Welcome aboard our coding journey! Today's adventure involves navigating through Python's primary data types: Numerical, String, Boolean, and None. Think of data types as different sea creatures, each playing unique roles in the Python ocean. Are you ready to explore these beasts? Let's dive right in!

Every value in Python has a datatype. This can be likened to packing for an imaginary sea journey. Items such as clothes, shoes, toys, books, and gadgets would need to be packed separately since they serve different purposes. Similarly, in Python, understanding the data type helps the program determine how best to store and operate on them. Look at the examples below, which illustrate Python's different native data types using an apple analogy:

Among the coral reefs of Python's ocean, we find the Numerical data types, which include integers, floating-point numbers, and complex numbers. These types are crucial for performing calculations.

For instance, consider the case of people on the boat. The number of people would be an integer, as you can't have half a person, right? However, the weight of the boat would be a floating-point number since it can be expressed in decimal points. So, long story short, a floating-point number (or a float) is a number with a decimal point in the middle (e.g., 3.52).

Here is how we manage these data types: