Lists and dictionaries in Python

Overview

Teaching: 15 min
Exercises: 5 min

Questions

• What other data structures are there?

Objectives

• Understand the use of lists and dictionaries in Python.

Lists

• Lists are ordered collections of items, which can be of any data type.
• They are mutable, meaning their elements can be changed after creation.
• Lists are created using square brackets [].

Example of list creation:

my_list = [1, 2, 3, 'apple', 'banana', 'cherry']

Indexing and accessing elements:

• Elements in a list are accessed using square brackets [] and indices (e.g. my_list[4]).
• Indexing starts from 0 for the first element.

last_element = my_list[-1]   # Accessing the last element

Manipulating lists:

• Lists support various operations like adding, removing, and updating elements.

Examples of manipulation:

my_list.append('orange')     # Adds 'orange' to the end of the list
my_list.insert(2, 'pear')    # Inserts 'pear' at index 2
my_list.remove('banana')     # Removes the first occurrence of 'banana'
my_list.pop()                # Removes and returns the last element
my_list[0] = 'grape'         # Updates the first element to 'grape'

Dictionaries

• Dictionaries are unordered collections of key-value pairs.
• They are mutable and can contain keys of any immutable data type.
• Dictionaries are created using curly braces {}.

Example of dictionary creation:

my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}

Accessing elements:

• Elements in a dictionary are accessed using square brackets [] and keys.

Example of accessing elements:

person_name = my_dict['name']   # Accessing value corresponding to 'name' key

Manipulating dictionaries:

• Dictionaries support various operations like adding, removing, and updating key-value pairs.

Examples of manipulation:

my_dict['gender'] = 'Male'     # Adds 'gender': 'Male' to the dictionary
del my_dict['age']             # Removes the key 'age' and its value
my_dict['city'] = 'Los Angeles' # Updates the value of 'city' key to 'Los Angeles'

Key Points

• Dictionaries: Efficient for fast key-based retrieval and storing data with unique identifiers.

• Lists: Versatile collections for storing ordered heterogeneous data types with various operations like indexing and appending.