Aggregate unique values from multiple columns with pandas GroupBy

melt + pivot_table

s = df.astype(str).melt(id_vars='prop1').drop_duplicates()

s.pivot_table(
  index='prop1',
  columns='variable',
  values='value',
  aggfunc=','.join)

variable       prop2       prop3      prop4
prop1
K20          12,1,66  travis,leo   10.0,4.0
L30       3,54,11,10    bob,john  11.2,10.0

Try this, it worked for me perfectly:

df.groupby(['prop1','prop2', 'prop4']).agg(lambda x: ','.join(x.unique())).reset_index()

This will give the result as:

prop1  prop2       prop3       prop4
L30    3,54,11,10  bob,john    11.2,10
K20    12,1,66     travis,leo  10,4

Use groupby and agg, and aggregate only unique values by calling Series.unique:

df.astype(str).groupby('prop1').agg(lambda x: ','.join(x.unique()))

            prop2       prop3      prop4
prop1                                   
K20       12,1,66  travis,leo   10.0,4.0
L30    3,54,11,10    bob,john  11.2,10.0

---

df.astype(str).groupby('prop1', sort=False).agg(lambda x: ','.join(x.unique()))

            prop2       prop3      prop4
prop1                                   
L30    3,54,11,10    bob,john  11.2,10.0
K20       12,1,66  travis,leo   10.0,4.0

---

If handling NaNs is important, call fillna in advance:

import re
df.fillna('').astype(str).groupby('prop1').agg(
    lambda x: re.sub(',+', ',', ','.join(x.unique()))
)

            prop2       prop3      prop4
prop1                                   
K20       12,1,66  travis,leo   10.0,4.0
L30    3,54,11,10    bob,john  11.2,10.0