Lists & Tuples

Q: list or tuple for return values?

Tuple is conventional for fixed-shape returns (value, error). Use NamedTuple when fields need names.

Q: sort vs sorted?

list.sort() mutates in place returning None. sorted(iterable) returns new list - works on any iterable.

Q: How does negative indexing work?

-1 is last element. Slice [:-1] excludes last item.

Q: What is list.extend vs +?

extend mutates in place. + creates new list - slower for large sequences.

Q: What is key= in sort?

Computes sort key once per element: sorted(users, key=lambda u: u.name.lower()).

Q: How do I reverse in place?

items.reverse() or items[::-1] for copy.

Lists and tuples are Python's primary ordered sequences. Lists excel when you need mutability and methods like sort; tuples signal fixed records and enable hashability when contents allow.

Recipe

nums = [3, 1, 4, 1, 5]
nums.sort()
unique_sorted = sorted(set(nums))
 
row = ("ada", 1842)
name, year = row
subset = nums[1:4:2]

When to reach for this:

Building ordered collections with append/pop
Returning fixed multi-value results from functions
Slicing and unpacking in data processing
Maintaining sorted sequences with bisect

Working Example

import bisect
from typing import NamedTuple
 
class Event(NamedTuple):
    ts: float
    kind: str
 
def top_k(items: list[int], k: int) -> list[int]:
    return sorted(items, reverse=True)[:k]
 
def insert_timestamped(events: list[Event], ts: float, kind: str) -> None:
    bisect.insort(events, Event(ts, kind))
 
def partition(pred, items: list[object]) -> tuple[list[object], list[object]]:
    yes, no = [], []
    for item in items:
        (yes if pred(item) else no).append(item)
    return yes, no
 
if __name__ == "__main__":
    timeline: list[Event] = []
    insert_timestamped(timeline, 1.0, "start")
    insert_timestamped(timeline, 0.5, "warmup")
    print(timeline)
    print(top_k([4, 1, 7, 3], 2))

What this demonstrates:

NamedTuple gives tuple immutability with named fields
bisect.insort keeps sorted order for insertions
sorted returns new list without mutating original
Tuple return packs two related lists from partition

Deep Dive

How It Works

Lists - Dynamic arrays; over-allocate for amortized O(1) append.
Tuples - Fixed size at creation; smaller memory footprint than lists.
Slicing - start:stop:step creates shallow copy of sequence portion.
Sorting - Timsort; stable; key= avoids decorating manually.
bisect - Binary search insertion point for sorted lists.

list vs tuple

Need	Pick
Mutate, append, sort	`list`
Dict key / set member	`tuple` (if hashable)
Function return bundle	`tuple` or `NamedTuple`
Homogeneous numeric buffer	`array.array` or numpy

Python Notes

# copy vs view
a = [1, 2, 3]
b = a[:]       # shallow copy
c = list(a)    # same
 
# tuple unpacking with *
first, *middle, last = (1, 2, 3, 4)

Gotchas

Shallow copy of nested list - Inner lists still shared. Fix: copy.deepcopy.
Sorting mixed types - TypeError in Python 3. Fix: Normalize to comparable values or use key.
bisect on unsorted list - Wrong insertion position silently. Fix: Keep invariant documented or sort first.
+= creates new tuple - t += (1,) rebinds; does not mutate tuple (immutability). Fix: Expected - assign new tuple.
Large list as set member - Lists unhashable. Fix: Convert to tuple or use dict keyed by id.

Alternatives

Alternative	Use When	Don't Use When
`deque`	Queue/stack at both ends	Need sorting/random access by index often
`array.array`	Compact numeric C types	Mixed object types
`numpy.ndarray`	Vectorized numerics	Small script lists
`dataclass`	Mutable records with methods	Need hashable/immutable record

FAQs

list or tuple for return values?

Tuple is conventional for fixed-shape returns (value, error). Use NamedTuple when fields need names.

sort vs sorted?

list.sort() mutates in place returning None. sorted(iterable) returns new list - works on any iterable.

How does negative indexing work?

-1 is last element. Slice [:-1] excludes last item.

When is bisect worth it?

Frequent inserts into a mostly sorted list where linear scan would hurt - modest sizes still fine with plain list.

Can tuples contain lists?

Yes but then tuple is unhashable. Cannot use as dict key.

What is list.extend vs +?

extend mutates in place. + creates new list - slower for large sequences.

How do I remove while iterating?

Iterate a copy or build new list - mutating during iteration skips elements.

Are list comprehensions always better?

Often for map/filter. Plain loop clearer for complex branches or side effects.

What is key= in sort?

Computes sort key once per element: sorted(users, key=lambda u: u.name.lower()).

How do I reverse in place?

items.reverse() or items[::-1] for copy.

Immutability & Hashability - hashable tuples
Dictionaries - when lists become values
heapq & Priority Queues - ordering by priority
Comprehensions & Generator Expressions - building lists

Python Tutorial (Official) - Official beginner-to-intermediate tutorial.
Python Standard Library Reference - Complete documentation for built-in modules and functions.
Real Python - In-depth tutorials, courses, and practical guides.
Python Crash Course (3rd Edition) - Beginner-friendly projects (Eric Matthes).
Fluent Python (2nd Edition, O'Reilly) - Same book on O'Reilly.

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Recipe

nums = [3, 1, 4, 1, 5]
nums.sort()
unique_sorted = sorted(set(nums))
 
row = ("ada", 1842)
name, year = row
subset = nums[1:4:2]

When to reach for this:

Building ordered collections with append/pop
Returning fixed multi-value results from functions
Slicing and unpacking in data processing
Maintaining sorted sequences with bisect

Working Example

import bisect
from typing import NamedTuple
 
class Event(NamedTuple):
    ts: float
    kind: str
 
def top_k(items: list[int], k: int) -> list[int]:
    return sorted(items, reverse=True)[:k]
 
def insert_timestamped(events: list[Event], ts: float, kind: str) -> None:
    bisect.insort(events, Event(ts, kind))
 
def partition(pred, items: list[object]) -> tuple[list[object], list[object]]:
    yes, no = [], []
    for item in items:
        (yes if pred(item) else no).append(item)
    return yes, no
 
if __name__ == "__main__":
    timeline: list[Event] = []
    insert_timestamped(timeline, 1.0, "start")
    insert_timestamped(timeline, 0.5, "warmup")
    print(timeline)
    print(top_k([4, 1, 7, 3], 2))

What this demonstrates:

NamedTuple gives tuple immutability with named fields
bisect.insort keeps sorted order for insertions
sorted returns new list without mutating original
Tuple return packs two related lists from partition

Deep Dive

How It Works

Lists - Dynamic arrays; over-allocate for amortized O(1) append.
Tuples - Fixed size at creation; smaller memory footprint than lists.
Slicing - start:stop:step creates shallow copy of sequence portion.
Sorting - Timsort; stable; key= avoids decorating manually.
bisect - Binary search insertion point for sorted lists.

list vs tuple

Need	Pick
Mutate, append, sort	`list`
Dict key / set member	`tuple` (if hashable)
Function return bundle	`tuple` or `NamedTuple`
Homogeneous numeric buffer	`array.array` or numpy

Python Notes

# copy vs view
a = [1, 2, 3]
b = a[:]       # shallow copy
c = list(a)    # same
 
# tuple unpacking with *
first, *middle, last = (1, 2, 3, 4)

Gotchas

Shallow copy of nested list - Inner lists still shared. Fix: copy.deepcopy.
Sorting mixed types - TypeError in Python 3. Fix: Normalize to comparable values or use key.
bisect on unsorted list - Wrong insertion position silently. Fix: Keep invariant documented or sort first.
+= creates new tuple - t += (1,) rebinds; does not mutate tuple (immutability). Fix: Expected - assign new tuple.
Large list as set member - Lists unhashable. Fix: Convert to tuple or use dict keyed by id.

Alternatives

Alternative	Use When	Don't Use When
`deque`	Queue/stack at both ends	Need sorting/random access by index often
`array.array`	Compact numeric C types	Mixed object types
`numpy.ndarray`	Vectorized numerics	Small script lists
`dataclass`	Mutable records with methods	Need hashable/immutable record

FAQs

list or tuple for return values?

Tuple is conventional for fixed-shape returns (value, error). Use NamedTuple when fields need names.

sort vs sorted?

list.sort() mutates in place returning None. sorted(iterable) returns new list - works on any iterable.

How does negative indexing work?

-1 is last element. Slice [:-1] excludes last item.

When is bisect worth it?

Frequent inserts into a mostly sorted list where linear scan would hurt - modest sizes still fine with plain list.

Can tuples contain lists?

Yes but then tuple is unhashable. Cannot use as dict key.

What is list.extend vs +?

extend mutates in place. + creates new list - slower for large sequences.

How do I remove while iterating?

Iterate a copy or build new list - mutating during iteration skips elements.

Are list comprehensions always better?

Often for map/filter. Plain loop clearer for complex branches or side effects.

What is key= in sort?

Computes sort key once per element: sorted(users, key=lambda u: u.name.lower()).

How do I reverse in place?

items.reverse() or items[::-1] for copy.

Immutability & Hashability - hashable tuples
Dictionaries - when lists become values
heapq & Priority Queues - ordering by priority
Comprehensions & Generator Expressions - building lists

Python Tutorial (Official) - Official beginner-to-intermediate tutorial.
Python Standard Library Reference - Complete documentation for built-in modules and functions.
Real Python - In-depth tutorials, courses, and practical guides.
Python Crash Course (3rd Edition) - Beginner-friendly projects (Eric Matthes).
Fluent Python (2nd Edition, O'Reilly) - Same book on O'Reilly.

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Lists & Tuples

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Lists & Tuples