Easy
Python
Guide
Lists
Lists are one of the most fundamental and versatile data structures in Python, serving as ordered collections that can store multiple items in a single variable. Think of a list as a container that holds various pieces of information in a specific sequence, much like a shopping list or a roster of employees. In Python, lists are created using square brackets and can contain different types of data - numbers, text, or even other lists. For example, temperatures = [18.5, 22.1, 19.8, 21.3] stores temperature readings. Lists are mutable, meaning you can modify them after creation by adding, removing, or changing elements.
Working with lists involves several common operations that make them incredibly useful for everyday programming tasks. You can access individual items using their position (called an index), starting from 0 for the first item. For instance, my_fruits[0] would return 'apple'. Adding items is straightforward with methods like append() to add to the end, or insert() to add at a specific position. You can remove items using remove() to delete by value or pop() to remove by position. Other useful operations include len() to find how many items are in the list, and you can easily loop through all items using a for loop: for fruit in my_fruits: print(fruit).
In customer service roles, lists prove invaluable for managing client information and tracking interactions. A customer service representative might maintain a list of pending enquiries like pending_tickets = ['Billing query - Smith', 'Product return - Jones', 'Technical support - Brown']. They could add new tickets as they arrive, remove resolved ones, and prioritise urgent cases by reordering the list. Similarly, call centre agents often work with lists of phone numbers to contact, systematically working through them and removing completed calls whilst adding new leads.
Marketing professionals frequently use lists to organise campaigns and track customer segments. A digital marketing specialist might create lists of email addresses for different customer groups: premium_customers = ['alice@email.com', 'bob@email.com'] and new_subscribers = ['charlie@email.com', 'diana@email.com']. They can merge lists for broader campaigns, filter them based on criteria, or remove unsubscribed addresses. Social media managers also use lists to track hashtags, schedule post topics, or monitor competitor accounts, making it easy to iterate through content ideas and track what's been published.
Data analysis roles across various industries rely heavily on lists for processing and organising information. A sales analyst might store monthly revenue figures in a list like monthly_sales = [45000, 52000, 48000, 61000] to calculate trends, find the highest performing month, or compute averages. HR professionals use lists to manage employee data, track training completion, or organise interview candidates. Even in healthcare, administrative staff might use lists to manage patient appointments, track medication schedules, or organise test results, demonstrating how this simple but powerful data structure underpins countless professional tasks across different sectors.
Syntax
Some examples of uses of lists:
# Define initial lists for examples
teams = ['Everton', 'Arsenal', 'Fulham']
numbers = [10, 25, 3, 45, 12]
mixed_list = ['hello', 42, True, 3.14]
empty_list = []
# 1. Creating lists: different ways to create and initialise lists
colours = ['red', 'green', 'blue']
temperatures = [18.5, 22.1, 19.8, 21.3]
shopping_list = ['milk', 'bread', 'eggs', 'cheese']
print("Creating lists - colours:", colours)
print("Creating lists - temperatures:", temperatures)
# 2. Accessing elements: using indices to get specific items
first_team = teams[0] # 'Everton' (first item, index 0)
last_team = teams[-1] # 'Fulham' (last item, negative indexing)
second_number = numbers[1] # 25 (second item, index 1)
print("Accessing elements - first team:", first_team)
print("Accessing elements - last team:", last_team)
print("Accessing elements - second number:", second_number)
# 3. Slicing: extracting portions of lists
first_two_teams = teams[0:2] # ['Everton', 'Arsenal'] (items 0 and 1)
last_three_numbers = numbers[-3:] # [3, 45, 12] (last three items)
middle_numbers = numbers[1:4] # [25, 3, 45] (items 1, 2, and 3)
print("Slicing - first two teams:", first_two_teams)
print("Slicing - last three numbers:", last_three_numbers)
# 4. Adding elements: different methods to add items to lists
teams.append('Liverpool') # Add to end
teams.insert(1, 'Chelsea') # Insert at specific position
colours.extend(['yellow', 'purple']) # Add multiple items
print("Adding elements - teams after append/insert:", teams)
print("Adding elements - colours after extend:", colours)
# 5. Removing elements: various ways to remove items from lists
teams.remove('Arsenal') # Remove first occurrence of 'Arsenal'
removed_number = numbers.pop() # Remove and return last item
removed_at_index = numbers.pop(0) # Remove and return item at index 0
print("Removing elements - teams after remove:", teams)
print("Removing elements - removed number:", removed_number)
print("Removing elements - numbers after pop operations:", numbers)
# 6. List methods: useful built-in methods for list manipulation
numbers_copy = [10, 25, 3, 45, 12, 3] # Reset for examples
count_of_3 = numbers_copy.count(3) # Count occurrences of 3
index_of_25 = numbers_copy.index(25) # Find index of first occurrence
numbers_copy.sort() # Sort in ascending order
colours.reverse() # Reverse the order
print("List methods - count of 3:", count_of_3)
print("List methods - index of 25:", index_of_25)
print("List methods - sorted numbers:", numbers_copy)
print("List methods - reversed colours:", colours)
# 7. List length and membership: checking size and contents
team_count = len(teams) # Number of items in list
has_everton = 'Everton' in teams # Check if 'Everton' exists
has_city = 'City' not in teams # Check if 'City' doesn't exist
print("Length and membership - team count:", team_count)
print("Length and membership - has Everton:", has_everton)
print("Length and membership - has City:", has_city)
# 8. Iterating through lists: different ways to loop through items
print("Iterating - simple loop through fruits:")
for fruit in fruits:
print(f" - {fruit}")
print("Iterating - loop with index and item:")
for index, colour in enumerate(colours):
print(f" {index}: {colour}")
# 9. List comprehensions: creating new lists based on existing ones
squared_numbers = [x**2 for x in [1, 2, 3, 4, 5]] # Square each number
long_fruits = [fruit for fruit in fruits if len(fruit) > 4] # Fruits with >4 letters
upper_colours = [colour.upper() for colour in colours] # Convert to uppercase
print("List comprehensions - squared numbers:", squared_numbers)
print("List comprehensions - long fruits:", long_fruits)
print("List comprehensions - upper colours:", upper_colours)
# 10. Nested lists and practical applications: lists containing other lists
customer_orders = [
['John Smith', 'Pizza', 15.99],
['Jane Doe', 'Burger', 12.50],
['Bob Wilson', 'Salad', 8.75]
]
weekly_temperatures = [
[18, 22, 20, 19], # Week 1
[21, 24, 23, 25], # Week 2
[16, 18, 17, 20] # Week 3
]
# Accessing nested list data
first_customer_name = customer_orders[0][0] # 'John Smith'
week_2_temp_3 = weekly_temperatures[1][2] # 23 (Week 2, Day 3)
print("Nested lists - first customer:", first_customer_name)
print("Nested lists - week 2 day 3 temperature:", week_2_temp_3)
# 11. Real-world examples: practical workplace scenarios
# Customer service ticket system
support_tickets = ['Password reset - Alice', 'Billing query - Bob', 'Bug report - Carol']
support_tickets.append('Feature request - Dave')
completed_ticket = support_tickets.pop(0) # Process first ticket
print("Real-world - support tickets:", support_tickets)
print("Real-world - completed ticket:", completed_ticket)
# Marketing email campaign
email_addresses = ['customer1@email.com', 'customer2@email.com', 'customer3@email.com']
new_subscribers = ['customer4@email.com', 'customer5@email.com']
email_addresses.extend(new_subscribers) # Add new subscribers
total_subscribers = len(email_addresses)
print("Real-world - total email subscribers:", total_subscribers)
# Sales data analysis
monthly_sales = [45000, 52000, 48000, 61000, 55000, 49000]
highest_sales = max(monthly_sales) # Find highest month
average_sales = sum(monthly_sales) / len(monthly_sales) # Calculate average
print("Real-world - highest monthly sales:", highest_sales)
print("Real-world - average monthly sales:", round(average_sales, 2))