Split a column into several columns based on several conditions and group by-CodePudding

I have a sample dataframe as given below.

import pandas as pd

data = {'ID':['A','A','A','A','A','A','A','A','A','C','C','C','C','C','C','C','C'],
    'Week': ['Week1','Week1','Week1','Week1','Week2','Week2','Week2','Week2','Week3',
             'Week1','Week1','Week1','Week1','Week2','Week2','Week2','Week2'],
    'Risk':['High','','','','','','','','','High','','','','','','',''],
    'Testing':['','Pos','','Neg','','','','','Pos', '', '','','Neg','','','','Pos'],
    'Week1_adher':['','','','','','','','','', '','','','','','','',''],
    'Week2_adher':['','','','','','','','','','','','','','','','',''],
    'Week3_adher':['','','','','','','','','','','','','','','','','']}
    
df1 = pd.DataFrame(data)
df1

Now I want to calculate adherence for each participant for each week. Its calculation is given as follows: If a participant has 2 or more entries(either positive/negative)in testing column for a week, then Adherence for that week is 'Yes' else its 'No'

Example, For participant A, week 1_adherence is 'Yes' because it has 2 entries in Testing column for Week1. Week2_adherence is 'No'

And I want the adherence results for all week to be displayed in the first row of each participant.

The final dataframe should like the image given below.

I have been stuck on this for quite some time. Any help is greatly appreciated. Thank you.

CodePudding user response：

Try:

adher = (df1.Testing.ne('')       # check for non-empty string
    .groupby([df1.ID, df1.Week])  # groupby ID and week
    .sum().ge(2)                  # count and check >= 2
    .unstack(fill_value=False)
    .replace({True:'Yes', False:'No'})
    .add_suffix('_adher')
)

# the first lines
mask = ~df1['ID'].duplicated()

df1.loc[mask, adher.columns] = adher.loc[df1.loc[mask,'ID']].values

Output:

   ID   Week  Risk   Testing Week1_adher Week2_adher Week3_adher
0   A  Week1  High                   Yes          No          No
1   A  Week1             Pos                                    
2   A  Week1                                                    
3   A  Week1             Neg                                    
4   A  Week2                                                    
5   A  Week2                                                    
6   A  Week2                                                    
7   A  Week2                                                    
8   A  Week3             Pos                                    
9   C  Week1  High                    No          No          No
10  C  Week1                                                    
11  C  Week1                                                    
12  C  Week1        Negative                                    
13  C  Week2                                                    
14  C  Week2                                                    
15  C  Week2                                                    
16  C  Week2        Positive