List Modification

Overview

Lists are mutable — they provide methods to add, remove, and rearrange elements in place. Knowing the time complexity (O(1) vs O(n)) of each operation is critical for performance-aware coding.

Syntax

python

lst = [1, 2, 3]

# Add elements
lst.append(4)       # [1, 2, 3, 4]     O(1)
lst.insert(1, 10)   # [1, 10, 2, 3, 4] O(n)
lst.extend([5, 6])  # adds multiple     O(k)

# Remove elements
lst.pop()           # removes last      O(1)
lst.pop(0)          # removes index 0   O(n)
lst.remove(10)      # removes by value  O(n)

# Other
lst.sort()          # in-place sort
lst.reverse()       # in-place reverse
lst.clear()         # empty the list

Common Pitfalls

list.pop(0) is O(n) because all elements shift — use collections.deque for O(1) operations on both ends.
list.remove() removes only the FIRST occurrence — use a list comprehension to remove all occurrences.
Interview tip: append() is O(1) amortized (list doubles capacity when full); insert(0, x) is O(n) due to shifting.

Real-World Example

Maintain a fixed-size history log using append and slicing

example

python

class HistoryLog:
    def __init__(self, max_size: int = 10):
        self._log: list[str] = []
        self._max = max_size

    def add(self, entry: str) -> None:
        self._log.append(entry)
        if len(self._log) > self._max:
            self._log.pop(0)   # remove oldest (consider collections.deque)

    def get(self) -> list[str]:
        return self._log.copy()

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