大熊猫：从其他数据框中填充缺失的数据框值

Question

I have two dataframes of different size:

df1 = pd.DataFrame({'A':[1,2,None,4,None,6,7,8,None,10], 'B':[11,12,13,14,15,16,17,18,19,20]})
df1

      A   B
0   1.0  11
1   2.0  12
2   NaN  13
3   4.0  14
4   NaN  15
5   6.0  16
6   7.0  17
7   8.0  18
8   NaN  19
9  10.0  20

df2 = pd.DataFrame({'A':[2,3,4,5,6,8], 'B':[12,13,14,15,16,18]})
df2['A'] = df2['A'].astype(float)
df2

     A   B
0  2.0  12
1  3.0  13
2  4.0  14
3  5.0  15
4  6.0  16
5  8.0  18

I need to fill missing values (and only them) in column A of the first dataframe with values from the second dataframe with common key in the column B. It is equivalent to a SQL query:

UPDATE df1 JOIN df2
  ON df1.B = df2.B
  SET df1.A = df2.A WHERE df1.A IS NULL;

I tried to use answers to similar questions from this site, but it does not work as I need:

df1.fillna(df2)

      A   B
0   1.0  11
1   2.0  12
2   4.0  13
3   4.0  14
4   6.0  15
5   6.0  16
6   7.0  17
7   8.0  18
8   NaN  19
9  10.0  20

df1.combine_first(df2)

      A   B
0   1.0  11
1   2.0  12
2   4.0  13
3   4.0  14
4   6.0  15
5   6.0  16
6   7.0  17
7   8.0  18
8   NaN  19
9  10.0  20

Intended output is:

      A   B
0   1.0  11
1   2.0  12
2   3.0  13
3   4.0  14
4   5.0  15
5   6.0  16
6   7.0  17
7   8.0  18
8   NaN  19
9  10.0  20

How do I get this result?

Answer 1

您正确使用combine_first() ，只是两个数据帧必须共享相同的索引，并且索引必须是列B：

df1.set_index('B').combine_first(df2.set_index('B')).reset_index()