numpy.loadtxt() in Python

numpy.load() in Python is used load data from a text file, with aim to be a fast reader for simple text files.

Note that each row in the text file must have the same number of values.

Syntax: numpy.loadtxt(fname, dtype=’float’, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)

Parameters:
fname : File, filename, or generator to read. If the filename extension is .gz or .bz2, the file is first decompressed. Note that generators should return byte strings for Python 3k.
dtype : Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array.
delimiter : The string used to separate values. By default, this is any whitespace.
converters : A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: converters = {0: datestr2num}. Default: None.
skiprows : Skip the first skiprows lines; default: 0.

Returns: ndarray

Code #1:

# Python program explaining
# loadtxt() function
import numpy as geek

# StringIO behaves like a file object
from io import StringIO

c = StringIO("0 1 2 \n3 4 5")
d = geek.loadtxt(c)

print(d)

Output :

[[ 0.  1.  2.]
 [ 3.  4.  5.]]

Code #2:

# Python program explaining
# loadtxt() function
import numpy as geek

# StringIO behaves like a file object
from io import StringIO

c = StringIO("1, 2, 3\n4, 5, 6")
x, y, z = geek.loadtxt(c, delimiter =', ', usecols =(0, 1, 2),
                                                unpack = True)

print("x is: ", x)
print("y is: ", y)
print("z is: ", z)

Output :

x is:  [ 1.  4.]
y is:  [ 2.  5.]
z is:  [ 3.  6.]

Code #3:

# Python program explaining
# loadtxt() function
import numpy as geek

# StringIO behaves like a file object
from io import StringIO

d = StringIO("M 21 72\nF 35 58")
e = geek.loadtxt(d, dtype ={'names': ('gender', 'age', 'weight'),
                                  'formats': ('S1', 'i4', 'f4')})

print(e)

Output :

[(b'M', 21,  72.) (b'F', 35,  58.)]