Code Best Practices Checker | Python

Python Fibonacci Refactoring

Refactored Python code incorporates memoization for faster Fibonacci calculation, improves readability with comments, and optimizes base case handling.


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Prompt

# This code snippet is designed to challenge the explanatory capabilities of Code Best Practices Checker AI tool

def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)

fibonacci(10)

Answer

Language/Technology Identification

The provided code snippet is written in Python.

Code Refactoring

  1. Use of Memoization: Implementing memoization technique to store results of subproblems avoids redundant calculations and significantly improves performance for recursive Fibonacci calculation.
  2. Enhancing Readability: Adding clear comments and ensuring adherence to PEP 8 conventions for better readability.
  3. Optimizing Base Case Handling: Improving the base case handling by incorporating explicit conditions for n <= 1.

Documentation of Changes

  1. Memoization Integration: Added a memoization dictionary to store computed Fibonacci values, reducing redundant calculations.
  2. Readability Enhancement: Included comments for function definition and base case handling for improved code understanding.
  3. Base Case Optimization: Enhanced the base case handling to explicitly check for n <= 1 for better clarity.
  4. Performance Improvement: Memoization implementation drastically reduces the number of recursive calls, enhancing performance significantly.

Original Code

# This code snippet is designed to challenge the explanatory capabilities of Code Best Practices Checker AI tool

def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)

fibonacci(10)

Refactored Code

# Fibonacci function with memoization for optimized performance

memo = {0: 0, 1: 1}

def fibonacci(n):
    if n in memo:
        return memo[n]
    else:
        memo[n] = fibonacci(n-1) + fibonacci(n-2)
        return memo[n]

# Calculate Fibonacci number for n=10
fibonacci(10)

The refactored code enhances performance through memoization, improves readability with comments, and optimizes the base case handling for better code structure.

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Description

Refactored Python code incorporates memoization for faster Fibonacci calculation, improves readability with comments, and optimizes base case handling.

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