Rolling window with group by and condition dataframe python

I am trying to create some rolling window features, after applying group by, like below:

my_data = [['2020-01-01', 'A' , '15'], 
          ['2020-01-01', 'B' , '150'],
          ['2020-01-02', 'A' , '1'], 
          ['2020-01-02', 'B' , '10'], 
          ['2020-01-03', 'A' , '4'], 
          ['2020-01-03', 'B' , '40'],
           ['2020-01-04', 'A' , '5'], 
          ['2020-01-04', 'B' , '50'],
           ['2020-01-05', 'A' , '13'], 
          ['2020-01-05', 'B' , '130'],
           ['2020-01-06', 'A' , '2'], 
          ['2020-01-06', 'B' , '20'],
           ['2020-01-07', 'A' , '14'], 
          ['2020-01-07', 'B' , '140'],
           ['2020-01-08', 'A' , '8'], 
          ['2020-01-08', 'B' , '80'],
           ['2020-01-09', 'A' , '6'], 
          ['2020-01-09', 'B' , '60'],
           ['2020-01-10', 'A' , '4'], 
          ['2020-01-10', 'B' , '40'],
          ['2020-01-11', 'A' , '3'], 
          ['2020-01-11', 'B' , '30'],
          ['2020-01-12', 'A' , '5'], 
          ['2020-01-12', 'B' , '50'],
          ['2020-01-13', 'A' , '12'], 
          ['2020-01-13', 'B' , '120'],
          ['2020-01-14', 'A' , '3'], 
          ['2020-01-14', 'B' , '33'],
             ['2020-01-15', 'A' , '53'], 
          ['2020-01-15', 'B' , '23'],
             ['2020-01-16', 'A' , '34'], 
          ['2020-01-16', 'B' , '26'],
             ['2020-01-17', 'A' , '98'], 
          ['2020-01-17', 'B' , '24'],
             ['2020-01-18', 'A' , '90'], 
          ['2020-01-18', 'B' , '902'],
             ['2020-01-19', 'A' , '42'], 
          ['2020-01-19', 'B' , '40'],
             ['2020-01-20', 'A' , '1'], 
          ['2020-01-20', 'B' , '4'],
             ['2020-01-21', 'A' , '23'], 
          ['2020-01-21', 'B' , '98'],
             ['2020-01-22', 'A' , '23'], 
          ['2020-01-22', 'B' , '10'],
           
          ]

df_1= pd.DataFrame(my_data, columns = ['Date', 'type', 'value'])
df_1['weekday'] = pd.to_datetime(df_1['Date']).dt.weekday
df_1.set_index(['Date'], inplace=True)

Here I group baased on the type and then merge the results together:

_grouped = df_1.groupby(["type"])['value'].rolling(2).mean()
df_2d_mean = pd.DataFrame(_grouped)
df_2d_mean = df_2d_mean.rename(columns={"value":"Mean 2D"})
df_2d_mean

result_df = df_1.copy()
result_df = result_df.merge(df_2d_mean, on=['Date', 'type'])
result_df

And the result look like this:

type    value   weekday Mean 2D
Date                
2020-01-01  A   15  2   NaN
2020-01-01  B   150 2   NaN
2020-01-02  A   1   3   8.0
2020-01-02  B   10  3   80.0
2020-01-03  A   4   4   2.5
2020-01-03  B   40  4   25.0
2020-01-04  A   5   5   4.5
2020-01-04  B   50  5   45.0
2020-01-05  A   13  6   9.0
2020-01-05  B   130 6   90.0
2020-01-06  A   2   0   7.5
2020-01-06  B   20  0   75.0
....

I would like to create 7 features "Mean 2D", such as "Mean 2D Monday", "Mean 2D Tuesday", etc., which will show the rolling window mean of only the previous 2 Mondays, 2 Tuesday, etc.. So I would like to perform a filter on weekday, to calculate the rolling window mean for each weekday...

Could you please provide any suggestion?