pandas :每组中均值填充缺失值 [英] Pandas: filling missing values by mean in each group

问题描述

这应该很简单，但是我发现最接近的是这篇文章: pandas:填充组中的缺失值，但我仍然可以不能解决我的问题......

This should be straightforward, but the closest thing I've found is this post: pandas: Filling missing values within a group, and I still can't solve my problem....

假设我有以下数据框

df = pd.DataFrame({'value': [1, np.nan, np.nan, 2, 3, 1, 3, np.nan, 3], 'name': ['A','A', 'B','B','B','B', 'C','C','C']})

  name  value
0    A      1
1    A    NaN
2    B    NaN
3    B      2
4    B      3
5    B      1
6    C      3
7    C    NaN
8    C      3

我想在每个名称"组中用平均值填写"NaN"，即

and I'd like to fill in "NaN" with mean value in each "name" group, i.e.

      name  value
0    A      1
1    A      1
2    B      2
3    B      2
4    B      3
5    B      1
6    C      3
7    C      3
8    C      3

我不确定该去哪里了

grouped = df.groupby('name').mean()

谢谢.

推荐答案

一种方法是使用transform:

>>> df
  name  value
0    A      1
1    A    NaN
2    B    NaN
3    B      2
4    B      3
5    B      1
6    C      3
7    C    NaN
8    C      3
>>> df["value"] = df.groupby("name").transform(lambda x: x.fillna(x.mean()))
>>> df
  name  value
0    A      1
1    A      1
2    B      2
3    B      2
4    B      3
5    B      1
6    C      3
7    C      3
8    C      3

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