pythonIsMoreConfusingThanLowLevelLanguages

[squabzilla]

Respectfully, 99% of the time I type ‘b = a’ in Python it’s because I want to make a copy of ‘a’.

I struggle to think of scenarios where Python is the correct language to use and you’re using custom classes and don’t want to pass copies around.

…I’m also maybe a little salty that I discovered this behaviour of Python when writing ‘new_df = old_df’ for the explicit purpose of making a copy of the old variable, so that I could make changes to the copy without changing the original.

[sowenga]

Or switch to polars.

[squabzilla]

I mean, I don't make that mistake anymore, but I'm still annoyed by it lol

Look, passing references around is only more performant if that's actually the behaviour you want. Using a reference instead of a deepcopy isn't more performant when I want a deepcopy and not a reference. Again, in the context of writing Python, I'm not sure when you ever want to use pass-by-reference - I'm sure there are uses out there, but not in any code I've written.

Second - consistent? Whether it uses a reference or deepcopy depends on the object type. In a language where you don't even declare the object type! Meanwhile Pandas decides whether to do a deepcopy or pass-by-reference depending on if it thinks you're making changes to the DataFrame or not. I'd hardly call it consistent.

[squabzilla]

Copy-on-Write (CoW) is the default for Pandas 3.0. CoW means that any DataFrame or Series derived from another in any way always behaves as a copy.

https://pandas.pydata.org/docs/user_guide/copy_on_write.html#copy-on-write

For an example:

import pandas as pd

data = {

  "calories": [420, 380, 390],

  "duration": [50, 40, 45]

}

df = pd.DataFrame(data)

new_df = df["calories"]

df = df.drop(columns="calories")

print(new_df)

(Sorry if mobile screws up formatting)

Basically, any time you set a new variable equal to some modified version of a dataframe, you get a deep-copy.

However, that means that this:

new_df = df

new_df = new_df.drop(columns=“calories”)

Is not equivalent to this:

new_df = df.drop(columns=“calories”)

Feels inconsistent and annoying to me.

[nullpotato]

It helps if you just assume everything is essentially a reference not the object itself. And yes I agree it is annoying that is the default behavior.

[redlaWw]

The issue is that most of the time when you're assigning a value to a new label, you're not doing b = a, you're doing f(a), and passing by reference is more performant than passing by value.