Feature engineered multiple columns of pandas data frame (add new columns based on existing ones)

Question

Sorry being naive. I have the following data and I want to feature engineered some columns. But I don't have how I can do multiple operations on the same data frame. One thing to mention I have multiple entries for each customer. So, in the end, I want aggregated values (ie 1 entry for each customer)

    customer_id purchase_amount date_of_purchase    days_since
 0    760             25.0         06-11-2009             2395
 1    860             50.0         09-28-2012             1190
 2   1200             100.0        10-25-2005             3720
 3   1420             50.0         09-07-2009             2307
 4   1940             70.0         01-25-2013             1071

new column based on min, count and mean

customer_purchases['amount'] = customer_purchases.groupby(['customer_id'])['purchase_amount'].agg('min')
customer_purchases['frequency'] = customer_purchases.groupby(['customer_id'])['days_since'].agg('count')
customer_purchases['recency'] = customer_purchases.groupby(['customer_id'])['days_since'].agg('mean')

nexpected outcome

customer_id purchase_amount date_of_purchase    days_since  recency frequency   amount  first_purchase
0   760         25.0      06-11-2009              2395       1273      5             38.000000  3293
1   860         50.0      09-28-2012              1190        118      10            54.000000  3744
2   1200       100.0      10-25-2005              3720        1192     9            102.777778  3907
3   1420        50.0      09-07-2009              2307         142     34            51.029412     3825
4  1940        70.0       01-25-2013              1071         686     10              47.500000    3984

One solution:

I can think of 3 separate operations for each needed column and then join all those to get a new data frame. I know it's not efficient for just sake what I need

df_1 = customer_purchases.groupby('customer_id', sort = False)["purchase_amount"].min().reset_index(name ='amount')

df_2 = customer_purchases.groupby('customer_id', sort = False)["days_since"].count().reset_index(name ='frequency')

df_3 = customer_purchases.groupby('customer_id', sort = False)["days_since"].mean().reset_index(name ='recency')

However, either I get an error or not data frame with correct data. Your help and patience will be appreciated.

Answer 1

SOLUTION

finally i found the solution

def f(x):
        recency        = x['days_since'].min()
        frequency      = x['days_since'].count()
        monetary_value = x['purchase_amount'].mean()
        c = ['recency','frequency, monetary_value']
        return pd.Series([recency, frequency, monetary_value], index =c )
    
    df1 = customer_purchases.groupby('customer_id').apply(f)
    print (df1)

Answer 2

Use instead

 customer_purchases.groupby('customer_id')['purchase_amount'].transform(lambda x : x.min()) 

Transform will give output for each row of original dataframe instead of grouped row as in case of using agg