Pandas how to apply a function to groupby().first()

Question

I have a df,the code is:

  df = """
      ValOption  RB test contrat
    0       SLA  4  3    23
    1       AC   5  4    12
    2       SLA  5  5    23
    3       AC   2  4    39
    4       SLA  5  5    26
    5       AC   3  4    52
    6       SLA  4  3    64
    0       SLA  4  3    23
    1       AC   5  4    12
    2       SLA  5  5    23
    3       AC   2  4    39
    4       SLA  5  5    26
    5       AC   5  4    52
    6       SLA  4  3    64
  
    """
df = pd.read_csv(StringIO(df.strip()), sep='\s+')

output:

       ValOption  RB  test  contrat

    0       SLA   4     3       23
    1        AC   5     4       12
    2       SLA   5     5       23
    3        AC   2     4       39
    4       SLA   5     5       26
    5        AC   3     4       52
    6       SLA   4     3       64
    0       SLA   4     3       23
    1        AC   5     4       12
    2       SLA   5     5       23
    3        AC   2     4       39
    4       SLA   5     5       26
    5        AC   5     4       52
    6       SLA   4     3       64

Now I group it and get the first by:

df_u=df.groupby(['RB','test']).first()

output:

Then I want to apply a function to each row,and for some reason I have to use pd.apply() function:

def func(row):
    v1=row['RB']*3
    v2=row['test']-1
    return v1+v2

df_u['new_col']=df_u.apply(lambda row:func(row), axis=1)

Notice:In real business the function is very complicated and long,so I need to use pd.apply()

Then I get an error:

KeyError: ('RB', 'occurred at index (2, 4)')

Answer 1

You have to reset_index to access the row 'RB' & 'test'. Use .values to set values to new_col :

df_u['new_col'] = df_u.reset_index().apply(func, axis=1).values
print(df_u)

# Output:
        ValOption  contrat  new_col
RB test                            
2  4           AC       39        9
3  4           AC       52       12
4  3          SLA       23       14
5  4           AC       12       18
   5          SLA       23       19

Update

How to return the new_col to the original df?

df = df.merge(df.drop_duplicates(['RB', 'test'])
                .assign(new_col=func)[['RB', 'test', 'new_col']],
              on=['RB', 'test'], how='left')

# Output
   ValOption  RB  test  contrat  new_col
0        SLA   4     3       23       14
1         AC   5     4       12       18
2        SLA   5     5       23       19
3         AC   2     4       39        9
4        SLA   5     5       26       19
5         AC   3     4       52       12
6        SLA   4     3       64       14
7        SLA   4     3       23       14
8         AC   5     4       12       18
9        SLA   5     5       23       19
10        AC   2     4       39        9
11       SLA   5     5       26       19
12        AC   5     4       52       18
13       SLA   4     3       64       14

Update2

The reason I drop_duplicates is for saving time,make it faster because the length of row is 60k,if I apply to each row,it spend lots of time,instead if I drop the duplicated first,I don't need to apply to each row,I directly assign the value to the same column value row

Apply is a for-loop like, use vectorization:

df['new_col'] = (df['RB']*3) + (df['test']-1)

Performance

For 140,000 records, the operation above took 361 µs:

%timeit (df1['RB']*3) + (df1['test']-1)
361 µs ± 9.02 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

For 14 records (it's not a mistake), the previous operation took 935 µs:

%timeit df.drop_duplicates(['RB', 'test']).apply(func, axis=1)
935 µs ± 5.44 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)