LeetCode Python Solutions: 349 Intersection of Two Arrays #python #coding #leetcode

ZeroStress LeetCode Python Solutions: 349 Intersection of Two Arrays Problem #python #leetcode

https://leetcode.com/problems/intersection-of-two-arrays/solutions/3577279/video-one-line-solution-efficient-space-complexity/

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The algorithm used in this code is straightforward. By converting the input lists to sets, duplicate elements are eliminated, and the & operator is used to find the common elements between the two sets. Converting the set back to a list ensures the desired output format.

The time complexity of this solution is determined by the set operations and list conversion. Let's analyze each step:

Converting nums1 and nums2 into sets using set() takes O(n) time complexity, where n is the length of the longer array.

Performing the set intersection using the & operator takes O(min(n1, n2)) time complexity, where n1 and n2 are the lengths of the two sets obtained in the previous step.

Converting the resulting set back to a list using list() takes O(k) time complexity, where k is the size of the resulting set.

Therefore, the overall time complexity is O(n + min(n1, n2) + k), where n is the length of the longer array, n1 and n2 are the lengths of the two sets, and k is the size of the resulting set.

As for space complexity:

Converting nums1 and nums2 into sets using set() requires additional space to store the unique elements. The space complexity for this step is O(n1 + n2), where n1 and n2 are the lengths of the two arrays.

The space complexity for the resulting set obtained from the set intersection is O(k), where k is the size of the resulting set.

Converting the set back to a list using list() requires additional space to store the list of elements. The space complexity for this step is O(k).

Therefore, the overall space complexity is O(n1 + n2 + k), where n1 and n2 are the lengths of the two arrays, and k is the size of the resulting list.

In summary, the time complexity of this solution is determined by the lengths of the arrays and the size of the resulting list, while the space complexity depends on the lengths of the arrays and the size of the resulting set/list.

Understanding and applying the concept of sets, set operations, and list conversion is crucial in solving this problem efficiently.

00:00 Code
02:00 Main
08:20 End