Copying multiple cell values (with a same gap) to a new variable and deleting them

[Alvin Ghimire]

Hi,
This is a portion of my large dataset, that I imported all .dta files using a loop. After which I got two variables, source and v1 (which I split into 3 variables). Now I want to do following things:

I want to delete rows 1 &2, 15&16...………...of my whole dataset, but before doing that I want to generate year variable using cell 1 of v11 i.e. I will have in year 2016 and Jan in v11 (for now, which I will rename to month later) , 2016 -- Feb, 2016 -- Mar,...……..2015 -- Jan, 2015-- Feb...…...1987 -- Jan, 1987 -- Feb...….. and like that with more than 30 years of time. Please note few years are missing, and also the variable source has more than 14 different values. I hope, you can understand what I am trying to do. Thanks in advance.

[Nick Cox]

Please follow FAQ Advice #12 and use dataex and CODE delimiters, not an image.

[Alvin Ghimire]

Thank you. Sorry for not binding with Statalist Norms, will try not to repeat in future. I have further cleaned the above data. Now, I want the only years (2016, 2015...………..1987) from month variable into a separate variable year, such that year variable will have 2016 from Jan to Dec, 2015 from Jan to Dec...……..and so on for the rest of the data. In addition to that after the year variable is generated, I want to delete the row containing year and place names respectively from month, tmax and tmin variables.

clear
input str9 source str5 month str9 tmax str8 tmin
"1030.dta" "2016" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "18.3"      "4.0"   
"1030.dta" "Feb"  "23.4"      "6.3"   
"1030.dta" "Mar"  "27.0"      "10.3"  
"1030.dta" "Apr"  "31.2"      "14.2"  
"1030.dta" "May"  "28.9"      "16.2"  
"1030.dta" "Jun"  "29.2"      "19.5"  
"1030.dta" "Jul"  "28.1"      "20.4"  
"1030.dta" "Aug"  "30.0"      "20.1"  
"1030.dta" "Sep"  "27.9"      "19.3"  
"1030.dta" "Oct"  "27.4"      "15.5"  
"1030.dta" "Nov"  "23.5"      "8.5"   
"1030.dta" "Dec"  "20.8"      "5.8"   
"1030.dta" "2015" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "20.2"      "4.4"   
"1030.dta" "Feb"  "22.2"      "7.0"   
"1030.dta" "Mar"  "24.7"      "9.9"   
"1030.dta" "Apr"  "26.4"      "12.6"  
"1030.dta" "May"  "30.6"      "16.6"  
"1030.dta" "Jun"  "31.4"      "19.7"  
"1030.dta" "Jul"  "29.5"      "19.8"  
"1030.dta" "Aug"  "29.1"      "20.1"  
"1030.dta" "Sep"  "29.4"      "18.9"  
"1030.dta" "Oct"  "27.2"      "13.7"  
"1030.dta" "Nov"  "23.6"      "9.5"   
"1030.dta" "Dec"  "18.8"      "4.3"   
"1030.dta" "2014" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "18.6"      "3.9"   
"1030.dta" "Feb"  "20.9"      "5.6"   
"1030.dta" "Mar"  "25.8"      "9.1"   
"1030.dta" "Apr"  "29.6"      "11.9"  
"1030.dta" "May"  "29.7"      "16.4"  
"1030.dta" "Jun"  "30.6"      "20.0"  
"1030.dta" "Jul"  "29.6"      "20.9"  
"1030.dta" "Aug"  "29.3"      "20.6"  
"1030.dta" "Sep"  "28.4"      "19.0"  
"1030.dta" "Oct"  "26.5"      "13.6"  
"1030.dta" "Nov"  "21.9"      "10.1"  
"1030.dta" "Dec"  "20.2"      "4.8"   
"1030.dta" "2013" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "18.9"      "1.6"   
"1030.dta" "Feb"  "23.8"      "6.1"   
"1030.dta" "Mar"  "28.1"      "10.1"  
"1030.dta" "Apr"  "29.3"      "12.4"  
"1030.dta" "May"  "30.0"      "17.1"  
"1030.dta" "Jun"  "30.0"      "20.1"  
"1030.dta" "Jul"  "29.4"      "20.4"  
"1030.dta" "Aug"  "29.6"      "20.2"  
"1030.dta" "Sep"  "29.8"      "18.9"  
"1030.dta" "Oct"  "26.6"      "15.9"  
"1030.dta" "Nov"  "22.6"      "7.7"   
"1030.dta" "Dec"  "18.8"      "4.8"   
"1030.dta" "2012" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "17.5"      "2.7"   
"1030.dta" "Feb"  "22.8"      "5.2"   
"1030.dta" "Mar"  "26.6"      "8.4"   
"1030.dta" "Apr"  "28.7"      "13.0"  
"1030.dta" "May"  "31.4"      "16.0"  
"1030.dta" "Jun"  "31.2"      "20.1"  
"1030.dta" "Jul"  "28.8"      "20.6"  
"1030.dta" "Aug"  "29.5"      "20.2"  
"1030.dta" "Sep"  "29.4"      "19.0"  
"1030.dta" "Oct"  "27.3"      "12.7"  
"1030.dta" "Nov"  "22.5"      "6.5"   
"1030.dta" "Dec"  "19.3"      "4.0"   
"1030.dta" "2011" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "19.6"      "2.4"   
"1030.dta" "Feb"  "23.8"      "5.3"   
"1030.dta" "Mar"  "27.0"      "8.8"   
"1030.dta" "Apr"  "28.7"      "11.9"  
"1030.dta" "May"  "28.9"      "16.2"  
"1030.dta" "Jun"  "29.4"      "19.0"  
"1030.dta" "Jul"  "28.6"      "20.5"  
"1030.dta" "Aug"  "29.5"      "20.0"  
"1030.dta" "Sep"  "28.4"      "19.1"  
"1030.dta" "Oct"  "28.1"      "14.2"  
"1030.dta" "Nov"  "22.4"      "9.5"   
"1030.dta" "Dec"  "18.9"      "4.1"   
"1030.dta" "2010" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "22.3"      "3.1"   
"1030.dta" "Feb"  "23.2"      "4.8"   
"1030.dta" "Mar"  "29.2"      "10.9"  
"1030.dta" "Apr"  "31.9"      "13.9"  
"1030.dta" "May"  "30.3"      "16.8"  
"1030.dta" "Jun"  "31.1"      "19.5"  
"1030.dta" "Jul"  "29.5"      "20.6"  
"1030.dta" "Aug"  "29.2"      "20.6"  
"1030.dta" "Sep"  "28.6"      "19.2"  
"1030.dta" "Oct"  "28.0"      "14.9"  
"1030.dta" "Nov"  "24.8"      "9.9"   
"1030.dta" "Dec"  "20.8"      "2.9"   
"1030.dta" "2009" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "22.5"      "3.7"   
"1030.dta" "Feb"  "25.8"      "5.9"   
"1030.dta" "Mar"  "27.5"      "8.2"   
"1030.dta" "Apr"  "31.4"      "13.3"  
"1030.dta" "May"  "29.1"      "16.0"  
"1030.dta" "Jun"  "30.6"      "18.9"  
"1030.dta" "Jul"  "29.8"      "20.7"  
"1030.dta" "Aug"  "29.1"      "20.5"  
end

I want my dataset in a format like below (in a continuous order, please note that some of the sources might have some missing years). Thank you so much in advance for your help.

Source	year	month	tmax	tmin
1030.dta	2016	Jan	18.3	4.0
1030.dta	2016	Feb	12.2	3.8
1030.dta	2016	Mar	23.1	12.4
1030.dta	2016	Dec	16.4	8.5
1030.dta	2015	Jan	16	5
1030.dta	1987	Dec	12	14
1213.dta	2016	Jan	12	11
1213.dta	2016	Feb	13	7

[Nick Cox]

Thanks for the data example. Climatological and meteorological data often seem to need some jiggery-pokery before you can do anything useful.

clear
input str9 source str5 month str9 tmax str8 tmin
"1030.dta" "2016" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "18.3"      "4.0"   
"1030.dta" "Feb"  "23.4"      "6.3"   
"1030.dta" "Mar"  "27.0"      "10.3"  
"1030.dta" "Apr"  "31.2"      "14.2"  
"1030.dta" "May"  "28.9"      "16.2"  
"1030.dta" "Jun"  "29.2"      "19.5"  
"1030.dta" "Jul"  "28.1"      "20.4"  
"1030.dta" "Aug"  "30.0"      "20.1"  
"1030.dta" "Sep"  "27.9"      "19.3"  
"1030.dta" "Oct"  "27.4"      "15.5"  
"1030.dta" "Nov"  "23.5"      "8.5"   
"1030.dta" "Dec"  "20.8"      "5.8"   
"1030.dta" "2015" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "20.2"      "4.4"   
"1030.dta" "Feb"  "22.2"      "7.0"   
"1030.dta" "Mar"  "24.7"      "9.9"   
"1030.dta" "Apr"  "26.4"      "12.6"  
"1030.dta" "May"  "30.6"      "16.6"  
"1030.dta" "Jun"  "31.4"      "19.7"  
"1030.dta" "Jul"  "29.5"      "19.8"  
"1030.dta" "Aug"  "29.1"      "20.1"  
"1030.dta" "Sep"  "29.4"      "18.9"  
"1030.dta" "Oct"  "27.2"      "13.7"  
"1030.dta" "Nov"  "23.6"      "9.5"   
"1030.dta" "Dec"  "18.8"      "4.3"   
"1030.dta" "2014" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "18.6"      "3.9"   
"1030.dta" "Feb"  "20.9"      "5.6"   
"1030.dta" "Mar"  "25.8"      "9.1"   
"1030.dta" "Apr"  "29.6"      "11.9"  
"1030.dta" "May"  "29.7"      "16.4"  
"1030.dta" "Jun"  "30.6"      "20.0"  
"1030.dta" "Jul"  "29.6"      "20.9"  
"1030.dta" "Aug"  "29.3"      "20.6"  
"1030.dta" "Sep"  "28.4"      "19.0"  
"1030.dta" "Oct"  "26.5"      "13.6"  
"1030.dta" "Nov"  "21.9"      "10.1"  
"1030.dta" "Dec"  "20.2"      "4.8"   
"1030.dta" "2013" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "18.9"      "1.6"   
"1030.dta" "Feb"  "23.8"      "6.1"   
"1030.dta" "Mar"  "28.1"      "10.1"  
"1030.dta" "Apr"  "29.3"      "12.4"  
"1030.dta" "May"  "30.0"      "17.1"  
"1030.dta" "Jun"  "30.0"      "20.1"  
"1030.dta" "Jul"  "29.4"      "20.4"  
"1030.dta" "Aug"  "29.6"      "20.2"  
"1030.dta" "Sep"  "29.8"      "18.9"  
"1030.dta" "Oct"  "26.6"      "15.9"  
"1030.dta" "Nov"  "22.6"      "7.7"   
"1030.dta" "Dec"  "18.8"      "4.8"   
"1030.dta" "2012" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "17.5"      "2.7"   
"1030.dta" "Feb"  "22.8"      "5.2"   
"1030.dta" "Mar"  "26.6"      "8.4"   
"1030.dta" "Apr"  "28.7"      "13.0"  
"1030.dta" "May"  "31.4"      "16.0"  
"1030.dta" "Jun"  "31.2"      "20.1"  
"1030.dta" "Jul"  "28.8"      "20.6"  
"1030.dta" "Aug"  "29.5"      "20.2"  
"1030.dta" "Sep"  "29.4"      "19.0"  
"1030.dta" "Oct"  "27.3"      "12.7"  
"1030.dta" "Nov"  "22.5"      "6.5"   
"1030.dta" "Dec"  "19.3"      "4.0"   
"1030.dta" "2011" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "19.6"      "2.4"   
"1030.dta" "Feb"  "23.8"      "5.3"   
"1030.dta" "Mar"  "27.0"      "8.8"   
"1030.dta" "Apr"  "28.7"      "11.9"  
"1030.dta" "May"  "28.9"      "16.2"  
"1030.dta" "Jun"  "29.4"      "19.0"  
"1030.dta" "Jul"  "28.6"      "20.5"  
"1030.dta" "Aug"  "29.5"      "20.0"  
"1030.dta" "Sep"  "28.4"      "19.1"  
"1030.dta" "Oct"  "28.1"      "14.2"  
"1030.dta" "Nov"  "22.4"      "9.5"   
"1030.dta" "Dec"  "18.9"      "4.1"   
"1030.dta" "2010" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "22.3"      "3.1"   
"1030.dta" "Feb"  "23.2"      "4.8"   
"1030.dta" "Mar"  "29.2"      "10.9"  
"1030.dta" "Apr"  "31.9"      "13.9"  
"1030.dta" "May"  "30.3"      "16.8"  
"1030.dta" "Jun"  "31.1"      "19.5"  
"1030.dta" "Jul"  "29.5"      "20.6"  
"1030.dta" "Aug"  "29.2"      "20.6"  
"1030.dta" "Sep"  "28.6"      "19.2"  
"1030.dta" "Oct"  "28.0"      "14.9"  
"1030.dta" "Nov"  "24.8"      "9.9"   
"1030.dta" "Dec"  "20.8"      "2.9"   
"1030.dta" "2009" "KATHMANDU" "AIRPORT"
"1030.dta" "Jan"  "22.5"      "3.7"   
"1030.dta" "Feb"  "25.8"      "5.9"   
"1030.dta" "Mar"  "27.5"      "8.2"   
"1030.dta" "Apr"  "31.4"      "13.3"  
"1030.dta" "May"  "29.1"      "16.0"  
"1030.dta" "Jun"  "30.6"      "18.9"  
"1030.dta" "Jul"  "29.8"      "20.7"  
"1030.dta" "Aug"  "29.1"      "20.5"  
end

gen place = tmax + " " + tmin if real(month) < . 
gen year = real(month) 
replace place = place[_n-1] if missing(place) 
replace year = year[_n-1] if missing(year) 
drop if real(month) < . 
destring tmin tmax, replace 
label def month 1 Jan 2 Feb 3 Mar 4 Apr 5 May 6 Jun 7 Jul 8 Aug 9 Sep 10 Oct 11 Nov 12 Dec 
encode month, gen(Month) label(month) 
drop month 
rename Month month 
gen mdate = ym(year, month) 
format mdate %tm 
order source place month year mdate 
list, sepby(year) 

     +---------------------------------------------------------------------+
     |   source               place   month   year     mdate   tmax   tmin |
     |---------------------------------------------------------------------|
  1. | 1030.dta   KATHMANDU AIRPORT     Jan   2016    2016m1   18.3      4 |
  2. | 1030.dta   KATHMANDU AIRPORT     Feb   2016    2016m2   23.4    6.3 |
  3. | 1030.dta   KATHMANDU AIRPORT     Mar   2016    2016m3     27   10.3 |
  4. | 1030.dta   KATHMANDU AIRPORT     Apr   2016    2016m4   31.2   14.2 |
  5. | 1030.dta   KATHMANDU AIRPORT     May   2016    2016m5   28.9   16.2 |
  6. | 1030.dta   KATHMANDU AIRPORT     Jun   2016    2016m6   29.2   19.5 |
  7. | 1030.dta   KATHMANDU AIRPORT     Jul   2016    2016m7   28.1   20.4 |
  8. | 1030.dta   KATHMANDU AIRPORT     Aug   2016    2016m8     30   20.1 |
  9. | 1030.dta   KATHMANDU AIRPORT     Sep   2016    2016m9   27.9   19.3 |
 10. | 1030.dta   KATHMANDU AIRPORT     Oct   2016   2016m10   27.4   15.5 |
 11. | 1030.dta   KATHMANDU AIRPORT     Nov   2016   2016m11   23.5    8.5 |
 12. | 1030.dta   KATHMANDU AIRPORT     Dec   2016   2016m12   20.8    5.8 |
     |---------------------------------------------------------------------|
 13. | 1030.dta   KATHMANDU AIRPORT     Jan   2015    2015m1   20.2    4.4 |
 14. | 1030.dta   KATHMANDU AIRPORT     Feb   2015    2015m2   22.2      7 |
 15. | 1030.dta   KATHMANDU AIRPORT     Mar   2015    2015m3   24.7    9.9 |
 16. | 1030.dta   KATHMANDU AIRPORT     Apr   2015    2015m4   26.4   12.6 |
 17. | 1030.dta   KATHMANDU AIRPORT     May   2015    2015m5   30.6   16.6 |
 18. | 1030.dta   KATHMANDU AIRPORT     Jun   2015    2015m6   31.4   19.7 |
 19. | 1030.dta   KATHMANDU AIRPORT     Jul   2015    2015m7   29.5   19.8 |
 20. | 1030.dta   KATHMANDU AIRPORT     Aug   2015    2015m8   29.1   20.1 |
 21. | 1030.dta   KATHMANDU AIRPORT     Sep   2015    2015m9   29.4   18.9 |
 22. | 1030.dta   KATHMANDU AIRPORT     Oct   2015   2015m10   27.2   13.7 |
 23. | 1030.dta   KATHMANDU AIRPORT     Nov   2015   2015m11   23.6    9.5 |
 24. | 1030.dta   KATHMANDU AIRPORT     Dec   2015   2015m12   18.8    4.3 |
     |---------------------------------------------------------------------|
 25. | 1030.dta   KATHMANDU AIRPORT     Jan   2014    2014m1   18.6    3.9 |
 26. | 1030.dta   KATHMANDU AIRPORT     Feb   2014    2014m2   20.9    5.6 |
 27. | 1030.dta   KATHMANDU AIRPORT     Mar   2014    2014m3   25.8    9.1 |
 28. | 1030.dta   KATHMANDU AIRPORT     Apr   2014    2014m4   29.6   11.9 |
 29. | 1030.dta   KATHMANDU AIRPORT     May   2014    2014m5   29.7   16.4 |
 30. | 1030.dta   KATHMANDU AIRPORT     Jun   2014    2014m6   30.6     20 |
 31. | 1030.dta   KATHMANDU AIRPORT     Jul   2014    2014m7   29.6   20.9 |
 32. | 1030.dta   KATHMANDU AIRPORT     Aug   2014    2014m8   29.3   20.6 |
 33. | 1030.dta   KATHMANDU AIRPORT     Sep   2014    2014m9   28.4     19 |
 34. | 1030.dta   KATHMANDU AIRPORT     Oct   2014   2014m10   26.5   13.6 |
 35. | 1030.dta   KATHMANDU AIRPORT     Nov   2014   2014m11   21.9   10.1 |
 36. | 1030.dta   KATHMANDU AIRPORT     Dec   2014   2014m12   20.2    4.8 |
     |---------------------------------------------------------------------|
 37. | 1030.dta   KATHMANDU AIRPORT     Jan   2013    2013m1   18.9    1.6 |
 38. | 1030.dta   KATHMANDU AIRPORT     Feb   2013    2013m2   23.8    6.1 |
 39. | 1030.dta   KATHMANDU AIRPORT     Mar   2013    2013m3   28.1   10.1 |
 40. | 1030.dta   KATHMANDU AIRPORT     Apr   2013    2013m4   29.3   12.4 |
 41. | 1030.dta   KATHMANDU AIRPORT     May   2013    2013m5     30   17.1 |
 42. | 1030.dta   KATHMANDU AIRPORT     Jun   2013    2013m6     30   20.1 |
 43. | 1030.dta   KATHMANDU AIRPORT     Jul   2013    2013m7   29.4   20.4 |
 44. | 1030.dta   KATHMANDU AIRPORT     Aug   2013    2013m8   29.6   20.2 |
 45. | 1030.dta   KATHMANDU AIRPORT     Sep   2013    2013m9   29.8   18.9 |
 46. | 1030.dta   KATHMANDU AIRPORT     Oct   2013   2013m10   26.6   15.9 |
 47. | 1030.dta   KATHMANDU AIRPORT     Nov   2013   2013m11   22.6    7.7 |
 48. | 1030.dta   KATHMANDU AIRPORT     Dec   2013   2013m12   18.8    4.8 |
     |---------------------------------------------------------------------|
 49. | 1030.dta   KATHMANDU AIRPORT     Jan   2012    2012m1   17.5    2.7 |
 50. | 1030.dta   KATHMANDU AIRPORT     Feb   2012    2012m2   22.8    5.2 |
 51. | 1030.dta   KATHMANDU AIRPORT     Mar   2012    2012m3   26.6    8.4 |
 52. | 1030.dta   KATHMANDU AIRPORT     Apr   2012    2012m4   28.7     13 |
 53. | 1030.dta   KATHMANDU AIRPORT     May   2012    2012m5   31.4     16 |
 54. | 1030.dta   KATHMANDU AIRPORT     Jun   2012    2012m6   31.2   20.1 |
 55. | 1030.dta   KATHMANDU AIRPORT     Jul   2012    2012m7   28.8   20.6 |
 56. | 1030.dta   KATHMANDU AIRPORT     Aug   2012    2012m8   29.5   20.2 |
 57. | 1030.dta   KATHMANDU AIRPORT     Sep   2012    2012m9   29.4     19 |
 58. | 1030.dta   KATHMANDU AIRPORT     Oct   2012   2012m10   27.3   12.7 |
 59. | 1030.dta   KATHMANDU AIRPORT     Nov   2012   2012m11   22.5    6.5 |
 60. | 1030.dta   KATHMANDU AIRPORT     Dec   2012   2012m12   19.3      4 |
     |---------------------------------------------------------------------|
 61. | 1030.dta   KATHMANDU AIRPORT     Jan   2011    2011m1   19.6    2.4 |
 62. | 1030.dta   KATHMANDU AIRPORT     Feb   2011    2011m2   23.8    5.3 |
 63. | 1030.dta   KATHMANDU AIRPORT     Mar   2011    2011m3     27    8.8 |
 64. | 1030.dta   KATHMANDU AIRPORT     Apr   2011    2011m4   28.7   11.9 |
 65. | 1030.dta   KATHMANDU AIRPORT     May   2011    2011m5   28.9   16.2 |
 66. | 1030.dta   KATHMANDU AIRPORT     Jun   2011    2011m6   29.4     19 |
 67. | 1030.dta   KATHMANDU AIRPORT     Jul   2011    2011m7   28.6   20.5 |
 68. | 1030.dta   KATHMANDU AIRPORT     Aug   2011    2011m8   29.5     20 |
 69. | 1030.dta   KATHMANDU AIRPORT     Sep   2011    2011m9   28.4   19.1 |
 70. | 1030.dta   KATHMANDU AIRPORT     Oct   2011   2011m10   28.1   14.2 |
 71. | 1030.dta   KATHMANDU AIRPORT     Nov   2011   2011m11   22.4    9.5 |
 72. | 1030.dta   KATHMANDU AIRPORT     Dec   2011   2011m12   18.9    4.1 |
     |---------------------------------------------------------------------|
 73. | 1030.dta   KATHMANDU AIRPORT     Jan   2010    2010m1   22.3    3.1 |
 74. | 1030.dta   KATHMANDU AIRPORT     Feb   2010    2010m2   23.2    4.8 |
 75. | 1030.dta   KATHMANDU AIRPORT     Mar   2010    2010m3   29.2   10.9 |
 76. | 1030.dta   KATHMANDU AIRPORT     Apr   2010    2010m4   31.9   13.9 |
 77. | 1030.dta   KATHMANDU AIRPORT     May   2010    2010m5   30.3   16.8 |
 78. | 1030.dta   KATHMANDU AIRPORT     Jun   2010    2010m6   31.1   19.5 |
 79. | 1030.dta   KATHMANDU AIRPORT     Jul   2010    2010m7   29.5   20.6 |
 80. | 1030.dta   KATHMANDU AIRPORT     Aug   2010    2010m8   29.2   20.6 |
 81. | 1030.dta   KATHMANDU AIRPORT     Sep   2010    2010m9   28.6   19.2 |
 82. | 1030.dta   KATHMANDU AIRPORT     Oct   2010   2010m10     28   14.9 |
 83. | 1030.dta   KATHMANDU AIRPORT     Nov   2010   2010m11   24.8    9.9 |
 84. | 1030.dta   KATHMANDU AIRPORT     Dec   2010   2010m12   20.8    2.9 |
     |---------------------------------------------------------------------|
 85. | 1030.dta   KATHMANDU AIRPORT     Jan   2009    2009m1   22.5    3.7 |
 86. | 1030.dta   KATHMANDU AIRPORT     Feb   2009    2009m2   25.8    5.9 |
 87. | 1030.dta   KATHMANDU AIRPORT     Mar   2009    2009m3   27.5    8.2 |
 88. | 1030.dta   KATHMANDU AIRPORT     Apr   2009    2009m4   31.4   13.3 |
 89. | 1030.dta   KATHMANDU AIRPORT     May   2009    2009m5   29.1     16 |
 90. | 1030.dta   KATHMANDU AIRPORT     Jun   2009    2009m6   30.6   18.9 |
 91. | 1030.dta   KATHMANDU AIRPORT     Jul   2009    2009m7   29.8   20.7 |
 92. | 1030.dta   KATHMANDU AIRPORT     Aug   2009    2009m8   29.1   20.5 |
     +---------------------------------------------------------------------+

[Alvin Ghimire]

Thank you so much. Yeah, these meteorological data are consuming time in just cleaning and making them ready for analysis. I am curious why don't they keep data in analyzable format!!! Anyways, thank you once again for your help.

[Romalpa Akzo]

If Alvin could be sure about the structure of the original data, below code might also contribute to a solution.

bys source: gen Year=2016-floor(_n/14)
by source: gen Month=mod(_n-1,13)
drop if Month==0
destring tmax tmin, replace