hguturu
hguturu
```py # pip install "pandas
Both above using `anndata==0.10.2`.
The usecase for us has been around multiple different intensities for the same set of features. E.g. if you have a sample x protein matrix. We might have a set...
Not sure, but I think at least a few years. The blame on the classic style suggests at least the `figure.titlesize` option is going back 8 years - https://github.com/matplotlib/matplotlib/blame/2a4d905ff2e6493264190f07113dc7f2115a8c1c/lib/matplotlib/mpl-data/stylelib/classic.mplstyle#L308. The...
Looks like `plt.rcParams` has `validate` property https://github.com/matplotlib/matplotlib/blob/main/lib/matplotlib/__init__.py#L664 that references https://github.com/matplotlib/matplotlib/blob/main/lib/matplotlib/rcsetup.py#L878. A safe check might be to see if `figure.labelsize in plt.rcParams.validate`?
Hi @eddiebergman, any new updates on the anticipated timeline for the `auto-sklearn` update? Leaving behind `scikit-learn < 0.25` would be very nice to have.
I want to request a similar feature for categorical variables. Currently they get formatted as `C({parameter})[T.{value}]`. E.g., ```py BinGrp = [0, 0, 0, 1, 1, 1] becomes C(BinGrp)[T.0] C(BinGrp)[T.1] 0...
Happy to submit a pull request if interested in implementing the change.
Good point. I was coming more from the perspective of having easier to handle variable names. e.g., ```py design = formulaic.model_matrix(["C(BinGrp, contr.treatment)"], all_phenotypes) model = sm.OLS([1,2,3,1,2,3], design).fit() model.summary() model.t_test("C(BinGrp, contr.treatment)[T.1]...