๐ฃ๐๐๐ต๐ผ๐ป ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฆ๐ฒ๐ฟ๐ถ๐ฒ๐: ๐ค๐ญ - ๐๐ฑ๐ฒ๐ป๐๐ถ๐ณ๐ ๐๐๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ฒ๐ ๐ถ๐ป ๐ฎ๐ป ๐๐ฟ๐ฟ๐ฎ๐ | ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ | ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ | ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐
ะะฒัะพั: DataSense
ะะฐะณััะถะตะฝะพ: 2024-11-26
ะัะพัะผะพััะพะฒ: 182
ะะฟะธัะฐะฝะธะต:
๐ช๐ฒ๐น๐ฐ๐ผ๐บ๐ฒ ๐๐ผ ๐๐ต๐ฒ ๐ณ๐ถ๐ฟ๐๐ ๐๐ถ๐ฑ๐ฒ๐ผ ๐ถ๐ป ๐ผ๐๐ฟ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฆ๐ฒ๐ฟ๐ถ๐ฒ๐ ๐ณ๐ผ๐ฟ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ฎ๐ป๐ฑ๐ถ๐ฑ๐ฎ๐๐ฒ๐!
In this tutorial, we tackle a common interview question: Identify Duplicates in an Array. This is a crucial skill for roles like Data Analyst, Data Engineer, and Python Developer. We'll explore three distinct methods to solve this problem, catering to different scenarios and optimizing for time and space complexity.
๐ ๐ฒ๐๐ต๐ผ๐ฑ ๐ญ: ๐จ๐๐ถ๐ป๐ด ๐ฎ ๐ฆ๐ฒ๐ - Understand how to leverage Python's set data structure for quick duplicate detection. Explanation of the add() method and how sets naturally handle duplicates. Ideal for scenarios where space efficiency isn't critical, and simplicity is key. Time Complexity: O(n), Space Complexity: O(n).
๐ ๐ฒ๐๐ต๐ผ๐ฑ ๐ฎ: ๐จ๐๐ถ๐ป๐ด ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต - Dive into an intuitive, straightforward approach using nested loops. Learn how to compare each element with others in the array to identify duplicates. While this method is less efficient (O(n^2)), it's a great way to build problem-solving intuition. Time Complexity: O(n^2), Space Complexity: O(1).
๐ ๐ฒ๐๐ต๐ผ๐ฑ ๐ฏ: ๐จ๐๐ถ๐ป๐ด ๐ฎ ๐๐ถ๐ฐ๐๐ถ๐ผ๐ป๐ฎ๐ฟ๐ - Explore how Python's dict can track element frequencies. Build a frequency map of the array and identify duplicates based on counts. Efficient for large datasets and a highly practical method in data analytics workflows. Time Complexity: O(n), Space Complexity: O(n).
By the end of this video, you'll have a clear understanding of all three approaches and know when to use each one based on the problem's requirements. Perfect for Python interviews and technical assessments! Don't forget to like, comment, and subscribe for more in this Python Interview Series. ๐
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