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numpy.loadtxt() in Python

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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.)]


Last Updated : 11 Dec, 2018
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