peppermint-lang 0.3.1

A pipe-first DSL for data and ML work

Peppermint

A pipe-first language for data and ML work. Every operation is a pipeline step. Errors propagate automatically. The transform is the notation.

Install

pip install peppermint-lang

Run

pep file.pep  # run a file
pep           # interactive REPL

Examples

Transform

load("employees.csv")
  |> filter(it.age > 18)
  |> add(tax: it.salary * 0.2)
  |> sort(by: "salary", dir: "desc")
  |> print()

Each step prints a live summary:

|> filter    → List  843 rows × 5 cols  (157 dropped)
|> add       → List  843 rows × 6 cols  (+tax)
|> sort      → List  843 rows × 6 cols

Aggregate

load("sales.csv")
  |> collapse(by: "region",
      avg: mean(col.revenue),
      n:   count()
  )
  |> sort(by: "avg", dir: "desc")
  |> print()

Top N per group

load("sales.csv")
  |> each(by: "region",
      |> add(rank: rank(col.revenue, dir: "desc"))
      |> filter(it.rank <= 3)
      |> drop("rank")
  )
  |> print()

ML pipeline

use ml
use viz
use env

load("data.csv")
  |> ml.embed(
      on: "text", out: "embedding",
      source: "deepinfra", model: "Qwen/Qwen3-Embedding-4B",
      apikey: env.get("DEEPINFRA_TOKEN"))
  |> ml.kmeans(k: 2..8, on: "embedding", out: "cluster")
  |> ml.umap(dims: 2, on: "embedding", out: "umap")
  |> viz.scatter(x: "umap1", y: "umap2", color: "cluster", label: "text", display: ["labels", "legend"])

Error handling

result = load("data.csv")
  |> filter(it.score > 0.5)

match(result,
  Ok(data): data |> print(),
  Err(msg):  print(msg)
)

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See docs/language.md for the full reference and examples/ for more.