# How to Square a Matrix in Numpy (3 Easy Ways)

Following is a tutorial on how to square a matrix in Numpy Python library.

## Using a numpy square method

The easiest way and the most convenient one is just to use a built-in function of Numpy square. It just take my array as an argument and squares it.

```import numpy as np

my_array = np.array([1, 2, 3, 4])
squared_array = np.square(my_array)
print(f"My array is equal to {my_array}")
print(f"Squared array is equal to {squared_array}")
```

As an output you can see that my array got squared as expected.

## Using a numpy power method

You can also use power Numpy method. Square is the same as second power so it will work the same.

```import numpy as np

my_array = np.array([1, 2, 3, 4])
squared_array = np.power(my_array, 2)
print(f"My array is equal to {my_array}")
print(f"Squared array is equal to {squared_array}")
```

## Using asterisks

The most pythonic way would be to use ** 2 which also square my array.

```import numpy as np

my_array = np.array([1, 2, 3, 4])
squared_array = my_array ** 2
print(f"My array is equal to {my_array}")
print(f"Squared array is equal to {squared_array}")
```

These are 3 different ways to square a matrix using Numpy. Of course Numpy square function is the most efficient one for a comercial purpose.

## When to Use Each Method

There are three different methods for squaring a matrix in Numpy. Each method has its own advantages and disadvantages.

`np.square()` Most efficient Least flexible
`np.power()` More versatile Less efficient
`**` operator Most concise Least efficient

The best method to use depends on your specific needs. If you need to square a matrix as efficiently as possible, then you should use the `np.square()` method. If you need to square a matrix to a specific power, then you should use the `np.power()` method. And if you need to square a matrix in a concise way, then you should use the `**` operator.

Ultimately, the best way to decide which method to use is to experiment and see which one works best for you.

## Conclusion

You have learned three different ways to square a matrix in Numpy. You have also learned about the advantages and disadvantages of each method.

The best method to use depends on your specific needs. If you need to square a matrix as efficiently as possible, then you should use the np.square() method. If you need to square a matrix to a specific power, then you should use the np.power() method. And if you need to square a matrix in a concise way, then you should use the ** operator.

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Ultimately, the best way to decide which method to use is to experiment and see which one works best for you.