Deep Nesting

“Code is like humor. When you have to explain it, it’s bad.”

Cory House

Deep nesting occurs when there are too many levels of indentation in the code, making it harder to understand, maintain, and debug. It can lead to reduced readability, and increases cognitive load for developers.

Symptoms

• Excessive indentation makes it hard to track logic.
• Many nested if statements or for loops.
• Hard-to-follow branching logic.
• Slow performance due to inefficient code.
• Increased likelihood of bugs due to complexity.

Example - Deeply nested conditional statements

def validate_model_convergence(model):
    if model.convergence > 1:
        if model.convergence < 0.1:
            if model.secondary_condition == True
                return True
            else:
                return False
        else:
            return False
    else:
        return False

Solutions

Refactoring deep nesting improves readability and maintainability. Techniques to reduce deep nesting include:

• Using early returns to eliminate unnecessary indentation.
• Extracting complex logic into helper functions for better modularity.
• Using built-in functions like any and all to simplify conditions.

Solution 1: Using early returns

def validate_model_convergence(model):
    if model.convergence <= 1:
        return False
    if model.convergence >= 0.1:
        return False
    if not model.secondary_condition:
        return False
    return True
        

Thia solution uses early returns to reduce the nesting level and make the code more readable. Each condition is checked separately, and if it fails, the function returns immediately, avoiding further nesting and evaluation of unnecessary conditions.

Solution 2: Using all for conciseness

Alternatively, we can use the all function to check multiple conditions in a single line, which can make the code more concise and easier to read.

def validate_model_convergence(model):
    return all([
        model.convergence > 1,
        model.convergence < 0.1,
        model.secondary_condition,
    ])

Example - Deeply nested loops

In this example, we have three nested loops to iterate over three 3D arrays and sum their corresponding elements. This code can be refactored using NumPy to improve performance and readability.

# Create three random 10x10x10 arrays
A = np.random.rand(10, 10, 10)
B = np.random.rand(10, 10, 10)
C = np.random.rand(10, 10, 10)

# Using nested loops (inefficient)
result = np.zeros((10, 10, 10))
for i in range(10):
    for j in range(10):
        for k in range(10):
            result[i, j, k] = A[i, j, k] + B[i, j, k] + C[i, j, k]

Solution

Using NumPy, we can perform the same operation without nested loops, which is more efficient and easier to read.

# Create three random 10x10x10 arrays
A = np.random.rand(10, 10, 10)
B = np.random.rand(10, 10, 10)
C = np.random.rand(10, 10, 10)

# Vectorized solution (fast & efficient)
result = A + B + C

Key takeaways

• Deep nesting makes code harder to read and maintain.
• Techniques like early returns, helper functions, and built-in functions can simplify complex logic.

Learn more

• RealPython - “Look Ma, No For-Loops”