Dear Statlists,

I have a dataset consisting of a large number of different products and their monthly import between June 2017-June 2019 as unit prices with including tariffs (unit_p_tariffinc). I would like to sum these values for every and each product for all month.

I know that it can be done for each at a time by

Code:
sum unit_p_tariffinc if product=="01011010"
display r(sum)
But this will be extremely time consuming, id there any suggestion on how this could be done in a more convenient way?

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input str8 product long product_ str14 valueQUANTITY_IN_100KG long quantity_in_100kg str14 valueVALUE_IN_EUROS double value_in_euros str4 y_m float(dummy tariff unit_price unit_p_tariffinc product_sum totalsum_treated weight) int(year month) float mdate
"01011010" 1011010 ":" . ":" . "1906" 0 1 . . 0 . . 2019  6 713
"01011010" 1011010 ":" . ":" . "1804" 0 1 . . 0 . . 2018  4 699
"01011010" 1011010 ":" . ":" . "1803" 0 1 . . 0 . . 2018  3 698
"01011010" 1011010 ":" . ":" . "1802" 0 1 . . 0 . . 2018  2 697
"01011010" 1011010 ":" . ":" . "1810" 0 1 . . 0 . . 2018 10 705
"01011010" 1011010 ":" . ":" . "1905" 0 1 . . 0 . . 2019  5 712
"01011010" 1011010 ":" . ":" . "1811" 0 1 . . 0 . . 2018 11 706
"01011010" 1011010 ":" . ":" . "1710" 0 1 . . 0 . . 2017 10 693
"01011010" 1011010 ":" . ":" . "1901" 0 1 . . 0 . . 2019  1 708
"01011010" 1011010 ":" . ":" . "1707" 0 1 . . 0 . . 2017  7 690
"01011010" 1011010 ":" . ":" . "1805" 0 1 . . 0 . . 2018  5 700
"01011010" 1011010 ":" . ":" . "1902" 0 1 . . 0 . . 2019  2 709
"01011010" 1011010 ":" . ":" . "1812" 0 1 . . 0 . . 2018 12 707
"01011010" 1011010 ":" . ":" . "1706" 0 1 . . 0 . . 2017  6 689
"01011010" 1011010 ":" . ":" . "1712" 0 1 . . 0 . . 2017 12 695
"01011010" 1011010 ":" . ":" . "1806" 0 1 . . 0 . . 2018  6 701
"01011010" 1011010 ":" . ":" . "1801" 0 1 . . 0 . . 2018  1 696
"01011010" 1011010 ":" . ":" . "1904" 0 1 . . 0 . . 2019  4 711
"01011010" 1011010 ":" . ":" . "1708" 0 1 . . 0 . . 2017  8 691
"01011010" 1011010 ":" . ":" . "1709" 0 1 . . 0 . . 2017  9 692
"01011010" 1011010 ":" . ":" . "1903" 0 1 . . 0 . . 2019  3 710
"01011010" 1011010 ":" . ":" . "1808" 0 1 . . 0 . . 2018  8 703
"01011010" 1011010 ":" . ":" . "1807" 0 1 . . 0 . . 2018  7 702
"01011010" 1011010 ":" . ":" . "1711" 0 1 . . 0 . . 2017 11 694
"01011010" 1011010 ":" . ":" . "1809" 0 1 . . 0 . . 2018  9 704
"01011090" 1011090 ":" . ":" . "1709" 0 1 . . 0 . . 2017  9 692
"01011090" 1011090 ":" . ":" . "1710" 0 1 . . 0 . . 2017 10 693
"01011090" 1011090 ":" . ":" . "1902" 0 1 . . 0 . . 2019  2 709
"01011090" 1011090 ":" . ":" . "1905" 0 1 . . 0 . . 2019  5 712
"01011090" 1011090 ":" . ":" . "1802" 0 1 . . 0 . . 2018  2 697
"01011090" 1011090 ":" . ":" . "1807" 0 1 . . 0 . . 2018  7 702
"01011090" 1011090 ":" . ":" . "1809" 0 1 . . 0 . . 2018  9 704
"01011090" 1011090 ":" . ":" . "1804" 0 1 . . 0 . . 2018  4 699
"01011090" 1011090 ":" . ":" . "1906" 0 1 . . 0 . . 2019  6 713
"01011090" 1011090 ":" . ":" . "1805" 0 1 . . 0 . . 2018  5 700
"01011090" 1011090 ":" . ":" . "1708" 0 1 . . 0 . . 2017  8 691
"01011090" 1011090 ":" . ":" . "1803" 0 1 . . 0 . . 2018  3 698
"01011090" 1011090 ":" . ":" . "1904" 0 1 . . 0 . . 2019  4 711
"01011090" 1011090 ":" . ":" . "1808" 0 1 . . 0 . . 2018  8 703
"01011090" 1011090 ":" . ":" . "1706" 0 1 . . 0 . . 2017  6 689
"01011090" 1011090 ":" . ":" . "1901" 0 1 . . 0 . . 2019  1 708
"01011090" 1011090 ":" . ":" . "1810" 0 1 . . 0 . . 2018 10 705
"01011090" 1011090 ":" . ":" . "1806" 0 1 . . 0 . . 2018  6 701
"01011090" 1011090 ":" . ":" . "1707" 0 1 . . 0 . . 2017  7 690
"01011090" 1011090 ":" . ":" . "1712" 0 1 . . 0 . . 2017 12 695
"01011090" 1011090 ":" . ":" . "1811" 0 1 . . 0 . . 2018 11 706
"01011090" 1011090 ":" . ":" . "1711" 0 1 . . 0 . . 2017 11 694
"01011090" 1011090 ":" . ":" . "1903" 0 1 . . 0 . . 2019  3 710
"01011090" 1011090 ":" . ":" . "1812" 0 1 . . 0 . . 2018 12 707
"01011090" 1011090 ":" . ":" . "1801" 0 1 . . 0 . . 2018  1 696
"01011100" 1011100 ":" . ":" . "1903" 0 1 . . 0 . . 2019  3 710
"01011100" 1011100 ":" . ":" . "1808" 0 1 . . 0 . . 2018  8 703
"01011100" 1011100 ":" . ":" . "1906" 0 1 . . 0 . . 2019  6 713
"01011100" 1011100 ":" . ":" . "1809" 0 1 . . 0 . . 2018  9 704
"01011100" 1011100 ":" . ":" . "1804" 0 1 . . 0 . . 2018  4 699
"01011100" 1011100 ":" . ":" . "1806" 0 1 . . 0 . . 2018  6 701
"01011100" 1011100 ":" . ":" . "1712" 0 1 . . 0 . . 2017 12 695
"01011100" 1011100 ":" . ":" . "1707" 0 1 . . 0 . . 2017  7 690
"01011100" 1011100 ":" . ":" . "1810" 0 1 . . 0 . . 2018 10 705
"01011100" 1011100 ":" . ":" . "1807" 0 1 . . 0 . . 2018  7 702
"01011100" 1011100 ":" . ":" . "1811" 0 1 . . 0 . . 2018 11 706
"01011100" 1011100 ":" . ":" . "1812" 0 1 . . 0 . . 2018 12 707
"01011100" 1011100 ":" . ":" . "1905" 0 1 . . 0 . . 2019  5 712
"01011100" 1011100 ":" . ":" . "1711" 0 1 . . 0 . . 2017 11 694
"01011100" 1011100 ":" . ":" . "1802" 0 1 . . 0 . . 2018  2 697
"01011100" 1011100 ":" . ":" . "1904" 0 1 . . 0 . . 2019  4 711
"01011100" 1011100 ":" . ":" . "1706" 0 1 . . 0 . . 2017  6 689
"01011100" 1011100 ":" . ":" . "1805" 0 1 . . 0 . . 2018  5 700
"01011100" 1011100 ":" . ":" . "1902" 0 1 . . 0 . . 2019  2 709
"01011100" 1011100 ":" . ":" . "1801" 0 1 . . 0 . . 2018  1 696
"01011100" 1011100 ":" . ":" . "1901" 0 1 . . 0 . . 2019  1 708
"01011100" 1011100 ":" . ":" . "1709" 0 1 . . 0 . . 2017  9 692
"01011100" 1011100 ":" . ":" . "1708" 0 1 . . 0 . . 2017  8 691
"01011100" 1011100 ":" . ":" . "1803" 0 1 . . 0 . . 2018  3 698
"01011100" 1011100 ":" . ":" . "1710" 0 1 . . 0 . . 2017 10 693
"01011910" 1011910 ":" . ":" . "1708" 0 1 . . 0 . . 2017  8 691
"01011910" 1011910 ":" . ":" . "1804" 0 1 . . 0 . . 2018  4 699
"01011910" 1011910 ":" . ":" . "1805" 0 1 . . 0 . . 2018  5 700
"01011910" 1011910 ":" . ":" . "1806" 0 1 . . 0 . . 2018  6 701
"01011910" 1011910 ":" . ":" . "1901" 0 1 . . 0 . . 2019  1 708
"01011910" 1011910 ":" . ":" . "1801" 0 1 . . 0 . . 2018  1 696
"01011910" 1011910 ":" . ":" . "1803" 0 1 . . 0 . . 2018  3 698
"01011910" 1011910 ":" . ":" . "1906" 0 1 . . 0 . . 2019  6 713
"01011910" 1011910 ":" . ":" . "1903" 0 1 . . 0 . . 2019  3 710
"01011910" 1011910 ":" . ":" . "1904" 0 1 . . 0 . . 2019  4 711
"01011910" 1011910 ":" . ":" . "1710" 0 1 . . 0 . . 2017 10 693
"01011910" 1011910 ":" . ":" . "1711" 0 1 . . 0 . . 2017 11 694
"01011910" 1011910 ":" . ":" . "1808" 0 1 . . 0 . . 2018  8 703
"01011910" 1011910 ":" . ":" . "1905" 0 1 . . 0 . . 2019  5 712
"01011910" 1011910 ":" . ":" . "1807" 0 1 . . 0 . . 2018  7 702
"01011910" 1011910 ":" . ":" . "1712" 0 1 . . 0 . . 2017 12 695
"01011910" 1011910 ":" . ":" . "1802" 0 1 . . 0 . . 2018  2 697
"01011910" 1011910 ":" . ":" . "1707" 0 1 . . 0 . . 2017  7 690
"01011910" 1011910 ":" . ":" . "1810" 0 1 . . 0 . . 2018 10 705
"01011910" 1011910 ":" . ":" . "1709" 0 1 . . 0 . . 2017  9 692
"01011910" 1011910 ":" . ":" . "1809" 0 1 . . 0 . . 2018  9 704
"01011910" 1011910 ":" . ":" . "1812" 0 1 . . 0 . . 2018 12 707
"01011910" 1011910 ":" . ":" . "1706" 0 1 . . 0 . . 2017  6 689
"01011910" 1011910 ":" . ":" . "1902" 0 1 . . 0 . . 2019  2 709
"01011910" 1011910 ":" . ":" . "1811" 0 1 . . 0 . . 2018 11 706
end
format %tm mdate

Thanks in advance!