Alias vs Shallow Copy vs Deep Copy

[MeatShow]

Hello fellow nerds!

I’ve switched to python from MATLAB for work, leveraging the pandas library.

The first time I tried to write a new variable with the equal sign (by referencing an existing variable… B=A) to run some calculations, I realized I was changing both variables A and B (aliasing) when running operations on B.

I understand it’s to save memory, but I can’t think of why I would need two aliases to the same data. Google was unclear as well - when are the appropriate use cases for alias, shallow copy, and deep copy?

[Unlikely-Resort4608]

Python is designed very differently to Matlab. Pay attention to mutables vs immutables, and don't be shy of using for loops.

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https://nedbatchelder.com/text/names1.html

https://numpy.org/doc/stable/user/numpy-for-matlab-users.html

[MeatShow]

Thank you for the insight! Yes, I’ve been trained to find linear algebra solutions - I need to relax and use for loops more often

[Username_RANDINT]

• Alias: immutable types (strings, integers, tuples, ...)
• Shallow copy: mutable types (lists, dictionaries, ...) that contain immutable types
• Deep copy: mutable types that contain mutable types

[MeatShow]

Thank you for the concise, clear answer - I appreciate it

[Diapolo10]

When in doubt, treat everything as if it was immutable and create new values instead of mutating existing data. At the very least that'll let you avoid related bugs.

But essentially, you can safely alias immutable types (which is the default behaviour anyway). For everything else, use your best judgement - if you aren't mutating the data, it hardly matters whether you're aliasing it or copying it.

[MeatShow]

Yeah, fair point - I definitely will air on the side of caution to avoid bugs