Mathematics has a handful of parameters using which we can gauge numbers. One among those is the square root. It might be a walk to find the square root when the given numbers are the product of themselves, but when it no longer seems to be the case, things get a tad bit difficult.

This article sets out to explore the different ways of using the built-in function –** sqrt( )** from the

*numpy*library of Python which is used to calculate the square roots in Python programming. We shall cover the aspects of the

*sqrt( )*function in the following sections:

**Syntax of***sqrt( )*function**Using***sqrt( )*function on One-Dimensional Array**Using***sqrt( )*function on N-Dimensional Array**Using***sqrt( )*function on Complex Numbers**Limitations of***sqrt( )*function

Before that let us import the *numpy *library using the below code:

```
import numpy as np
```

## Syntax of *sqrt( ) *Function

We shall understand the construction of the *sqrt( ) *function using the syntax given below which details the basic constituents required for its effective functioning.

**Syntax:**

```
numpy.sqrt(x, out=None, where=True, dtype=None)
```

**Parameters:**

input array or scalar entity for which the square root is to be deduced*x –*an optional construct set to*out –**none*by default, and can be used to have the results stored in a specific array that is of the same length as the outputan optional construct set to*where –**True*by default and is used to pass the function at all positions that are declared*True*and retain those positions as is, which are set to*False*an optional construct used to specify the data type that is to be returned*dtype –*

## Using *sqrt( ) *Function on One-Dimensional Array

In this section, we shall construct a one-dimensional array using the below code in order to calculate the square root for its elements.

```
ar1 = [[12, 36, 71, 99]]
```

Once done, the above array is passed through the *sqrt( ) *function to find the results.

```
np.sqrt(ar1)
```

**Output:**

## Using *sqrt( ) *Function on N-Dimensional Array

The square root calculation for N-dimensional arrays shall be demonstrated in this section and it works in the same way as done earlier for the one-dimensional array. Given below is a two-dimensional array that shall be passed through the *sqrt( ) *function to determine the square root of its entities.

```
ar2 = np.array([[12.5, 33.3, 25],
[26, 79, 14.5]], dtype = float)
np.sqrt(ar2)
```

**Output:**

## Using *sqrt( ) *Function on Complex Numbers

When calculating the square root for decimals with a pen and paper can give you a migraine, imagine what can the square root calculation of complex numbers give you. Just to have a fair idea of what is being implied here, have a look at the below formula to calculate the square root of the complex numbers.

But Python being all the more friendly, has induced the capabilities in the *sqrt( ) *function for determining the square root of complex numbers too. But it comes with a price! When the input array has a complex number, then the *sqrt( ) *function goes on to consider every other element in the input as a complex number and deduce its corresponding square root.

```
ar3 = [[12+5j, 33-3j, 25],
[26, 7-9j, 14+5j]]
np.sqrt(ar3)
```

**Output:**

## Limitations of *sqrt( ) *Function

This function is limited to calculating the square roots of only positive numbers. When those that are negative come into the picture, then the below result comes up:

## Conclusion

Now that we have reached the end of this article, hope it has elaborated on the different ways to find the square root using the *sqrt( ) *function from the *numpy *library. Here’s another article that explains how to use the *positive( ) *function for N-dimensional arrays in Python. There are numerous other enjoyable & equally informative articles in *CodeforGeek* that might be of great help to those who are looking to level up in Python. Whilst you enjoy those, *adios*!

## Reference

https://numpy.org/doc/stable/reference/generated/numpy.sqrt.html