Coding interview practice
Python Coding Interview Practice
Practice Python interview problems with an AI mentor that evaluates correctness, edge cases, complexity, and code quality. The unified editor also lets you select Java, C, or C++ when you want to solve the same style of problem in another language.
What you will practice
Sessions cover the patterns interviewers expect candidates to recognize, from arrays and hash maps to trees, graphs, dynamic programming, object-oriented design, and concurrency. Topic-wise and company-targeted modes help you build a plan around your current level.
After a submission, inspect test feedback, a reference solution, theory, examples, and an optional SQL version when a data problem has a natural relational solution. Per-language buffers and automatic checkpoints keep unfinished code available when you switch languages or return later.
- Lists, strings, dictionaries, sets, and comprehensions
- Two pointers, sliding windows, hashing, and binary search
- Stacks, queues, linked lists, trees, heaps, and graphs
- Recursion, backtracking, dynamic programming, and greedy reasoning
- OOP, generators, decorators, asyncio, and practical design
- Complexity analysis, hidden tests, debugging, and optimization
How a practice session works
Step 1
Choose a difficulty, topic, or targeted plan. The engine creates an interview-style prompt with the context and constraints needed to reason about a correct solution.
Step 2
Write and run your answer in the browser. Ask for a focused hint or theory explanation when you need help without immediately revealing the final answer.
Step 3
Review semantic feedback, complexity notes, examples, and the reference approach. Automatic checkpoints preserve the latest question and code for your next visit.