Blind 75 — Python Solutions

A recruiter-facing record of my progress through the Blind 75 interview problem set.

This repository is not only a collection of accepted answers. Each completed problem documents how I approached it, the tradeoffs I considered, the final Python solution, its time and space complexity, the mistakes I made, and the amount of focused time I spent learning it.

Status: Active learning project
Language: Python 3.11+
Started: June 25, 2026
Problem set: NeetCode Blind 75

Progress

Metric	Current value
Completed	14 / 75
In progress	1
Total focused time	8h 50m
Completion	18.7%

Detailed tracking is available in PROGRESS.md.

What This Repository Demonstrates

• Pattern recognition across common interview problem families.
• Clean and readable Python with type hints.
• The ability to compare brute-force and optimized approaches.
• Accurate time and space complexity analysis.
• Testing against normal cases, edge cases, and failure cases.
• Written technical communication instead of code-only submissions.
• Consistent reflection on mistakes, tradeoffs, and reusable lessons.

Topics Covered

Topic	Problems
Arrays & Hashing	8
Two Pointers	3
Sliding Window	4
Stack	1
Binary Search	2
Linked List	6
Trees	11
Heap / Priority Queue	1
Backtracking	2
Tries	3
Graphs	6
Advanced Graphs	1
1-D Dynamic Programming	10
2-D Dynamic Programming	2
Greedy	2
Intervals	5
Math & Geometry	3
Bit Manipulation	5
Total	75

Repository Structure

blind-75-python/
├── README.md
├── PROGRESS.md
├── LEARNING_LOG.md
├── progress.json
├── arrays-and-hashing/
│   ├── README.md
│   └── contains-duplicate/
│       ├── description.md
│       ├── notes.md
│       ├── solution.py
│       └── test_solution.py
├── two-pointers/
├── sliding-window/
├── ...
├── templates/
│   ├── description.md
│   ├── notes.md
│   ├── solution.py
│   └── test_solution.py
├── scripts/
│   ├── start_problem.py
│   ├── track_problem.py
│   └── update_progress.py
└── .github/workflows/python-tests.yml

Every problem folder uses the same format:

File	Purpose
description.md	Original summary, links, examples, constraints, and completion checklist
notes.md	First thoughts, brute force, optimized reasoning, mistakes, and lessons
solution.py	Final clean Python solution
test_solution.py	Focused tests for normal and edge cases

Problem statements are summarized in my own words. Full copyrighted statements are not copied into this repository; each folder links back to the original problem.

My Process for Every Problem

1. Read the problem and restate it in my own words.
2. Attempt it without looking at a solution.
3. Record the first approach, including why it may be inefficient.
4. Identify the core data structure or algorithmic pattern.
5. Implement and test the optimized solution.
6. Write the time and space complexity.
7. Record mistakes and the general lesson that transfers to future problems.
8. Revisit the problem later without notes to verify retention.

Time Tracking

Focused time includes:

• Independent attempt time.
• Studying hints or explanations.
• Implementing and debugging.
• Writing tests and documentation.
• Reviewing the problem later.

The root progress summary is generated from progress.json.

Start a problem:

python scripts/start_problem.py valid-anagram

Add time and update its status:

python scripts/track_problem.py valid-anagram --status in_progress --add-minutes 35

Mark it complete:

python scripts/track_problem.py valid-anagram --status completed --add-minutes 20

Regenerate the progress files:

python scripts/update_progress.py

Running the Project

Create and activate a virtual environment:

python3 -m venv .venv
source .venv/bin/activate

Install development tools:

python -m pip install -r requirements-dev.txt

Run all tests:

pytest

Run code-quality checks:

ruff check .

Completion Standard

A problem counts as complete only when:

• The final implementation works.
• The solution is written cleanly in Python.
• Tests cover important edge cases.
• Time and space complexity are documented.
• The reasoning is explained in my own words.
• The time spent is recorded.
• At least one reusable lesson is documented.

Learning Goals

By the end of the project, I expect to be able to:

• Recognize common patterns before writing code.
• Explain why an approach works, not only reproduce it.
• Choose appropriate data structures based on constraints.
• Analyze algorithmic complexity accurately.
• Write interview-ready Python under time pressure.
• Communicate technical decisions clearly to engineers and recruiters.

Attribution

The study list is based on the NeetCode Blind 75, and individual problem folders link to their original practice pages. All explanations, notes, tests, and implementations in this repository are my own unless explicitly attributed.

License

Code in this repository is available under the MIT License.