如何在Python中按两列分组，计算加权平均值，返回DataFrame

Question

目标：获取一个 DataFrame，按该 DataFrame 的两列进行分组，计算第三列的加权平均值，并返回一个 DataFrame。下面的 Reprex 需要进行哪些调整？

##  REPREX
import pandas as pd
import numpy as np

# Turn into DataFrame
md = {"group1": ["A", "A", "B", "B", "A"],
      "group2": [1, 0, 1, 1, 1],
      "var1":[343, 345, 567, 345, 212],
      "wt":[1.2, 1.3, 1.8, .9, 1.9]}

# Turn into DataFrame
my_df = pd.DataFrame(md)

# This works to get unweighted means:
grouped__means = my_df.groupby(["group1", "group2"], as_index=False)[["var1"]].mean()

# This does NOT work for weighted means:
grouped__wtd_means = my_df.groupby(["group1", "group2"], as_index=False)[["var1"]].agg(np.average(weights=my_df["wt"]))

# What needs to change?

Answer 1

您可以在这里使用

GroupBy.apply

。

df.groupby(['group1', 'group2']).apply(lambda x: np.average(x['var1'], weights=x['wt']))

group1  group2
A       0         345.000000
        1         262.709677
B       1         493.000000
dtype: float64

Answer 2

许多小组自己计算加权平均值以避免申请。将值乘以权重和总和，然后除以权重总和。

pandas

将在分区索引上对齐。

(df['var1'].mul(df['wt']).groupby([df['group1'], df['group2']]).sum()
     .divide(df.groupby(['group1', 'group2'])['wt'].sum()))

group1  group2
A       0         345.000000
        1         262.709677
B       1         493.000000
dtype: float64

Answer 3

另一种方法是将

set_index

与

GroupBy.agg

结合使用。

例如在你的情况下：

(
    my_df
    .set_index('wt')
    .groupby(["group1", "group2"])
    .agg({'var1': lambda ser : np.average(ser, weights=ser.index)})
)