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Python Exercises: 315+ Coding Challenges with Solutions
Role Play
Rating: 4.4 out of 5(502 ratings)
77,641 students

Python Exercises: 315+ Coding Challenges with Solutions

Python coding exercises for beginners and intermediate learners: practice problem solving, algorithms, files and OOP.
Last updated 9/2026
English
English [Auto],

What you'll learn

  • Solve Python exercises by breaking a problem into inputs, processing steps and expected outputs.
  • Write step-by-step algorithms and translate them into working Python programs.
  • Practise Python data structures, including lists, tuples, dictionaries and sets, alongside conditions, loops and functions.
  • Apply Python to files and data tasks, including text files, CSV, JSON and selected Pandas examples.
  • Build small Python applications through object-oriented, Django and Tkinter practice exercises.
  • Review and improve Python solutions, using AI assistance thoughtfully while checking and understanding generated code.

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

13 sections • 373 lectures • 35h 6m total length
  • 01 Why you need this course in AI Era3:49
  • 02 Course Outlines3:37
  • 03 Job Oppertunities after this course5:22
  • 04 Instructor Guide How to complete this course3:30
  • Discover the Power of 300+ Python Exercises

Requirements

  • A computer with permission to install Python and the packages used in relevant exercises.
  • You did not need to buy any paid software for this course
  • Basic Python knowledge is helpful; beginners should complete the included fundamentals first.
  • You did not need to have advanced Python Programming knowledge, you will learn in this course

Description

Build your programming confidence with 315+ Python exercises, coding challenges and explained solutions. Practise turning a problem into a step-by-step algorithm, writing the Python code and checking whether the result meets the requirements.

This course is designed for learners who want to move from understanding Python examples to solving problems themselves. Start with a review of the fundamentals, work through beginner and intermediate exercises, and explore applied tasks involving files, data, web applications and desktop interfaces.

If you often understand a solution when someone explains it but struggle to start with a blank editor, this course gives you a structured way to practise.

Learn through Python exercises with solutions

Knowing the syntax of a programming language is one part of learning to code. You also need practice deciding which tools to use and how to combine them.

The exercises in this course provide opportunities to work with user input, calculations, conditions, loops, collections and functions. As you progress, you will encounter tasks that bring several concepts together.

The solution demonstrations help you compare your approach with an explained example. Use them to understand the steps, investigate mistakes and identify another way to solve a problem.

For the strongest learning experience, attempt each exercise before watching the solution. Even an incomplete attempt can help you identify what you understand and where you need more practice.

Start with the Python fundamentals

The course includes introductory material covering Python setup, code editors, variables, data types and essential programming structures.

Review lists, tuples, dictionaries and sets. Explore conditional statements, loops and functions before applying them in the exercise sections.

If you are new to Python, use these lessons as your starting point and take time to run the examples yourself. If you already know the basics, use the fundamentals as a reference when an exercise reveals a gap in your understanding.

The course then gives you repeated opportunities to apply those concepts in different situations.

Develop a Python problem-solving method

Before writing code, take a moment to understand the task.

What information will the program receive? What should it produce? Which calculations, conditions or repeated steps are needed?

The course includes lessons on reading problems carefully, breaking larger tasks into smaller steps, planning before coding, writing algorithms and using flowcharts.

An algorithm is a sequence of steps for solving a problem. Learning to express those steps clearly can make the transition to Python code easier.

Practise explaining your approach in plain language, then turn it into code. After running the program, compare its behavior with the original requirement.

Practise beginner Python coding exercises

Begin with manageable tasks that reinforce the core language.

Examples include printing numbers and their squares, calculating a circle’s area, checking voting eligibility, generating multiplication tables and processing student marks.

You will also work with strings and collections. Practise storing information, filtering values, updating dictionary entries and using set operations.

These exercises give you opportunities to repeat important patterns while changing the details of the problem. That repetition helps you recognize when a condition, loop, function or collection is useful.

Where an exercise includes an extension or assignment, try it independently after completing the demonstrated example.

Progress to intermediate Python practice

The intermediate exercises introduce a wider variety of tasks and combinations of concepts.

Explore examples involving file extensions, unit conversions, input validation, regular expressions, classes and objects. Work with collections and practise transforming or selecting information.

You will also encounter examples involving CSV, JSON and Excel-related data tasks. These help connect Python syntax with common forms of stored information.

Some problems may be familiar, while others introduce a library or technique you have not used before. Treat that difference as a guide to where you need additional practice rather than a reason to rush through the solution.

Apply Python to files and data

File handling allows programs to work with information beyond a single interactive session.

The course includes exercises involving reading and writing text, selecting information from files and working with structured formats such as CSV and JSON.

Selected Pandas exercises introduce tasks such as reading spreadsheet columns, calculating values and working with Series.

These examples provide practice applying Python to data. They are part of a broader programming exercise course, giving you a foundation you can build on if you later study data analysis in greater depth.

Check your results against the source information and pay attention to how the program handles different inputs.

Explore small web and desktop applications

The applied exercises show how familiar programming tasks can be presented through different interfaces.

Django examples include forms, calculations and displaying results in a web application. Tkinter examples include windows, input fields, buttons, messages and other interface elements.

You can see how a calculation or validation task changes when a user interacts with a form or graphical window instead of the console.

These activities help you connect basic programming logic with small application examples. They also provide opportunities to revisit conditions, functions, collections and input processing in another context.

The focus is on practising through manageable applications rather than promising complete mastery of a web or desktop framework.

Use AI assistance while continuing to learn

AI tools can explain code, suggest changes and help investigate errors. However, copying a generated answer does not show that you understand the solution.

The course includes guidance on when to ask AI for help, when to attempt a task independently and how to check AI-generated answers.

Explore lessons on explaining, debugging and improving code with AI assistance. Keep the exercise requirement in view and inspect whether a suggested solution actually satisfies it.

A useful practice habit is to attempt the problem first, ask for a hint when needed and then explain the final solution in your own words. You should be able to describe what the code does and why the important steps are there.

Build a consistent practice routine

You do not need to complete a large number of exercises in one sitting.

Choose a manageable set of problems, attempt them carefully and keep a record of the ones that need another attempt. Return to difficult exercises after some time and try solving them without reopening the solution immediately.

As a suggested routine:

  1. Read the problem and identify the expected result.

  2. Write a short plan or algorithm.

  3. Attempt the Python solution.

  4. Run the code and inspect the output.

  5. Compare your approach with the explanation.

  6. Change the input or requirement and try again.

This routine helps make practice active and gives you a clearer picture of your progress.

Who should take this course?

This course is suitable for beginners who have started learning Python, students who need additional coding practice and intermediate learners who want a broader collection of problems.

It is especially useful if you understand tutorials but find it difficult to build a solution independently.

You need a computer, basic digital skills and a willingness to practise. Basic Python knowledge is helpful, and the included fundamentals provide a starting point for learners who need a review.

As you reach exercises involving additional libraries or frameworks, follow the relevant setup instructions and use the supplied learning materials where available.

Strengthen your Python problem-solving skills through regular practice, explained algorithms and practical coding exercises. Work at your own pace, revisit difficult tasks and build confidence by writing and understanding your own solutions.

Who this course is for:

  • Beginners who have started learning Python and need structured coding practice.
  • Students who understand explanations but struggle to write programs independently.
  • Intermediate learners who want to strengthen problem-solving skills through varied exercises.
  • Self-taught programmers who want to identify and practise weaker topics.
  • Learners interested in progressing from console programs to small data, web and desktop applications.
  • Python learners who want to use AI assistance without relying on copied answers.