简单的转换

首先，让我们创建一个虚拟数据帧

我们假设客户可以有 n 个订单，订单可以有 m 个商品，而商品可以多次订购

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

。
。

现在，我们将使用 pandas transform 函数来计算每个客户的订单数量

# 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