Data manipulation in pandas on monthly, quarterly and annual level on multiple columns

Question

I need to create a function which take an input as dictionary and update column values in the dataframe. My data looks following

Date	Col_1	Col_2	Col_3	Col_4	Col_5
01/01/2021	10	20	10	20	10
02/01/2021	10	20	10	20	10
03/01/2021	10	20	10	20	10
04/01/2021	10	20	10	20	10
05/01/2021	10	20	10	20	10
06/01/2021	10	20	10	20	10
07/01/2021	10	20	10	20	10
08/01/2021	10	20	10	20	10
09/01/2021	10	20	10	20	10
10/01/2021	10	20	10	20	10
11/01/2021	10	20	10	20	10
12/01/2021	10	20	10	20	10

Now, if pass monthly level update in percentage for 'Col_1' and 'Col_2', say

{Date: ['01/01/2021','02/01/2021','03/01/2021','04/01/2021','05/01/2021','06/01/2021',
        '07/01/2021','08/01/2021','09/01/2021','10/01/2021','11/01/2021','12/01/2021',],
 'Col_1': [20,20,20,20,30,30,40,40,20,20,20,20],
 'Col_2': [0,0,0,0,0,0,0,0,0,0,10,10]}

After performing this my desired out looks like this for monthly change

Date	Col_1	Col_2	Col_3	Col_4	Col_5
01/01/2021	12	20	10	20	10
02/01/2021	12	20	10	20	10
03/01/2021	12	20	10	20	10
04/01/2021	12	20	10	20	10
05/01/2021	13	20	10	20	10
06/01/2021	13	20	10	20	10
07/01/2021	14	20	10	20	10
08/01/2021	14	20	10	20	10
09/01/2021	12	20	10	20	10
10/01/2021	12	20	10	20	10
11/01/2021	12	24	10	20	10
12/01/2021	12	24	10	20	10

Similarly, I want to update my data on Quarterly and Yearly Level too. I am able to do the Yearly update, here is my code. Please help me with MONTHY AND QUARTERLY updation based on the inputs.

Thank you!!

dic = {'col_1':10,'col_2':-5)
year = 2021
def update_df(dic,df,year):
    df = df[df['date'].dt.year == year]
    df = (df+df.select_dtypes(include = 'number').mul(pd.Series(dic)/100)).combine_first(df)[df.columns]
    return df

I am trying like this

def update_df(dic,df,year,choice):     
    if choice == annual:         
        df = df[df['date'].dt.year == year]         
        df = (df+df.select_dtypes(include =                 
'number').mul(pd.Series(dic)/100)).combine_first(df)[df.columns]        
    elif choice == quarterly :          
        df["quarter"] = df.date.dt.quarter           
        df = (df+df.select_dtypes(include =                   
        'number').mul(pd.Series(dic)/100)).combine_first(df)[df.columns]
    else choice == monthly : 
        df["month"] = df.date.dt.month           
        df = (df+df.select_dtypes(include =                   
        'number').mul(pd.Series(dic)/100)).combine_first(df)[df.columns]
    return df

Answer 1

**pandas captures 4 general time related concepts:

Date times: A specific date and time with timezone support. Similar to datetime.datetime from the standard library.

Time deltas: An absolute time duration. Similar to datetime.timedelta from the standard library.

Time spans: A span of time defined by a point in time and its associated frequency.

Date offsets: A relative time duration that respects calendar arithmetic. Similar to dateutil.relativedelta.relativedelta from the dateutil package.**