numpy.zeros () in Python

numpy.zeros (shape, dtype = None, order = & # 39; C & # 39;): return a new array of the given shape and type with zeros. 
Parameters :

  shape:  integer or sequence of integers  order:  C_contiguous or F_contiguous C-contiguous order in memory (last index varies the fastest) C order means that operating row-rise on the array will be slightly quicker FORTRAN-contiguous order in memory (first index varies the fastest). F order means that column-wise operations will be faster.  dtype:  [optional, float (byDeafult)] Data type of returned array. 

Returns :

 ndarray of zeros having given shape, order and datatype. 


Code 1 :

# Python program illustrating
# numpy.zeros method

 

import numpy as geek

 

b = geek.zeros ( 2 , dtype = int )

print ( "Matrix b:" , b)

 

a = geek.zeros ([ 2 , 2 ], dtype = int )

print ( "Matrix a:" , a)

 

c = geek.zeros ([ 3 , 3 ])

print ( "Matrix c:" , c)

Output:

 Matrix b: [ 0 0] Matrix a: [[0 0] [0 0 ]] Matrix c: [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] 


Code 2: Type management data

# Python program illustrating
# numpy.zeros method

 

import numpy as geek

 
# manipulating data types

b = geek.zeros (( 2 ,), dtype = [ ( 'x' , ' float' ), ( 'y'  , 'int' )])

print (b)

Output:

 [(0.0, 0) (0.0, 0)] 

Link:
https://docs.scipy.org/doc/numpy- dev / reference / generated / numpy.zeros.html # numpy.zeros
Note: zeros, unlike zeros and nulls, do not set array values ​​to zero or random values respectively. Also, these codes will not work with an online ID. Please run them on your systems to see how they work.

This article is courtesy of Mohit Gupta_OMG



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