type

.shape

tqdm

.swapaxes

einsum

Import

print

👆

ipdb.set_trace()

*reshape

parallel_apply.

.where

you can't reshape without knowing the shape bro

Pandas .apply() and .to_datetime(). Pandas as a whole is a work of art!

Why this? I have always seen people praise tqdm a lot, but I don't understand why.

Not necessarily a function but the njit decorator from numba.

[deleted]

[deleted]

pandas.DataFrame.query()

In many interesting cases this is actually very possible, e.g. broadcasting x.reshape(1, -1) * y.reshape(-1, 1) Rarely do I need to query the shape explicitly. Mostly a squeeze/unsqueeze leads to the goal. And often one knows the structure of the data explicitly, e.g. the dimensionality of a Euclidean space. I don't want to deny the use case for .shape entirely, but I think often it can be avoided.

can't agree, for unknown shapes I use permute or x.reshape(x.shape/2, x.shape/2) etc

Decorators are functions!

It's nice to know how long something is going to take

to_exel

.pipe() is another game changer, along with .assign(). I recently discovered you can pass callables to almost ANYTHING in pandas. Things like df.assign(newcolumn= lambda df: ... or better yet df[lambda x:....] and df.loc[lambda x:].

Tqdm.pandas()

exit()

apply() with lambdas

I like pudb with classic blue screen

Import pytorch_lightning as pl

poor debugability