df = pd.DataFrame({'Timestamp': [datetime.date(2019, 4, 15), datetime.date(2019, 4, 16), datetime.date(2019, 4, 17), datetime.date(2019, 4, 18), datetime.date(2019, 4, 19), datetime.date(2019, 4, 20), datetime.date(2019, 4, 21), datetime.date(2019, 4, 22), datetime.date(2019, 4, 23), datetime.date(2019, 4, 24), datetime.date(2019, 4, 25), datetime.date(2019, 4, 26), datetime.date(2019, 4, 27), datetime.date(2019, 4, 28)], 'Price': ['3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988', '3988']})
df['Timestamp'] = pd.to_datetime(df['Timestamp'])
df['Price'] = df['Price'].astype('int')

df['Week'] = [1 if x == 4 else 0 for x in df['Timestamp'].dt.weekday]
df['Week'] = df['Week'].cumsum()

df[['Price', 'Week']].groupby('Week').mean()

df[['Timestamp', 'Week']].groupby(['Week']).agg({'Timestamp':[np.min,np.max]})