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User guide |Discord

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.

• 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.

Browse through some examples inbinder or in ourplayground.

See theQuick Start guide for setup and installation instructions.

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|1001|1202|502|203|403|30   dataframe>:declareColumns 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|2202|703|70

• 📚 User guide:https://dataframe.readthedocs.io/en/latest/
• 📖 API reference:https://hackage.haskell.org/package/dataframe/docs/DataFrame.html