An intuitive, dynamically-typed DataFrame library.
A tool for exploratory data analysis.
- Install Haskell (ghc + cabal) via ghcup selecting all the default options.
- 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.
- Jupyter notebook is still underway with some local tests/examples in the works.
- For a preview check out the California Housing notebook.
We provide a primer here and show how to do some common analyses.
Familiar with another dataframe library? Get started:
import qualified DataFrame as D
import DataFrame ((|>))
main :: IO ()
df <- D.readTsv "./data/chipotle.tsv"
print $ df
|> D.select ["item_name", "quantity"]
|> D.groupBy ["item_name"]
|> D.aggregate (zip (repeat "quantity") [D.Maximum, D.Mean, D.Sum])
|> D.sortBy D.Descending ["Sum_quantity"]Output:
----------------------------------------------------------------------------------------------------
index | item_name | Sum_quantity | Mean_quantity | Maximum_quantity
------|---------------------------------------|--------------|--------------------|-----------------
Int | Text | Int | Double | Int
------|---------------------------------------|--------------|--------------------|-----------------
0 | Chips and Fresh Tomato Salsa | 130 | 1.1818181818181819 | 15
1 | Izze | 22 | 1.1 | 3
2 | Nantucket Nectar | 31 | 1.1481481481481481 | 3
3 | Chips and Tomatillo-Green Chili Salsa | 35 | 1.1290322580645162 | 3
4 | Chicken Bowl | 761 | 1.0482093663911847 | 3
5 | Side of Chips | 110 | 1.0891089108910892 | 8
6 | Steak Burrito | 386 | 1.048913043478261 | 3
7 | Steak Soft Tacos | 56 | 1.018181818181818 | 2
8 | Chips and Guacamole | 506 | 1.0563674321503131 | 4
9 | Chicken Crispy Tacos | 50 | 1.0638297872340425 | 2
Full example in ./app folder using many of the constructs in the API.
- Jupyter/ihaskell support (soon)
- Apache arrow and Parquet compatability
- Integration with common data formats (currently only supports CSV)
- Support windowed plotting (currently only supports ASCII plots)
- Create a lazy API that builds an execution graph instead of running eagerly (will be used to compute on files larger than RAM)
- Please first submit an issue and we can discuss there.