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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA Python function packages reusable behavior behind a name. Define it with def, pass arguments when you call it, and use return when the caller needs a result. The details that make functions reliable—parameter kinds, defaults, scope, variadic arguments, docstrings, annotations and lambdas—are all part of Python’s function model.
Define and call a function
A definition binds a name to a function object; its indented body runs only when the function is called.
def greet(name):
"""Return a greeting for one person."""
return f"Hello, {name}!"
message = greet("Mina")
print(message) # Hello, Mina!The first string literal in the body is the function’s docstring. It is available through greet.__doc__ and to documentation tools, so documenting public functions is a useful habit.
Parameters are the names written in the definition, such as name. Arguments are the values supplied by a call, such as "Mina". A function that reaches the end without an explicit value returns None.
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def announce(text):
print(text)
result = announce("Ready")
print(result is None) # TruePrinting is an output side effect; returning is how a function gives a value back for assignment, comparison or further computation.
Understand names, scope and object references
Each call gets a local symbol table. Arguments become local names, and assignments in the body normally create or update local names. Python resolves names through its local, enclosing, global and built-in scopes; use nonlocal or global only when deliberately changing an enclosing or module-level binding.
def add_item(items, item):
items.append(item) # mutates the object supplied by the caller
return items
basket = ["book"]
add_item(basket, "pen")
print(basket) # ['book', 'pen']The parameter receives a reference to the object. Rebinding the parameter does not rebind the caller’s variable, while mutating a shared mutable object is visible to the caller.
Choose a parameter kind deliberately
Without special markers, parameters can normally be supplied positionally or by keyword. A slash makes parameters before it positional-only; a standalone asterisk makes parameters after it keyword-only.
def f(pos_only, /, flexible, *, named):
return pos_only, flexible, named
f(1, 2, named=3) # valid
f(1, flexible=2, named=3) # valid
# f(pos_only=1, flexible=2, named=3) # TypeError| Kind | How callers provide it | When it helps |
|---|---|---|
| Positional-only | By position, before / |
When the parameter name is an implementation detail or may change without breaking callers. |
| Positional-or-keyword | By position or matching keyword | For ordinary values where both concise calls and named calls are useful. |
| Keyword-only | By keyword, after * |
When names carry meaning or you want to prevent callers relying on argument position. |
Keyword arguments can appear in a different order from the definition, but each parameter can receive a value only once. Required parameters must be supplied, and an unknown keyword raises TypeError unless the signature accepts extra keywords.
Defaults: definition-time evaluation and the mutable-default trap
Python evaluates a default expression when the def statement executes, not afresh for every call. As the Python tutorial puts it, “The default value is evaluated only once.”
def add_tag(tag, tags=[]):
tags.append(tag)
return tags
print(add_tag("python")) # ['python']
print(add_tag("guide")) # ['python', 'guide']The same list is reused, so mutations persist between calls. That persistence can be intentional, but it is usually surprising when a caller expects a new container.
def add_tag(tag, tags=None):
if tags is None:
tags = []
tags.append(tag)
return tags
print(add_tag("python")) # ['python']
print(add_tag("guide")) # ['guide']Use a sentinel such as None when each call should receive fresh state. Do not use a mutable default merely as a per-call initializer.
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Use *args, **kwargs and unpacking with intent
In a definition, *args collects extra positional arguments into a tuple, while **kwargs collects extra keyword arguments into a mapping.
def log_event(event, *args, **kwargs):
print(event, args, kwargs)
log_event("download", "report.pdf", user="Mina")
# download ('report.pdf',) {'user': 'Mina'}At a call site, the same symbols unpack values instead of collecting them:
numbers = [2, 4, 6]
options = {"sep": ", "}
print(*numbers, **options) # 2, 4, 6Use variadic parameters for forwarding, wrappers and genuinely open-ended inputs. Otherwise, explicit parameters communicate the contract more clearly; arbitrary argument lists are the least frequently used signature style in the official tutorial.
Write lambdas only for small expressions
A lambda creates a function from one expression:
records = [("Ada", 91), ("Lin", 84), ("Sam", 97)]
records.sort(key=lambda record: record[1])Lambda is useful where a short function object is needed, such as a sorting key. It cannot contain statements, and it has no room for a descriptive multi-line interface. Prefer a named def when the logic needs several steps, a meaningful name or a docstring.
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Add annotations without mistaking them for enforcement
def area(width: float, height: float) -> float:
"""Return the area of a rectangle."""
return width * heightAnnotations are optional metadata stored on the function. They can document intended types and support editors, linters and type checkers, but ordinary Python calls do not automatically enforce them.
Design signatures for clarity and change
When several signatures could solve the same task, evaluate them against the caller’s needs:
- Clarity: use keyword-only parameters when a value’s meaning is easy to miss by position.
- Compatibility: use positional-only parameters when exposing a parameter name would unnecessarily tie future API changes to that name.
- State safety: avoid unintended mutable default reuse; initialize per-call containers inside the body.
- Contract precision: prefer named parameters over unrestricted
*argsand**kwargsunless flexibility is part of the design. - Return behavior: return data for callers to use; reserve printing for deliberate side effects such as command-line output.
A compact signature can express these decisions directly:
def render(title, /, body, *, width=80, theme="light"):
...Here, title must be positional, body accepts either style, and width and theme must be named. Such constraints make calls self-documenting and reduce accidental dependence on argument order.
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