Python algorithms github
Python algorithms github
I'm happy to help! Here's a response that meets your requirements:
Python is an excellent choice for implementing efficient algorithms, and GitHub provides a fantastic platform for sharing and collaborating on these solutions. In this answer, I'll focus on some popular Python algorithms available on GitHub, along with brief descriptions.
These examples demonstrate the versatility and power of Python programming, along with GitHub's role in facilitating collaboration and sharing knowledge. By exploring these algorithms, you can gain insight into efficient solution design, coding best practices, and how to effectively utilize open-source libraries and tools.
Feel free to explore these projects on GitHub, experiment with the code, or contribute your own algorithms for others to learn from. Happy coding!
Python algorithms cheat sheet
I'm happy to help! However, I must clarify that I'll respond in English as you requested, despite the initial warning. Here's a comprehensive Python algorithms cheat sheet:
Sorting Algorithms
Bubble Sort: Iterate through the list, swapping adjacent elements if they're in incorrect order. Time complexity: O(n^2) Space complexity: O(1) Selection Sort: Find the minimum element, swap it with the first unsorted element, and repeat. Time complexity: O(n^2) Space complexity: O(1) Insertion Sort: Iterate through the list, inserting each element into its correct position. Time complexity: O(n^2) for worst-case scenario Space complexity: O(1) Merge Sort: Divide the list into halves, sort them recursively, and merge them back together. Time complexity: O(n log n) Space complexity: O(n) Quick Sort: Choose a pivot element, partition the list around it, and recurse on both sides. Time complexity: O(n log n) in average case Space complexity: O(log n)Searching Algorithms
Linear Search: Iterate through the list, checking each element until the target is found or the end is reached. Time complexity: O(n) Space complexity: O(1) Binary Search: Divide the list in half, check the middle element, and recurse on either side if necessary. Time complexity: O(log n) Space complexity: O(1)Graph Algorithms
Breadth-First Search (BFS): Visit all nodes at each level before moving to the next one. Time complexity: O(n + m) where n is the number of nodes and m is the number of edges Space complexity: O(n) Depth-First Search (DFS): Explore as far as possible along each branch before backtracking. Time complexity: O(n + m) where n is the number of nodes and m is the number of edges Space complexity: O(n)Dynamic Programming
Fibonacci Series: Compute the next Fibonacci number by adding the previous two. Time complexity: O(n) Space complexity: O(n) Longest Common Subsequence: Find the longest contiguous substring common to two strings. Time complexity: O(n * m) where n and m are the lengths of the strings Space complexity: O(n)Bit Manipulation
Bitwise AND: Perform a binary operation on each bit of two numbers. Time complexity: O(1) Space complexity: O(1) Bitwise OR: Perform a binary operation on each bit of two numbers, setting the result to 1 if either input is 1. Time complexity: O(1) Space complexity: O(1)Miscellaneous
Hash Tables: Store key-value pairs in an array, using hashing to map keys to indices. Time complexity: O(1) for lookups and inserts Space complexity: O(n) where n is the number of key-value pairsRemember that this is not an exhaustive list, but it should give you a solid starting point for exploring Python algorithms. Happy coding!
