# Utilizzo delle funzioni di transform che restituiscono calcoli secondari per gruppo

Nell'esempio precedente, avevamo un risultato per cliente. Tuttavia, è possibile applicare anche funzioni che restituiscono valori diversi per il gruppo.

# Create a dummy dataframe
orders_df = pd.DataFrame()
orders_df['customer_id'] = [1,1,1,1,1,2,2,3,3,3,3,3]
orders_df['order_id'] = [1,1,1,2,2,3,3,4,5,6,6,6]
orders_df['item'] = ['apples', 'chocolate', 'chocolate', 'coffee', 'coffee', 'apples',
'bananas', 'coffee', 'milkshake', 'chocolate', 'strawberry', 'strawberry']

# Let's try to see if the items were ordered more than once in each orders

# First, we define a fuction that will be applied per group
def multiple_items_per_order(_items):
# Apply .duplicated, which will return True is the item occurs more than once.
multiple_item_bool = _items.duplicated(keep=False)
return(multiple_item_bool)

# Then, we transform each group according to the defined function
orders_df['item_duplicated_per_order'] = (                    # Put the results into a new column
orders_df                             # Take the orders dataframe
.groupby(['order_id'])['item']        # Create a seperate group for each order_id & select the item
.transform(multiple_items_per_order)) # Apply the defined function to each group separately

# Inspecting the results ...
print(orders_df)
#     customer_id  order_id        item  item_duplicated_per_order
# 0             1         1      apples                      False
# 1             1         1   chocolate                       True
# 2             1         1   chocolate                       True
# 3             1         2      coffee                       True
# 4             1         2      coffee                       True
# 5             2         3      apples                      False
# 6             2         3     bananas                      False
# 7             3         4      coffee                      False
# 8             3         5   milkshake                      False
# 9             3         6   chocolate                      False
# 10            3         6  strawberry                       True
# 11            3         6  strawberry                       True