I have a dataset (Product_ID,date_time, Sold) which has products sold on various dates. The dates are not consistent and are given for 9 months with random 13 days or more from a month. I have to segregate the data in a such a way that the for each product how many products were sold on 1-3 given days, 4-7 given days, 8-15 given days and >16 given days. . So how can I code this in python using pandas and other packages

PRODUCT_ID DATE_LOCATION Sold 0E4234 01-08-16 0:00 2 0E4234 02-08-16 0:00 7 0E4234 04-08-16 0:00 3 0E4234 08-08-16 0:00 1 0E4234 09-08-16 0:00 2 . . (same product for 9 months sold data) . 0G2342 02-08-16 0:00 1 0G2342 03-08-16 0:00 2 0G2342 06-08-16 0:00 1 0G2342 09-08-16 0:00 1 0G2342 11-08-16 0:00 3 0G2342 15-08-16 0:00 3 . . .(goes for 64 products each with 9 months of data) .

I don't know even how to code for this in python The output needed is

PRODUCT_ID      Days   Sold
0E4234          1-3      94-7      38-15     16>16     (remaing values sum)
0G2342          1-3      34-7      18-15     7>16    (remaing values sum)
.
.(for 64 products)
.

Would be happy if at least someone posted a link to where to start

You can first convert dates to dtetimes and get days by dt.day:

df['DATE_LOCATION'] = pd.to_datetime(df['DATE_LOCATION'], dayfirst=True)
days = df['DATE_LOCATION'].dt.day

Then binning by cut:

rng = pd.cut(days, bins=[0,3,7,15,31], labels=['1-3', '4-7','8-15', '>=16'])
print (rng)
0      1-3
1      1-3
2      4-7
3     8-15
4     8-15
5      1-3
6      1-3
7      4-7
8     8-15
9     8-15
10    8-15
Name: DATE_LOCATION, dtype: category
Categories (4, object): [1-3 < 4-7 < 8-15 < >=16]

And aggregate sum by product and binned Series:

df = df.groupby(["PRODUCT_ID",rng])['Sold'].sum()
print (df)
PRODUCT_ID  DATE_LOCATION
0E4234      1-3              94-7              38-15             3
0G2342      1-3              34-7              18-15             7
Name: Sold, dtype: int64

If need also count per years:

df = df.groupby([df['DATE_LOCATION'].dt.year.rename('YEAR'), "PRODUCT_ID",rng])['Sold'].sum()
print (df)YEAR  PRODUCT_ID  DATE_LOCATION
2016  0E4234      1-3              94-7              38-15             30G2342      1-3              34-7              18-15             7
Name: Sold, dtype: int64