Data types in Python

If you haven’t yet installed python and set up a development environment, I would suggest that you first read this post on getting started with python.

In this post, we will be looking broadly at the following data types:

• Numeric values
• Boolean values
• Sequential types
• Strings
• Dictionaries

This is by no means an exhaustive list, but is likely to be the data types you use most often when programming in python.

Numerics

In python, numerical values can be stored in 3 distinct data types: integers, floating point numbers and complex numbers.

Integers

Integers in python are represented by the int class and can contain positive or negative whole numbers.

Floats

Decimal numbers in python are represented by the float class. These can be positive or negative real numbers specified by the presence of a decimal point.

Complex numbers

Complex numbers are are represented by the complex class. These are specified as (real part) + (imaginary part)j.

# integer
x = 3

# float
y = 2.0

# complex number - j represents the imaginary part of your complex number
z = 1 + 3j

When working with complex numbers you can access the real and imaginary parts of your complex number - in this case z - by using the z.real and z.imag attributes respectively.

Let’s look at how we can perform some basic operations with these data types.

# sum of x and y
x + y

# difference of x and y
x - y

# product of x and y
x * y

# quotient of x and y
x / y

# floored quotient of x and y
x // y

# remainder of x / y
x % y

# x to the power y
x ** y

In python, it is possible to convert data types into others using various built-in functions. Be sure to try out int(), float() and complex().

Booleans

Boolean data types can take on one of two built-in values: True or False. They are used to represent truth values. Other values can also be used as ‘truth’ values, for example, the integers 0 and 1 in a numerical context. The built-in function bool() can be also used to convert any value to a Boolean, if the value can be interpreted as a truth value (see section Truth Value Testing in the python docs). Take note of the capitalization as the usage of true and false will throw errors.

# boolean values
a = True
b = False

print("The type of a is: ", type(a))
print("The type of b is: ", type(b))

Boolean values are useful when checking if two variables have the same value or are of the same data type.

# let x be an integer
x = 5

# let y be a floating point number
y = 5.0

# check if the value of x is the same as the value of y - result is True
x == y

# check if x and y are of the same data type - result is False
type(x) == type(y)

Above we can see that 5 and 5.0 are seen to have the same value, but as we are aware, belong to the different data type classes int and float.

Lists

In python, lists are synonymous with arrays declared in other languages. A key feature of a list that makes it super powerful is that it need not be homogeneous. This means that a single list may contain other data types like integers and strings or even other lists in your list.

# create an empty list
mylist = []

# a list with various data types
mylist = ['Text', 23, 3.14]

# a list within a list
mylist = ['Fruit', ['apples','oranges','bananas']]

Lists are also mutable, which means they can be altered after they have been created. The elements in a list are indexed according to a definite sequence and the indexing of a list is done with 0 being the first index. Elements in a list can easily be duplicated and/or re-ordered as needed. Lists are represented in python by the list class.

# create a list
mylist = ['Text', 23, 3.14]

# select items from the list
mylist[0]
mylist[1]
mylist[2]

# append item to end of the list
mylist.append('New list item')

# select the last item in the list
last_item = mylist[-1]

# remove an item from a list - also try the .pop() method
mylist.remove('Text')

Sets

A set in python, is very similar to a list with a few exceptions. The biggest difference being that a set only contains unique elements. Sets can be created using curly braces, with elements separated by commas. Alternatively an iterable object (such as a list, tuple or string) can be turned into a set using the set() function. Sets are represented by the set class.

# create an empty set
myset = {}

# convert a list into a set
mylist = ['Text', 23, 3.14]
myset = set(mylist)

# a set containing various elements
myset = {'a', 'b', 'c', 1, 2, 3}

# a set will only keep unique values
myset = {1, 1, 1, 2, 2, 3}

It is important to note that ordinary sets cannot be nested as seen above with lists as sets are not hashable.

Tuples

Much like the list, a tuple is an ordered collection of python objects. The sequence of values stored in a tuple can be of any type, and they are indexed by integers. A notable difference that exists between a list and a tuple is that tuples are immutable. Tuples can be created using round brackets, with elements separated by commas or by using the tuple() function. It is represented by the tuple class.

# create an empty tuple
mytuple = ()

# convert a list into a set
mytuple = ('Text', 23, 3.14)
print(mytuple)

# convert a list into a tuple
mylist = ['Text', 23, 3.14]
mytuple = tuple(mylist)

Strings

A string is also a sequential data type in python. It is an immutable sequence of Unicode characters or put simply - ordinary text. String literals are written by enclosing a sequence of characters in either single, double or triple quotes. It is represented by str class.

# an empty string
mystring = ""

# a string with single quotes
mystring = 'Hello, this is a string!'

# a string with double quotes
mystring = "It's useful if you need to use an apostrophe."

# a string with triple quotes
mystring = ''' These are useful for
creating strings that span
multiple lines!
'''

Dictionaries

In python, a dictionary is an unordered collection of key-value pairs used to store data values. Dictionaries can be created by placing a comma-separated list of key : value pairs within curly braces. Dictionaries are represented by dict class.

# create an empty dictionary
mydict = {}

# create a dictionary
mydict = {'abc' : ['a','b','c'], 123 : 'One-two-three', 'eight' : 8 }

# create a dictionary using built-in function
mylist = [('abc', ['a','b','c']), (123, 'One-two-three'), ('eight', 8)]
mydict = dict(mylist)

We can access the values in a dictionary by making reference to it’s key name, used inside square brackets, similar to retrieving an item from a list. We can also add or remove items from a dictionary as seen below.

# create a dictionary
mydict = {'abc' : ['a','b','c'], 123 : 'One-two-three', 'eight' : 8 }

# get the value 8
myvalue = mydict['eight']

# delete a key:value pair
del mydict[123]

# Deleting entire Dictionary
mydict.clear()

# add new key:value pair
mydict['key'] = 'value'

# update a key's value
mydict['key'] = 'A different value'

That should be enough for now - be sure to play around with these to see what you can come up with.