In NumPy, you can find the square root of a number of sequence with `np.sqrt()`

:

```
import numpy as np
np.sqrt(4)
# Expected result
# 2.0
np.sqrt([4, 16, 64])
# Expected result
# array([2., 4., 8.])
```

The input is “array-like”, meaning it can be a number, a Python sequence or a NumPy array (with any number of dimensions). The result will be a NumPy scalar (if you pass in a scalar) or a NumPy array (if you pass in a sequence or an array).

`np.sqrt()`

is an element-wise function, meaning it operates on each element of an array independently. The output will be the same shape as the input and each of the output elements will be the square root of its corresponding input element. Easy!

A few other fun facts about `np.sqrt()`

:

- Negative numbers will be
`np.nan`

(you will also see a RuntimeWarning) - The square root of
`np.inf`

is`np.inf`

- If you pass in integers, NumPy will coerce the result to one of its float data types. As of version 1.19.4, it seems to convert int types to float with double the precision up to
`float64`

. So`int8`

becomes`float16`

,`int16`

becomes`float32`

and`int32`

and above become`float64`

. - Complex numbers work as expected (e.g.
`np.sqrt(8 - 6j)`

). The result will be one of the complex NumPy types.

By the way, other popular tensor libraries like PyTorch and TensorFlow follow roughly the same API. See the documentation for torch.sqrt() and tf.sqrt().