DSA

Status

Contents

• A: Mathematics
• B: Bit Magic
• C: Recursion
• D: Arrays
• E: Searching
• F: Sorting
• G: Matrix
• H: Hashing
• I: String
• J: Linked List
• K: Stack
• L: Queue
• M: Dequeue
• N: Tree
• O: Binary Search Tree
• P: Heap
• Q: Graphs
• R: Greedy
• S: Backtracking
• T: Dynamic Programming
• U: Trie
• V: Segment Tree
• W: Disjoint Set

Ugh, not another DSA repository but anyway. What the fork is this?

This repository is the implementation of the contents DSA self paced series by yours truly but in Python

Aren't we supposed to learn DSA in say C++?

YOU SHOULD but we both know you won't or can't write C++

So, what's up with your code? Why should I look into it?

Yours truly for each topic he came across has written the code in 1-2 or more than 2 methods which perform the exact same task. So that you could choose whichever method works for you. Each method is tested under the tests/ with pytest

So what's the difference between method 1 and 2?

Well, if you look closely, I mean really closely you could see the timeit module in __main__ which shows the time taken by that particular function to run

def method1(n: int) -> int:
    return len(str(n)) if n >= 0 else len(str(n)) - 1


def method2(n: int) -> int:
    import math

    return int(math.log10(n) + 1 if n > 0 else (1 if n == 0 else math.log10(-n) + 1))


if __name__ == "__main__":
    """
    from timeit import timeit
    print(timeit(lambda: method1(1234567890), number=10000)) # 0.0022839140001451597
    print(timeit(lambda: method2(1234567890), number=10000)) # 0.004222848001518287
    """

So is the time taken by timeit reliable?

Fork no, but still for the best result I ran the methods 10 thousand times each. My machine is Asus-UX425JA with Fedora 33 on kernel 5.9 with several stackoverflow tabs and a spotify tab open when I ran each timeit

TODO:

• See Projects

Contributors

• See CONTRIBUTING.md