derive

Transforms the data mid-pipeline with an arbitrary Python function. This is the Python API's most powerful operator — your function runs in your kernel, with the full power of pandas, NumPy, or plain Python.

from gofish import chart, spread, derive, stack, rect, normalize

chart(data).flow(
    spread(by="origin", dir="x"),
    derive(lambda d: normalize(d, "count")),
    stack(by="cylinders", dir="y"),
).mark(rect(h="count", fill="origin", stroke="white", strokeWidth=2)).render(
    w=500, h=300, axes=True
)

Signature

derive(fn) -> DeriveOperator

Parameters

Parameter	Type	Description
fn	callable	A function that receives the current data and returns new data

Returns a DeriveOperator for use inside .flow().

How it works

When the chart renders, the engine calls back into your Python kernel for each derive step: it sends the current group's data to Python, runs fn, and uses the returned data for the rest of the pipeline. The function can return a list of dicts or a pandas DataFrame.

# Keep only large catches
chart(seafood).flow(
    spread(by="lake", dir="x"),
    derive(lambda rows: [r for r in rows if r["count"] > 20]),
).mark(rect(h="count"))

Utility functions

GoFish ships small helpers that pair well with derive:

• normalize(data, field) — scale field so the values sum to 1.
• repeat(row, field) — repeat a row row[field] times.

from gofish import derive, normalize

derive(lambda d: normalize(d, "count"))

Notes

• derive runs in your kernel — anything importable in your notebook is fair game.
• Because it round-trips to Python, a derive step is a callback, not a static transform; it re-runs whenever the chart re-renders.