根据NaN进行检查时如何选择特定的列Pandas数据框 [英] How to pick specific columns Pandas dataframe when checking against NaN

问题描述

我在Python中有一个熊猫数据框，看起来像

I have a pandas dataframe in Python that looks something like

       AccountID_x    AccountId  AmountCD_x  AmountDOC_x  AmountDoc_x  
1              NaN  4001001copa       52.53        52.53          NaN   
2              NaN  4001001copa       52.53        52.53          NaN   
3      4001001copa          NaN       52.53        52.53          NaN   
4              NaN  4001001copa       52.53        52.53          NaN   

此数据帧是合并命令的结果，该命令将2个数据帧合并在一起.我现在想做的是创建一个新列，该列将添加AccountID_x或AccountId而不是Nan，因此在上面的示例中，第1、2、4行将具有AccountId的值，而第3行将具有某些新列中AccountID_x中的值.

This dataframe is the result of a merge command to merge 2 dataframes together. What I want to do now is create a new column that will add either AccountID_x or AccountId based on which on is not Nan, so in the above example rows 1, 2, 4 would have the value of AccountId in it and row 3 would have the value from AccountID_x in some new column.

推荐答案

您还可以使用apply传播fillna:

df2['newcolumn'] = df2[['AccountID_x','AccountId']].apply(lambda x: x.fillna(method='ffill')[-1], axis=1)

或等效地(在您的情况下):

Or equivalently (in your case):

df2['newcolumn'] = df2[['AccountID_x','AccountId']].apply(lambda x: x.fillna(method='bfill')[0], axis=1)

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