On 5/1/22 00:21, Christopher Barker wrote:
On Sat, Apr 30, 2022 at 2:17 PM Pablo Alcain wrote:
It shows that out of 20k analyzed classes in the selected libraries (including black, pandas, numpy, etc), ~17% of them could benefit from the usage of auto-assign syntax.
I only read English, and haven't studied the coe, so I don't know how that works, but assuming it's accurately testing for the simple cases that auto-assigning could work for;
That's not that much actually -- for approx every six-parameter function, one of them could be auto-assigned.or for every six functions, one could make good use of auto- assignment (and maybe be a dataclass?)
I think you place too much emphasis on dataclasses -- none of my projects use them, nor could they. Going through a one of my smaller projects, this is what I found: - number of `__init__`s: 11 - number of total params (not counting self): 25 - number of those params assigned as-is: 19 - number of `__init__`s where all are assigned as-is: 6 - number of non-`__init__`s where this would useful: 0
And I'm not trying to be a Negative Nelly here -- I honestly don't know, I actually expected it to be higher than 17% -- but in any case, I think it should be higher than 17% to make it worth a syntax addition.
17% is a massive amount of code.
But pandas and numpy may not be the least bit representative [...]?
This would not be the first time Python was improved to help the scientific community. My own thoughts about the proposal: It seems interesting, and assigning as-is arguments is a chore -- but I'm not sure using up a token to help only one method per class is a good trade. -- ~Ethan~