A structured collection of Python implementations for LeetCode problems, designed for algorithm practice, interview preparation, and reusable problem-solving patterns.
This repository serves as both a solution archive and a long-term algorithm reference.
This repository demonstrates:
- Consistent problem-solving patterns across algorithm categories
- Clean and reusable Python implementations
- Systematic organization for long-term knowledge retention
- Automated repository maintenance using GitHub Actions
It reflects a structured approach to algorithm learning and engineering discipline.
- 98 solved problems
- Topic-tagged catalog for fast lookup
- Reusable notes for common algorithm patterns
- Consistent directory structure for maintainability
- Runnable Python solutions with explanations
Each problem is stored in an individual directory with:
README.md– explanation, key insight, and complexity analysissolution.py– clean and runnable implementation
Additional resources:
CATALOG.md– full index of solved problems with topic tags0000-notes/– reusable algorithm notes and patterns
####-problem-name/
0000-notes/
CATALOG.md
LICENSE
| Path | Description |
|---|---|
####-problem-name/ |
One directory per LeetCode problem |
0000-notes/ |
Algorithm notes and reusable problem-solving patterns |
CATALOG.md |
Full list of solved problems with topic tags |
LICENSE |
License information |
All problem directories follow:
####-problem-name
Where:
####– four-digit LeetCode problem IDproblem-name– lowercase kebab-case
Examples:
0001-two-sum
1458-max-dot-product-of-two-subsequences
3453-separate-squares-i
Full list:
Includes:
- Problem ID
- Title
- Directory link
- Topic tags
git clone https://github.com/GioiaZheng/Leetcode-Solutions.git
cd Leetcode-Solutions
cd 0001-two-sum
python solution.pyTopics covered:
- Arrays
- Hash tables
- Two pointers
- Sliding window
- Binary search
- Prefix sums
- Greedy algorithms
- Dynamic programming
- Graph algorithms
- Recursion & backtracking
See:
0000-notes/README.md
Algorithm practice is most effective when solutions are easy to revisit and generalize.
This repository is designed to support:
- fast review before interviews
- pattern recognition across problems
- reusable knowledge for common techniques
Contributions are welcome.
Guidelines:
- Use directory format
####-problem-name - Keep solutions clear and self-contained
- Include both explanation and implementation
After adding problems, update statistics:
python scripts/update_stats.pyIf you find this repository useful, consider starring ⭐ the project.