/coding-problems

Solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures

Primary LanguagePythonMIT LicenseMIT

Coding Problems

Here you can find solutions for various coding/algorithmic problems and many useful resources for learning algorithms and data structures.
Also, this repo will be updated with new solutions and resources from time to time.

Note that this repo is meant to be used for learning and researching purposes only and it is not meant to be used for production.

Solutions

Algorithms and data structures are not language-specific (it's true that some languages are faster, and some are easier to use), but if you are good with the logic and pseudocode, any language would be good.
So I've decided to use Python because I think it's very close to pseudocode and it's easily readable (so it'll be easy for someone from another environment to implement the same solutions).
As I said previously, all solutions are written in Python (more precisely, Python 3), using the Built-in Functions (print, len, range, sorted, sum, min, max, etc...) and a few modules from the Python Standard Library like:

So, to execute these solutions there is no need from installing any external packages.
Coding style and name conventions are described in the official PEP8 page.

Note that I'm not the author of these problems, they are from sites like LeetCode (you can find more than 40 sites like this in the Training Sites section). Only the solutions and explanations are mine. If you find any bug or incorrect implementation (or faster/better implementation) in this repo, please let me know by opening an issue or pull request.

Template

For easier navigation into the solutions, each file with a solution in this repo will have the following template:

'''
Problem Name

Problem explanation.

Input: XXX
Output: XXX
Output explanation: XXX

=========================================
Solution 1 explanation.
    Time Complexity:    O(X)
    Space Complexity:   O(X)
Solution 2 explanation.
(some of the problems are solved in more than one way)
    Time Complexity:    O(X)
    Space Complexity:   O(X)
'''


##############
# Solution 1 #
##############

def name_of_solution_1(params):
    # description of code
    pass


##############
# Solution 2 #
##############

def name_of_solution_2(params):
    # description of code
    pass


###########
# Testing #
###########

# Test 1
# Correct result => 'result1'
test_val = 'example1'
print(name_of_solution_1(test_val))
print(name_of_solution_2(test_val))

# Test 2
# Correct result => 'result2'
test_val = 'example2'
print(name_of_solution_1(test_val))
print(name_of_solution_2(test_val))

Note that here I'm using the simplest way of testing, printing the results using the print method. Why? Because I think that the bigger part of the users of this repo isn't familiar with unit testing and I wanted this part to be intuitive. Btw, I strongly recommend using some unit testing framework for this kind of testing. The Python Standard Library contains a great framework for unit testing called unittest, or you can install some third-party unit testing framework like pytest.

Categories

Each solution/problem in this repo belongs to one of these categories:

  1. Arrays - Array Manipulations, Sorting, Binary Search, Divide and Conquer, Sliding Window, etc.
  2. Linked Lists - Linked List Searching, Pointer Manipulations, etc.
  3. Trees - Binary Search Trees, Tree Traversals: Breadth-First (Level Order) Traversal, Depth-First Traversal (Inorder, Preorder, Postorder), etc.
  4. Hashing DS - Hashing Data Structures: Sets/HashSets and Dictionaries/HashMaps.
  5. Dynamic Programming - 2D and 1D Dynamic Programming, LCS, LIS, Knapsack, etc.
  6. Strings - String Manipulations, Reversing, Encodings/Decodings, etc.
  7. Math - GCD, LCM, Factorization, Geometry, Math Formulas, etc.
  8. Other - Backtracking, BFS, DFS, Stacks, Queues, Deques, Priority Queues (Heaps), Matrices, etc.

Learning Resources

The learning resources are divided into 4 categories: Courses, Books, Training Sites, Other Resources.

Courses

Collection of free courses from one of the best CS universities.

  1. Standford University

  2. Princeton University

  3. UC San Diego

  4. MIT University

  5. Harvard University

  6. UC Berkeley

Books

Several books that have made an impression on me:

  1. Grokking Algorithms by Aditya Bhargava - The best book for complete beginners in algorithms! I wish this book existed when I started learning algorithms.
  2. Introduction to Algorithms by CLRS - This book is called the "bible textbook of algorithms" by many programmers.
  3. Algorithms by Robert Sedgewick & Kevin Wayne - These authors are instructors of the previously mentioned Coursera courses: Algorithms Part 1 and Algorithms Part 2. Also, this book has an excellent and free site with exercises, presentations, and examples.
  4. The Algorithm Design Manual by Steven Skiena - The book describes many advanced topics and algorithms and it focuses on real-life practical examples. This book has one of the best sites with resources (solutions, algorithms and data structures, python implementations).
  5. Algorithms by S. Dasgupta, C. Papadimitriou, and U. Vazirani - This book is an official book for algorithms and data structures classes in several famous universities.
  6. Competitive Programming 3 by Steven Halim & Felix Halim - A great book that prepares you for competitive programming (not for complete beginners). You can learn many things and tricks about competitive programming. But if your goal is to prepare for competitive programming then choose a faster language than Python, C/C++ (or Java, it's faster than Python but not like C/C++).
  7. Cracking the Coding Interview by Gayle Laakmann McDowell - A bit different from the previous books. Prepares you for coding interviews using great coding problems.

Training Sites

If the problems from LeetCode are not enough and you need more problems like those, you can find much more on these platforms:

Other Resources

  1. Geeks For Geeks - The site which all interested in algorithms (no matter if beginners or experts) should know! YouTube channel with many useful videos.
  2. The Algorithms - Python - Great GitHub repo with many algorithms written in Python (Link from the same repo written in other programming languages).
  3. CP Algorithms - Great page with excellent explanations for various algorithms.
  4. Visualizers:
    • USFCA Visualization Tool - Great tool for visualizing data structures and algorithms, created by the University of San Francisco.
    • VisuAlgo - Another great tool for visualizing data structures and algorithms through animation.
    • Algorithm Visualizer - Interactive online platform that visualizes algorithms from code. This platform is an open-source project, here you can find the source code.
  5. Courses and tutorials (but not from universities like the Courses section):
  6. YouTube playlists with tutorials: