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DataFrame

A fast, safe, and intuitive DataFrame library.

Why use this DataFrame library?

• Encourages concise, declarative, and composable data pipelines.
• Static typing makes code easier to reason about and catches many bugs at compile time—before your code ever runs.
• 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.

Example usage

Interactive environment

Key features in example:

• Intuitive, SQL-like API to get from data to insights.
• Create typed, completion-ready references to columns in a dataframe using :exposeColumns
• Type-safe column transformations for faster and safer exploration.
• Fluid, chaining API that makes code easy to reason about.

Standalone script example

-- Useful Haskell extensions.
{-# LANGUAGE OverloadedStrings #-} -- Allow string literal to be interpreted as any other string type.
{-# LANGUAGE TypeApplications #-} -- Convenience syntax for specifiying the type `sum a b :: Int` vs `sum @Int a b'. 

import qualified DataFrame as D -- import for general functionality.
import qualified DataFrame.Functions as F -- import for column expressions.

import DataFrame ((|>)) -- import chaining operator with unqualified.

main :: IO ()
main = do
    df <- D.readTsv "./data/chipotle.tsv"
    let quantity = F.col "quantity" :: D.Expr Int -- A typed reference to a column.
    print (df
      |> D.select ["item_name", "quantity"]
      |> D.groupBy ["item_name"]
      |> D.aggregate [ (F.sum quantity)     `F.as` "sum_quantity"
                     , (F.mean quantity)    `F.as` "mean_quantity"
                     , (F.maximum quantity) `F.as` "maximum_quantity"
                     ]
      |> D.sortBy D.Descending ["sum_quantity"]
      |> D.take 10)

Output:

------------------------------------------------------------------------------------------
index |          item_name           | sum_quantity |    mean_quanity    | maximum_quanity
------|------------------------------|--------------|--------------------|----------------
 Int  |             Text             |     Int      |       Double       |       Int      
------|------------------------------|--------------|--------------------|----------------
0     | Chicken Bowl                 | 761          | 1.0482093663911847 | 3              
1     | Chicken Burrito              | 591          | 1.0687160940325497 | 4              
2     | Chips and Guacamole          | 506          | 1.0563674321503131 | 4              
3     | Steak Burrito                | 386          | 1.048913043478261  | 3              
4     | Canned Soft Drink            | 351          | 1.1661129568106312 | 4              
5     | Chips                        | 230          | 1.0900473933649288 | 3              
6     | Steak Bowl                   | 221          | 1.04739336492891   | 3              
7     | Bottled Water                | 211          | 1.3024691358024691 | 10             
8     | Chips and Fresh Tomato Salsa | 130          | 1.1818181818181819 | 15             
9     | Canned Soda                  | 126          | 1.2115384615384615 | 4 

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

Installing

Jupyter notebook

• We have a hosted version of the Jupyter notebook on azure sites. This is hosted on Azure's free tier so it can only support 3 or 4 kernels at a time.
• To get started quickly, use the Dockerfile in the ihaskell-dataframe to build and run an image with dataframe integration.
• For a preview check out the California Housing notebook.

CLI

• Run the installation script curl '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/mchav/dataframe/refs/heads/main/scripts/install.sh | sh
• Download the run script with: curl --output dataframe "https://raw.githubusercontent.com/mchav/dataframe/refs/heads/main/scripts/dataframe.sh"
• Make the script executable: chmod +x dataframe
• Add the script your path: export PATH=$PATH:./dataframe
• Run the script with: dataframe

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:

• Coming from Pandas
• Coming from Polars
• Coming from dplyr

Supported input formats

• CSV
• Apache Parquet

Supported output formats

• CSV

Future work

• Apache arrow compatability
• Integration with common data formats (currently only supports CSV)
• Support windowed plotting (currently only supports ASCII plots)
• Host the whole library + Jupyter lab on Azure with auth and isolation.