How do I group by Week and Year, and depending on that, assign values to a third column having 'N' as all values initially?

I have a dataframe with 4 columns. I created a new column and assigned all values as 'N' to it.

Assume that first two columns have some random info, Column3 = Year, Column4= week No., Now Column5 =week-ES (initally assigned all 'N')should be equal to 'Week No.' for the recent 5 weeks, and should be equal to 'Pastwk' for all weeks before that. How can I use group by along with the top 5 weeks clause? How do I do that? I used this code, But did not get desire result. the desired result is the table below:

    df.sort_values(['Year','Week No.'],ascending=[False,False],inplace = True)
df['Week-ES'] = 'N'
df = df.groupby(['Year','Week No.']).size()
df['Week-ES'][:5]= df['Week No.'][:5]
#for i in range(5):
#    df.loc[df['Week-ES'].index == i, 'Week-ES'] = df['Week No.'].iloc[i]   
df.iloc[5:]['Week-ES'] = 'Past WK'

Col1	Col2	Year	WeekNo.	Week-ES
v1	v2	2020	48	Recent
v2	v3	2020	47	Recent
v3	v4	2020	47	Recent
v4	v5	2020	46	Recent
v5	v6	2020	40	Pastwk
v6	v7	2019	52	PastWk

Solution

Idea is convert values to week periods, subtract 5 Weeks and compare by weeks periods from datetimes from columns Year and Week by Series.ge for greater or equal and pass to numpy.where:

last = pd.to_datetime('now').to_period('W') - 5
print (last)
2020-11-09/2020-11-15

s = df['Year'].astype(str).add(df['Week'].astype(str).add('-1'))
dates = pd.to_datetime(s, format='%Y%W-%w').dt.to_period('W')
df['C'] = np.where(dates.ge(last), 'Recent', 'Pastwk')

print (df)
   Year  Week       C
0  2020    48  Recent
1  2020    47  Recent
2  2020    47  Recent
3  2020    46  Recent
4  2020    40  Pastwk
5  2019    52  Pastwk