numpy.ones() in Python

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.

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')]

Reference: API Doc