aggregate pivot in pandas with multiple repeated fields

Question

I have a dataframe that looks like this:

id       Field_name  Field_value
1           consent          yes
1   _REACTION TIME_         5547
1              age            24
1           gender             X
1   _REACTION TIME_        45396
1         education          uni
1          language           EN
1   _REACTION TIME_       105187
2           consent          yes
2   _REACTION TIME_         3547
2              age            25
2           gender             F
2   _REACTION TIME_        42396
2         education          uni
2          language           EU
2   _REACTION TIME_       115427

and I would like to have it as a row per id, with every _REACTION TIME_ row being a different column, such as:

id  consent  _REACTION TIME_1  age gender  _REACTION TIME_2  education language _REACTION TIME_3
1       yes              5547   24      X             45396        uni       EN           105187
2       yes              3547   25      F             42396        uni       EU           115427

I have been looking for an answer to this all over SO but I can’t find it for this particular issue when only some of the entries are repeated, but they are repeated multiple times.

Thanks in advance!

Asked By: baskcat

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Source

Answer 1

Use GroupBy.cumcount only for rows with duplicates by DataFrame.duplicated, so possible pivoting by DataFrame.pivot, last for original order of columns add DataFrame.reindex:

m = df.duplicated(['id','Field_name'], keep=False)
df.loc[m, 'Field_name'] += df[m].groupby(['id','Field_name']).cumcount().add(1).astype(str)

cols = df['Field_name'].unique()
df = df.pivot(index='id', columns='Field_name', values='Field_value').reindex(cols, axis=1)
print (df)
Field_name consent _REACTION TIME_1 age gender _REACTION TIME_2 education  
id                                                                          
1              yes             5547  24      X            45396       uni   
2              yes             3547  25      F            42396       uni   

Field_name language _REACTION TIME_3  
id                                    
1                EN           105187  
2                EU           115427

Solution avoiding overwrite original DataFrame is similar:

m = df.duplicated(['id','Field_name'], keep=False)
s = df['Field_name'].add(df.groupby(['id','Field_name']).cumcount().add(1)
                           .astype(str)).where(m, df['Field_name'])


df1 = (df.assign(Field_name=s)
        .pivot(index='id', columns='Field_name', values='Field_value')
        .reindex(s.unique(), axis=1))
print (df1)
Field_name consent _REACTION TIME_1 age gender _REACTION TIME_2 education  
id                                                                          
1              yes             5547  24      X            45396       uni   
2              yes             3547  25      F            42396       uni   

Field_name language _REACTION TIME_3  
id                                    
1                EN           105187  
2                EU           115427

Answered By: jezrael

Answer 2

If you want to remain _REACTION TIME_ instead of renaming it as _REACTION TIME_1 in column header, you can do groupby.apply

out = (df.groupby('id').apply(lambda g: g.drop('id', axis=1).set_index('Field_name').T)
       .reset_index(level=0).reset_index(drop=True)
       .rename_axis('', axis=1))

print(out)

   id consent _REACTION_TIME_ age gender _REACTION_TIME_ education language _REACTION_TIME_
0   1     yes            5547  24      X           45396       uni       EN          105187
1   2     yes            3547  25      F           42396       uni       EU          115427

Answered By: Ynjxsjmh

aggregate pivot in pandas with multiple repeated fields

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