Python Language
Panda's transformeren: bewerkingen op groepen vooraf en voeg de resultaten samen

Eenvoudige transformatie

Laten we eerst een dummy-dataframe maken

We gaan ervan uit dat een klant n bestellingen kan hebben, een bestelling m artikelen kan bevatten en artikelen meerdere keren kunnen worden besteld

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']

# And this is how the dataframe looks like:
print(orders_df)
#     customer_id  order_id        item
# 0             1         1      apples
# 1             1         1   chocolate
# 2             1         1   chocolate
# 3             1         2      coffee
# 4             1         2      coffee
# 5             2         3      apples
# 6             2         3     bananas
# 7             3         4      coffee
# 8             3         5   milkshake
# 9             3         6   chocolate
# 10            3         6  strawberry
# 11            3         6  strawberry

.
.

Nu zullen we de pandas- transform om het aantal bestellingen per klant te tellen

# First, we define the function that will be applied per customer_id 
count_number_of_orders = lambda x: len(x.unique())

# And now, we can tranform each group using the logic defined above
orders_df['number_of_orders_per_cient'] = (               # Put the results into a new column that is called 'number_of_orders_per_cient'
                     orders_df                            # Take the original dataframe
                    .groupby(['customer_id'])['order_id'] # Create a seperate group for each customer_id & select the order_id
                    .transform(count_number_of_orders))   # Apply the function to each group seperatly 

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

Meerdere resultaten per groep

transform gebruiken die subberekeningen per groep retourneren

In het vorige voorbeeld hadden we één resultaat per klant. Functies die verschillende waarden voor de groep retourneren, kunnen echter ook worden toegepast.

# 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