获取不在特定商店购物的客户列表

Question

假设我有一个事务和客户的数据框架：

df = pd.DataFrame({'shop': pd.Series(['McDonalds', 'McDonalds', 'McDonalds', 'McDonalds', 'Burger King', 'Burger King', 'Burger King', 'Burger King', 'Burger King', 'Trump Golf Course', 'Trump Golf Course', 'Trump Golf Course', 'Trump Golf Course', 'Trump Golf Course', 'Trump Golf Course'],dtype='object',index=pd.RangeIndex(start=0, stop=15, step=1)), 'Customer': pd.Series(['John Ryan', 'Jim Bob', 'Mary Ryan', 'Michael Patric', 'John Ryan', 'Jim Bob', 'Mary Ryan', 'Sean Connery', 'Brad Pitt', 'John Ryan', 'John Ryan', 'Michael Patric', 'Mary Ryan', 'John Ryan', 'Jim Bob'],dtype='object',index=pd.RangeIndex(start=0, stop=15, step=1)), 'Customer ID': pd.Series([1, 2, 3, 4, 1, 2, 3, 5, 6, 1, 1, 4, 3, 1, 2],dtype='int64',index=pd.RangeIndex(start=0, stop=15, step=1)), 'Amount': pd.Series([50, 32, 15, 65, 32, 51, 54, 84, 52, 51, 2, 32, 54, 87, 65],dtype='int64',index=pd.RangeIndex(start=0, stop=15, step=1))}, index=pd.RangeIndex(start=0, stop=15, step=1))

print(df)

                 shop        Customer  Customer ID  Amount
0           McDonalds       John Ryan            1      50
1           McDonalds         Jim Bob            2      32
2           McDonalds       Mary Ryan            3      15
3           McDonalds  Michael Patric            4      65
4         Burger King       John Ryan            1      32
5         Burger King         Jim Bob            2      51
6         Burger King       Mary Ryan            3      54
7         Burger King    Sean Connery            5      84
8         Burger King       Brad Pitt            6      52
9   Trump Golf Course       John Ryan            1      51
10  Trump Golf Course       John Ryan            1       2
11  Trump Golf Course  Michael Patric            4      32
12  Trump Golf Course       Mary Ryan            3      54
13  Trump Golf Course       John Ryan            1      87
14  Trump Golf Course         Jim Bob            2      65

我想提取或标记那些没有在麦当劳购物的汉堡王顾客。（在本例中，肖恩·康纳利和布拉德·皮特）

我试图创建一个掩码，其中shop == McDonalds，并获取客户ID

mask1 = df.shop == 'McDonalds'

mcdonalds_customer_ids = df[mask1]['Customer ID'].values

array([1, 2, 3, 4], dtype=int64)

然后创建一个单独的掩码，其中shop=='Burger King'和客户ID不在麦当劳客户ID列表中：

mask = (df['shop'] == 'Burger King' & df['Customer ID'] not in mcdonalds_customer_ids)

我得到以下错误：

TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

TypeError: cannot compare a dtyped [int64] array with a scalar of type [bool]

我也尝试过使用np.where，但它变得更加混乱。你知道吗

我的预期产出只是提取两个没有在麦当劳购物的汉堡王顾客：

                 shop        Customer  Customer ID  Amount
7         Burger King    Sean Connery            5      84
8         Burger King       Brad Pitt            6      52

或者用np.哪里地址：

                 shop        Customer  Customer ID  Amount  No_McDonalds
7         Burger King    Sean Connery            5      84  True
8         Burger King       Brad Pitt            6      52  True

我可以用一个函数来实现这一点，但我希望能以某种方式将它矢量化。完全失败，感谢任何帮助。你知道吗

Answer 1

这应该做到：

aux = df.groupby('Customer').shop.sum()

df['No_McDonalds'] = df.Customer.map(aux.apply(lambda x: ('Burger King' in x) & ('McDonalds' not in x)))

输出：

                 shop        Customer  Customer ID  Amount  No_McDonalds
0           McDonalds       John Ryan            1      50         False
1           McDonalds         Jim Bob            2      32         False
2           McDonalds       Mary Ryan            3      15         False
3           McDonalds  Michael Patric            4      65         False
4         Burger King       John Ryan            1      32         False
5         Burger King         Jim Bob            2      51         False
6         Burger King       Mary Ryan            3      54         False
7         Burger King    Sean Connery            5      84          True
8         Burger King       Brad Pitt            6      52          True
9   Trump Golf Course       John Ryan            1      51         False
10  Trump Golf Course       John Ryan            1       2         False
11  Trump Golf Course  Michael Patric            4      32         False
12  Trump Golf Course       Mary Ryan            3      54         False
13  Trump Golf Course       John Ryan            1      87         False
14  Trump Golf Course         Jim Bob            2      65         False

如果你需要解释，请告诉我，我会帮助你的。你知道吗

Answer 2

在您的情况下，我想提取或标记那些没有在麦当劳购物的汉堡王顾客，您只需执行以下操作：

s = (set(df.loc[df.shop.eq('Burger King'), 'Customer ID']) 
    - set(df.loc[df.shop.eq('McDonalds'), 'Customer ID'])
    )

输出s：

{5, 6}

要使用Buger King提取这些客户记录：

df[df.shop.eq('Burger King') & df['Customer ID'].isin(s)]

输出：

          shop      Customer  Customer ID  Amount
7  Burger King  Sean Connery            5      84
8  Burger King     Brad Pitt            6      52

Answer 3

这是一个带有^{}和^{}的向量化解决方案。 首先我们得到Burger King的行，然后我们从McDonalds得到Customers

最后检查来自Burger King的哪些客户没有去过McDonalds：

bk = df.loc[df['shop'].eq('Burger King')]
mc = df.loc[df['shop'].eq('McDonalds'), 'Customer']

bk[~bk['Customer'].isin(mc)]

          shop      Customer  Customer ID  Amount
7  Burger King  Sean Connery            5      84
8  Burger King     Brad Pitt            6      52