On top of what has been said, if you want to do some more advanced statistical analyses (in the inference area, not ML/predictive field), then chances are that these algorithms are either published as R or STATA packages (usually R).
In Python, there is statsmodels. Here, you'll find a lot of GLM stuff, which is sort of an older approach. Modern inferential statistics, if not just Bayesian, is usually in the flavor of semi-parametric models that rely on asymptotics.
As R is used by professional researchers, it is simply more on the edge of things. Python has most of the "statistics course" schoolbook methods, but not much beyond that.
For example, it has become very common to have dynamic panel data which require dynamic models.
Now if you want to do a Blundell-Bond type model in PYthon you have to... code it yourself using GMM, if it exists even.
For statistics, that's pretty much like saying you have a Deep Learning package that maybe has GRU but no transformer modules at all.
So yeah, you can code it yourself. Or you use the other one.
In Python, there is statsmodels. Here, you'll find a lot of GLM stuff, which is sort of an older approach. Modern inferential statistics, if not just Bayesian, is usually in the flavor of semi-parametric models that rely on asymptotics.
As R is used by professional researchers, it is simply more on the edge of things. Python has most of the "statistics course" schoolbook methods, but not much beyond that.
For example, it has become very common to have dynamic panel data which require dynamic models. Now if you want to do a Blundell-Bond type model in PYthon you have to... code it yourself using GMM, if it exists even.
For statistics, that's pretty much like saying you have a Deep Learning package that maybe has GRU but no transformer modules at all. So yeah, you can code it yourself. Or you use the other one.