Python Pandas : pivot table with aggfunc = count unique distinct

Question

df2 = pd.DataFrame({\'X\' : [\'X1\', \'X1\', \'X1\', \'X1\'], \'Y\' : [\'Y2\',\'Y1\',\'Y1\',\'Y1\'], \'Z\' : [\'Z3\',\'Z1\',\'Z1\',\'Z2\']})

    X   Y   Z
0  X1  Y2         


        

Answer 1

For best performance I recommend doing DataFrame.drop_duplicates followed up aggfunc='count'.

Others are correct that aggfunc=pd.Series.nunique will work. This can be slow, however, if the number of index groups you have is large (>1000).

So instead of (to quote @Javier)

df2.pivot_table('X', 'Y', 'Z', aggfunc=pd.Series.nunique)

I suggest

df2.drop_duplicates(['X', 'Y', 'Z']).pivot_table('X', 'Y', 'Z', aggfunc='count')

This works because it guarantees that every subgroup (each combination of ('Y', 'Z')) will have unique (non-duplicate) values of 'X'.