They help to digest the individual code blocks. My mind doesn’t digest whitespace the same way, it simply interprets it as formatting.
It’s also much more frustrating to edit imo since the formatter generally has no idea what to do with misaligned whitespace. I also find it frustrating that you can’t do multiline lambdas, last I used it.
The same meme with “wiring and lights” at the top. Then you descend to motors, transformers delta-y phases, RC and RL circuits, op amps, BJT circuits, reverse bias what?, differential equations, and eventually signals and systems.
What’s the difference? I rarely use Python and every time I do I have to relearn which tools are the go to ones. In Java it’s a little simpler, we really just have Maven and Gradle. They have their own problems, sure, what tool doesn’t, but the thing that annoys me about python is the quantity of tools. There often isn’t a clear winner.
Now, to be fair to python, a lot of the ones mentioned on this post are very specifically for data science use cases and not general purpose development.
Very little of this is uniquely a problem in Python. It seems to me that your problem is with software development in general.
My problem is with semantic whitespace
That’s really the part I hate the most. it just feels wrong
That’s the part I like the most. I don’t want to work on any code that isn’t properly formatted, and at that point why bother with curly braces, etc?
They help to digest the individual code blocks. My mind doesn’t digest whitespace the same way, it simply interprets it as formatting.
It’s also much more frustrating to edit imo since the formatter generally has no idea what to do with misaligned whitespace. I also find it frustrating that you can’t do multiline lambdas, last I used it.
I used to love it so much more…
come into the light, my child. become an electrical engineer.
The same meme with “wiring and lights” at the top. Then you descend to motors, transformers delta-y phases, RC and RL circuits, op amps, BJT circuits, reverse bias what?, differential equations, and eventually signals and systems.
at least then you’re dealing with the laws of nature instead of man-made BS. if you’re like me and have 0 tolerance for BS, it’s an absolute win.
No, the dependency management in Python is a nightmare. There’s like a billion options for it.
Use pipenv and don’t think about it anymore.
What’s the difference? I rarely use Python and every time I do I have to relearn which tools are the go to ones. In Java it’s a little simpler, we really just have Maven and Gradle. They have their own problems, sure, what tool doesn’t, but the thing that annoys me about python is the quantity of tools. There often isn’t a clear winner.
Now, to be fair to python, a lot of the ones mentioned on this post are very specifically for data science use cases and not general purpose development.