使用

Use transform for Series with same size as original DataFrame, binning with cut and aggregate sum:

df['DATE_LOCATION'] = pd.to_datetime(df['DATE_LOCATION'], format='%d-%m-%y %H:%M')

df = df.sort_values("DATE_LOCATION")
s = (df["DATE_LOCATION"].diff().dt.days > 1).cumsum()

count = s.groupby(s).transform('size')
print (count)
0     2
1     2
2     4
3     4
4     4
5     4
6     2
7     2
8     1
9     3
10    3
11    3
Name: DATE_LOCATION, dtype: int32

bins = pd.cut(count, bins=[0,3,7,15,31], labels=['1-3', '4-7','8-15', '>=16'])
df = df.groupby(['PRODUCT_ID', bins])['Sold'].sum().reset_index()
print (df)
  PRODUCT_ID DATE_LOCATION  Sold
0     0E4234           1-3     9
1     0E4234           4-7     7
2     0G2342           1-3    11