numpy.loadtxt() in Python
Last Updated :
11 Dec, 2018
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:
import numpy as geek
from io import StringIO
c = StringIO( "0 1 2 \n3 4 5" )
d = geek.loadtxt(c)
print (d)
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Output :
[[ 0. 1. 2.]
[ 3. 4. 5.]]
Code #2:
import numpy as geek
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)
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Output :
x is: [ 1. 4.]
y is: [ 2. 5.]
z is: [ 3. 6.]
Code #3:
import numpy as geek
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)
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Output :
[(b'M', 21, 72.) (b'F', 35, 58.)]
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