# numpy.ones() in Python

Published on August 3, 2022

Pankaj

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')]
``````

Reference: API Doc

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Pankaj

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