Eliminating rows with a specific value in a column using Python

goodrows = [row for row in data if row.split()[4] != '0']

or

goodrows = [row for row in data if not (-50 <= float(row.split()[4]) <= 30)]

Edit:

If your data is actually in a NumPy array, which your comment seems to indicate even if your post didn't:

goodrows = [row for row in data if row[4] != 0]

or

goodrows = [row for row in data if not (-50 <= row[4] <= 30)]

should work. There is definitely a NumPy internal way to do this though.

you can use numpy to do this quickly:

data="""
0  4028.44  4544434.50    -6.76  -117.00  0.0002   0.12
0  4028.50  3455014.50    -5.86  0        0.0003   0.39
0  7028.56  4523434.50    -4.95  -137.00  0.0005   0.25
0  8828.62  4543414.50    -3.05  0        0.0021   0.61
0  4028.44  4544434.50    -6.76  -107.00  0.0002   0.12
0  4028.50  3455014.50    -5.86  -11.00   0.0003   0.39
0  7028.56  4523434.50    -4.95  -127.00  0.0005   0.25
0  8828.62  4543414.50    -3.05  0        0.0021   0.61
"""
from StringIO import StringIO
import numpy as np
d = np.loadtxt(StringIO(data)) # load the text in to a 2d numpy array

print d[d[:,4]!=0]  # choose column 5 != 0
print d[(d[:,4]>=50)|(d[:,4]<=-30)] # choose column 5 >=50 or <=-30

Assuming your data is in a plain text file like this:

$ cat data.txt 
0  4028.44  4544434.50    -6.76  -117.00  0.0002   0.12
0  4028.50  3455014.50    -5.86  0        0.0003   0.39
0  7028.56  4523434.50    -4.95  -137.00  0.0005   0.25
0  8828.62  4543414.50    -3.05  0        0.0021   0.61
0  4028.44  4544434.50    -6.76  -107.00  0.0002   0.12
0  4028.50  3455014.50    -5.86  -11.00   0.0003   0.39
0  7028.56  4523434.50    -4.95  -127.00  0.0005   0.25
0  8828.62  4543414.50    -3.05  0        0.0021   0.61

And you are not using any external libraries. The following will read the data into a list of strings, omiting the undesirable lines. You can feed these lines into any other function you choose. I call print merely to demonstrate. N.B: The fifth column has index '4', since list indices are zero-based.

$ cat data.py 
#!/usr/bin/env python

print "1. Delete the rows which have '0' as a value on 5th column:"

def zero_in_fifth(row):
    return row.split()[4] == '0'

required_rows = [row for row in open('./data.txt') if not zero_in_fifth(row)]
print ''.join(required_rows)

print '2. Choose the range (i.e. remove the rows which have values between -50 and 30 on 5th column):'

def should_ignore(row):
    return -50 <= float(row.split()[4]) <= 30

required_rows = [row for row in open('./data.txt') if not should_ignore(row)]
print ''.join(required_rows)

When you run this you will get:

$ python data.py 
1. Delete the rows which have '0' as a value on 5th column:
0  4028.44  4544434.50    -6.76  -117.00  0.0002   0.12
0  7028.56  4523434.50    -4.95  -137.00  0.0005   0.25
0  4028.44  4544434.50    -6.76  -107.00  0.0002   0.12
0  4028.50  3455014.50    -5.86  -11.00   0.0003   0.39
0  7028.56  4523434.50    -4.95  -127.00  0.0005   0.25

2. Choose the range (i.e. remove the rows which have values between -50 and 30 on 5th column):
0  4028.44  4544434.50    -6.76  -117.00  0.0002   0.12
0  7028.56  4523434.50    -4.95  -137.00  0.0005   0.25
0  4028.44  4544434.50    -6.76  -107.00  0.0002   0.12
0  7028.56  4523434.50    -4.95  -127.00  0.0005   0.25