pandas - 合并两个具有相同列名称的DataFrames [英] Pandas - merge two DataFrames with Identical Column Names

问题描述

我有两个数据帧在第一列中具有相同的列名称和相同的ID。除了ID列之外，在一个DataFrame中包含值的每个单元格都包含另一个数据库中的NaN。
这是一个他们看起来像的例子：

  ID Cat1 Cat2 Cat3 
 1 NaN 75 NaN 
 2 61 NaN 84 
 3 NaN NaN NaN 
 
 
 ID Cat1 Cat2 Cat3 
 1 54 NaN 44 
 2 NaN 38 NaN 
 3 49 50 53 
  

我想将它们合并到一个DataFrame中，同时保留相同的列名。所以结果将如下所示：

  ID Cat1 Cat2 Cat3 
 1 54 75 44 
 2 61 38 84 
 3 49 50 53 
  

我试过：

  df3 = pd.merge（df1，df2，on ='ID'，how ='outer'）
  / pre> 
 
 
其中给了我一个包含两倍的列的DataFrame。如何将每个DataFrame的值合并为一个？
解决方案
您可能希望 df.update  。请参阅文档。
  df1.update（df2，raise_conflict = True）
  
 
I have two Data Frames with identical column names and identical IDs in the first column. With the exception of the ID column, every cell that contains a value in one DataFrame contains NaN in the other. 
Here's an example of what they look like:
ID    Cat1    Cat2    Cat3
1     NaN     75      NaN
2     61      NaN     84
3     NaN     NaN     NaN


ID    Cat1    Cat2    Cat3
1     54      NaN     44
2     NaN     38     NaN
3     49      50      53
I want to merge them into one DataFrame while keeping the same Column Names. So the result would look like this:
ID    Cat1    Cat2    Cat3
1     54      75      44
2     61      38      84
3     49      50      53
I tried:
df3 = pd.merge(df1, df2, on='ID', how='outer')
Which gave me a DataFrame containing twice as many columns. How can I merge the values from each DataFrame into one?
解决方案
You probably want df.update. See the documentation.
df1.update(df2, raise_conflict=True)


                        
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