Create a Numpy array filled with all zeros - Python

In this article, we will learn how to create a Numpy array filled with all zeros, given the shape and type of array. We can use Numpy.zeros() method to do this task.

Let's understand with the help of an example:

import numpy as np
# Create a 1D array of zeros with 5 elements
array_1d = np.zeros(5)
print(array_1d)

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

Explanation:

• np.zeros(5) function creates a 1D array with 5 elements, all initialized to 0.
• By default, the data type of the array is float, so the zeros are represented as 0.0.

Syntax of Creating a NumPy Array filled with all zeros

numpy.zeros(shape, dtype=float, order='C')

Parameters

• shape: Tuple or int specifying the dimensions of the array (e.g., (rows, columns)).
• dtype (optional): Data type of the array (default is float).
• order (optional): Memory layout of the array. 'C' (row-major) or 'F' (column-major).

Creating a 2D Zero Array

import numpy as np
# Create a 2D array of zeros (3 rows, 4 columns)
array_2d = np.zeros((3, 4))
print(array_2d)

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

Explnation:

• np.zeros((3, 4)) function creates a 2D array with 3 rows and 4 columns.
• The shape of the array is specified as a tuple (3, 4).

Specifying Data Type

import numpy as np
# Create an integer zero array
array_int = np.zeros((2, 3), dtype=int)
print(array_int)

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

Explanation:

• Here, the dtype=int argument is used to specify that the array elements should be integers.
• The resulting array is 2x3 (2 rows, 3 columns) with all elements as 0, but they are now integers instead of floats.

Using Column-Major Order

import numpy as np
# Create an array with column-major order
array_column_major = np.zeros((2, 3), order='F')
print(array_column_major)

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

Explanation:

• By default, NumPy arrays are stored in row-major order (C order). However, specifying order='F' changes this to column-major order (Fortran-style).
• In column-major order, elements in the same column are stored contiguously in memory.