Map values using multiple columns with a specific condition in Python

I have a dataset where I would like to map values based on a specific condition.
I would like to add a new column and then map a label to an ID if it meets the condition of:

**If ID == AA AND Date >= to Q121:  Status = 'closed' AND values within the Used column will be null.**

Data

ID  Date    Location    Used    
AA  Q121    NY          20  
AA  Q221    NY          50  
AA  Q321    NY          10  
BB  Q121    CA          1   
BB  Q221    CA          0   
BB  Q321    CA          500 
BB  Q421    CA          700 
CC  Q121    AZ          50  

Desired

ID  Date    Location    Used    Status
AA  Q121    NY                  closed
AA  Q221    NY                  closed
AA  Q321    NY                  closed
BB  Q121    CA          1   
BB  Q221    CA          0   
BB  Q321    CA          500 
BB  Q421    CA          700 
CC  Q121    AZ          50

Doing

df['Status']=df['ID'].map({'AA':'closed' ,  })

Is it possible to map using two columns, or would a loop be better fit for this?
Any suggestion is appreciated.

>Solution :

You can use np.where for this:

df['Used'] = np.where(((df.ID == 'AA') & (df.Date >= 'Q121')), '', df['Used'])
df['Status'] = np.where(((df.ID == 'AA') & (df.Date >= 'Q121')), 'closed', '')

# or use np.nan instead of '' above, that's actually preferable (generating NaN values)

print(df)

   ID  Date Location   Used  Status
0  AA  Q121       NY         closed
1  AA  Q221       NY         closed
2  AA  Q321       NY         closed
3  BB  Q121       CA    1.0        
4  BB  Q221       CA    0.0        
5  BB  Q321       CA  500.0        
6  BB  Q421       CA  700.0        
7  CC  Q121       AZ   50.0