Converting monthly data to daily data in pandas when some months are missing

I have one time series dataset with different daily interest rates. I want to merge the data with a panel dataset, where I also have a daily time component but each day exists a couple of times since it is attributed to different asset classes.

The data looks like this
Dataframe A:

Dataframe B:

What I want to have is:

How would you merge these two dataframes?
When I use the merge command:
Merge = pd.merge(Dataframe A, Dataframe B, on="Time", how = "inner"), I get the following error message.

ValueError: You are trying to merge on datetime64[ns] and object columns. If you wish to proceed you should use pd.concat

But I do not want to use pd.concat since it is not the outcome I wish to achieve.

>Solution :

pd.merge on Time column. Merge will work only when both the column have same data type. Your columns may have different type due to which its raising exception.

df1.Time = pd.to_datetime(df1.Time,  format = '%d/%m/%Y').dt.strftime('%m/%d/%Y')
df2.Time = pd.to_datetime(df2.Time,  format = '%d/%m/%Y').dt.strftime('%m/%d/%Y')

pd.merge(df1, df2, how='inner', on='Time')

This gives us the expected output :

         Time Asset  Rate
0  08/01/2021     A  2.30
1  08/01/2021     B  2.30
2  08/01/2021     C  2.30
3  08/01/2021     D  2.30
4  08/02/2021     A  2.34
5  08/02/2021     B  2.34
6  08/02/2021     C  2.34
7  08/02/2021     D  2.34
8  08/03/2021     A  2.33