Returning the average of a column based on row values

Question

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I have the below dataframe and I would like to return the average values of 'Age' and 'Sales' for each Flavor 'Chocolate' or 'Vanilla' so the average Age of 'Vanilla' is x, the average Age of 'Chocolate' is y, etc.

I haven't been able to find the answer anywhere on the web and I'm stuck.

print(MergeData.head())

   Customer      Type     Flavor  Age  Sales    Store  Goals  Goal FlavorCode  \
0         1     Adult  Chocolate   45   4.25  Greeley     25    25          C   
1         2     Child    Vanilla    5   2.90  Greeley     25    25          V   
2         6  Teenager  Chocolate   16   4.10  Greeley     25    25          C   
3         8     Child    Vanilla    4   3.00  Greeley     25    25          V   
4        10     Child    Vanilla    6   2.50  Greeley     25    25          V   

         AgeBin1 AgeBin2  
0   (28.0, 72.0]       B  
1  (3.999, 14.0]       A  
2   (14.0, 28.0]       A  
3  (3.999, 14.0]       A  
4  (3.999, 14.0]       A  

Answer 1

you can even use df.loc ..

just using an example dataset here

>>> df
         Name  Score1  Score2
0       Alisa    62.2      89
1       Bobby    47.4      87
2    Cathrine    55.5      67
3     Madonna    74.6      55
4       Rocky    31.2      47
5   Sebastian    77.5      72
6    Jaqluine    85.6      76
7       Rahul    63.5      79
8       David    42.8      44
9      Andrew    32.3      92
10       Ajay    71.2      99
11     Teresa    57.4      69

mean of the dataFrame ..

>>> df.mean()
Score1    58.433333
Score2    73.000000
dtype: float64

For a particular column:

>>> df.loc[:,"Score1":"Score2"].mean()
Score1    58.433333
Score2    73.000000
dtype: float64

Answer 2

IIUC:

df.groupby(['Flavor'])['Age','Sales'].transform('mean')

Demo:

print(df.groupby(['Flavor'])['Age','Sales'].transform('mean'))

Output:

    Age  Sales
0  30.5  4.175
1   5.0  2.800
2  30.5  4.175
3   5.0  2.800
4   5.0  2.800