Start with the exercises before installing the web example
Open the first lesson and you're asked to write arithmetic in Python's interactive shell, then put it in a file. That small step explains the appeal of 30 Days of Python. The English lessons give a beginner somewhere specific to start, with screenshots for opening an editor and exercises after the explanations. You can begin learning without choosing a framework or signing up for a database.
Our checkout contained 440 files and occupied 71.7 MB, so this is a substantial folder of teaching material. The reading order moves through collections and functions before reaching pandas, Flask, and MongoDB. That breadth helps you discover what Python can do. It also makes the title a poor deadline: the README explicitly asks learners to work at their own pace. I'd choose it for a study group with someone available to explain mistakes.
What happened when we ran it
Our run installed the python_for_web/ project in 8 seconds, adding 41 packages and using 40 MB on disk. The build succeeded in 1 second. We cloned commit 50c11a8 on October 8, 2026, into an unprivileged Debian container with 3 CPUs and 8 GB of RAM, using the Python 3.12 uv image without secrets. Those results establish that the measured setup steps completed in that environment.
Pip-audit reported 32 known vulnerabilities. There was no test script or target, so the lab skipped tests. The scan also found no tests directory, no Dockerfile, and 0 CI workflow files. Our measurement method covers repository setup, rather than completed lessons or browser behavior. A successful build cannot tell you whether the Flask form produces a useful result, and the supplied audit summary doesn't identify advisory severity or affected packages.
A function named square_number computes something else
The functions lesson includes a higher-order-function example where square_number(n) returns n ** n. Its displayed result for an input of three is twenty-seven. The code and comment agree, which makes the misleading function name especially awkward for a beginner. An earlier example in the same lesson correctly squares its argument. Someone learning alone has to decide which example to trust while also learning how functions work.
Our scan found 0 CI workflow files, so the repository provides no visible automated check through that route. A contributor's open correction, PR 1007, addresses the square function, but an open proposal hasn't repaired the lesson you're reading. I'd have a learner predict each example's output before running it, then explain any discrepancy. An instructor can turn this into a useful discussion. A learner expecting consistently correct worked examples should choose another resource.
MongoDB adds credentials after the local lessons
The database chapter walks through creating a MongoDB Atlas account, choosing a cluster, setting network access, and adding a database user. Its connection string needs your own credentials. That adds account setup to what began as local arithmetic exercises. Keep database passwords outside committed source files, even though the chapter demonstrates connection strings inline. Joining the Telegram group is optional, and the opening lessons need no hosted API credential.
The 40 MB installed footprint belongs to the measured web project, rather than a complete deployment of every course exercise. The web chapter also introduces virtual environments and a Heroku account for deployment. In the checked-out application, the main block enables Flask debugging and binds to all interfaces. Treat that launch configuration as something to revise before hosting. The lab's successful build doesn't establish that the application is ready for public traffic.
Recent correction requests show activity and unfinished work
The repository's last push was September 30, 2026. GitHub showed 229 open issues and PRs when checked on October 17, and recent contributor activity included the square-function correction and a proposal to fix string-method examples. The latest-release endpoint returned no published release. Contributor activity is current, though proposed fixes remain unmerged. The missing release tag alone tells us little about how quickly lesson corrections will land.
Across roughly 4,562 source lines, the lab found no runnable test target. For this kind of repository, the consequence is educational: learners don't get an automated verdict on whether they've understood an exercise. The README's conversational explanations and editor screenshots reduce the effort of starting, but they don't supply feedback on your own answer. A mentor who reads your solution fills a gap that another explanatory paragraph cannot.
Choose checked exercises when you need feedback
Exercism's Python track groups work into concept and practice exercises and includes checks for the exercises. I'd pick it when you want a defined problem and a way to check your answer. Trekhleb's learn-python organizes examples by language topic and puts assertions beside expected behavior. That suits a developer who wants to change a small example and inspect what the change breaks. Neither comparison is a performance result from our sandbox.
For a reading group, 30 Days of Python still gives you a useful sequence and plenty to discuss. Keep the web example in a separate environment: its 32 known vulnerabilities make copying that environment into a real service a poor starting decision. Before progressing past the functions chapter, explain why n ** n doesn't match the name square_number. If you can't resolve that mismatch, get feedback before carrying the example into your own code.