Python Data Structures and Time Complexity Practice

Vocabulary and concept flashcards covering Python time complexity analysis, set vs list operations, dictionary characteristics, list slicing, and code output evaluation from the lecture transcript.

Fastest Growing Time Complexity

Among $O(1)$, $O(n)$, $O(n^2)$, and $O(2^n)$, the fastest growing time complexity is $O(2^n)$.

List vs. Set

A set is unordered, and a list is ordered.

The time complexity is $O(n)$ because the inner loop only executes for indices where $i < 120$, which is a constant bound, leaving the outer loop $O(n)$ as dominant.

Iterating through range(0, 15, 3) prints $0, 3, 6, 9, 12$.

Which of the following best describes how a dictionary works?

A data structure that stores key/value pairs and uses a hash function to help find values quickly.

For $lst = [1, 2, 3, 4, 5]$, the slice $lst[1:4]$ returns elements from index $1$ up to index $3$, resulting in $[2, 3, 4]$.

Assigning d['b'] = 99 updates the value corresponding to key 'b' in dictionary d.

For a nested loop where the inner loop runs from $i$ to $len(nums)$, the time complexity is $O(n^2)$.

Which operation is faster: checking if an element is in a set or in a list?

Checking if an element is in a set is faster than in a list because sets use hash tables for lookups.

True