Q:

fast, accurate, reliable way to remove undesirable values from a csv file

I have a large-ish csv file that has a substantial ammount of dirty data in it, I'd like to clean it up a bit by eliminating all the values that are not absolutely necessary. 
Here is the file I'm talking about.
It has the components:
Website,Title,Start Date,Employer,Location,lat,lon,Country,Skills11,Jobs 
but I would like to obliterate all but:
Employer,Location,Country,Jobs
Is there a particular tool that is uniquely suited for this task? 
Or maybe someone has a handy Python script that can get the job done?  

A:

You can easily do it with python writing to a temporary file then replacing the original. 
import  csv
from operator import itemgetter
from tempfile import NamedTemporaryFile
from shutil import move

with open("edsa_data.csv") as f, NamedTemporaryFile(dir=".", delete=False) as tmp:
    # itertools.imap python2
    csv.writer(tmp).writerows(map(itemgetter(3, 5, 7, 9), csv.reader(f)))
move(tmp.name, "edsa_data.csv")

For a more generic approach:
import csv
from operator import itemgetter
from tempfile import NamedTemporaryFile
from shutil import move

def keep_columns(csv_f, keep_cols, **kwargs):
    with open(csv_f) as f, NamedTemporaryFile("w", dir=".", delete=False) as tmp:
        csv.writer(tmp, **kwargs).writerows(itemgetter(*keep_cols)(row) 
                                            for row in csv.reader(f, **kwargs))
    move(tmp.name, csv_f)

keep_columns("edsa_data.csv", (3, 4, 7, 9))

For the kwargs you can pass sep="," skipinitialspace=True etc.. 

