Reading Non-uniform Data From File Into Array With Numpy

Suppose I have a text file that looks like this: 33 3 46 12 23 10 23 11 23 12 23 13 23 14 23 15 23 16 24 10 24 11 24 12 24 13 24 14 24 15 24 16 25 14 25 15 25 16 26 16 27 1

Solution 1:

Here's a one-liner:

arrays = [np.array(map(int, line.split())) for line in open('scienceVertices.txt')]

arrays is a list of numpy arrays.

Solution 2:

for line in textfile:
  a = np.array([int(v) for v in line.strip().split(" ")])
  # Work on your array

Solution 3:

You can also use numpy.fromstring()

for line in f:
    a = numpy.fromstring(line.strip(), dtype=int, sep=" ")

or -- if you want full flexibility -- even numpy.loadtxt():

for line in f:
    a = numpy.loadtxt(StringIO.StringIO(line), dtype=int)

For long lines, these solution will perform better than the Python code in the other answers.

Solution 4:

f = open("file", "r")
array = []
line = f.readline()
index = 0while line:
    line = line.strip("\n")
    line = line.split()
    array.append([])
    for item in line:
        array[index].append(int(item))
    line = f.readline()
    index += 1
f.close()

print array