Count unique values using pandas groupby

Question

I have data of the following form   df   pd DataFrame        group    1  1  2  3  3  3  4        param     a    a    b   np nan   a    a   np nan     print df        group param   0      1     a   1      1     a   2      2     b   3      3   NaN   4      3     a   5      3     a   6      4   NaN   Non-null values within groups are always the same  I want to count the non-null value for each group  where it exists  once  and then find the total counts for each value    I m currently doing this in the following  clunky and inefficient  way   param      for    group in df df param notnull    groupby  group        param append group param unique   0   print pd DataFrame   param   param   param value counts       a    2   b    1   I m sure there s a way to do this more cleanly and without using a loop  but I just can t seem to work it out  Any help would be much appreciated

User · Answer

This is just an add-on to the solution in case you want to compute not only unique values but other aggregate functions:

df.groupby(['group']).agg(['min','max','count','nunique'])

Hope you find it useful

User · Answer

I know it has been a while since this was posted  but I think this will help too  I wanted to count unique values and filter the groups by number of these unique values  this is how I did it   df groupby  group   agg   min   max   count   nunique    reset index drop False

User · Answer

I think you can use SeriesGroupBy nunique   print  df groupby  param    group   nunique    param a    2 b    1 Name  group  dtype  int64   Another solution with unique  then create new df by DataFrame from records  reshape to Series by stack and last value counts   a   df df param notnull    groupby  group    param   unique   print  pd DataFrame from records a values tolist    stack   value counts    a    2 b    1 dtype  int64

[python] Count unique values using pandas groupby

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