下面是我对原始question的previous答案。

更新：

这是因为pandas静默地将grouped_df的索引与布尔值mask对齐。
下面是一个会导致类似场景的配置：

np.random.seed(20)

df = pd.DataFrame({
    "col_a": np.random.choice(list("ABCDEFGHIJ"), 100),
    "col_b": np.random.randint(0, 20, 100),
    "col_c": np.random.randint(0, 30, 100)
})

grouped_df = (
    df.groupby("col_a").sum()["col_b"]
    .sort_values().reset_index() # length of 10
)

mask = df["col_c"] > 10 # length of 100

out = grouped_df[mask] # length of 5

输出：

print(out)

  col_a  col_b
1     H     52
2     C     70
3     I     70
6     F     87
9     G    190

• 中间体/详细信息：*

#A friendly warning
UserWarning: Boolean Series key will be reindexed to match DataFrame index.
 
>>> grouped_df
  col_a  col_b
0     B     41
1     H     52
2     C     70
3     I     70
4     D     76
5     E     86
6     F     87
7     A    107
8     J    116
9     G    190

>>> grouped_df.index
RangeIndex(start=0, stop=10, step=1)

>>> mask[mask.eq(True)].index
Index([ 1,  2,  3,  6,  9, 10, 13, 14, 15, 17, 18, 19, 20, 23, 24, 25, 26, 27,
       29, 30, 33, 34, 35, 37, 38, 39, 40, 41, 43, 44, 45, 49, 50, 51, 53, 54,
       61, 63, 64, 67, 68, 69, 70, 71, 74, 75, 78, 79, 81, 83, 84, 85, 88, 89,
       90, 91, 92, 93, 94, 96, 97],
      dtype='int64')

>>> grouped_df.index.intersection(mask[mask.eq(True)].index)
Index([1, 2, 3, 6, 9], dtype='int64')