Easy
Python
Guide
Variables
Variables in Python are containers that store data values, much like labelled boxes in a warehouse. They allow programmers to give meaningful names to pieces of information, making code easier to read and maintain. Unlike some programming languages, Python doesn't require you to declare the type of variable beforehand—you simply assign a value using the equals sign. For example, employee_name = "Sarah" creates a string variable, whilst salary = 35000 creates a numeric variable. This flexibility makes Python particularly accessible for beginners and efficient for experienced developers.
In the financial sector, variables are essential for managing monetary calculations and client data. A financial analyst might use variables like monthly_income = 4500, tax_rate = 0.20, and net_pay = monthly_income * (1 - tax_rate) to calculate take-home pay. Investment firms use variables to track portfolio performance, where share_price = 150.50 and shares_owned = 1000 help calculate total investment value. Variables also store client information such as account_number = "GB29NWBK60161331926819" for UK bank accounts or risk_tolerance = "moderate" for investment profiles.
Healthcare professionals increasingly rely on variables for patient management systems and medical research. A GP surgery might use variables like patient_id = "NHS123456789", blood_pressure = 120/80, and appointment_date = "2025-07-15" to track patient records. In pharmaceutical research, variables store clinical trial data: participant_age = 45, dosage_mg = 50, and side_effects = ["drowsiness", "nausea"]. Hospital systems use variables to manage bed availability (beds_available = 12) and staff rotas (shift_pattern = "07:00-19:00"), ensuring efficient resource allocation across the NHS.
The retail and e-commerce industry depends heavily on variables for inventory management and customer data. A supermarket chain like Tesco might use variables such as product_code = "5000169086124", stock_level = 250, and reorder_point = 50 to manage their supply chain automatically. Online retailers use variables to personalise customer experiences: customer_postcode = "OX1 2JD", delivery_preference = "next_day", and basket_total = 89.99. Marketing departments utilise variables to track campaign performance, storing data like click_through_rate = 0.045 and conversion_rate = 0.032 to measure ROI.
In the education sector, variables help manage student information and academic performance tracking. A university might use variables like student_id = "UNI2025001", course_code = "CS101", and grade_percentage = 78.5 to maintain academic records. Teachers use variables in educational software to track progress: lessons_completed = 15, quiz_score = 8.5, and attendance_rate = 0.95. School administrators rely on variables for timetabling systems, where classroom = "Room A101", period = 3, and subject = "Mathematics" help organise daily schedules efficiently across different year groups.
Syntax
Some examples of the syntax for variables and what can be done with them:
# String Variables
name = "Alice"
surname = "Johnson"
city = "Manchester"
# String concatenation: joins strings together
full_name = name + " " + surname # "Alice Johnson"
print("Full name:", full_name)
# String repetition: repeats a string multiple times
repeated = name * 3 # "AliceAliceAlice"
print("Repeated name:", repeated)
# String length: returns the number of characters
name_length = len(name) # 5
print("Length of name:", name_length)
# String methods: various string operations
uppercase = name.upper() # "ALICE"
lowercase = surname.lower() # "johnson"
print("Uppercase:", uppercase)
print("Lowercase:", lowercase)
# String formatting: inserting variables into strings
greeting = f"Hello, my name is {name} and I live in {city}"
print("Formatted string:", greeting)
# Integer Variables
age = 25
year = 2025
score = 100
# Basic arithmetic operations
next_year_age = age + 1 # 26
birth_year = year - age # 2000
double_score = score * 2 # 200
print("Next year age:", next_year_age)
print("Birth year:", birth_year)
print("Double score:", double_score)
# Boolean Variables
is_student = True
has_job = False
is_adult = age >= 18
# Boolean operations
can_vote = is_adult and age >= 18 # True
needs_support = is_student and not has_job # True
print("Can vote:", can_vote)
print("Needs support:", needs_support)
# List Variables
teams = ["Everton", "Arsenal", "Fulham"]
numbers = [1, 2, 3, 4, 5]
mixed_list = [name, age, is_student]
# List operations
teams.append("Chelsea") # Add item to end
first_team = teams[0] # Access first item: "Everton"
list_length = len(numbers) # 5
print("Teams after adding Chelsea:", teams)
print("First team:", first_team)
print("Number of items in numbers:", list_length)
# Dictionary Variables
person = {"name": "Bob", "age": 30, "city": "London"}
grades = {"maths": 85, "english": 92, "science": 78}
# Dictionary operations
person["job"] = "Engineer" # Add new key-value pair
student_age = person["age"] # Access value: 30
all_keys = list(person.keys()) # Get all keys
print("Person dictionary:", person)
print("Student age:", student_age)
print("All keys:", all_keys)
# Variable reassignment and type changes
flexible_var = 42 # Initially an integer
print("As integer:", flexible_var, type(flexible_var))
flexible_var = "Now I'm a string" # Changed to string
print("As string:", flexible_var, type(flexible_var))
flexible_var = [1, 2, 3] # Changed to list
print("As list:", flexible_var, type(flexible_var))
# Multiple assignment
x, y, z = 10, 20, 30
print("Multiple assignment - x:", x, "y:", y, "z:", z)
# Swapping variables
a, b = "first", "second"
print("Before swap - a:", a, "b:", b)
a, b = b, a # Swap values
print("After swap - a:", a, "b:", b)
# Constants (by convention, use uppercase)
PI = 3.14159
MAX_ATTEMPTS = 3
COMPANY_NAME = "Tech Solutions Ltd"
# Using constants in calculations
radius = 5
circle_area = PI * radius ** 2 # 78.53975
print("Circle area:", circle_area)
print("Company:", COMPANY_NAME)