How do I import data with different types from file into a Python Numpy array?

Question

Say I have a file myfile.txt containing:

1   2.0000  buckle_my_shoe
3   4.0000  margery_door

How do I import data from the file to a numpy array as an int, float and string?

I am aiming to get:

array([[1,2.0000,"buckle_my_shoe"],
[3,4.0000,"margery_door"]])

I've been playing around with the following to no avail:

a = numpy.loadtxt('myfile.txt',dtype=(numpy.int_,numpy.float_,numpy.string_))

EDIT: Another approach might be to use the ndarray type and convert afterwards.

b = numpy.loadtxt('myfile.txt',dtype=numpy.ndarray)

    array([['1', '2.0000', 'buckle_my_shoe'],
       ['3', '4.0000', 'margery_door']], dtype=object)

Answer 1

Use numpy.genfromtxt:

import numpy as np
np.genfromtxt('filename', dtype= None)
# array([(1, 2.0, 'buckle_my_shoe'), (3, 4.0, 'margery_door')], 
#       dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '|S14')])

Answer 2

Pandas can do that for you. The docs for the function you could use are here.

Assuming your columns are tab separated, this should do the trick (adapted from this question):

df = DataFrame.from_csv('myfile.txt', sep='\t')
array = df.values # the array you are interested in