Algorithms for Coding Interviews in Python

Beginner or advanced-level developers should take courses to study Python before their coding interview. Some areas to focus on should include algorithms, familiarity with Python’s standard libraries and data structures, and practicing their skills through coding challenges.

While preparing for your Python interview, you should invest more time learning key areas, such as Python’s key libraries, concepts such as time and space complexity, data structures and algorithms, etc. Take some time to go through Python courses and practice solving coding problems to test your skills regularly so you can track your progress.

LeetCode offers diverse problems for interview prep, and HackerRank is great for learning Python basics. However, Educative stands out by combining interactive lessons to learn Python deeply and coding challenges to practice, all in one platform, making it the ideal choice for comprehensive interview preparation.

Essential algorithms for coding interviews include sorting (merge, quick, heap), searching (binary search), graph algorithms (BFS, DFS), dynamic programming (knapsack, longest common subsequence), and data structure-based algorithms (hashing, heaps, and trees).

Python’s built-in sort() and sorted() use TimSort, a hybrid sorting algorithm combining merge sort and insertion sort. It’s highly efficient for real-world data, making it the best choice for most sorting tasks in Python.

Start by studying algorithm basics, solving problems on platforms like Educative, and practicing frequently. Focus on understanding core concepts like recursion, dynamic programming, and divide-and-conquer strategies, and gradually tackle more complex problems.

Start with basic sorting algorithms like bubble, selection, and insertion sort, then move on to more complex algorithms like merge sort, quicksort, and graph traversal algorithms like BFS and DFS.

Memorize algorithms by practicing regularly, solving problems multiple times, and understanding the underlying principles. Break down algorithms into smaller steps, visualize their workings, and implement them in different programming languages for better retention.