heidi: Tidy data in Haskell

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Tidy data in Haskell, via generics.

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Versions [RSS] 0.0.0, 0.1.0, 0.2.0, 0.3.0
Change log CHANGELOG.md
Dependencies base (>4.9 && <5), boxes (>=0.1.4), containers (>=, exceptions (>=0.8.3), generics-sop (>0.3.0), hashable (>=, heidi, microlens (>=0.4.8), microlens-th (>=0.4.1), scientific (>=, text (>=, unordered-containers (>0.2.8), vector (>= [details]
License MIT
Copyright (c) 2019-2020, Marco Zocca
Author Marco Zocca
Maintainer Marco Zocca
Category Data Science, Data Mining, Generics
Home page https://github.com/ocramz/heidi#readme
Bug tracker https://github.com/ocramz/heidi/issues
Source repo head: git clone https://github.com/ocramz/heidi
Uploaded by ocramz at 2021-06-17T09:24:35Z
Executables app
Downloads 640 total (16 in the last 30 days)
Rating 2.0 (votes: 1) [estimated by Bayesian average]
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Status Docs available [build log]
Last success reported on 2021-06-17 [all 1 reports]

Readme for heidi-0.3.0

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heidi : tidy data in Haskell

This library aims to bridge the gap between Haskell's precise but inflexible type discipline and the dynamic world of dataframes.

More specifically, heidi aims to make it easy to analyze collections of Haskell values; users encode their data (lists, maps and so on) into dataframes, and use functions provided by heidi for manipulation.

If this sounds interesting, read on!


A "dataframe" is conceptually a table of data that can be manipulated with a computer program; it potentially contains numbers, text and anything else that can be rendered as text.

In scientific practice, a "tidy" dataframe is a specific way of arranging the data in which each row represents a distinct observation ("data point") and each column a "feature" (i.e. some observable aspect) of the data.

Nowadays, data science is a very established practice and many software libraries offer excellent functionality for working with such dataframes. R has tidyverse , Python has pandas, and so on.

What about Haskell?

The Frames [1] library offers rigorous type safety and good runtime performance, at the cost of some setup overhead. Heidi's main design goal instead is to have minimal overhead and possibly very low cognitive load to data science practitioners, at the cost of some type safety.


The following snippet demonstrates the minimal setup necessary to use heidi :

{-# language DeriveGeneric #-}   (1)
module MyDataScienceTask where
import GHC.Generics    (2)

import Heidi

data Sales = Sales { item :: String, amount :: Int } deriving (Eq, Show, Generic)     (3)
instance Heidi Sales     (4)

All datatypes that are meant to be used within dataframes must be in the Heidi typeclass, which in turn requires a Generic instance.

The DeriveGeneric language extension (1) enables the compiler to automatically write the correct incantations (3), as long as the user also imports the GHC.Generics module (2) from base.

The automatic dataframe encoding mechanism is made possible by the empty Heidi instance (4).

It is also convenient to use DeriveAnyClass to avoid writing the empty typeclass instance :

{-# language DeriveGeneric, DeriveAnyClass #-}
data Foo = Foo Int String deriving (Generic, Heidi)


Out of the box, Haskell offers record types, e.g.

data Row a = MkRow { column1 :: Int, column2 :: String } deriving (Eq, Show)

which is handy because in one declaration you get a constructor method MkRow and accessors column1, column2, so a simple "data table" could be constructed as a list of such records, simply enough.

One thing that the language doesn't natively support is lookup by accessor name. For example column1 :: Row -> Int can only access a value of type Row, since the column1 name is globally unique (for a discussion on modern techniques to deal with this, see the Advanced section below).

In addition to lookup, many data tasks require relational operations across pairs of data tables; algorithmically, these require lookups both across rows and columns, and there's nothing in Haskell's implementation of records that supports this.

There are a number of additional tasks that are routine in data analysis such as plotting, rendering the dataset to various tabular formats (CSV, database ...), and this library aims to support those too with a convenient syntax.


Haskell offers a number of advanced workarounds for manipulating types, such as generic traversals, lookups, etc. A brief list of keywords is given in the following, for those inclined to dive into the rabbit hole.

Row polymorphism

Elm, Purescript etc.



Row types

As you might know, the "row types" problem is well understood and has been explored in practice; discussing the various tradeoffs between approaches would be lengthy and quite technical (and your humble author is not too qualified to do full justice to the topic either).

In Haskell , the Frames [2] library and related ecosystem stands out as a full-featured dataframe implementation that does not compromise on type safety.

Heidi instead offers generic transformations from the source datatypes to uni-typed values (conceptually, each row is a Map String T where data T = TInt Int | TChar Char etc.), a domain in which it's convenient to perform lookups and similar operations.

Exploring further : vinyl [3], heterogeneous lists, sums-of-products ...


[1] OverloadedRecordFields : https://downloads.haskell.org/ghc/latest/docs/html/users_guide/glasgow_exts.html#record-field-selector-polymorphism

[2] Frames : https://hackage.haskell.org/package/Frames

[3] vinyl : https://hackage.haskell.org/package/vinyl

[4] generics-sop : https://hackage.haskell.org/package/generics-sop