Welcome to my Data Structures and Algorithm repository!👋🏻 Here, you will find a curated collection of programs implemented in Java, Python, and C++. These programs cover a wide range of data structures and algorithms, including but not limited to arrays, linked lists, trees, graphs, sorting algorithms, searching techniques, and dynamic programming.
Whether you are a beginner looking to understand the fundamentals or an experienced programmer aiming to reinforce your skills, this repository is designed to cater to all levels of expertise. Each program comes with detailed explanations and code comments to aid in your understanding.
By exploring this repository, you will deepen your understanding of how data structures work, learn various algorithms, and become proficient in implementing them using Java, Python, or C++. You can use these programs as a reference, study material, or even as a starting point for your own projects.
So delve into our collection and embark on a journey of discovery and learning. Sharpen your problem-solving skills, gain insights into efficient coding practices, and strengthen your grasp on crucial concepts in the world of data structures and algorithms. Happy coding! :)
⭐️ If you find my repository useful or if you like it, please consider giving it a star. Your support encourages me to continue working on it and improve its functionality. Thank you! ⭐️
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Start by exploring the existing issues in the repository and identify an issue you are interested in or something you want to work on that is not already there.
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If the feature or bug fix you want to work on is not present as an issue, create a new issue for it. Clearly explain the problem or feature request and wait for it to get assigned to you.
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Once the issue is assigned to you, begin working on it. If there is an existing issue related to your chosen topic, make sure it is assigned to you before proceeding.
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Fork the repository:
a. Go to the repository's website (e.g., GitHub) and click on the "Fork" button.
b. This will create a copy of the repository under your GitHub account.
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Clone the forked repository:
a. Open a terminal and navigate to the location where you want to store the repository.
b. Use the git clone command followed by the URL of the forked repository to create a local copy.
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Create a new branch:
a. Switch to the repository's directory using the terminal.
b. Create a new branch using the git checkout -b branch-name command.
c. Replace "branch-name" with a descriptive name for your branch (e.g., "add-binary-search").
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Add your code:
a. Write your data structure or algorithm code in the appropriate folder.
b. Include comments that explain your approach, time complexity, and space complexity.
c. Provide a sample input and output to demonstrate the functionality of your code.
d. Add comments to clarify to explain the logic and any important steps.
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Ensure your code style matches the repository style:
a. Review the existing code in the repository to understand the preferred style.
b. Follow the established naming conventions, indentation, and formatting.
c. Use meaningful variable and function names that convey the purpose of the code.
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Test your code:
a. Verify that your code is correct by running relevant test cases.
b. Ensure all sample inputs produce the expected outputs.
c. Consider edge cases and handle them appropriately, if applicable.
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Commit and push your changes:
a. Use git add -A to stage all the changes you made.
b. Run git commit -m "Brief description of your changes" to commit your changes.
c. Finally, use git push origin branch-name to push your changes to the forked repository.
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Create a pull request:
a. Go to the forked repository on the website (e.g., GitHub).
b. Click on the "New Pull Request" button.
c. Select the original repository from the base repository dropdown.
d. Choose your branch in the "compare" dropdown.
e. Provide a descriptive title and detailed description for your pull request including the issue number.
f. Click on the "Create Pull Request" button to submit your changes for review.
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Review and address feedback:
a. Monitor the pull request for any feedback or suggestions from the maintainer.
b. Make necessary changes to your code based on the feedback received.
c. Commit and push the changes to the same branch.
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Wait for approval:
a. The maintainer will review your code and either approve or provide further feedback.
b. Once approved, your changes will be merged into the main repository.
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Once approved, your code will be merged into the main repository. Congratulations on your contribution!
FOR CONTRIBUTION IN C++
/*
Approach:
- Explain the general approach or algorithm behind the code in brief.
Time Complexity: O(n)
- Explain the time complexity of the code.
Space Complexity: O(1)
- Explain the space complexity of the code.
Sample Input:
- Provide an example input.
Sample Output:
- Provide the expected output for the given input.
*/
#include <iostream>
using namespace std;
int main() {
// Main function to test the code
return 0;
}FOR CONTRIBUTION IN JAVA
/*
Approach:
- Explain the general approach or algorithm behind the code in brief.
Time Complexity: O(n)
- Explain the time complexity of the code.
Space Complexity: O(1)
- Explain the space complexity of the code.
Sample Input:
- Provide an example input.
Sample Output:
- Provide the expected output for the given input.
*/
public class CodeExample {
public static void main(String[] args) {
// Main function to test the code
}
}FOR CONTRIBUTION IN PYTHON
'''
Approach:
- Explain the general approach or algorithm behind the code in brief.
Time Complexity: O(n)
- Explain the time complexity of the code.
Space Complexity: O(1)
- Explain the space complexity of the code.
Sample Input:
- Provide an example input.
Sample Output:
- Provide the expected output for the given input.
'''
def code_example():
# Function to implement the code logic
pass
if __name__ == "__main__":
code_example() # Call the function to test the codeNote: You should replace the comments with the actual explanation, complexities, sample input, output, and code logic as per your specific requirements.
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