DataHaskell Starter

A ready-to-use VS Code Dev Container for doing data science in Haskell with Jupyter notebooks.

This repo is batteries included environment for the DataHaskell ecosystem:

• VS Code + Dev Containers
• IHaskell Jupyter kernel
• DataHaskell libraries (e.g. dataframe, hasktorch, etc.)
• Example notebooks to get you started

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Who is this for?

• You prefer an easy to use, pinned environment over installing everything locally.
• You like notebooks (Jupyter / VS Code) for exploration.

If you’re new to DataHaskell, this is the recommended way to get a working setup.

Setting up VS Code

You can set up a devcontainer in your current folder by running the command below:

• Linux/MacOS: curl -sSL https://raw.githubusercontent.com/DataHaskell/datahaskell-starter/refs/heads/main/setup.sh | sh

When you next open VS Code you should see a modal asking if you want to re-open the project in a devcontainer.

Running GHCi scripts

hscript allows you to run GHCi scripts without a main and with inline imports. It enables writing code like:

:set -package text
:set -XOverloadedStrings
:set -XTemplateHaskell

import qualified DataFrame as D
import qualified DataFrame.Functions as F
import Data.Text (Text)
import DataFrame ((|>))
import DataFrame.Functions ((.==), (.>))

-- Read Iris dataset
iris <- D.readParquet "../dataframe/data/iris.parquet"

-- Filter large setosas
iris |>
  D.filterWhere (F.col @Text "variety" .== "Setosa") |>
  D.filterWhere (F.col @Double "sepal.length" .> 5.4)

-- Declare column variables
_ = (); F.declareColumns iris

-- Create a new feature
D.derive "ratio" (sepal_width / sepal_length) iris

Linux/MacOS: Download https://raw.githubusercontent.com/DataHaskell/datahaskell-starter/refs/heads/main/hscript.sh and add it to your PATH.

You can then run files like the one above by typing:

hscript runme.hs

Example notebooks

Check out the example project in the notebooks directory.

Requirements

You’ll need:

• VS Code
• Docker (Docker Desktop on macOS/Windows, or Docker Engine on Linux)
• VS Code extensions:
  • Jupyter
  • Dev Containers
  • Haskell (recommended)

You do not need to install GHC, Cabal, or IHaskell on your host machine. Everything lives inside the container.