The 50 Best Python Practice Problems (Beginner to Advanced) — Runnable in Your Browser

50 Python problems — every one with a working, runnable solution. Click Run on any code block to execute it in your browser. Modify the solution, break it, fix it, extend it. That's how you learn.

The problems are arranged in three tiers: Beginner (1–20) covers fundamentals, Intermediate (21–35) covers algorithms and data structures, and Advanced (36–50) covers performance patterns, dynamic programming, and the data science stack.

These problems cover the foundations: strings, numbers, lists, loops, and functions. If you're new to Python, work through these in order. If you already know the basics, use them as warm-ups.

Topic: Strings, f-strings

Write a function greet(name) that returns "Hello, {name}! Welcome to Python.". Test it with several names.

Topic: Arithmetic, functions

Write celsius_to_fahrenheit(c) using the formula F = C × 9/5 + 32. Test with 0°C (freezing), 100°C (boiling), and -40°C.

Topic: Modulo operator, conditionals

Write even_or_odd(n) that returns the string "even" or "odd". Handle negative numbers correctly.

Topic: Modulo, conditionals, loops

Print numbers 1–30. For multiples of 3 print "Fizz", for multiples of 5 print "Buzz", for multiples of both print "FizzBuzz".

Topic: String slicing

Reverse any string using slice notation. Then test it on a palindrome to confirm it reads the same forwards and backwards.

Topic: String iteration, conditionals

Count the vowels (a, e, i, o, u) in a string. Case-insensitive.

Topic: String manipulation, comparison

Check if a string is a palindrome. Ignore spaces and case.

Topic: Loops, accumulators

Sum a list using a loop — no sum() allowed. Then compare to sum() to verify.

Topic: Loops, comparison

Find the largest number in a list without using max(). Handle empty lists gracefully.

Topic: Strings, split()

Count words in a sentence. Handle extra whitespace correctly.

Topic: Type conversion, iteration

Sum all the digits of an integer. Works for negative numbers too.

Topic: Loops, string formatting

Print a formatted multiplication table for any number from 1 to 10.

Topic: Sets

Return True if a list contains any duplicate values. Use the set trick for O(n) performance.

Topic: dict.fromkeys(), order preservation

Remove duplicates from a list while keeping the original order. set() doesn't preserve order — use dict.fromkeys() instead.

Topic: Arithmetic, functions, f-strings

Calculate simple interest: SI = (principal × rate × time) / 100. Format the output neatly.

Topic: List comprehension, nested loops

Flatten a list of lists into a single list using a comprehension.

Topic: Sets, sorting

Find the second largest unique value in a list.

Topic: Sets

Return elements that appear in both lists. Use set intersection for efficiency.

Topic: zip(), list comprehension

Transpose a 2D matrix (swap rows and columns) using zip(*matrix).

Topic: collections.Counter

Count how often each character appears in a string. Show the top 5 most frequent.

These problems require combining concepts: recursion, generators, algorithmic thinking, and more complex data structures. Spend 10–15 minutes trying each one before reading the solution.

Topic: Generators, yield

Create a generator function that yields Fibonacci numbers on demand — memory-efficient for large sequences.

Topic: Sieve of Eratosthenes, list manipulation

Find all primes up to N using the Sieve of Eratosthenes — more efficient than checking each number individually.

Topic: String manipulation, ord/chr, modular arithmetic

Encode and decode text using a Caesar cipher shift. Preserve case and non-letter characters.

Topic: collections.Counter, string processing

Count word frequencies in a text, ignoring punctuation and case.

Topic: defaultdict, sorting as a key

Group a list of words by their anagram family. Words that are anagrams of each other go in the same group.

Topic: Divide and conquer, O(log n)

Implement binary search from scratch. Returns the index of the target or -1 if not found.

Topic: Two pointers, merging

Merge two sorted lists into one sorted list in O(n) time, without sorting the result.

Topic: String parsing, iteration

Compress a string using run-length encoding: "aaabbc" → "a3b2c". Single characters don't get a number.

Topic: Stacks

Check if a string of brackets is balanced. Every opening bracket must have a matching closing bracket in the correct order.

Topic: List slicing, modular arithmetic

Rotate a list left by k positions. Handle k larger than the list length with modulo.

Topic: Recursion, isinstance()

Flatten a deeply nested list structure of arbitrary depth.

Topic: sorted(), lambda, key functions

Sort a list of dictionaries by a specified key. Support both ascending and descending order.

Topic: Greedy algorithm, lookup tables

Convert an integer to its Roman numeral representation.

Topic: String comparison, zip

Find the longest common prefix shared by all strings in a list.

Topic: zip(), dict comprehension

Create a dictionary from two parallel lists: one for keys, one for values. Handle lists of different lengths safely.

These problems cover performance patterns (decorators, LRU cache, memoization), dynamic programming, graph algorithms, and the data science stack. They represent the level of Python used in professional software engineering and data science roles.

Topic: Hash maps, O(n) solutions

Given a list and a target, return the indices of two numbers that add up to the target. Solve it in O(n) time using a hash map.

Topic: Sliding window

Find the length of the longest substring without repeating characters.

Topic: OrderedDict, O(1) get/put

Implement an LRU (Least Recently Used) Cache with get and put operations, both in O(1) time.

Topic: Decorators, functools.wraps, time.perf_counter

Write a decorator that measures and prints execution time for any function.

Topic: Decorators, caching, recursion optimization

Build a memoization decorator from scratch. Then use it to make naive recursive Fibonacci fast.

Topic: Deque, sliding window, O(n)

Find the maximum value in every window of size k in a list. The naive O(n×k) approach is too slow — use a deque for O(n).

Topic: Dynamic programming, bottom-up DP

Find the minimum number of coins needed to make an amount. Classic DP problem.

Topic: Dynamic programming, subsequences

Find the length of the longest strictly increasing subsequence in an array.

Topic: BFS, queue, graph traversal

Find the shortest path between two nodes in an unweighted graph using Breadth-First Search.

Topic: Trees, hash maps, prefix searching

Implement a Trie (prefix tree) with insert, search, and starts_with operations. Used in autocomplete systems.

Topic: NumPy, z-scores, statistical detection

Use NumPy to find outliers in a dataset using z-score normalization.

Topic: Pandas groupby, agg, merge

Analyze a multi-region sales dataset. Find top performers, calculate margins, and identify trends.

Topic: Matplotlib subplots, styling

Create a 2×2 dashboard with a line chart, bar chart, histogram, and scatter plot.

Topic: Decorators with parameters, exception handling, backoff strategies

Write a @retry decorator that retries a failing function with exponential backoff. Essential for network calls and flaky APIs.

Topic: Metaclasses, design patterns

Implement the Singleton design pattern using a metaclass — so that only one instance of a class can ever exist, no matter how many times it's instantiated.