We're going to take in this guide a short overview of each one of the main number data types that we have access to in python.

I'm gonna start off with some examples. I'm going to say we have a variable here called Product ID. And I'm going to provide it with the number 123. The data type this is going to represent is the integer data type. Now if you're not certain what integers are or how they work in computer science an integer is a whole number. So it's not a decimal it's not a fraction. It's not a complex number. It is simply a number like this. It can be 123 could be 42 it could be 3. Any of those are going to represent integers.

With the way that Python works and we discuss this a little bit when we talked about the data types in python but with how python works all of this is decided and it's all processed. When we run our python program and when the interpreter comes across this value it's going to understand that this is an integer and it's going to assign that automatically. We don't have to define it ourselves like we would have to if we were working in Java or C++ or a language like that. So this is an integer and it'll be automatically parsed by the system.

Now the next type we're going to look at is one I'm going to create a variable here called sale price and I'm going to say 14.99.

Now, this is a floating-point type of data type it may look like a decimal but it's very important to not call this a decimal and we're going to have a guide that we see exactly why that is. And so the main point to remember is this is a float and there actually is a decimal library inside of python you have to import it manually. And we're going to do that in one of the upcoming guides. But the important thing to note is that a float's precision is not anywhere near as granular as a decimals precision which means that if you're performing very complex calculations that involve decimals then a float may not be your best choice and in fact, there have been some pretty big disasters including a shuttle crash just because floating-point numbers were not used properly so it's very important to understand that we're going to have a guide that goes through that and I'm going to show you where the differences are.

But for right now you can think of a float as a type of decimal. It simply is a type that isn't precise it wouldn't be an issue with saying that the sale prices $14.99

However, if you want to try to build a scientific program then this is going to not work properly and you have to be careful. But at the end of the day if you want a very basic decimal this float is going to be understood exactly like it is right here by the Python interpreter. So far we've covered integers and floats.

The next one we're going to cover is going to be fractions so I'm going to say tip percentage and I can say one fifth.

Now, this is going to represent 20 percent and we can check that here by print out percentage. You'll see that it prints out point 2 which is equivalent to 20 percent.

And so the cool thing about each one of these is notice how we didn't have to do any work to tell python what kinds of numbers that we're working with. And if you're brand new to programming then this may not seem like a big deal this may seem intuitive which is part of the reason why the creators of Python did it this way because it does seem logical that you should be able to place any type of number store it in a variable and have the system decide how to treat it.

But if you have been programming for a long time and you've worked with much more low-level type languages like C and those type of languages then this is going to look very odd because we're simply allowing the system the python languages self to manage the parsing and decide what data type to use so as it goes through it looks and it finds product I.D. It looks and finds 123 and it says this is an integer it comes to sale price looks at the value that's assigned and it says this is a floating-point number. And then it finds 1/5 and it says it's a fraction and it converts it just like you see in the above image.

I'm going to create one other one. I'm going to say "new product" and we're going to say this is 150 and this is just another integer. But the main thing that I wanted to show you is what happens when you combine such as when you perform calculations on these various data types because that's another nice thing about Python is it's a very flexible in allowing you to switch back and forth and if you remember back into our string section you may have remembered that when we tried to combine a string with a number we got an error and that's because you can't have that kind of overlap. Python doesn't want you to be able to become that flexible with your programming because you're going to end up with some very weird bugs.

But one thing it is flexible is with these numbers so you have all of these subtypes such as float and integer and they all can work together. And so if I do something like this where I say sale price plus new product and I go and I print this out you'll see it says 164.99

What happened here is we took two different data types or I should say two different numbered data types of float and integer and when you combine those such as when you add them or subtract them or multiply them Python goes through and it actually converts that final output into a float. And so if we were to have two different types of integers I'm going to copy this all commented out. And so if I were for some reason in a logical sense you wouldn't usually combine a product ID with a new product but just for the sake of being able to see the way that this works. These are two integers 123 and 150. If I add these together you see that it didn't convert it into a float it kept it as an integer but whenever we had sale price and new product. And one of these sale price, in this case, was a float then automatically converted the output to be a float to be that type of decimal-based value.

And so that's something that is also very helpful. So there are going to be many times where you're going to be dealing with situations where you need to perform calculations and you may have many different data types you may have a decimal or you may have a float you may have a fraction and an integer and you need them to all work together and that's what's so helpful with Python is it allows you to do that. It will automatically allow you to process each one of those perform the calculations and then it will perform dynamically the types of data type conversions just like we saw right there.

So that is your overview of the various number types inside of Python.

Code

product_id = 123
sale_price = 14.99
tip_percentage = 1/5
new_product = 150

print(sale_price + new_product)