The Numpy module of python is the toolkit. Because it is a package of functions to perform various operations, these operations are high scientific computations in python. Numpy supports multiple dimensions. The toolkits work on them. An array in numpy can be one dimension and two, three, or higher. Thus we have a quick review. As of now, we will read about **the numpy square root**. An easy function to use and understand.

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## About numpy square root

np.sqrt() function gets the square root of the matrix elements. To say that the function is to determine the positive square-root of an array, element-wise. Sqrt () is a mathematical tool which does this:

There are only non-negative outputs from this function.

## Syntax numpy square root

We use sqrt in place of the square root.

The standard syntax of the Function np.sqrt() is:

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

while the usual way of writing the syntax is:

```
numpy.sqrt()
```

## Parameters Used:

Parameters | Mandatory or not |

x | mandatory |

out | optional |

where | optional |

casting | optional |

keepdims | optional |

axes | optional |

order | optional |

subok | optional |

dtype | optional |

signature | optional |

**x : array_like**

This parameter is* *Input values whose square-roots have to be determined. In other words, it specifies the radicand. The radicand is the value under the radical when you compute the square root.

**out**

The ‘out’ keyword argument expects to be a tuple with one entry per output (which can be None for arrays to be allocated by the ufunc). The `out`

parameter enables you to specify an array where the output will be stored. This parameter is not used in simpler calculations but at a higher level. This parameter provides a location to store the result. If provided, it must have a shape for the inputs broadcast. A freshly-allocated array returned if not provided or None. A tuple (possible only as a keyword argument) must have a length equal to the number of outputs.

**where**

This parameter Accepts a boolean array, which is broadcast together with the operands. Values of True indicate that to calculate the ufunc at that position and False values indicate to leave the value in the output alone. Generalized ufuncs cannot use this argument because those take non-scalar input.

**axes**

A list of tuples with indices of axes a generalized ufunc should operate. For instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix multiplication, the base elements are two-dimensional matrices, and these take to store in the two last axes of each argument.

**order**

This parameter specifies the calculation iteration order/memory layout output array. Defaults to ‘K.’ ‘C’ means the output should be C-contiguous, ‘F’ means F-contiguous, ‘A’ means F-contiguous if the inputs are F-contiguous and not also not C-contiguous

**axes**

A list of tuples with indices of axes a generalized ufunc should operate. For instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix multiplication, the last two axes of each argument take and store the base elements are two-dimensional matrices.

**signature**

This argument allows you to provide a specific signature for the 1-d loop to use in the underlying calculation. If the loop specified does not exist for the ufunc, then a TypeError is raised. Usually, a suitable loop is found automatically by comparing the input types with what is available and searching for a loop with data-types. The **types** attribute of the ufunc object provide a list of known signatures.

Still, there are more parameters to mention. But you do not need to know all. To know all we need to see the unfunc docs. So you would get an idea of all the parameters.

### Returning value

As per NumPy, return the non-negative square-root of an array, element-wise.

## Examples to comprehend

now we import the module first

`import numpy `

Now using numpy in a better way.

```
array_2d = numpy.array([[1, 16], [25, 49]], dtype=numpy.float)
print(array_2d)
[[ 1. 16.]
[ 25. 49.]]
array_2d_sqrt = numpy.sqrt(array_2d)
print(array_2d_sqrt)
[[1. 4.]
[5. 7.]]
```

Here we also used the dtype parameter to fix datatype. This example also shows that it retains the dimensionality of the original array.

```
#for complex numbers
array = numpy.array([4, -1, -5 + 9J], dtype=numpy.complex)
print(array)
[4, -1, -5 + 9J]
numpy.sqrt(array)
array([2.00000000+0.j 0.00000000+1.j 1.62721083+2.76546833j])
```

The above example shows the output for complex numbers.

```
#negative numbers
array = numpy.array([-4, 5, -6])
numpy.sqrt(array)
__main__:1: RuntimeWarning: invalid value encountered in sqrt
array([nan 2.23606798 nan])
```

The output for negative number is NaN(Not a Number).

## Can I calculate the square root of -1?

```
numpy.sqrt(-1)
__main__:1: RuntimeWarning: invalid value encountered in sqrt
nan
```

Yes, for -1, you can use this function. But it would return NaN as discussed in the last example.

All the examples give the idea of using the function. Now you are ready to go.

**What’s Next?**

NumPy is very powerful and incredibly essential for information science in Python. That being true, if you are interested in data science in Python, you really ought to find out more about Python.

You might like our following tutorials on numpy.

- Mean: Implementation and Importance
- Using Random Function to Create Random Data
- Reshape: Reshaping Arrays With Ease
- In-depth Explanation of np.power() With Examples
- Clip Function

## Conclusion

In conclusion, this function is useful for all calculations. Those computations can be of broader aspects—both primary and scientific as well.

Still have any doubts or questions do let me know in the comment section below. I will try to help you as soon as possible.

*Happy Pythoning!*