How to have relative frequencies histograms in seaborn jointplot

Question

I'd like to make a jointplot to compare the distributions of 2 conditions, but one of the conditions has much fewer cases, so its histograms is not visible on the x and y margins. I tried to get normalized histograms per condition with:

sns.jointplot(x=var_x, y=var_y, data=df, kind="kde", hue="condition", alpha=0.7, joint_kws={'norm_hist':True})

I also tried norm_hist={'norm_hist':True} and normalize instead of norm_hist, but it didn't work. I've seen this post about distplot, but the argument norm_hist=True doesn't work for joinplot. I had looked at the source code but there is too much abstraction for me to see how I could tweak it to get normalized histogram.
Would you have any idea of how to get that results?
Thanks!

Answer 1

sns.jointplot(..., kind='kde') uses sns.kdeplot both for the central ("joint") and the marginal subplots. You can set common_norm=False to either or both of them.

Also note that distplot is an old function; seaborn's interface has been cleaned up and extended.

Here is an example:

import seaborn as sns

penguins = sns.load_dataset('penguins')[:230]

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species", kind="kde",
              joint_kws={'common_norm': False}, marginal_kws={'common_norm': False})

penguins.value_counts('species') shows the uneven counts:

species
Adelie       152
Chinstrap     68
Gentoo        10