Python data types

In this part of the Python programming tutorial, we talk about Python data types.

Computer programs, such as spreadsheets, text editors, calculators, or chat clients, work with data. Tools to work with various data types are essential part of a modern computer language.

Python data type definition

Data type is a set of values and the allowable operations on those values.

Python has a great set of useful data types. Python's data types are built in the core of the language. They are easy to use and straightforward.

Python Boolean values

There is a duality built in our world. There is a Heaven and Earth, water and fire, jing and jang, man and woman, love and hatred. In Python programming language, the Boolean datatype is a primitive datatype having one of two values: True or False. This is a fundamental data type. Interestingly, this data type was not there from the beginning, but it was created later on.

Happy parents are waiting a child to be born. They have chosen a name for both possibilities. If it is going to be a boy, they might have chosen John. If it is going to be a girl, they might have chosen Victoria.


# kid.py

import random

male = False
male = bool(random.randint(0, 1))

if (male):
   print("We will use name John")
   print("We will use name Victoria")

The script uses a random integer generator to simulate our case.

import random

Here we import the random module that is used to calculate random numbers.

male = bool(random.randint(0, 1))

Here we use two functions. the randint() function returns a random number from the given integer boundaries. In our case 0 or 1. The bool() function converts the integers to boolean values.

if (male):
   print("We will use name John")
   print("We will use name Victoria")

We print the name. The if keyword works with boolean values. If the male is True, we print the "We will use name John" to the console. If it has a False value, we print the other string.

The following script shows some common values that are considered to be True or False.


# bool_fun.py

print(bool(' '))

The example prints nine boolean values.

$ ./bool_fun.py 

This is the output of the bool_fun.py script.

Python None

There is another special data type — None. This data type means non existent, not known, or empty.


# none.py

def function():


In our example, we define a function. Functions will be covered later in the tutorial. The function does nothing. It does not explicitly return any value. Such a function will implicitly return None.

$ ./none.py 

This is the output of the none.py script.

Python numbers

In Python programming language, we have integer numbers, floating point numbers, and complex numbers.

If we work with integers, we deal with discrete entities. We would use integers to count apples.


# apples.py

# number of baskets
baskets = 16

# number of apples in a basket
apples_in_basket = 24

# we get the total number of apples
total = baskets * apples_in_basket

print("There are total of", total, "apples")

In our script, we count the total amount of apples. We use the multiplication operation.

$ ./apples.py 
There are total of 384 apples

This is the output of the script.

Floating point numbers represent real numbers in computing. Real numbers measure continuous quantities. Let's say a sprinter for 100m ran 9.87s. What is his speed in km/h?


# sprinter.py

# 100 m is 0.1 km

distance = 0.1

# 9.87 s is 9.87/60*60 h

time = 9.87 / 3600

speed = distance / time

print("The average speed of a sprinter is {0} km/h".format(speed))

To get the speed, we divide the distance by the time.

print("The average speed of a sprinter is {0} km/h".format(speed))

We build the message with the format() function and print it to the console.

$ ./sprinter.py 
The average speed of a sprinter is  36.4741641337 km/h

This is the output of the sprinter.py script. Value 36.4741641337 is a floating point number.

Python strings

String is a data type representing textual data in computer programs.

Strings in Python can be created using single quotes, double quotes, and triple quotes. When we use triple quotes, strings can span several lines without using the escape character.


# strings.py

a = "proximity alert"
b = 'evacuation'
c = """


In our example we assign three string literals to a, b, and c variables. Then we print them to the console.

$ ./strings.py 
proximity alert


This is the output of the strings.py script.

When we work with strings, we can use escape sequences. Escape sequences are special characters that have a specific purpose when used within a string.

print("   bbb\raaa") # prints aaabbb

The carriage return \r is a control character for end of line return to beginning of line.


# strophe.py

print("Incompatible, it don't matter though\n'cos someone's bound to hear my cry")
print("Speak out if you do\nYou're not easy to find")

The new line is a control character which begins a new line of text.

$ ./strophe.py 
Incompatible, it don't matter though
'cos someone's bound to hear my cry
Speak out if you do
You're not easy to find

Next we examine the backspace control character.

print("Python\b\b\booo") # prints Pytooo

The backspace control character \b moves the cursor one character back. In our case, we use three backspace characters to delete three letters and replace them with three o characters.

print("Towering\tinferno") # prints Towering        inferno

The horizontal tab puts a space between text.

"Johnie's dog"
'Johnie\'s dog'

Single and double quotes can be nested. Or in case we use only single quotes, we can use the backslash to escape the default meaning of a single quote.

print("eagle has", len("eagle"), "characters")

We can use the len() function to calculate the length of the string in characters.

If we append an r to the string, we get a raw string. The escape sequences are not interpreted.


# raw.py

print(r"Another world\n")
$ ./raw.py 
Another world\n

We get the string with the new line character included.

In the next example, we show string multiplication and concatenation.


# strings2.py

print("eagle " * 5)

print("eagle " "falcon")

print("eagle " + "and " + "falcon")

The * operator repeats the string n times. In our case five times. Two string literals next to each other are automatically concatenated. We can also use the + operator to explicitly concatenate strings.

$ ./strings2.py 
eagle eagle eagle eagle eagle 
eagle falcon
eagle and falcon

This is the output of the strings.py script.

Python has several built-in data types for working with collections of values: tuples, lists, sets, and dictionaries.

Python tuples

A tuple is an immutable sequence data type. The tuple can contain mixed data types.

fruits = ("oranges", "apples", "bananas")

Tuples are created using round brackets. Here we have a tuple consisting of three fruit types.

fruits = "apples", "oranges", "bananas"
print(fruits)  # prints  ('apples', 'oranges', 'bananas')

The parentheses are not mandatory. We can omit them.


# tuples.py

first = (1, 2, 3)
second = (4, 5, 6)

print("len(first) : ", len(first))
print("max(first) : ", max(first))
print("min(first) : ", min(first))
print("first + second :", first + second)
print("first * 3 : ", first * 3)
print("1 in first : ", 1 in first)
print("5 not in second : ", 5 not in second)

This example shows several basic operations with tuples. The len() function returns the number of elements in the first tuple. The max() function returns the maximum value and the min() the minimum value. The addition operator adds two tuples, the multiplication operator multiplies the tuple. The in operator determines if the value is in the tuple.

$ ./tuples.py
len(first) :  3
max(first) :  3
min(first) :  1
first + second : (1, 2, 3, 4, 5, 6)
first * 3 :  (1, 2, 3, 1, 2, 3, 1, 2, 3)
1 in first :  True
5 not in second :  False

This is the output of the script.

Next we will do some indexing.


# tuples2.py

five = (1, 2, 3, 4, 5)

print("five[0] : ", five[0])
print("five[-1] : ", five[-1])
print("five[-2] : ", five[-2])
print("five[:] : ", five[:])
print("five[0:4] : ", five[0:4])
print("five[1:2] : ", five[1:2])
print("five[:2] : ", five[:2])
print("five[:-1] : ", five[:-1])
print("five[:9] : ", five[:9])

To get a value from a tuple, we use square brackets []. Note that we count indexes from 0. If there are five objects in a tuple, the indexes are 0...4. If we use a negative index, we get a value from the end of the tuple. So index -1 gets the last element, -2 gets the last but one element. Python enables to create slices from tuples. For this we use the : delimiter. For instance, [0:4] gives (1, 2, 3, 4). Note that the last element is not included.

We can omit one or both indexes in a slice. The [:4] gives (1, 2, 3, 4). It goes from the first element. The [0:] gives (1, 2, 3, 4, 5). This time, the last element is included. If we go out of bounds, we simply get all elements in the tuple.

$ ./tuples2.py 
five[0] :  1
five[-1] :  5
five[-2] :  4
five[:] :  (1, 2, 3, 4, 5)
five[0:4] :  (1, 2, 3, 4)
five[1:2] :  (2,)
five[:2] :  (1, 2)
five[:-1] :  (1, 2, 3, 4)
five[:9] :  (1, 2, 3, 4, 5)

This is the output.

Tuples can contain several mix data types.


# tuples_mix.py

mix = (1, 2, "solaris", (1, 2, 3))

print("mix[1] :", mix[1])
print("mix[2] :", mix[2])
print("mix[3] :", mix[3])
print("mix[3][0] :", mix[3][0])
print("mix[3][1] :", mix[3][1])
print("mix[3][2] :", mix[3][2])

In our example, we have put numbers, a string, and a tuple into the mix tuple.

$ ./tuples_mix.py 
mix[1] : 2
mix[2] : solaris
mix[3] : (1, 2, 3)
mix[3][0] : 1
mix[3][1] : 2
mix[3][2] : 3

To get the elements from the nested tuple, we use two square brackets.

We have an exception when we work with tuples containing one element. Parentheses are also used in expressions. How do we distinguish between an expression and a one element tuple? The creators of the Python programming language decided to use a comma to denote that we are using a tuple.


# tuple_one.py

print((3 + 7))
print((3 + 7, ))

In the first case we have and expression. We print number 10 to the console. In the second case we deal with a tuple. We print a tuple containing number 10.

$ ./tuple_one.py 

This is the output.

Python lists

A list is a mutable sequence data type. It can contain mixed data types. A list and a tuple share many common features. Because a list is a modifiable data type, it has some additional operations. A whole chapter is dedicated to the Python list.

actors = ["Jack Nicholson", "Antony Hopkins", "Adrien Brody"]

The list is created using the square brackests [].


# simple_list.py

num = [0, 2, 5, 4, 6, 7]

print(num + [8, 9])

As we have stated previously, we can use the same operations on lists as on tuples.

$ ./list_simple.py 
[5, 4, 6, 7]
[0, 2, 5, 4, 6, 7, 8, 9]

This is the output.

Next we will sort a list.


# list_sorting.py

numbers = [4, 3, 6, 1, 2, 0, 5]


In our script we have a list of numbers. To sort those numbers, we use the built-in sort() function.

$ ./list_sorting.py 
[4, 3, 6, 1, 2, 0, 5]
[0, 1, 2, 3, 4, 5, 6]

The reverse() function will sort the elements of a list in reverse order.

numbers.reverse()   #  [5, 4, 3, 2, 1, 0]

Counting elements in a list is done with the count() method.


# list_counting_elements.py

numbers = [0, 0, 2, 3, 3, 3, 3]

print("zero is here",  numbers.count(0), "times")
print("one is here",   numbers.count(1), "times") 
print("two is here",   numbers.count(2), "time") 
print("three is here", numbers.count(3), "times") 

The script counts number occurrences in a list.

$ ./list_counting_elements.py 
zero is here 2 times
one is here 0 times
two is here 1 time
three is here 4 times

Next, we will deal with inserting and deleting items from the list.


# list_modify.py

names = []



names.insert(0, "Adriana")

del names[1]

del names[0]

In our example we first create an empty names list. We use the append() function to append new items to the list. The elements are appended in the consecutive way. The insert() function inserts new elements at a given position. The existing elements are not deleted, they are relocated. The remove() function removes a specific item from the list. If we want to delete an item based on the index, we use the del keyword.

$ ./list_modify.py 
['Frank', 'Alexis', 'Erika', 'Ludmila']
['Adriana', 'Frank', 'Alexis', 'Erika', 'Ludmila']
['Adriana', 'Ludmila']

This is the output of the list_modify.py script.

There following program presents additional two functions.


# list_modify2.py

first = [1, 2, 3]
second = [4, 5, 6]


first[0] = 11
first[1] = 22
first[2] = 33


The extend() method appends a whole list to another list. To modify an element in a list, we can use the assignment operator. The pop() method takes an item from the list and returns it.

$ ./list_modify2.py 
[1, 2, 3, 4, 5, 6]
[11, 22, 33, 4, 5, 6]
[11, 22, 33, 4, 5]

This is the output.

In the following example, we will find out indexes of elements.


# list_index.py

numbers = [0, 1, 2, 3, 3, 4, 5]


To find an index in a list, we use the index() method. If there are more occurrences of an element, the method returns the index of the first element.

$ ./list_index.py 

This is the output of the list_index.py script.

Next we will do some transformations.


# list_transform.py

first = [1, 2, 3]
second = (4, 5, 6)



We can use tuple() function to create a tuple from a list and list() function to create a list from a tuple. Note that the original objects are not modified; the functions only return those transformed collections.

$ ./list_transform.py 
(1, 2, 3)
[4, 5, 6]
[1, 2, 3]
(4, 5, 6)

Python sets

A set is an unordered collection of data with no duplicate elements. A set supports operations like union, intersection, or difference; similar as in Mathematics.


set1 = set(['a', 'b', 'c', 'c', 'd'])
set2 = set(['a', 'b', 'x', 'y', 'z'])

print("set1: " , set1)
print("set2: " , set2)
print("intersection: ", set1 & set2)
print("union: ", set1 | set2)
print("difference: ", set1 - set2)
print("symmetric difference: ", set1 ^ set2)

In our example we have two sets. We use the set() function to create sets. The intersection operation returns elements that are both in set1 and set2. The union operation returns all elements from both sets. The difference returns elements that are in the set1 but not is set2. And finally, the symmetric difference returns elements that are in set1 or set2, but not in both.

$ ./sets.py 
set1: set(['a', 'c', 'b', 'd'])
set2: set(['a', 'x', 'b', 'y', 'z'])
intersection: set(['a', 'b'])
union: set(['a', 'c', 'b', 'd', 'y', 'x', 'z'])
difference: set(['c', 'd'])
symmetric difference: set(['c', 'd', 'y', 'x', 'z'])

This is the output of the sets.py script.

Next we introduce some other operations with sets.


# sets2.py

set1 = set([1, 2])

set2 = set([1, 2, 3, 4, 6, 7, 8])


print("Is set1 subset of set2 ? :", set1.issubset(set2))
print("Is set1 superset of set2 ? :", set1.issuperset(set2))


The add() method adds an item to the set. The remove() item removes an item from a set. The clear() method removes all items from the set. The set1 is superset of set2 if every element in set2 is also in set1. The set1 is a subset of set2 if every element in set1 is also in set2.

$ ./sets2.py 
set([1, 2, 3, 4])
set([1, 2, 3, 4, 6, 7])
Is set1 subset of set2 ? :  True
Is set1 superset of set2 ? :  False

If we need an immutable set, we can create a frozen set with the frozenset() function.

fs = frozenset(['a', 'b', 'c'])

This line creates a frozen set from a list.

Python dictionaries

A Python dictionary is a group of key-value pairs. The elements in a dictionary are indexed by keys. Keys in a dictionary are required to be unique. Because of the importance of the dictionary data type, a whole chapter covers the dictionary in this Python tutorial.


# dictionary_simple.py

words = { 'girl': 'Maedchen', 'house': 'Haus', 'death': 'Tod' }





Our first example shows some basic usage of the dictionary data type. We print a specific value, keys and values of the dictionary. The items() method returns a list of dictionarie's (key, value) pairs as tuples.

$ ./dictionary_simple.py 
['house', 'girl', 'death']
['Haus', 'Maedchen', 'Tod']
[('house', 'Haus'), ('girl', 'Maedchen'), ('death', 'Tod')]
{'house': 'Haus', 'death': 'Tod'}

In this part of the Python tutorial, we have described Python data types.