# NumPy ones_like – A Complete Guide

In this tutorial, we will be learning about the NumPy ones_like method and also seeing a lot of examples regarding the same. So let us begin!

Recommended read: NumPy ones – A Complete Guide

## What is NumPy ones_like?

The `ones_like` method in NumPy is a function that returns an array of ones having the same shape and size as the given array.

## Syntax of NumPy ones_like

Let us have a look at the syntax of the `numpy.ones_like()` method first.

```numpy.ones_like(a, dtype=None, order='K', subok=True, shape=None)
```

Returns:
An array with the same shape and data type as the given array, filled with all ones.

## Examples of Numpy ones_like function

Let’s now take a look at how the `numpy.ones_like()` function works and what is the expected output for different types of inputs.

### 1-dimensional array using ones_like

```import numpy as np

a = np.arange(10)
print("a =", a)

b = np.ones_like(a)
print("b =", b)
```

Output:

```a = [0 1 2 3 4 5 6 7 8 9]
b = [1 1 1 1 1 1 1 1 1 1]
```

### 2-dimensional array using ones_like

N x N array

```import numpy as np

a = np.arange(10).reshape(5, 2)
print("a =\n", a)

b = np.ones_like(a)
print("b =\n", b)
```

Output:

```a =
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]
b =
[[1 1]
[1 1]
[1 1]
[1 1]
[1 1]]
```

1 x N array

```import numpy as np

a = np.arange(12).reshape(1, 12)
print("a =\n", a)

b = np.ones_like(a)
print("b =\n", b)
```

Output:

```a =
[[ 0  1  2  3  4  5  6  7  8  9 10 11]]
b =
[[1 1 1 1 1 1 1 1 1 1 1 1]]
```

N x 1 array

```import numpy as np

a = np.arange(12).reshape(12, 1)
print("a =\n", a)

b = np.ones_like(a)
print("b =\n", b)
```

Output:

```a =
[[ 0]
[ 1]
[ 2]
[ 3]
[ 4]
[ 5]
[ 6]
[ 7]
[ 8]
[ 9]
[10]
[11]]
b =
[[1]
[1]
[1]
[1]
[1]
[1]
[1]
[1]
[1]
[1]
[1]
[1]]
```

### 1-dimensional float-type array using Numpy ones_like

```import numpy as np

a = np.arange(10)
print("a =", a)

b = np.ones_like(a, dtype=float)
print("b =", b)
```

Output:

```a = [0 1 2 3 4 5 6 7 8 9]
b = [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
```

### 2-dimensional float-type array

```import numpy as np

a = np.arange(10).reshape(2, 5)
print("a =\n", a)

b = np.ones_like(a, dtype=float)
print("b =\n", b)
```

Output:

```a =
[[0 1 2 3 4]
[5 6 7 8 9]]
b =
[[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]]
```

## What’s the difference between Numpy ones vs ones_like

• Note that, in the `ones` method we are creating a new array of our desired shape and data type having all the values as 1. But, here, we are directly passing an array or an array-like object to get an array of the same shape and data type.
• The NumPy `ones_like` function takes more time than the NumPy `ones` function to produce an array with all 1.

## Conclusion

That’s all! In this tutorial, we learned about the Numpy ones_like method and practiced different types of examples using the same. If you want to learn more about NumPy, feel free to go through our NumPy tutorials.