Fast and memory efficient solution for loading well formed large csv files

iter_loadtxt.py

# Code copied from Joe Kington's StackOverflow answer 
# http://stackoverflow.com/a/8964779/1135883
# For large files with regular records this is a much faster and memory 
# efficient solution

def iter_loadtxt(filename, delimiter=' ', skiprows=0, dtype=float):
    def iter_func():
        with open(filename, 'r') as infile:
            for _ in range(skiprows):
                next(infile)
            for line in infile:
                line = line.rstrip().split(delimiter)
                for item in line:
                    yield dtype(item)
        iter_loadtxt.rowlength = len(line)

    data = np.fromiter(iter_func(), dtype=dtype)
    data = data.reshape((-1, iter_loadtxt.rowlength))
    return data

#generate_text_file()
data = iter_loadtxt('Area3_final 0_5m.XYZ')