numpy.ones() in Python

Python numpy.ones() function returns a new array of given shape and data type, where the element’s value is set to 1. This function is very similar to numpy zeros() function.

numpy.ones() function arguments

The numpy.ones() function syntax is:

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

The shape is an int or tuple of ints to define the size of the array. If we just specify an int variable, a one-dimensional array will be returned. For a tuple of ints, the array of given shape will be returned.
The dtype is an optional parameter with default value as a 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.

Python numpy.ones() Examples

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

1. Creating one-dimensional array with ones

import numpy as np

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

Output:

[1. 1. 1.]

Notice that the elements are having the default data type as the float. That’s why the ones are 1. in the array.

2. Creating Multi-dimensional array

import numpy as np

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

Output:

[[1. 1. 1.]
 [1. 1. 1.]]

3. NumPy ones array with int data type

import numpy as np

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

Output:

[[1 1 1]
 [1 1 1]]

4. NumPy Array with Tuple Data Type and Ones

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

import numpy as np

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

Output:

[[(1, 1.) (1, 1.)]
 [(1, 1.) (1, 1.)]]
[('x', '<i8'), ('y', '<f8')]