How to use a weighted mean estimator in seaborn factor plot (incl bootstrapping)?

Question

I have a dataframe where each of the rows has a certain weight which needs to be accounted for in the mean computations. I love seaborn factorplots and their bootstrapped 95% confidence intervals but haven't been able to get seaborn to accept a new weighted mean estimator.

Here is an example of what I would like to do.

tips_all = sns.load_dataset("tips")
tips_all["weight"] = 10 * np.random.rand(len(tips_all))
sns.factorplot("size", "total_bill", 
               data=tips_all, kind="point")
# here I would like to have a mean estimator that computes a weighted mean
# the bootstrapped confidence intervals should also use this weighted mean estimator
# something like (tips_all["weight"] * tips_all["total_bill"]).sum() / tips_all["weight"].sum()
# but on bootstrapped samples (for the confidence interval)

Answer 1

From @mwaskom: https://github.com/mwaskom/seaborn/issues/722

It's not really supported, but I think it is possible to hack together a solution. This seems to work?

tips = sns.load_dataset("tips")
tips["weight"] = 10 * np.random.rand(len(tips))

tips["tip_and_weight"] = zip(tips.tip, tips.weight)

def weighted_mean(x, **kws):
    val, weight = map(np.asarray, zip(*x))
    return (val * weight).sum() / weight.sum()

g = sns.factorplot("size", "tip_and_weight", data=tips,
                   estimator=weighted_mean, orient="v")
g.set_axis_labels("size", "tip")