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How to Create Array of zeros using Numpy in Python

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  • Last Updated : 01 Jul, 2022
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In this article, we will cover how to create a Numpy array with zeros using Python.

Python Numpy Zeros Array

In Numpy, an array is a collection of elements of the same data type and is indexed by a tuple of positive integers. The number of dimensions in an array is referred to as the array’s rank in Numpy. Arrays in Numpy can be formed in a variety of ways, with different numbers of Ranks dictating the array’s size. It can also be produced from a variety of data types, such as lists, tuples, etc. To create a NumPy array with zeros the numpy.zeros() function is used which returns a new array of given shape and type, with zeros. Below is the syntax of the following method.

Syntax: numpy.zeros(shape, dtype=float, order=’C’)

Parameter:

  • shape: integer or sequence of integers
  • order: {‘C’, ‘F’}, optional, default: ‘C’
  • dtype : [optional, float(byDefault)].

Return: Array of zeros with the given shape, dtype, and order.

Example 1: Creating a one-dimensional array with zeros using numpy.zeros()

Python3




import numpy as np
 
arr = np.zeros(9)
print(arr)


Output:

[0. 0. 0. 0. 0. 0. 0. 0. 0.]

Example 2: Creating a 2-dimensional array with zeros using numpy.zeros()

Python3




import numpy as np
 
# create a 2-D array of 2 row 3 column
arr = np.zeros((2, 3))
print(arr)


Output:

[[0. 0. 0.]
 [0. 0. 0.]]

Example 3: Creating a Multi-dimensional array with zeros using numpy.zeros()

Python3




import numpy as np
 
# creating 3D array
arr = np.zeros((4, 2, 3))
 
print(arr)


Output:

[[[0. 0. 0.]
  [0. 0. 0.]]

 [[0. 0. 0.]
  [0. 0. 0.]]

 [[0. 0. 0.]
  [0. 0. 0.]]

 [[0. 0. 0.]
  [0. 0. 0.]]]

Example 4: NumPy zeros array with an integer data type

Python3




import numpy as np
 
# Creating array of 2 rows 3 column 
# as Datatype integer
arr = np.zeros((2, 3), dtype=int)
print(arr)


Output:

[[0 0 0]
 [0 0 0]]

Example 5: NumPy Array with Tuple Data Type and Zeroes

In the output, i4 specifies 4 bytes of integer data type, whereas f8 specifies 8 bytes of float data type.

Python3




import numpy as np
 
# Specifying array as a tuple, and
# Specify their data types.
arr = np.zeros((2, 2), dtype=[('x', 'int'),
                              ('y', 'float')])
print(arr)
print(arr.dtype)


Output:

[[(0, 0.) (0, 0.)]
 [(0, 0.) (0, 0.)]]
[('x', '<i4'), ('y', '<f8')]

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