Python lambda functions: one expression and the same closure rules

Operation contract

The dispatch sort key selects priority and then receipt ID so tied priorities have an explicit presentation order. Sorting produces a separate outer list. The callback list binds the current receipt ID as a default argument; otherwise later calls would read the loop name’s final binding. Lambda changes the syntax, not the ownership model.

Failure and ownership boundary

A key function should not mutate the collection being sorted. A captured mutable object remains shared even when its reference was bound through a default. Multi-step validation, logging and failure handling are easier to review in a named function than in a crowded expression. Python scope: local assignment, nonlocal state and late binding and Python sorting: stable keys, independent output and explicit tie rules supply the underlying behavior.

Working program

receipts = [(2, "R-0042"), (1, "R-0041"), (2, "R-0040")]
ordered = sorted(receipts, key=lambda receipt: (receipt[0], receipt[1]))
print(ordered)
callbacks = [lambda receipt_id=receipt_id: receipt_id for receipt_id in ("R-0041", "R-0042")]
print([callback() for callback in callbacks])
print("input unchanged:", receipts[0] == (2, "R-0042"))

Output

[(1, 'R-0041'), (2, 'R-0040'), (2, 'R-0042')]
['R-0041', 'R-0042']
input unchanged: True

Costs and limits

Sorting n entries has O(n log n) worst-case comparison work and O(n) auxiliary storage in this use. Key construction adds its own per-entry work. Creating n callbacks retains O(n) functions/default references.

Common Mistakes

• Lambda does not freeze every captured value.
• Use a named function when an expression hides several domain decisions.

Connected lessons

Python scope: local assignment, nonlocal state and late binding, Python function arguments: avoid shared mutable defaults, Python sorting: stable keys, independent output and explicit tie rules.