numpy.zeros() in Python

numpy.zeros(shape, dtype = None, order = ‘C’) : Return a new array of 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 :



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# Python Program illustrating
# numpy.zeros method
  
import numpy as geek
  
b = geek.zeros(2, dtype = int)
print("Matrix b : \n", b)
  
a = geek.zeros([2, 2], dtype = int)
print("\nMatrix a : \n", a)
  
c = geek.zeros([3, 3])
print("\nMatrix c : \n", c)

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Output :

Matrix b : 
 [0 0]

Matrix a : 
 [[0 0]
 [0 0]]

Matrix c : 
 [[ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]


Code 2 : Manipulating data types

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# Python Program illustrating
# numpy.zeros method
  
import numpy as geek
  
# manipulation with data-types
b = geek.zeros((2,), dtype=[('x', 'float'), ('y', 'int')])
print(b)

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Output :

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

Reference :
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.zeros.html#numpy.zeros
Note : zeros, unlike zeros and empty, does not set the array values to zero or random values respectively.Also, these codes won’t run on online-ID. Please run them on your systems to explore the working.

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