Understanding Data Types in Python 3

Updated on August 20, 2021
Understanding Data Types in Python 3


In Python, like in all programming languages, data types are used to classify one particular type of data. This is important because the specific data type you use will determine what values you can assign to it and what you can do to it (including what operations you can perform on it).

In this tutorial, we will go over the important data types native to Python. This is not an exhaustive investigation of data types, but will help you become familiar with what options you have available to you in Python.


You should have Python 3 installed and a programming environment set up on your computer or server. If you don’t have a programming environment set up, you can refer to the installation and setup guides for a local programming environment or for a programming environment on your server appropriate for your operating system (Ubuntu, CentOS, Debian, etc.)


One way to think about data types is to consider the different types of data that we use in the real world. An example of data in the real world are numbers: we may use whole numbers (0, 1, 2, …), integers (…, -1, 0, 1, …), and irrational numbers (π), for example.

Usually, in math, we can combine numbers from different types, and get some kind of an answer. We may want to add 5 to π, for example:

5 + π

We can either keep the equation as the answer to account for the irrational number, or round π to a number with a brief number of decimal places, and then add the numbers together:

5 + π = 5 + 3.14 = 8.14 

But, if we start to try to evaluate numbers with another data type, such as words, things start to make less sense. How would we solve for the following equation?

sky + 8

For computers, each data type can be thought of as being quite different, like words and numbers, so we will have to be careful about how we use them to assign values and how we manipulate them through operations.


Any number you enter in Python will be interpreted as a number; you are not required to declare what kind of data type you are entering. Python will consider any number written without decimals as an integer (as in 138) and any number written with decimals as a float (as in 138.0).


Like in math, integers in computer programming are whole numbers that can be positive, negative, or 0 (…, -1, 0, 1, …). An integer can also be known as an int. As with other programming languages, you should not use commas in numbers of four digits or more, so when you write 1,000 in your program, write it as 1000.

Info: To follow along with the example code in this tutorial, open a Python interactive shell on your local system by running the python3 command. Then you can copy, paste, or edit the examples by adding them after the >>> prompt.

We can print out an integer like this:


Or, we can declare a variable, which in this case is essentially a symbol of the number we are using or manipulating, like so:

my_int = -25

We can do math with integers in Python, too:

int_ans = 116 - 68

Integers can be used in many ways within Python programs, and as you continue to learn more about the language you will have a lot of opportunities to work with integers and understand more about this data type.

Floating-Point Numbers

A floating-point number or a float is a real number, meaning that it can be either a rational or an irrational number. Because of this, floating-point numbers can be numbers that can contain a fractional part, such as 9.0 or -116.42. In general, for the purposes of thinking of a float in a Python program, it is a number that contains a decimal point.

Like we did with the integer, we can print out a floating-point number like this:


We can also declare a variable that stands in for a float, like so:

my_flt = 17.3

And, just like with integers, we can do math with floats in Python, too:

flt_ans = 564.0 + 365.24

With integers and floating-point numbers, it is important to keep in mind that 3 ≠ 3.0, as 3 refers to an integer while 3.0 refers to a float.


The Boolean data type can be one of two values, either True or False. Booleans are used to represent the truth values that are associated with the logic branch of mathematics, which informs algorithms in computer science.

Whenever you see the data type Boolean, it will start with a capitalized B because it is named for the mathematician George Boole. The values True and False will also always be with a capital T and F respectively, as they are special values in Python.

Many operations in math give us answers that evaluate to either True or False:

  • greater than
    • 500 > 100 True
    • 1 > 5 False
  • less than
    • 200 < 400 True
    • 4 < 2 False
  • equal
    • 5 = 5 True
    • 500 = 400 False

Like with numbers, we can store a Boolean value in a variable:

my_bool = 5 > 8

We can then print the Boolean value with a call to the print() function:


Since 5 is not greater than 8, we will receive the following output:


As you write more programs in Python, you will become more familiar with how Booleans work and how different functions and operations evaluating to either True or False can change the course of the program.


A string is a sequence of one or more characters (letters, numbers, symbols) that can be either a constant or a variable. Strings exist within either single quotes ' or double quotes " in Python, so to create a string, enclose a sequence of characters in quotes:

'This is a string in single quotes.'
"This is a string in double quotes."

You can choose to use either single quotes or double quotes, but whichever you decide on you should be consistent within a program.

The basic program “Hello, World!” demonstrates how a string can be used in computer programming, as the characters that make up the phrase Hello, World! are a string.

print("Hello, World!")

As with other data types, we can store strings in variables:

hw = "Hello, World!"

And print out the string by calling the variable:

Hello, World!

Like numbers, there are many operations that we can perform on strings within our programs in order to manipulate them to achieve the results we are seeking. Strings are important for communicating information to the user, and for the user to communicate information back to the program.


A list is a mutable, or changeable, ordered sequence of elements. Each element or value that is inside of a list is called an item. Just as strings are defined as characters between quotes, lists are defined by having values between square brackets [ ].

A list of integers looks like this:

[-3, -2, -1, 0, 1, 2, 3]

A list of floats looks like this:

[3.14, 9.23, 111.11, 312.12, 1.05]

A list of strings:

['shark', 'cuttlefish', 'squid', 'mantis shrimp']

If we define our string list as sea_creatures:

sea_creatures = ['shark', 'cuttlefish', 'squid', 'mantis shrimp']

We can print them out by calling the variable:


And the output looks exactly like the list that we created:

['shark', 'cuttlefish', 'squid', 'mantis shrimp']

Lists are a very flexible data type because they are mutable in that they can have values added, removed, and changed. There is a data type that is similar to lists but that can’t be changed, and that is called a tuple.


A tuple is used for grouping data. It is an immutable, or unchangeable, ordered sequence of elements.

Tuples are very similar to lists, but they use parentheses ( ) instead of square brackets and because they are immutable their values cannot be modified.

A tuple looks like this:

('blue coral', 'staghorn coral', 'pillar coral')

We can store a tuple in a variable and print it out:

coral = ('blue coral', 'staghorn coral', 'pillar coral')
('blue coral', 'staghorn coral', 'pillar coral')

Like in the other data types, Python prints out the tuple just as we had typed it, with parentheses containing a sequence of values.


The dictionary is Python’s built-in mapping type. This means that dictionaries map keys to values and these key-value pairs are a useful way to store data in Python. A dictionary is constructed with curly braces on either side { }.

Typically used to hold data that are related, such as the information contained in an ID, a dictionary looks like this:

{'name': 'Sammy', 'animal': 'shark', 'color': 'blue', 'location': 'ocean'}

You will notice that in addition to the curly braces, there are also colons throughout the dictionary. The words to the left of the colons are the keys. Keys can be made up of any immutable data type. The keys in the dictionary above are: 'name', 'animal', 'color', 'location'.

The words to the right of the colons are the values. Values can be comprised of any data type. The values in the dictionary above are: 'Sammy', 'shark', 'blue', 'ocean'.

Like the other data types, let’s store the dictionary inside a variable, and print it out:

sammy = {'name': 'Sammy', 'animal': 'shark', 'color': 'blue',  'location': 'ocean'}
{'color': 'blue', 'animal': 'shark', 'name': 'Sammy', 'location': 'ocean'}

If we want to isolate Sammy’s color, we can do so by calling sammy['color']. Let’s print that out:


As dictionaries offer key-value pairs for storing data, they can be important elements in your Python program.


At this point, you should have a better understanding of some of the major data types that are available for you to use in Python. Each of these data types will become important as you develop programming projects in the Python language.

You can learn about each of the data types above in more detail by reading the following specific tutorials:

Once you have a solid grasp of data types available to you in Python, you can learn how to convert data types.

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Python is a flexible and versatile programming language that can be leveraged for many use cases, with strengths in scripting, automation, data analysis, machine learning, and back-end development. It is a great tool for both new learners and experienced developers alike.


You should have Python 3 installed and a programming environment set up on your computer or server. If you don’t have a programming environment set up, you can refer to the installation and setup guides for a local programming environment or for a programming environment on your server appropriate for your operating system (Ubuntu, CentOS, Debian, etc.)

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