如何在Panda Data框架中结合两列时删除nan值？

Question

我正在尝试但是nan在组合两列a时无法删除DataFrame.

数据如下:

feedback_id                  _id
568a8c25cac4991645c287ac     nan    
568df45b177e30c6487d3603     nan    
nan                          568df434832b090048f34974       
nan                          568cd22e9e82dfc166d7dff1   
568df3f0832b090048f34711     nan
nan                          568e5a38b4a797c664143dda   

我想要:

feedback_request_id
568a8c25cac4991645c287ac
568df45b177e30c6487d3603
568df434832b090048f34974
568cd22e9e82dfc166d7dff1
568df3f0832b090048f34711
568e5a38b4a797c664143dda

这是我的代码:

df3['feedback_request_id'] = ('' if df3['_id'].empty else df3['_id'].map(str)) + ('' if df3['feedback_id'].empty else df3['feedback_id'].map(str))

输出我得到:

feedback_request_id
568a8c25cac4991645c287acnan
568df45b177e30c6487d3603nan
nan568df434832b090048f34974
nan568cd22e9e82dfc166d7dff1
568df3f0832b090048f34711nan
nan568e5a38b4a797c664143dda

我试过这个,也是:

df3['feedback_request_id'] = ('' if df3['_id']=='nan' else df3['_id'].map(str)) + ('' if df3['feedback_id']=='nan' else df3['feedback_id'].map(str))

但它给出了错误:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

你可以使用combine_first或fillna:

print df['feedback_id'].combine_first(df['_id'])
0    568a8c25cac4991645c287ac
1    568df45b177e30c6487d3603
2    568df434832b090048f34974
3    568cd22e9e82dfc166d7dff1
4    568df3f0832b090048f34711
5    568e5a38b4a797c664143dda
Name: feedback_id, dtype: object

print df['feedback_id'].fillna(df['_id'])
0    568a8c25cac4991645c287ac
1    568df45b177e30c6487d3603
2    568df434832b090048f34974
3    568cd22e9e82dfc166d7dff1
4    568df3f0832b090048f34711
5    568e5a38b4a797c664143dda
Name: feedback_id, dtype: object

Answer 2

如果您想要一个不需要df明确引用两次或其任何列的解决方案：

df.bfill(axis=1).iloc[:, 0]

对于两列，这会将非空值从右列复制到左列，然后选择左列。

Answer 3

对于就地解决方案，您可以使用pd.Series.updatewith pd.DataFrame.pop：

df['feedback_id'].update(df.pop('_id'))

print(df)

                feedback_id
0  568a8c25cac4991645c287ac
1  568df45b177e30c6487d3603
2  568df434832b090048f34974
3  568cd22e9e82dfc166d7dff1
4  568df3f0832b090048f34711
5  568e5a38b4a797c664143dda