Looping through grouped and assign value in new column in python

Question

I would like to create a new column and assign value for each group (in this case is Color), and in each group:

if Function column is View and Access column is no, the value for the new column will be 'No' for this group;

if Function column = View and Access column = yes, the value for new column will be same as 'Access' for the rows of the group

Data:

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

Desired Outcome:

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

Given that you want a 'no' for all other cases (except from the pair View-Yes), you can do the following:

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