Best Resources to Learn Python as a Beginner
Learning Python in 2025 shouldn't cost you a fortune. The internet has democratized programming education to the point where some of the best resources are completely free. However, too many options can paralyze you before you write your first line of code.
After digging through Reddit threads, developer forums, and course reviews, a clear pattern emerges. The resource matters less than how you use it. But some paths are definitely smoother than others.
Start Where You Actually Are
Mimo offers a mobile-first approach that fits into your daily routine. The platform teaches Python through bite-sized lessons and includes an AI assistant to answer questions as you code. You can build projects directly in the app, which helps bridge the gap between theory and practice. For people learning on their commute or during lunch breaks, this format works well.
Mimo also recently launched a building experience where you can create real apps and websites using AI-assisted development. You describe what you want to build in plain language, then collaborate with AI to implement changes in an actual codebase. The platform includes a modern editor, built-in SQL database, instant preview, and the ability to publish your projects with a custom domain. It's designed to teach you both coding fundamentals and the AI-assisted workflows that are becoming industry standard.
Futurecoder takes a different angle. This free browser-based course includes built-in debuggers and enhanced error messages that explain what went wrong. Users praise its ability to teach how code executes step-by-step. The instant feedback loop prevents you from building bad habits early on.
Want someone to actually review your code? Exercism offers a mentor-reviewed system where experienced developers critique your work. Some beginners find it too challenging at first. Those who stick with it appreciate the personalized feedback. You'll learn not just how to make code work, but how to make it good.
Two Free University Courses Worth Your Time
When people hear about free university courses, they often assume they're watered-down versions of the real thing. They're not.
University of Helsinki Python MOOC strips away the fluff. This text-based course mimics a proper computer science semester:
- 14 sections with roughly 30 exercises each
- Test My Code system auto-grades your work instantly
- You need to pass 80% of exercises before advancing
- Expect 120 to 160 hours of work total
- Dedicated Discord channel for peer support
That 80% requirement sounds harsh. It prevents the illusion of understanding that plagues many self-taught programmers. Students report gaining the independence to write programs without constantly Googling syntax. Later sections transition from a browser editor to Visual Studio Code. You'll learn professional workflows like setting up development environments and using extensions. These skills separate hobbyists from job-ready developers.
Harvard CS50P brings Hollywood production values to programming education. Professor David Malan makes compiler errors feel exciting, which is a weird thing to say but somehow true.
The course deliberately teaches 80% of what you need for each problem set. You research the remaining 20% independently. This gap forces you to develop the search skills that professional developers use daily.
CS50P's auto-graders are unforgiving. You might spend hours debugging because you capitalized a letter wrong. Students curse this at first, then thank it later when they're catching bugs in production code. The course costs $219 for a verified certificate on edX, but Harvard offers the identical curriculum free through their OpenCourseWare platform.
For People Who Learn by Building
Some people need to see results immediately. Abstract theory kills their motivation. Show them a for-loop in isolation and their eyes glaze over. Show them how it could rename 500 files in two seconds and suddenly they're taking notes.
Mimo's building experience fits this learning style perfectly. You can build full-stack apps with front and back ends, work with a built-in SQL database, and publish stable versions for real users.
The instant preview shows your changes as you implement them, and you can export your source code or connect a custom domain for portfolio-ready projects. Unlike pure AI generation tools that create code you can't explain, Mimo keeps you close to what you're building.
Angela Yu's 100 Days of Code on Udemy promises 100 projects in 100 days. You'll build console games, data visualizations, and web applications while forming a daily coding habit. The first 50 days earn universal praise for pacing and clarity.
The quality drops in the latter half as projects become more self-guided. Web development sections feel slightly dated. But here's why it still works: the course teaches you concepts you didn't know existed. That unknown-unknown problem trips up many self-taught programmers. You can't Google something if you don't know it has a name.
Price matters here. The course lists for over $100 but goes on sale for $15-$20 regularly. Many U.S. public libraries offer free access through "Gale by Udemy." Check before buying.
Automate the Boring Stuff with Python by Al Sweigart targets a specific audience: people who want Python for their current job, not to become developers.
What you'll learn:
- Rename files in bulk
- Update spreadsheets automatically
- Scrape websites for data
- Send automated emails
Accountants and data entry professionals swear by this approach because it solves immediate pain points. One Reddit user credited this book with launching their data engineering career. However, the code style leans toward beginner-friendly rather than pythonic. Use this alongside a more rigorous course if you're aiming for software development roles.
Physical Books Still Have Their Place
Physical books force you away from digital distractions. Many developers report better retention when studying from paper, particularly during deep focus sessions.
Python Crash Course by Eric Matthes consistently ranks as the number one beginner book. Half covers syntax and theory, half walks through projects including a space invaders-style game and Django web app. The 3rd edition includes updated content on modern Python practices. Users recommend it for striking the balance that many online courses miss.
Think Python by Allen Downey focuses on computational thinking. If you want to understand not just Python but programming as a discipline, this book delivers. The approach feels more academic. Works for some learners, alienates others.
Skip Learn Python the Hard Way by Zed Shaw. Experienced developers describe it as "radioactive garbage" due to idiosyncratic style and outdated Python 3 information. The controversy runs deep enough that recommending it will get you downvoted on Reddit.
YouTube Works If You're Disciplined About It
YouTube tutorials get a bad rap for encouraging passive learning. Watch a video, feel smart, then freeze at a blank screen. But some creators break this pattern.
Corey Schafer produces the clearest Python tutorials on the platform. His explanations of Object-Oriented Programming, Django, and regular expressions beat many paid textbooks.
The catch? His videos lack built-in exercises. You need to create your own practice problems or risk falling into tutorial hell.
Other solid options:
- Programming with Mosh: Slower, methodical pacing
- Bro Code: High energy, broad topic coverage
Avoid 6-12 hour "Full Course" videos unless you're ready to pause every five minutes and code along. Otherwise, you're just watching someone else solve problems.
The Tools You'll Actually Need
Your choice of IDE sparks religious debates in programmer circles. The 2025 consensus has settled on two options.
PyCharm Community Edition works brilliantly out of the box. Powerful autocomplete catches errors before you run code. The introspection helps you understand type annotations and available methods through exploration rather than memorization. Beginners benefit from this immediate feedback.
Visual Studio Code requires more setup. You'll need to install Python and Jupyter extensions at minimum. But it offers flexibility across languages. For long-term career development, learning VS Code pays dividends when you inevitably need to work with JavaScript, HTML, or other technologies.
Jupyter Notebooks inside VS Code transform the learning experience for data science paths. Line-by-line execution with instant visual output reduces debugging burnout. You see results immediately, which maintains motivation during difficult concepts.
Breaking Out of Tutorial Hell
The biggest trap isn't picking the wrong resource.
Tutorial hell happens when you become a passive consumer, following along perfectly but unable to create anything independently. I spent two months in this trap with web development. Could follow any tutorial flawlessly. Couldn't build a contact form from scratch. Breaking out required three deliberate changes in approach.
The Parallel Project Rule
After completing a tutorial that builds a calculator, you build a unit converter. Similar concepts, different implementation. This forces you to apply knowledge rather than copy it. The struggle of adapting what you learned to a new context teaches more than any smooth tutorial walkthrough.
Deliberate Breakage
Got code working? Now change it until it breaks. See what error messages appear. Fix them without consulting the tutorial.
This builds debugging skills that no polished walkthrough can teach. After a week of deliberately breaking scripts, you'll read Python error messages like sentences instead of cryptic warnings.
Search Before Asking
Google, Stack Overflow, and official documentation are separate skills to master. They're as important as Python syntax. Fighting with documentation for 30 minutes teaches you more than getting a quick answer from ChatGPT.
Good rule of thumb: struggle for at least 20 minutes before asking anyone for help.
The AI Question Nobody Wants to Address
ChatGPT and Claude have become standard tools in 2025. Use them right, and you'll learn syntax in weeks. Use them wrong, and you'll never learn at all.
How to use AI without crippling yourself:
- Ask it to explain cryptic error messages
- Request progressive difficulty drills based on your current level
- Have it generate custom exercises for weak areas
- Never ask it to just write the solution
The framework: Use AI for explanations, minimal debugging hints, and practice generation. Write the code yourself. Always.
What Separates Success from Failure
Here's what nobody wants to hear: grit matters more than your resource choice. The ability to sit with confusion, read error messages carefully, and keep trying after your code fails for the hundredth time. That's the skill.
People with expensive bootcamp degrees wash out because they quit when things get hard. High school dropouts become senior developers because they refuse to stop learning.
The best resources teach you how to think through problems, not just how to type syntax.
Top comments (1)
A very nice list of resources - thanks for sharing, Srdan.
JetBrains unifed all PyCharm editions in December last year, migrating Community users to a single product with continued free access and integrated Jupyter support - far less confusing now!