User guide
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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.
## Features
- Type-safe column operations with compile-time guarantees
- Familiar, approachable API designed to feel easy coming from other languages.
- Interactive REPL for data exploration and plotting.
## Quick start
Browse through some examples in [binder](https://mybinder.org/v2/gh/mchav/ihaskell-dataframe/HEAD) or in our [playground](https://ulwazi-exh9dbh2exbzgbc9.westus-01.azurewebsites.net/lab).
## Install
### Cabal
To use the CLI tool:
```bash
$ cabal update
$ cabal install dataframe
$ dataframe
```
As a prodject dependency add `dataframe` to your .cabal file.
### Stack (in stack.yaml add to extra-deps if needed)
Add to your package.yaml dependencies:
```yaml
dependencies:
- dataframe
```
Or manually to stack.yaml extra-deps if needed.
## Example
```haskell
dataframe> df = D.fromNamedColumns [("product_id", D.fromList [1,1,2,2,3,3]), ("sales", D.fromList [100,120,50,20,40,30])]
dataframe> df
------------------
product_id | sales
-----------|------
Int | Int
-----------|------
1 | 100
1 | 120
2 | 50
2 | 20
3 | 40
3 | 30
dataframe> :exposeColumns df
"product_id :: Expr Int"
"sales :: Expr Int"
dataframe> df |> D.groupBy [F.name product_id] |> D.aggregate [F.sum sales `as` "total_sales"]
------------------------
product_id | total_sales
-----------|------------
Int | Int
-----------|------------
1 | 220
2 | 70
3 | 70
```
## Documentation
* 📚 User guide: https://dataframe.readthedocs.io/en/latest/
* 📖 API reference: https://hackage.haskell.org/package/dataframe/docs/DataFrame.html
