numpy.zeros() in Python

The numpy.zeros() function syntax is:

zeros(shape, dtype=None, order='C')

• The shape is an int or tuple of ints to define the size of the array.
• The dtype is an optional parameter with default value as float. It’s used to specify the data type of the array, for example, int.
• The order defines the whether to store multi-dimensional array in row-major (C-style) or column-major (Fortran-style) order in memory.

Let’s look at some examples of creating arrays using the numpy zeros() function.

1. Creating one-dimensional array with zeros

import numpy as np

array_1d = np.zeros(3)
print(array_1d)

Output:

[0. 0. 0.]

Notice that the elements are having the default data type as the float. That’s why the zeros are 0.

2. Creating Multi-dimensional array

import numpy as np

array_2d = np.zeros((2, 3))
print(array_2d)

Output:

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

3. NumPy zeros array with int data type

import numpy as np

array_2d_int = np.zeros((2, 3), dtype=int)
print(array_2d_int)

Output:

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

4. NumPy Array with Tuple Data Type and Zeroes

We can specify the array elements as a tuple and specify their data types too.

import numpy as np

array_mix_type = np.zeros((2, 2), dtype=[('x', 'int'), ('y', 'float')])
print(array_mix_type)
print(array_mix_type.dtype)

Output:

[[(0, 0.) (0, 0.)]
 [(0, 0.) (0, 0.)]]
[('x', '<i8'), ('y', '<f8')]

Reference: API Doc