Question

我有一个数据框，其中每一行都有一定的权重，需要在平均计算中加以考虑。我喜欢seaborn factorplots和他们95％的置信区间，但是无法接受新的加权均值估计。

以下是我想要做的一个例子。

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

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

它并没有真正支持，但我认为可以将解决方案整合在一起。这似乎有用吗？

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")

如何在seaborn因子图（包括自举）中使用加权平均估计量？

1 个答案: