python - Rearranging Data in CSV by Certain Data in a Column -

i have csv file 30,000 lines of data 24 columns. last column geographic column , looks this:

 ethiopia  il  il  tx  tx  md  ny  ny  ethiopia  ethiopia  sweden  ca  ca  hi  latvia  oh 

right want entire csv rows correspond geographic locations of united states 2 character state abbreviations (ca, hi, oh, etc.)

basically want data in csv remove non usa related, or better if possible, arrange first x amount of lines usa based locations , rest else @ end of csv.

here code far:

import csv  ask = "y"  while ask != "n":     inputfile = input("please enter filename: ")     filename = open(inputfile, "r")      data = []     filename f:         reader = csv.reader(f, delimiter=',')         row in reader:             if len(row[24]) == 3:                 data = row[24]         datalist = row[0:23].join(data)         output = open("newly created data.csv","w")         output.write(datalist)         print ("done.")      output.close()      ask = input("another file, y or n? ") 

it arranges data in column 24 correctly reading usa locations, don't know how sort rest of file , other 23 columns match usa locations.

i'm using python 3, thanks.

for purely standard library solution, maybe like

import csv  open('location.csv', newline='') fp_in:     reader = csv.reader(fp_in, delimiter=',')     data = list(reader)  data.sort(key=lambda x: (len(x[-1].strip()) != 2, x[-1].strip()))  open("locout.csv", "w", newline='') fp_out:     writer = csv.writer(fp_out, delimiter=',')     writer.writerows(data) 

the sort key function, lambda x: (len(x[-1].strip()) != 2, x[-1].strip())), means it'll sort data first whether or not last column has 2 characters or not, putting 2-character locations first, , secondly name (effectively alphabetizing them, @ least if start capital letter.)

i'm assuming file isn't large: 30000 lines isn't many, 24 columns, might work entirely in memory.

(aside: if you're doing lot of csv manipulations, might interested in pandas library -- makes lot of operations simpler they'd otherwise.)