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() functionimport numpy as geek # StringIO behaves like a file objectfrom 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() functionimport numpy as geek # StringIO behaves like a file objectfrom 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() functionimport numpy as geek # StringIO behaves like a file objectfrom 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.)]
