Reduce Dependency on Global Variables

Global variables, while accessible from anywhere in the code, can become a double-edged sword when used excessively.

Relying heavily on global variables can complicate code readability, hinder debugging efforts, and make code maintenance a daunting task.

In this article, we’ll explore the drawbacks of overusing global variables and highlight the advantages of using function parameters and return values to pass information between different parts of the code.

Let’s dive in!

Complexity and Readability Concerns

When global variables are scattered throughout your code, it becomes difficult to track their origins and understand how they are being used. This can lead to confusion and hinder collaboration within a team.

name = "John"

def greet():
    print("Hello, " + name + "!")

def update_name(new_name):
    global name
    name = new_name

greet()  # Output: Hello, John!

update_name("Alice")
greet()  # Output: Hello, Alice!

In this example, the name variable is accessed and modified within multiple functions. While it might seem convenient, it creates hidden dependencies and makes it challenging to reason about the behavior of each function.

Debugging Challenges:

When debugging issues within your code, global variables can introduce uncertainty and make it harder to isolate problems. Since any function can modify global variables, pinpointing the source of a bug becomes more complex.

count = 0

def increment():
    global count
    count += 1

def decrement():
    global count
    count -= 1

def reset():
    global count
    count = 0

increment()
increment()
decrement()
print(count)  # Output: 1

reset()
print(count)  # Output: 0

In this scenario, tracking how the count variable is changed becomes cumbersome, especially when dealing with larger codebases.

Maintainability and Scalability

As codebases grow, maintaining and scaling them becomes increasingly challenging when global variables are heavily used. It becomes harder to reason about the interactions between different parts of the code, and making changes or adding new features becomes more error-prone.

# Global variables
customer_data = []
order_data = []

def add_customer(name):
    global customer_data
    customer_data.append(name)

def add_order(order):
    global order_data
    order_data.append(order)

def process_order(customer_name, order):
    add_customer(customer_name)
    add_order(order)

process_order("Alice", "Product A")
print(customer_data)  # Output: ['Alice']
print(order_data)  # Output: ['Product A']

In this example, both add_customer and add_order functions modify global variables. As the codebase expands, it becomes harder to keep track of which functions interact with these variables, potentially leading to unintended side effects.

Adopting Function Parameters and Return Values

To mitigate the issues associated with overusing global variables, it’s generally better to embrace function parameters and return values. By passing information explicitly between functions, you create more modular and maintainable code.

def greet(name):
    print("Hello, " + name + "!")

def update_name(current_name):
    return "Alice"

name = update_name(name)
greet(name)  # Output: Hello, Alice!

In this revised example, the name variable is passed as a parameter to the greet function, removing the reliance on a global variable. The update_name function returns a new name, and the value is assigned back to name. This approach ensures clear data flow and reduces the complexity of the code.