If you wish to learn something like a real college class, then this is for you. CS50P (which is the name it’s commonly known by) divides its topics into 10 weeks: functions and variables, conditionals, loops, exceptions, libraries, unit testing, file I/O, regular expressions, object oriented programming, and a catch all “et cetera” week, culminating in a final project of your own design.

This was custom made by Dr. Chuck Severance for those with little or no coding knowledge, and it reflects that in its gentle ramp. The entire free Python course is 16 chapters long, beginning with the question of “why program?” and proceeding through variables, conditions, functions, loops, strings, files, lists, dictionaries, tuples, regular expressions, network programming, web services, basic OOP, and databases using SQL.

It is the real material used within Google to introduce folks with some coding expertise to Python, all in the span of two days. It covers topics of setup, introduction to language, strings, lists, sorting, dictionaries and files, regular expressions and utility functions, all accompanied by written materials, lecture videos, and practical exercises.

To obtain this certification track, create five authentic projects: Arithmetic Formatter, Time Calculator, Budget App (which teaches you object oriented programming using a Category class), Polygon Area Calculator (which uses Rectangle and Square classes, with Square subclassing Rectangle), and a Probability Calculator, where you simulate drawing balls from a hat.

It covers the fundamentals (data types, strings, conditionals, loops) then keeps going into object oriented programming, threading and scripting, and a tour through NumPy, Pandas, Matplotlib, and Scikit Learn, before wrapping with a web scraping module and a set of interview questions.

This is a shorter, 3 hour course which is closer to the core syntax: installation and print statements, variables, loops, if/else constructs, functions and scope, error handling, file operations, and a quick project.

After the basics, this 10 hour course ventures into some of the territory that many “advanced” free Python courses go over too quickly: looping tricks, lambda functions, full OOP with inheritance, iterators & generators, scope & modules, exception handling, sorting algorithms, virtual environments and a real introduction to NumPy and Pandas.

Content-wise it’s a 2 hour crash course over the questions that actually happen in Python interviews: list comprehension, list vs. tuple, common map and lambda questions and a walk through a mock interview near the end.

NumPy and Pandas for data, Matplotlib for plotting, Scikit Learn for basic machine learning, a full module on web scraping and an intro to TensorFlow for deep learning.

After 5 hours of focused study, a deep dive into one library, Pandas, along with learning the basics of Series, DataFrame manipulation and a hands-on project analyzing weather data.

One of the more in-depth Python programming for beginners courses featured here, with almost 4 hours of content ranging from the importance of programming and memory allocation, to conditionals, loops, functions, and a very in-depth OOP section that covers multiple types of OOP inheritance. It even covers topics you don’t typically find in a beginner course such as regular expressions, testing with Pytest, and GitHub Copilot.

It’s actually three guided exploratory data analysis (EDA) projects that cover both IPL cricket data, football data, and a Marvel Superheroes data set, all done using Pandas, Matplotlib and Seaborn. View it as a real-world extension once you have picked up Pandas elsewhere.

This is an actual complete beginner course, where everything is explained in Hindi, and is 4.5 hours long, including a robust section on OOP (classes, constructors, multiple and multi level inheritance), conditionals, loops, functions, strings and string functions, variables and data types.

It is an introduction, one and half hours worth of learning, not a whole game development class. It introduces you to the installation of Pygame, to draw the first “hello world” window, to know how to use the game loop and game state, to work with pixel coordinates and colors, the Rect object, to add images and text, to draw shapes, some animation and to play sounds.

The Python Programming MOOC is a practical, exercise-heavy course from the University of Helsinki. The 2026 version is split into an Introduction to Programming course covering parts 1–7 and an Advanced Course covering parts 8–14. The lessons move from basic programming concepts and conditional statements to functions, lists, file handling, object-oriented programming, and more advanced Python topics.

The Programiz website is a good free source to learn Python programming language. You will find there all the basics (variables, control flow, data types, functions, files) and it is in the advanced level that Programiz shines: list comprehensions, lambda functions, iterators, generators, closures, decorators, the @property decorator, regular expressions and even metaprogramming using metaclasses.

From the outset DataCamp presents Python not as a general purpose language, but as a data tool and thus the entire four-hour course is organised around these aspects: Python basics (including using it as a calculator), lists, functions and packages, and finally an introduction to NumPy arrays and basic statistical operations.

As you write, you write real code in an editor that is integrated into the browser: 14 projects over the course, ranging from Hello World to control flow, lists, loops, functions and strings.

As for the SoloLearn app, it is specifically designed for mobile-first learning. So, in case you need to learn some fundamentals like strings, user input, variables, if/else statements, lists and functions, you can do that while waiting in line or while taking a ride on the bus. It is the lightest course of them all, as in the name.

Kaggle’s Python course takes a hands-on approach, with short lessons followed by coding exercises. It covers variables and numbers, functions, conditionals, lists, loops, strings, dictionaries, and working with external libraries. The course is designed with data science in mind and can lead naturally into Kaggle’s Pandas and machine learning courses.