1 year ago

Introduction to Python Programming - Data Structures

Overview
Sequences
- List
- Tuples
Set
Dictionary

Overview

Data structures are containers that enable storage, grouping, access, iteration, and manipulation of basic data types. Data structures include Sequences, Set, and Dictionary.

A sequence is a container holding data (elements) in an ordered manner. Sequence types include list, str, tuple, and bytes. Among them, only list is mutable while the rest are immutable. The sequence below contains 10 integer elements (n = 10).

Python Sequences

The element at the first position takes an index value of 0 while the last takes an index value of n - 1, 9.

Note: str refers to strings.

Operations on Sequences

You can check if an element exists in a sequence using the in keyword or operator. The result is the boolean value True or False. You can also do this for other containers like Set and Dictionary. For example
```
sequence = [99, 22, 43, 40, 85, 66, 17, 81, 2, 8]

40 in sequence # True
82 in sequence # False
```
If you put the code in a Python file (.py), use the print statement to see the output. For example print(40 in sequence). At a Python REPL, you can run the code as is.
You can access the elements of a sequence using the integer indices. This operation, known as indexing is fast. For example, to get the fifth (5) element at index position four (4), you would write
```
sequence[4] #85
```
To retrieve a slice of elements, specify the start and end index.

For example, the code below will return a slice of the first to the fifth element.
```
sequence[0:5] # [99, 22, 43, 40, 85]
```
Note that the sixth element (at index 5) is excluded. You may shorten the above statement to sequence[:5] by omitting the start index. You may also omit the end index in certain cases. Omitting the start index will return a slice from the start index to the rest elements. For example sequence[3:] will yield [40, 85, 66, 17, 81, 2, 8]. Also, omitting the start and end index together (sequence[:]) will return a slice containing all elements.
Sequences also support iteration. You can go through each element in a sequence using a for loop statement (you will see more loops in my next article). For example, in the for loop below, we multiply each element by three (3) and print the results.
```
for element in sequence:
    print(3 * element)
```
Other operations on sequences include counting using the len() function; reversing, appending, and popping using reverse(), append() and pop() methods/functions in the case of mutable sequences. In list and str, you can count the number of times an element appears using the count() method. You can find an element's position using the find() or index() method if the method(s) is available on the sequence. For example
```
len(sequence) # 10

a_string_is_also_a_sequence = "Hello reader!"

len(a_string_is_also_a_sequence) # 13

sequence.index(81) #7 meaning 81 is at index 7

a_string_is_also_a_sequence.count("l") #2, letter l occurs twice
```

Let's look at the sequence types starting with the list data structure.

List

The list data structure is an ordered sequence of elements enclosed in square brackets ([]). The content could be any data type or data structure, including a list. Since lists are mutable, adding or removing elements modifies the list.

Creation

Use the literal square brackets [] or the list() function. Below is a list of names, numbers, two empty lists, and so on.

names = ["John", "Doe", "Peter", "Griffin", "Lois", "Lane"]

numbers = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

empty_1 = []

empty_2 = list()

# you can convert a sequence to a list using the list function
list_of_letters_abcd = list("abcd")

# The string method split creates a list of strings
list_of_letters_efgh = "e f g h".split(" ")

A list can contain other lists (nesting). For example


nested_list = [names, numbers]

Python Nested List

Note: A list can contain a mixture of types e.g. ["a", 1, [], 2.3]. However, you should not do this as it makes working with such a list difficult.

Operations

Indexing (Access) and Slicing

As mentioned above, you can retrieve the elements of a list by indexing and slicing.

# Indexing
# The first element or name
names[0]  # 'John'

# The third name starting from the back
names[-3]  # 'Griffin'

# Slicing

# The first three numbers
numbers[0:3]  # [10, 20, 30]

# same as above(we can omit the start index)
numbers[:3] # [10, 20, 30]

# The third number to the end
numbers[2:] # [30, 40, 50, 60, 70, 80, 90, 100]

Modifying (Mutation)

# Replace the first element 'John' with 'James'
names[0] = "James"

# Replace 'Lois' with 'Mary'
names[-2] = "Mary"

names # ['James', 'Doe', 'Peter', 'Griffin', 'Mary', 'Lane']

Deletion

To delete an element from a list, use the del keyword. For example, del some_list[0] will remove the item in the first position. Afterward, the list is re-ordered so that the element initially at position 2 (index 1) takes position 1 (index 0), and so on.

Copying

Assigning a list variable to a new variable does not create a copy. The new variable refers to the original one. Python does what we call referencing behind the scenes. Referencing saves memory. For example,

two variables pointing to the same Python list

numbers_a = numbers

numbers_a == numbers # True

We can check if both lists point to the same object using the is keyword. For example

numbers_a is numbers  # True

numbers_a and numbers are equal and the same. Modification to either will reflect in both variables. Let us add 110 at the end of the list using numbers_a.

# We add 110 to the numbers_a list
numbers_a.append(110)

Print the content of the list through the numbers variable.

# numbers also contains the '110' added to numbers_a
numbers # [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]

In cases where you need to make a copy of the original list, use either a full slice [:], the list() function, or the copy() method.

#1 Copying with a full slice
a = numbers[:]

# Copying the list function
b = list(numbers)

# Use the copy method
c = numbers.copy()

Concerning content, the new copy is equal to the original list. However, the lists are not identical.

# Do we have the same elements?
a == numbers # True
b == numbers # True
c == numbers # True

# Are we identical?
a is numbers # False
b is numbers # False
c is numbers # False

a and numbers are equal but not the same. They are still equal because they contain the same elements. Variables b and c are also equal but not identical.

Addition and Multiplication

You can combine two or more lists with the + operator. For example

[1, 2] + [3, 4]  # [1, 2, 3, 4]

You can also use the * operator to generate a new list containing repetitions of the elements of the old list. For example

[1] * 5 # [1, 1, 1, 1, 1]

[1, 2] * 5 # [1, 2, 1, 2, 1, 2, 1, 2, 1, 2]

Some List Methods

List methods include but are not limited to append(), pop(), reverse(), index(), and count().

# append: add elements to the end/back of the list

names.append('Owen')
names.append('Wilson')

names # ['James', 'Doe', 'Peter', 'Griffin', 'Mary', 'Lane', 'Owen', 'Wilson']

# pop: remove and return the last element from a list
# Specify an index to remove the item in that position
# The removed item is returned (can be stored in a variable)

names.pop() # 'Wilson'

names  # ['James', 'Doe', 'Peter', 'Griffin', 'Mary', 'Lane', 'Owen']

# pop(index): remove and return an element at a given index name.pop(0) # 'James'

names  # ['Doe', 'Peter', 'Griffin', 'Mary', 'Lane', 'Owen']

# reverse: Reverse the positions of the element

# before reversal
numbers # [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]

numbers.reverse()

# after reversal
numbers # [110, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]

# index: get the position/index of an element.
# returns a ValueError if the element is not found
names.index('Lane') # 4

# count: how many "Peter" do we have in the names list
names.count('Peter') # 1

# Use the len function to determine the number of elements in a list
len(numbers) # 10

Tuples

Tuples are very similar to lists except that they are immutable and their elements are enclosed within a parenthesis (). Prefer tuple to list if you do not want the content of your collection to change.

Creation

Use the literal parenthesis () or the tuple() function.

# Literally
empty_tuple = ()

my_tuple = (1, 2, 3)

# you do not always need the parenthesis
my_other_tuple = 4, 4, 5, 5, 5

d = 1,

# from sequences such as string, list
names_tuple = tuple(names)

numbers_tuple = tuple(numbers)

Note that a single element in () without a comma , will not give you a tuple.

not_a_tuple = (2)

type(not_a_tuple) # <class 'int'>

names_tuple # ('Doe', 'Peter', 'Griffin', 'Mary', 'Lane', 'Owen')

numbers_tuple # (110, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10)

my_other_tuple  # (4, 4, 5, 5, 5)

Operations

You can retrieve elements of a tuple via indexing and slicing.

names_tuple[2] # 'Griffin'

names_tuple[:4] # ('Doe', 'Peter', 'Griffin', 'Mary')

However, you cannot modify a tuple.

names_tuple[0] = 'Fail'

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-106-f0d527e00ac0> in <module>
----> 1 names_tuple[0] = 'Fail'


TypeError: 'tuple' object does not support item assignment

Like all sequences, you can count the number of elements in a tuple using the len function.

# count
len(names_tuple) # 6

You can also use the list() function to create a new list from a tuple.

# convert to list
list(names_tuple)  # ['Doe', 'Peter', 'Griffin', 'Mary', 'Lane', 'Owen']

Like in a list, the + and * operators perform similar actions.

# Combine tuples: create a new one
my_tuple + my_other_tuple # (1, 2, 3, 4, 4, 5, 5, 5)

# Repeat the elements and create a new tuple
my_tuple * 5  # (1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3)

Tuple unpacking

You can assign the elements of a tuple to variables using tuple unpacking. For example, the variable my_tuple contains three elements.

my_tuple # (1, 2, 3)

To store these three elements in three different variables, you would write

# unpack my_tuple

first, second, third = my_tuple

first # 1

second # 2

third # 3

Tuple Methods

index() and count() are examples of methods/functions of a tuple.

# How many 5s do we have in the my_other_tuple variable
my_other_tuple.count(5) # 3

# Mary is at which position/index?
names_tuple.index('Mary')  # 3

Check out my previous article on data types where I discussed strings briefly. Note that the bytes sequence is similar to strings. So, I will not discuss it in this article :)

Sets

A set is a mutable collection of unique elements enclosed in curly braces ({}). However, unlike lists, tuples, and strings, sets are not ordered. So you cannot index into or slice sets.

Creation

Use either literal {} or the set function.

letters_set = {'a', 'b', 'c', 'c', 'a', 'b', 'c'}

numbers_set = set([1, 2, 2, 3, 3, 5, 5, 4, 4, 2, 5])

empty_set = set()

not_an_empty_set = {}

type(empty_set) # set
type(not_an_empty_set) # dict

Note that an empty {} gives an empty dictionary and not an empty set. Use set() to create an empty set.

numbers_set  # {1, 2, 3, 4, 5}

letters_set # {'a', 'b', 'c'}

Set Operations

Let's create some sets to perform some set operations.

set_1 = {1, 2, 3, 4, 5}
set_2 = {1, 2, 3}
set_3 = {4, 5}
set_4 = {6, 7}

Union: all elements in both sets

set_1.union(set_2)  # {1, 2, 3, 4, 5}

set_1.union(set_4)  # {1, 2, 3, 4, 5, 6, 7}

Intersection: Only elements that appear in both sets

set_1.intersection(set_2) # {1, 2, 3}

set_1.intersection(set_3) # {4, 5}

Difference: elements present in the first set but not in the second set

set_1.difference(set_2) # {4, 5}

set_3.difference(set_4) # {4, 5}

Set Methods

add, update, union, intersection, and difference are some examples of set methods. See python documentation for more information.

Update: fill a set with the elements of another set

set_1.update(set_4)

set_1  # {1, 2, 3, 4, 5, 6, 7}

Add: Add element(s) to a set

set_1.add(8)
set_1.add(9)
set_1.add(9)
set_1.add(10)

set_1  # {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

Remove an element from a set


# remove 10. Then remove 1
set_1.remove(10)
set_1.remove(1)

set_1  # {2, 3, 4, 5, 6, 7, 8, 9}

Dictionaries

Just like the English dictionary, a Python dictionary is a collection of keys and their values (key-value pair) enclosed in curly braces ({}). Like sets, dictionaries are mutable and unordered

Note: Dictionary keys must be immutable (for example, strings, and integers). Therefore, a mutable type (such as a list) cannot be a dictionary key. Dictionary values can be of any data type, including a dictionary.

Creation

Use the literal curly bracket {} in the following format { "key": value } or use the dict function.

# Literal
items = { "pen": 30, "chalk": 20, "book": 45 }

empty_dict = {}

# Use the dict function
same_items = dict(pen=30, chalk=20, book=45)

# dict function taking a list of tuples
its_all_i_see = dict([("pen", 30), ("chalk", 20), ("book", 45)])

empty_dict_2 = dict()

items  # {'pen': 30, 'chalk': 20, 'book': 45}

same_items # {'pen': 30, 'chalk': 20, 'book': 45}

its_all_i_see # # {'pen': 30, 'chalk': 20, 'book': 45}

type(items) # dict

key_types = {"string": 1, 1 : 5, (5,): "tuple" }

key_types  # {'string': 1, 1: 5, (5,): 'tuple'}

A mutable key like a list, set, or dictionary will result in an error.

key_types_fail = { [1, 2]: "whatever" }

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-163-59389ba2d9af> in <module>
----> 1 key_types_fail = { [1, 2]: "whatever" }


TypeError: unhashable type: 'list'

Operations

Access and Modification

Use dictionary[key] or dictionary.get(key) to retrieve the value associated with the key.

items["pen"] # 30

items.get("pen") # 30

# Return the value 0 if there is no pen.
items.get("pen", 0)

dictionary[key] throws an error if the key does not exist. This may crash your program. Prefer .get(key) as it returns None if the key is not present. With the get() method, you can also specify a default value to return if the key is present

Use dictionary[key] = value to add a new item to a dictionary or to modify an existing key with a new value. For example:

# Add a new item table to the items
items["table"] = 100

items # # {'pen': 30, 'chalk': 50, 'book': 45, 'table': 100}

# change the value of chalk to 50. The original was 20

items["chalk"] = 50

items  # {'pen': 30, 'chalk': 50, 'book': 45, 'table': 100}

Deletion

# Remove table from the dictionary
del items['table']

Addition and Multiplication

The dictionary type does not support the + and * operators.

del dictionary[key] throws a KeyError if the key is not in the dictionary.

Dictionary Methods

Some dictionary methods are keys, values, clear, items, and update.

same_items  # {'pen': 30, 'chalk': 20, 'book': 45}

# get a list of keys only using the keys() method
same_items.keys()  # dict_keys(['pen', 'chalk', 'book'])

# the values only
same_items.values()  # dict_values([30, 20, 45])

# The items method gives you a tuple of the keys and values of the dictionary
same_items.items() #dict_items([('pen', 30), ('chalk', 20), ('book', 45)])

# clear the dictionary
same_items.clear()

# Now empty
same_items # {}

# add the elements from another dictionary
same_items.update(items)

same_items # {'pen': 30, 'chalk': 50, 'book': 45}

Conclusion

In this article, we have looked at some data structures. In the next, we will look at control flow in Python. Please follow me on Twitter to get notified whenever I release a new article. Thanks for reading!

(0) (0)

Introduction To Python Programming - A Word-counter Program
06 Sep, 2023
Introduction To Python Programming - Control Flow
06 Sep, 2023
Introduction To Python Programming - Data Types And Operators
06 Sep, 2023
Introduction To Python Programming - Getting Started
02 Sep, 2023

Introduction to Python Programming - Data Structures

Table of Contents

Overview

Sequences

Operations on Sequences

List

Creation

Operations

Indexing (Access) and Slicing

Modifying (Mutation)

Deletion

Copying

Addition and Multiplication

Some List Methods

Tuples

Creation

Operations

Tuple unpacking

Tuple Methods

Sets

Creation

Set Operations

Union: all elements in both sets

Intersection: Only elements that appear in both sets

Difference: elements present in the first set but not in the second set

Set Methods

Update: fill a set with the elements of another set

Add: Add element(s) to a set

Remove an element from a set

Dictionaries

Creation

Operations

Access and Modification

Deletion

Addition and Multiplication

Dictionary Methods

Conclusion

Related Posts

Our Blog

Introduction to Python Programming - Data Structures

Table of Contents

Overview

Sequences

Operations on Sequences

List

Creation

Operations

Indexing (Access) and Slicing

Modifying (Mutation)

Deletion

Copying

Addition and Multiplication

Some List Methods

Tuples

Creation

Operations

Tuple unpacking

Tuple Methods

Sets

Creation

Set Operations

Union: all elements in both sets

Intersection: Only elements that appear in both sets

Difference: elements present in the first set but not in the second set

Set Methods

Update: fill a set with the elements of another set

Add: Add element(s) to a set

Remove an element from a set

Dictionaries

Creation

Operations

Access and Modification

Deletion

Addition and Multiplication

Dictionary Methods

Conclusion

Related Posts