## Table of Contents

Welcome back to another article in my Intro to Programming series.

In the previous article, we discussed data types in programming and we briefly went over the list of basic data types that we have available in Python.

It's worth mentioning that the particular data types supported by each programming language vary a bit. The concept of data types, however, is universal.

In this article we begin diving into the Python data types to learn what they can do for us. I will say that a large part of beginner programming is getting familiar with the various data types available, so let's get started.

First, we'll cover numbers, as they are probably the most familiar and easiest to work with. You will remember from my article on data types that there are two basic types of numbers: **integer numbers** and **floating-point numbers**.

**Integers **are the easiest - **these are whole numbers, so numbers that don't have a decimal part, such as 20, 10, and -5.**

## What Are Floating-Point Numbers?

Floating-point numbers are numbers that have a decimal component, so numbers such as 3.14, 2.73, etc. You may be wondering why this weird-term, "floating-point." The answer to that question is a bit more complicated, but if you're curious, it goes back to how computers actually represent numbers.

As you know, computers only operate with ones and zeros, so when a computer looks at any data, it only sees a sequence of zeros and ones. Like so:

# 10010001

In human speak, this number would be ten million ten thousand and one. In computer speak, it's actually the number 145 written in 8 bits.

Imagine you're a computer. You can only use ones and zeros, *and* have a limited space in your memory. How do you represent numbers that have decimals?

Well, you simply decide where in that sequence of zeros and ones to "put" the decimal. Depending on what number you need to represent, the decimal point may be closer or farther from the beginning of the number.

# 10010.001

# 100.10001

This is called "floating," where the **decimal point floats left and right to represent different numbers**. Don't worry too much if this is a bit confusing, understanding this is not essential at this point in your learning and it will make more sense as you learn more programming.

## How to Use Integers and Floating-Point Numbers in Python

Ok, so let's explore a bit what we can do with integers and floating-point numbers in Python. To do this, I'm going to start a Jupyter notebook. For a refresher, follow this quick guide on how to start a new Jupyter notebook:

If you don't have Jupyter installed, read more here:

We're going to try out a few operations. For best outcomes, follow along on your computer.

**Note: In the examples below, the Python code is what follows the arrows: >>>. While the result of the code is printed immediately underneath without any leading >>>. This mimics the behavior you will encounter when using Jupyter notebooks or the Python command line.**

### How to Compute Addition in Python

Numbers in Python can be added using the + sign. In Python, + is called an **operator**. We'll talk more about operators in just a moment, but for now you can think of all mathematical signs (+, -, /, *, etc.) as operators.

Here's an example of addition in Python in Jupyter notebooks:

```
>>> 2 + 4
6
```

In the example above, 2 and 4 are called **operands**. **Operands **are the data on which **operators **act on. Pay attention for one moment to the data types involved: 2 and 4 are integers. The result, 6, is also an integer.

Compare that with the following example shown in Jupyter notebooks:

```
>>> 2.0 + 4.0
6.0
```

In this case, 2.0 and 4.0 are floating-point numbers. 6.0 is also a floating point number.

So far, we notice that the result of an addition operation will have the same data type as the operands involved in the addition operation. But what happens when we add an integer to a floating-point number?

Here we'll add an integer to a floating-point number in Jupyter notebooks:

```
>>> 2 + 4.0
6.0
```

We get a floating-point number!

Why? Well, generally speaking, floating-point numbers have more *precision*. Therefore, when adding a number with less precision and a number with higher precision (i.e., with decimal points), Python will return a number with more precision.

This is very useful in situations like this shown in Jupyter notebooks:

```
>>> 2 + 4.2
6.2
```

Think about it: it would be quite unexpected to have 2 + 4.2 return 6.

### How to Compute Subtraction in Python

Numbers can be subtracted using the - operator.

Again, in the example below using Jupyter notebooks, the minus is the **operator **and 2 and 10 are **operands***.*

```
>>> 2 - 10
-8
```

Of course, we can also subtract floating-point numbers shown in Jupyter notebooks:

```
>>> 2.1 - 10.1
-8.0
```

And, we can mix and match integers and floating-point numbers using Jupyter notebooks:

```
>>> 2 - 10.1
-8.1
```

### How to Compute Multiplication in Python

Numbers can be multiplied using the * operator.

Here's an example of multiplication in Jupyter notebooks:

```
>>> 2 * 2
4
```

And, as you'd expect, this is the result in Jupyter notebooks:

```
>>> 2.1 * 2.1
4.41
```

But also, something you might not expect. Below is an actual result I got on my computer using Jupyter notebooks:

```
>>> 2.1 * 2.2
4.620000000000001
```

That makes no sense, right?

If you do the math on a pocket calculator, you'll get 4.62, which is the correct answer. So why does Python return 4.620000000000001?

We mentioned earlier that floating-point numbers are represented using a **fixed number of binary digits**. Because we don't have infinite digits at our disposal, not all floating-point numbers can be represented accurately, so we will encounter **rounding errors***.*

**This is very important!** Floating-point arithmetic in Python *can be imprecise.* As a word of caution, **never use floating point numbers to represent quantities that need to be strictly accurate, such as money****.**

To do math with money in Python, for example, it is customary to work with cents, rather than full dollars. So instead of computing $1.5+$0.75, you would compute 150 + 75, with the understanding that the result will be in cents, not dollars.

For other quantities that need to be accurate, Python provides a more complex data type, called the *decimal* data type. We will be covering this in future blog posts.

### How to Compute Exponents (Power) in Python

You can use the ** operator to compute powers. For example, you can compute 2^{3} by writing 2 ** 3. This is equivalent to 2 * 2 * 2.

Here's an example of computing 2 ** 3 in Jupyter notebooks:

```
>>> 2 ** 3
8
```

Of course, you can also use floating-point numbers, but be aware of the potential rounding errors as shown in Jupyter notebooks:

```
>>> 2.1 ** 3
9.261000000000001
```

### How to Compute Division in Python

For division, you can use the / operator.

Here's an example of computing division in Python using Jupyter notebooks:

```
>>> 5 / 2
2.5
```

As you see above, 5 is an integer, 2 is an integer, but the result is 2.5, which is a float.

That makes sense. Let's try another one, shown in Jupyter notebooks:

```
>>> 4 / 2
2.0
```

Four divided by two is two. So why does Python return 2.0? Well, when using the / operator, Python 3 **always returns the result of division as a floating-point number even if the result is a whole number.**

What if you want the integer approximation of a division?

For this use case, Python provides the // operand, as shown here in Jupyter notebooks:

```
>>> 2 // 3
0
```

Please note that when using the // operator, Python performs **floor division**. In other words, it rounds down the result to the nearest integer if the operands are integers.

When at least one of the operands is a floating-point number, something interesting happens. The result is a floating-point number, except the decimal part is always 0.

Here's an example in Jupyter notebooks:

```
>>> 7.0 // 3
2.0
```

### How to Compute Modulo (Mod) in Python

The modulo operator % allows us to calculate *the remainder* when we perform division.

```
>>> 5 % 3
2
```

And you can verify that 5 = 3 * 1 + 2, where, of course, 2 is the remainder we get when dividing 5 by 3.

Similarly as before, whether the remainder is an integer or a floating-point number depends on the data types of the operands. For example shown in Jupyter notebooks:

```
>>> 5.0 % 3
2.0
```

In the example above, because at least one of the operands 5.0 is a floating-point number, the remainder will also be a floating point number.

This modulo operator is actually pretty useful to programmatically answer a very simple question: is a number odd or even?

From math, we know that when we divide a number by 2, if the remainder is 1, then the number is odd, and if it is 0, then the number is even. We know that 15, for example, is odd.

Here's an example using Jupyter notebooks:

```
>>> 15 % 2
1
```

And indeed, the remainder of dividing 15 by 2 is 1, so 15 is odd.

What about 20? We can again use the modulo operator and we get 0, so 20 is even, as shown here in Jupyter notebooks.

```
>>> 20 % 2
0
```

This may again sound silly, because we can tell that by just looking at the number. But what if we had a list of 10 million numbers and we needed to count how many of them are even? Rather than doing it by hand, we'd write a program that would use the modulo operator to determine which ones are even, and count them.

## How to Use Operator Precedence in Python

So far, we've only seen very simple calculations. However, in Python, you can have as many operands and operators as you want.

For example, shown here in Jupyter notebooks:

```
>>> 15 + 2 * 10
35
```

How does Python compute the result? Well, it first multiplies 2 by 10 and then it adds 15 to the result.

But how come it knows not to first add 15 and 2, and then multiply the result by 10? It knows because of built-in rules defining the order in which operators should be evaluated.

This is called** operator precedence** and it's exactly the same one you learned in math.

To refresh your memory, the order is as follows:

**E**xponent (power)**M**ultiplication**D**ivision**A**ddition**S**ubtraction

In other words, **EMDAS**.

What if you really do want to tell Python to first add 15 and 2, and *then* multiply the result by 10?

That's simple: use parentheses as shown here using Jupyter notebooks:

```
>>> (15 + 2) * 10
170
>>> (3 + 4) * (2 + 3)
35
```

This works because parentheses are always evaluated first. So, to be thorough, the full operator precedence rule is as follows:

**P**arentheses**E**xponent (power)**M**ultiplication**D**ivision**A**ddition**S**ubtraction

To more easily remember this, you can use the **PEMDAS** acronym.

### A Cheat Sheet of Operators and Operands For Python

Let's summarize what we've learned so far in a table:

Operation |
Operator |
Operands |
Result |

x + y | + | x, y | Returns the sum of x and y. |

x - y | - | x, y | Returns the result of subtracting y from x. |

x * y | * | x, y | Returns the result of multiplying x by y. |

x ** y | ** | x, y | Returns the result of raising x to the power of y (equivalent of multiplying x by x, y times). |

x / y | / | x, y | Returns the result of dividing x by y. The result will always be a floating-point number (in Python 3 and above). |

x // y | // | x, y | Divides x by y and rounds down the result to the nearest whole number. |

x % y | % | x, y | Returns the remainder of dividing x by y. |

In the table above, x and y can be any integer or floating point number.

That's it for numbers for now. In this blog post I showed you how to do very basic math using Python and we learned about the various operators we can use, such as the plus sign (+) for addition and star (*) for multiplication. This is very basic, but this is actually where computers got started and where they will forever be unmatched by any human being: they are very, very fast and accurate when it comes to math.

Thanks for reading!