Applying group by then condition

How can the value of 'same_month=yes' be updated for entries grouped by "code" when the "Date" is identical?

below are examples of the data frame

import pandas as pd

df = pd.DataFrame([[1,'2023-01-02 00:00:00','01','2023',''],
                   [2,'2023-01-02 00:00:00','01','2023',''],
                   [3,'01/16/2023','01','2023',''],
                   [4,'01/17/2023','01','2023',''],
                   [5,'01/16/2023','01','2023',''],
                   [6,'02/13/2023','02','2023',''],
                   [7,'02/13/2023','02','2023',''],
                   [8,'02/13/2023','02','2023',''],
                   [9,'26/11/2018','11','2018',''],
                   [10,'26/11/2018','11','2018',''],
                   [11,'26/11/2018','11','2018',''],
                   [12,'26/11/2018','11','2018',''],
                   [13,'05/11/2018','11','2018',''],
                   [14,'09/11/2018','11','2018',''],

                  ],
                  columns=['Number','Date','Code','year','Same_Month'])

df 

so the expected result is that all of them are yes except entries number (4,13,14)

This should do the trick?

df['Date'] = pd.to_datetime(df['Date'],format='mixed') #Ignore this line if already in datetime format
duplicates = df.duplicated(['Code','Date'], keep=False) #Find duplicate date values per code group
df.loc[duplicates, 'Same_Month'] = 'Yes' #Add yes where duplicates value is true

This produces a table like this when using the data provided: