A fast, safe, and intuitive 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.
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.
-- 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.
- 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.
- 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
We provide a primer here and show how to do some common analyses.
Familiar with another dataframe library? Get started:
- CSV
- Apache Parquet
- CSV
- 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.