe loaded by running cabal repl --build-depends dataframe.
  • Follow along any one of the tutorials below.

Jupyter notebook

What is exploratory data analysis?

We provide a primer here and show how to do some common analyses.

Coming from other dataframe libraries

Familiar with another dataframe library? Get started:

Example usage

Code example

import qualified DataFrame as D

import DataFrame ((|>))

main :: IO ()
    df <- D.readTsv "./data/chipotle.tsv"
    print $ df
      |> D.select ["item_name", "quantity"]
      |> D.groupBy ["item_name"]
      |> D.aggregate (zip (repeat "quantity") [D.Maximum, D.Mean, D.Sum])
      |> D.sortBy D.Descending ["Sum_quantity"]

Output:

----------------------------------------------------------------------------------------------------
index |               item_name               | Sum_quantity |   Mean_quantity    | Maximum_quantity
------|---------------------------------------|--------------|--------------------|-----------------
 Int  |                 Text                  |     Int      |       Double       |       Int       
------|---------------------------------------|--------------|--------------------|-----------------
0     | Chips and Fresh Tomato Salsa          | 130          | 1.1818181818181819 | 15              
1     | Izze                                  | 22           | 1.1                | 3               
2     | Nantucket Nectar                      | 31           | 1.1481481481481481 | 3               
3     | Chips and Tomatillo-Green Chili Salsa | 35           | 1.1290322580645162 | 3               
4     | Chicken Bowl                          | 761          | 1.0482093663911847 | 3               
5     | Side of Chips                         | 110          | 1.0891089108910892 | 8               
6     | Steak Burrito                         | 386          | 1.048913043478261  | 3               
7     | Steak Soft Tacos                      | 56           | 1.018181818181818  | 2               
8     | Chips and Guacamole                   | 506          | 1.0563674321503131 | 4               
9     | Chicken Crispy Tacos                  | 50           | 1.0638297872340425 | 2

Full example in ./app folder using many of the constructs in the API.

Visual example

Screencast of usage in GHCI

Supported input formats

  • CSV
  • Apache Parquet (still buggy and experimental)

Future work

  • Apache arrow compatability
  • Integration with common data formats (currently only supports CSV)
  • Support windowed plotting (currently only supports ASCII plots)

Contributing

  • Please first submit an issue and we can discuss there.