Problem with a splitting string from file

Some modules already do the job for you !

Have a look at numpy.loadtxt. It loads a text file and returns a numpy array ready to use.

Here an example:

# Import module
import numpy as np

# Load text
data = np.loadtxt("filename.txt")
print(data)
# [[  0.  41. 216.]
#  [ 10.  42. 214.]
#  [ 20.  43. 215.]
#  [ 30.  39. 222.]
#  [ 40.  34. 222.]
#  [ 50.  35. 215.]
#  [ 60.  42. 218.]
#  [ 70.  37. 213.]
#  [ 80.  41. 216.]
#  [ 90.  43. 222.]
#  [100.  33. 220.]]

Then you can easily have the average of a column with np.mean()

print(np.mean(data[:, 1]))
# 39.09090909090909

There are already some great answers using numpy and pandas, but if you want to process it by yourself, you could do it with list comprehension

Sample:

# line has a variable number of spaces as delimiters
line = '1   3     5'
# split line into a list by spaces
split_line = line.split(' ')
# filter spaces out, keeping only those list elements which have values,
# because if '' evaluates to false, they will be discarded
# also convert the string numbers to integers
only_numbers = [int(num) for num in split_line if num]

Then you'll get

print(only_numbers)
[1, 3, 5]

As suggested, you could use Pandas or Numpy to solve the task. However, if you really want to do it yourself in pure Python, without using extra libraries, here is a quite readable and Pythonic version of your code. Also, use simply split() instead split(" ") since you have more than one blank-space separating the data in your file.

bonds = []
with open("inputfile.txt", "r") as inputfile:
    for line in inputfile:
        line = line.strip()
        if len(line) and line[0] not in '#@':
            data = line.split()
            bonds.append(float(data[1]))

avg = sum(bonds)/len(bonds)