After importing data from a .csv file, I have some data that looks similar to this (albeit order hundreds of columns and thousands of rows):
4 5 6 7 8 9 10 11 12 13 14 15 16
0 302255Z 09005KT 1 1/4SM BR CLR M00/M00 A3044 RMK AO2A SLP311 T10021002 $;
1 302232Z 08003KT 1 1/4 BR CLR M00/M00 A3044 RMK AO2A SLP310 $; NaN
2 302225Z 09005KT 1 1/2SM BR CLR M00/M00 A3044 RMK AO2A SLP309 $; NaN
3 302155Z 08003KT 2 1/2SM BR CLR M00/M00 A3043 RMK AO2A SLP306 T10001000 $;
4 302055Z 09004KT 3SM BR CLR 00/00 A3042 RMK AO2A SLP304 T00020002 56001 $;
5 301955Z 00000KT 3SM BR CLR 01/01 A3042 RMK AO2A SLP304 T00080008 $; NaN
6 301855Z 09006KT 3SM BR FEW055 01/01 A3042 RMK AO2A SLP303 T00110011 $; NaN
7 301655Z 10004KT 2 1/2SM BR FEW050 M00/M00 A3041 RMK AO2A SLP301 T10031003 $;
8 301610Z 09004KT 2 1/2SM BR CLR 00/00 A3041 RMK AO2A SLP301 $; NaN
9 301555Z AUTO 08005KT 4800 BR CLR 01/01 A3041 RMK AO2 SLP300 T00070007 $;
10 301509Z AUTO 06003KT 4800 BR CLR 01/01 A3041 RMK AO2 SLP300 $; NaN
11 301449Z AUTO 10003KT 4000 BR CLR 01/01 A3041 RMK AO2 SLP300 $; NaN
12 301355Z AUTO 07004KT 6000 BR CLR 02/02 A3041 RMK AO2 SLP300 T00230023 $;
13 301255Z AUTO 07003KT 6000 BR CLR 02/02 A3041 RMK AO2 SLP299 T00200020 $;
14 301055Z AUTO 00000KT 9000 BR CLR 04/04 A3040 RMK AO2 SLP298 T00360036 $;
I abandoned trying to shift everything to match up correctly. Instead, I'm trying to create a new column that combines entries from columns 5 and 6 for those values ending in KT. And I'm creating a second new column for those values starting in T.
To start, I attempted pulling out all of the data that satisfied my criterion in rows 5 and 6 like so:
df1=df[df[5].str.contains("KT")].iloc[:,[0,5]]
df2=df[df[6].str.contains("KT")].iloc[:,[0,6]]
the .iloc value was an attempt to merge the results together. There has to be a slicker way to get this formatted. Any thoughts?
If helpful, here's a more simple data set:
row1=['a','b','c1K','d','e','foo','foo','f1111T','g','$']
row2=['a','b','foo','c2K','d','e','f4321T','g','$','$']
row3=['a','b','c3K','d','e','f1234T','g','$']
df=ps.DataFrame(zip(row1,row2,row3)).T
df1=df[df[2].str.contains("K")].iloc[:,[0,2]]
df2=df[df[3].str.contains("K")].iloc[:,[0,3]]
trying ps.concat([df1,df2],axis=0,join='outer') doesn't give what I'd like, it gives
0 2 3
0 a c1K NaN
2 a c3K NaN
1 a NaN c2K
something like this would be prettier:
0
1 a c1K
2 a c3K
3 a c2K