mastering-pycharm-course: the demo code and handouts behind an Effective PyCharm course

Course material, not a product

The repository is described as course demos and handouts for Talk Python's Effective PyCharm course, and the README is a course page rather than a software README. There is no library here, no command to install and nothing to import.

The course itself is what the course is built around. PyCharm is introduced as the premier Python IDE, and the stated problem is that while IDEs are powerful, they can be daunting to learn with all their features, which is why the course covers how they all work. The course summary says it teaches you to be extremely proficient with PyCharm, diving into every aspect of the IDE, from project management and the editor through advanced database management features and the refactoring tools.

The specific topics in the README list give a good sense of the coverage. Managing Python projects large and small, creating web applications with Pyramid, Flask, Django and more, using the data science mode, refactoring with confidence, learning about code smells and duplicate code tooling, accessing git, GitHub and using git flow, using the visual debugger to understand code flow and state, making code more reliable with unit testing and pytest, and creating new Python packages.

The audience is stated in two parts. For anyone who has never used PyCharm, it is a concrete and demo-focused tour. For existing PyCharm users, the pitch is that the IDE has a wealth of features you might not even know exist, and the course explores most of them.

The licence is GPL-2.0 and the last push was on 2026-10-13, so the course is being kept current with the IDE rather than frozen at a launch.

The directory layout maps to the exercises

The top level tree is short and every entry is course material: `code/`, `demos/`, `extra_samples/`, `your-turn/`, `readme_resources/`, the two requirements files, a `ruff.toml` and a `LICENSE`.

`demos/` is the live coding material, the code that is written and run during the videos. `code/` is the finished version of that code, which is the part a viewer wants when they want to compare against what they typed. `extra_samples/` holds additional samples beyond what the course walks through.

`your-turn/` is the one that tells you the most about how the course is structured. The README says the course has hands-on exercises for almost every chapter, and the exercises range in length from 10 minutes to about 30 minutes each. The directory name matches the README's phrase Take the course, and it is where a learner does the work. That is a deliberate split: a course that only shows you code teaches you nothing you cannot watch on YouTube.

`readme_resources/` holds the images the README embeds, including the course banner and a concept example image, and the README links JetBrains' own PyCharm cheat sheet for Windows and Linux and a separate macOS version.

That cheat sheet link is a small sign of how the project relates to JetBrains. The course teaches a commercial product, and the repository points you at the vendor's own reference card rather than reproducing it, which keeps the course material about the workflow rather than about the key bindings.

Dependencies pinned by uv, and what they reveal

The requirements file is generated, and the header says so: it was autogenerated by uv via `uv pip compile requirements.piptools --output-file requirements.txt --exclude-newer 1 week`.

That one line tells you several things about the project. There is a separate `requirements.piptools` file with loose constraints, and the pinned file is compiled from it, which is the standard uv pattern for keeping a lockfile and a source of truth apart. The `--exclude-newer 1 week` argument is the detail worth noticing, because it means the resolution deliberately ignores anything published in the last seven days, so that a course recording does not break the week after a dependency release lands.

The dependency list is where the course topics become concrete. Flask is there, along with the `blinker` and `click` packages it depends on, which matches the web application material. The Jupyter stack is present with `jupyterlab`, `jupyterlab-server`, `jupyter-server`, `ipykernel`, `ipython` and `debugpy`, which is the data science and notebook work. `matplotlib` appears with `contourpy`, `cycler` and `fonttools` as its dependencies, which is the plotting side of the data science chapter. `pytest` is there with `iniconfig` and `coverage` via `pytest-cov`, matching the testing material, and `alembic` in the list points at the database and migration chapters.

So the dependency set is not incidental, it is the course's table of contents in the form of a lockfile. Every major topic in the README list has a library attached to it.

Why the course stops to draw diagrams

The README explains its own teaching method, and it is the part of the README that has the most to say about whether the course works for you.

The reasoning goes like this. Exploring a topic interactively with demos and live code is engaging, but it can lose the forest for the trees. So when the course hits a new topic, it stops and discusses it with concise and clear visuals. There is an example image of the concept review covering the database tools inside PyCharm.

That is a defensible position and an unusual one to see written down. Most screencast courses assume the demo is the teaching, and the demo becomes the lesson, so the student is left to reconstruct the mental model afterwards from what they saw. This course says explicitly that it does both, and it puts the diagram before the demonstration rather than after it.

The exercises are the other half of the method. The stated reason for having them for almost every chapter is that learning IDEs is hard because there are so many features, and unless you build that muscle memory with practice, most of them will be forgotten. The framing is about retention rather than about tests, which is the right way to think about IDE muscle memory, since the value of a keybinding or a refactoring shortcut is entirely in whether your hand reaches for it without thinking.

The 10 to 30 minute length is a sensible design choice for that, long enough to actually use the feature and short enough to fit in a lunch break.

Where this fits among the PyCharm learning options

The realistic alternative for learning PyCharm is the vendor's own material plus the reference card the README links, and it is a fair comparison to make.

JetBrains' documentation and the cheat sheet cover the features and the shortcuts. What they do not do is walk you through a realistic project with a database, a web framework, a test suite and a debugger session, in a fixed order, with someone explaining why you would want each feature. That is the gap this course is aimed at, and it is a real gap, since the hardest part of an IDE is not learning what a feature does but learning when to reach for it.

The second alternative is learning PyCharm by using it, which is what most people do and what the course itself is a reaction to. The README's argument is that unless you build the muscle memory with practice, most of the features will be forgotten, which is the strongest case for the exercise-per-chapter structure.

The third option is a different editor entirely, and that is worth naming plainly. The course is specific to PyCharm's data tools, its database and refactoring features, its git integration and its visual debugger. If your work is not in PyCharm, those chapters do not transfer.

What the course does not offer is the thing people often want from an IDE course and cannot get: it is not a PyCharm configuration to copy, and it is not a set of keybinding profiles. The repository is a set of example projects and exercises, and the value is in the walkthrough rather than in the files themselves.

Editorial conclusion

mastering-pycharm-course is worth pulling if you are following the Effective PyCharm course and want the exact demo code and exercise files rather than writing your own alongside the videos. It is not a PyCharm plugin, a configuration template for your own project, or a general Python tutorial. Before you start, read the requirements header to see the uv command that generated the lock, install with the pinned requirements rather than a loose range, and expect the code to assume a configured PyCharm with its database and debugger tools already in place.

Frequently asked questions

Official sources

1. Issues
2. License: GPL-2.0
3. Project website
4. README
5. talkpython/mastering-pycharm-course on GitHub