## Python numpy square method explanation with examples:

Python *numpy* square method is used to find the squares of each element in an array. It takes an *array_like* input and returns an newly created array.

In this post, we will learn how to use the *numpy.square* method with different types of array inputs.

### Definition of numpy square method:

This method is defined as like below:

`numpy.square(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'square'>`

Where,

*x*is the input*array_like*data.*out*is an optional value. It can be*ndarray*, None or tuple of ndarray and None. It should have the same shape as the input data. This is a location to put the result. If this is not provided, a new array is created and returned.*where*is another optional value. This is a condition that is broadcast over the elements of the input array.

It returns a new array or *array_like* object with each element as the square. It has the same *shape* and *dtype* as the input array.

### Example of numpy square method with an integer array:

Let’s try it with an integer array:

```
import numpy as np
given_arr = [1, 2, 3, 4, 5, 6, 7]
print(np.square(given_arr))
```

If you run this program, it will print:

`[ 1 4 9 16 25 36 49]`

So, as you can see here, all elements are square of the elements.

We can also use it with any other dimension arrays. For example, let’s try it with a *2-D* array:

```
import numpy as np
given_arr = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
print(np.square(given_arr))
```

It will print:

```
[[ 1 4 9]
[16 25 36]
[49 64 81]]
```

### Example with complex numbers array:

Similar to the above example, we can also use an array with complex numbers. For example:

```
import numpy as np
given_arr = [1 + 2j, 3 - 4j]
print(np.square(given_arr))
```

It will print:

`[-3. +4.j -7.-24.j]`

### Reference:

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