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I have a DataFrame with an index called SubjectID and a column Visit. Subjects have multiple Visits and either an integer value or an N/A for Value1 and Value2. I want to collapse the rows that have the same SubjectID and the same Visit number.

Here is my data frame:

SubjectID    Visit    Value1    Value2    
B1           1         1.57      N/A
B1           1         N/A       1.75
B1           2         N/A       1.56

I want to it to look like this:

Subject ID    Visit     Value1    Value2
B1            1          1.57      1.75
B1            2          N/A       1.56

I was trying to use groupby() to solve this problem but I'm not sure how to make it take into account both the index and the values in the Visit column.

1 Answer 1

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You can use groupby.first or groupby.last to get the first/last non-null value for each column within the group. For the example data, the output would be the same for either method:

df = df.groupby(['SubjectID', 'Visit']).first().reset_index()

The resulting output:

  SubjectID  Visit  Value1  Value2
0        B1      1    1.57    1.75
1        B1      2     NaN    1.56
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