Compose multiple unary functions into a pipeline.

Express the idea of function composition in a reusable, type‑annotated form.

When you need to apply a series of transformations to data in a fixed order, e.g., data cleaning pipelines or middleware chains.

Functional programming, data processing pipelines, middleware chains, API middleware.

def pipeline(*funcs: Callable[[Any], Any]) -> Callable[[Any], Any]:

def pipeline(*funcs: Callable[[Any], Any]) -> Callable[[Any], Any]:

*funcs (variadic Callable[[Any], Any]) -> return Callable[[Any], Any]

• Callable
• Any
• lambda
• functools.reduce
• operator.iconcat

• functools.reduce with operator.iconcat
• toolz.pipe
• itertools.chain

forgetting to import Callable from typing, returning None instead of a lambda, forgetting to handle zero functions (should return identity), misordering of function application.

functools.reduce, operator.iconcat (Python 3.8+), itertools.chain, toolz.pipe, Unix pipelines.

Confusing *funcs with *args, assuming right‑to‑left composition like mathematics, forgetting that each function must accept exactly one argument.

• function composition
• higher‑order functions
• functional pipelines

The returned function applies the functions left‑to‑right (first function first), matching Unix‑pipeline intuition; if zero functions are given it should return the identity function.

Signals the intention to compose transformations into a reusable processing step.

Think of a Unix pipe: data flows through each function in order.

Remember to import Callable from typing and handle the zero‑function case by returning lambda x: x.

functools.reduce with operator.iconcat or toolz.pipe for more advanced composition.