Add a new (binary) column in a dataframe, based on certain condition, for every group/id

I have the below dataframe:

#Load the required libraries
import pandas as pd

#Create dataset
data = {'id': [1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1,
               2, 2,2,2,2,
               3, 3, 3, 3, 3, 3,
               4, 4,4,4,4,4,4,4,
               5, 5, 5, 5, 5,5, 5, 5,5,],
        'cycle': [1,2, 3, 4, 5,6,7,8,9,10,11,
                  1,2, 3,4,5,
                  1,2, 3, 4, 5,6,
                  1,2,3,4,5,6,7,8,
                  1,2, 3, 4, 5,6,7,8,9,],
        'Salary': [5, 6, 7,8,9,6,4,12,5,14,15,
                   4, 5,6,7,8,
                   5,8,4,7,12,1,
                   8,1,2,7,4,5,8,1,
                   1, 4,9,10,11,7,13,4,15,],
        'Children': ['No', 'Yes', 'Yes', 'Yes', 'Yes', 'No','No', 'Yes', 'Yes', 'Yes', 'No',
                     'Yes', 'No', 'Yes', 'No', 'Yes',
                     'No','Yes', 'Yes', 'No','No', 'Yes',
                     'Yes','Yes', 'Yes', 'No','No', 'Yes', 'Yes', 'Yes',
                      'No',  'Yes', 'No', 'No', 'Yes', 'Yes', 'Yes', 'Yes', 'No',],
        'Days': [123, 128, 66, 66, 120, 141, 52,96, 120, 141, 52,
                 96, 120,128, 66, 120,
                 15,123, 128, 66, 120, 141,
                 141,128, 66, 123, 128, 66, 120,141, 
                 123, 128, 66, 123, 128, 66, 120, 141, 52,],
        }

#Convert to dataframe
df = pd.DataFrame(data)
print("df = \n", df)

The above dataframe looks as such:

Now, I need to add a binary column in this dataframe such that, in every group/id, whenever Salary >= 7, Binary value should be 1, else 0.

For, example, for id=1, the ‘Salary’ column is [5, 6, 7,8,9,6,4,12,5,14,15]. Hence, the Binary column should be [0, 0 , 1, 1, 0, 0 ,0 ,1 , 0 , 1 ,1]

The new dataframe looks as such:

Can somebody please let me know how do I achieve this task in Python?

>Solution :

One way is:

df['Binary']=0
df.loc[df['Salary']>=7,'Binary']=1

# another way:
df['Binary']=np.where(df['Salary'] >=7,1,0)