Use of numpy.min_scalar_type( ) method in Python

In this tutorial, we will learn the use of the numpy.min_scalar_type( ) method in Python with some basic and easy examples. In many situations, you may come across such a function that is provided by the numpy package.

I know you are here to learn some of the cool features offered by the NumPy package in Python. First of all, let’s learn what NumPy actually is and why it is one of the most important and widely used package in Python.
NumPy is used for all the scientific computations in Python. It also provides us high-performance multidimensional array object and set of tools for these arrays. Hence enabling us to deal with complex scientific computations and data analysis.
Many users confuse between NumPy and lists. NumPy deals with arrays whereas lists are ordered sequences of different or same objects.
NumPy has many advantages over lists: they require less memory space, they are fast to access and convenient for operations. Easy way to solve linear algebra, generation of random numbers, and solving different transforms.

The numpy.min_scalar_type( ) Method in Python

Now that we are clear about the features provided by NumPy let’s move to the min_scalar_type( ) method in Python.
By using the numpy.min_scalar_type( ) method, we get the minimum scalar type of a value that is passed as a parameter in the method.
The demotion of a datatype is not allowed, like floating-point values are not demoted to integers and so on.

Syntax:
np.min_scalar_type(value)

Note that “np” is just a name given by the user and it can be anything.

Examples:

# import numpy package 
import numpy as xyz 
  
# use of xyz.min_scalar_type() 
val = xyz.min_scalar_type(-50) 
  
print(val)
Output: 
int8
import numpy as xyz

xyz.min_scale_type(22)
Output:
dtype('uint8')
import numpy as xyz

xyz.min_scalar_type(-22.11)
Output:
dtype('float16')

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