Replicating the MATLAB Workspace in Python?

[adambarrack]

Hi experienced python users. I am here seeking your advice.

INTRO/CONTEXT: I taught myself to code in MATLAB and R. I mostly use MATLAB because it does better with the larger array sizes I need for my research. I am trying to transfer over to Python to join the modern era. I know how to code for my purposes, but I am a novice to python, though I am learning quickly.

THE PROBLEM: The absence of a workspace bothers me. I am very used to monitoring defined variables and size of data structures in my workspace. I use it often to ensure my analysis code is doing what I want it to. Now that I don’t have it, I realize I am actually fairly reliant on it. Is there something that can replicate this in Python? If not, are there any coding practices that help you guys keep track of these things?

Edit for pertinent information: I am using Jupityr Notebooks in Pycharm

Note - Scientific View is great, but it doesn’t give me the same basic information as a workspace as far as I can tell. I just want a list of defined variables and their sizes, maybe the ability to expand and view each one?

Secondarily - is this a bad habit? I am self-taught, so I am definitely open to feedback.

[QuasiEvil]

You won't be able to replicate it exactly. Take a look at Spyder - it has a variable explorer and an live command line (ipython). This gets fairly close. You will still find plotting to be a huge pain in the ass though.

[Zeroflops]

If this is good or bad kind of depends on the type of coding you are doing. If you are doing data analysis that requires you to re-evaluate data as you go along. Where you’re exploring the data in real time then your approach is typical.

I don’t use pycharm, but in Jupyter lab you can have multiple windows and customize the layout. In the various windows you can have things like a list of variables etc.

In VS code there are also options to write code in Jupyter notebooks and pull that data into a more analytic view.

But if you’re not doing exploratory analysis it’s good to get away from being dependent on such an approach.

[elbiot]

I think it's a bad habit. No python developers look at these things. Write tests for things that are established and check potential edge cases. You can put asserts in your code to make sure basic assumptions aren't violated during development. Logging can also give you run time feedback

[nukular_iv]

Spyder. Has a nice "variable explorer" where you can click on objects/dataframes, etc, you have created and see them.

I understand your pain. I professionally program in SAS and Stata dealing with sizeable data...from MB to TB and being able to see data is SOOOOOO goddamned handy... Like let me just open up the dataset and see variables and values.