在python的新列中循环分组并赋值

Question

我想创建一个新列并为每个组（在本例中为颜色）和每个组分配值：

如果 Function 列是 View 而 Access 列是 no，则该组的新列的值为“No”；

如果 Function column = View and Access column = yes，则新列的值将与组中行的“Access”相同

数据：

Type	Color	Function	Access
A	Blue	Add	yes
A	Blue	View	no
A	Red	Add	no
A	Red	View	yes
B	Blue	Add	yes
B	Blue	View	no

期望的结果：

Type	Color	Function	Access	New Column
A	Blue	Add	yes	no
A	Blue	View	no	no
A	Red	Add	no	no
A	Red	View	yes	yes
B	Blue	Add	yes	no
B	Blue	View	no	no

# I created a new column first before grouping them
data['New Column'] = ''
data_grouped = data.groupby(['Type', 'Color']

# attempted to loop but stuck here #
for group_name, df_group in data_grouped:
    print(format(group_name))

    for row_index, row in df_group.iterrows():

        if (row['Function'] == 'View') & (row['Access'] == 'no'):
            row['New Column'] = 'no'

        print(col, column_type)

Answer 1

考虑到您希望对所有其他情况都使用“否”（View-Yes 对除外），您可以执行以下操作：

data['New Column'] = np.where((data['Function']=='View') & (data['Access']=='yes'), 'yes', 'no')