dataframe
A fast, safe, and intuitive DataFrame library.
| Version on this page: | 0.3.0.0 |
| LTS Haskell 24.2: | 0.2.0.2 |
| Stackage Nightly 2025-07-29: | 0.3.0.0 |
| Latest on Hackage: | 0.3.0.1 |
Maintained by [email protected]
This version can be pinned in stack with:
dataframe-0.3.0.0@sha256:1660d9f3ea193b1d9762336ced0b0c8f6e95915deb5af7a4aaaf26c3bc8d4357,12247Module documentation for 0.3.0.0
Depends on 17 packages(full list with versions):
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.
Example usage
Interactive environment
ghci> import qualified DataFrame as D
ghci> import DataFrame ((|>))
ghci> df <- D.readCsv "./data/housing.csv"
ghci> D.columnInfo df
--------------------------------------------------------------------------------------------------------------------
index | Column Name | # Non-null Values | # Null Values | # Partially parsed | # Unique Values | Type
------|--------------------|-------------------|---------------|--------------------|-----------------|-------------
Int | Text | Int | Int | Int | Int | Text
------|--------------------|-------------------|---------------|--------------------|-----------------|-------------
0 | total_bedrooms | 20433 | 207 | 0 | 1924 | Maybe Double
1 | ocean_proximity | 20640 | 0 | 0 | 5 | Text
2 | median_house_value | 20640 | 0 | 0 | 3842 | Double
3 | median_income | 20640 | 0 | 0 | 12928 | Double
4 | households | 20640 | 0 | 0 | 1815 | Double
5 | population | 20640 | 0 | 0 | 3888 | Double
6 | total_rooms | 20640 | 0 | 0 | 5926 | Double
7 | housing_median_age | 20640 | 0 | 0 | 52 | Double
8 | latitude | 20640 | 0 | 0 | 862 | Double
9 | longitude | 20640 | 0 | 0 | 844 | Double
ghci> :exposeColumns df
ghci> import qualified DataFrame.Functions as F
ghci> df |> D.groupBy ["ocean_proximity"] |> D.aggregate [(F.mean median_house_value) `F.as` "avg_house_value" ]
--------------------------------------------
index | ocean_proximity | avg_house_value
------|-----------------|-------------------
Int | Text | Double
------|-----------------|-------------------
0 | <1H OCEAN | 240084.28546409807
1 | INLAND | 124805.39200122119
2 | ISLAND | 380440.0
3 | NEAR BAY | 259212.31179039303
4 | NEAR OCEAN | 249433.97742663656
ghci> df |> D.derive "rooms_per_household" (total_rooms / households) |> D.take 10
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
index | longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | rooms_per_household
------|-----------|----------|--------------------|-------------|----------------|------------|------------|--------------------|--------------------|-----------------|--------------------
Int | Double | Double | Double | Double | Maybe Double | Double | Double | Double | Double | Text | Double
------|-----------|----------|--------------------|-------------|----------------|------------|------------|--------------------|--------------------|-----------------|--------------------
0 | -122.23 | 37.88 | 41.0 | 880.0 | Just 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY | 6.984126984126984
1 | -122.22 | 37.86 | 21.0 | 7099.0 | Just 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY | 6.238137082601054
2 | -122.24 | 37.85 | 52.0 | 1467.0 | Just 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY | 8.288135593220339
3 | -122.25 | 37.85 | 52.0 | 1274.0 | Just 235.0 | 558.0 | 219.0 | 5.6431000000000004 | 341300.0 | NEAR BAY | 5.8173515981735155
4 | -122.25 | 37.85 | 52.0 | 1627.0 | Just 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY | 6.281853281853282
5 | -122.25 | 37.85 | 52.0 | 919.0 | Just 213.0 | 413.0 | 193.0 | 4.0368 | 269700.0 | NEAR BAY | 4.761658031088083
6 | -122.25 | 37.84 | 52.0 | 2535.0 | Just 489.0 | 1094.0 | 514.0 | 3.6591 | 299200.0 | NEAR BAY | 4.9319066147859925
7 | -122.25 | 37.84 | 52.0 | 3104.0 | Just 687.0 | 1157.0 | 647.0 | 3.12 | 241400.0 | NEAR BAY | 4.797527047913447
8 | -122.26 | 37.84 | 42.0 | 2555.0 | Just 665.0 | 1206.0 | 595.0 | 2.0804 | 226700.0 | NEAR BAY | 4.294117647058823
9 | -122.25 | 37.84 | 52.0 | 3549.0 | Just 707.0 | 1551.0 | 714.0 | 3.6912000000000003 | 261100.0 | NEAR BAY | 4.970588235294118
ghci> df |> D.derive "nonsense_feature" (latitude + ocean_proximity) |> D.take 10
<interactive>:14:47: error: [GHC-83865]
• Couldn't match type ‘Text’ with ‘Double’
Expected: Expr Double
Actual: Expr Text
• In the second argument of ‘(+)’, namely ‘ocean_proximity’
In the second argument of ‘derive’, namely
‘(latitude + ocean_proximity)’
In the second argument of ‘(|>)’, namely
‘derive "nonsense_feature" (latitude + ocean_proximity)’
Key features in example:
- Intuitive, SQL-like API to get from data to insights.
- Create type-safe references to columns in a dataframe using
:exponseColumns - 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.
Visual example
Installing
Jupyter notebook
- We have a hosted version of the Jupyter notebook on azure sites.
- 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
- Install Haskell (ghc + cabal) via ghcup selecting all the default options.
- Install snappy (needed for Parquet support) by running:
sudo apt install libsnappy-dev. - 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.
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:
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)
- Host the whole library + Jupyter lab on Azure with auth and isolation.