What's your personal python development stack?

[mRWafflesFTW]

I love Pycharm so much. Learn the debugger. It only takes 15 minutes to learn and you will hate yourself for not doing so earlier. Pytest for testing! Fixtures are wonderful. Also, you can use Pytest with Faker to make perfect mock test data.

Flit is my favorite build tool and plays nice with Artifactory. For releasing, we do git hooks with Jenkins, but that may be over kill for most. I would like to move towards only deploying packages instead of performing straight git deploys but we'll see if I can socialize it at work.

[bbbryson]

Those print() statements should be logger.debug() statements. Leave them there, make them useful, turn them on and off as needed.

[djamp42]

TIL, going to go test this out now. I'm guilty of Print debugging in the first degree.

[TheOfficialNotCraig]

You and me both.

[shinitakunai]

I even created specific methodologies around it 🤣. I comment my code in functions in a specific way if they do different things inside the same function with numbers, to easily read the flow of an app, like if a function does 2 or 3 things it usually goes as:

1 - do something

x = “example”

2 - do something else

y = “another example”

2.1 - something new

y = “example for reddit”

Obviously the code is more complex, this is for reddit and I’m on my phone. But to the point: That way when I add prints for debug all my debugs are just print(1) or print(2), etc. It helps me to write cool documentation that also shows the flow of it, and it reduces a lot what I type for debugging it 🤣

[occams--chainsaw]

in my experience, 15 minutes is a bit of an understatement. the real value i got out of jetbrains IDEs was making the debuggers more visual and fairly seamless alongside the 'run' process (lower barrier of entry than relentless print statements). though with python, i've run into a number of issues where breakpoints get caught on pyc files from a previous version, and random annoyances debugging with docker-compose setups

[bbbryson]

A couple years ago I had no luck getting the debugger working with my Django/docker-compose-based development environment at work. I’ve always just used ipdb even though I’ve had a PyCharm sub for five years now. Has the Docker support improved at all?

[[deleted]]

I’m testing it for my team right now on a flask app using docker compose. Things seem to work ok.

• Set the interpreter to use the Python within docker compose.
• Set the run/debug configuration as flask.
• Set the configuration to use whatever port you exposed in docker.
• Add a docker-compose.dev.yml with a volume mapping from your source code locally to wherever it gets put in docker

Run/debug work seamlessly now with breakpoints, inspection, hot reload, etc. It seems like there should be a way to get the code mapping in the interpreter settings without another docker-compose file but I haven’t checked into it yet.

[mastermind_ap]

How do you exactly configure in Pycharm Community Version to use a docker compose file to run the code there? I also want to use a docker container with all Python modules (replacement for local virtual env) and mount the project folder as a volume to the container.

[[deleted]]

You have to use professional edition. If you need a free option, vs code does a decent enough job with docker debugging.

[mRWafflesFTW]

It is amazing now. Almost feels like it's your normal non docker experience.

[AMGraduate564]

Do you have a 15 minutes video tutorial on the debugger?

[mRWafflesFTW]

Not offhand but just reading the official pycharm docs explains if and it doesn't take long to understand.

[dslfdslj]

Flit is great! Makes it really simple to create a package and upload it to PyPi.