pythonadvanced15 minutes

Refactor and Optimize Complex Data Processing Function

Given a large-function script that processes a nested data structure with redundant loops and poor readability, refactor the code to improve maintainability, reduce complexity, and optimize performance without changing its output.

Challenge prompt

You are provided a Python function `process_data(data)` that takes a list of dictionaries with nested data. The function performs multiple nested loops, redundant computations, and has low readability due to variable naming and structure. Your task is to refactor this function to: - Improve readability and maintainability by using meaningful variable names and modularizing repeated logic into helper functions. - Optimize performance by minimizing redundant iterations and using efficient data access techniques. - Keep the behavior of the function exactly the same, ensuring output correctness. Submit the refactored version of the function only.

Guidance

• Break down the monolithic function into smaller helper functions with single responsibilities.
• Use list comprehensions or generator expressions where appropriate to simplify loops.
• Avoid redundant loops by combining data processing steps when possible.
• Rename variables to clearly indicate their purpose and data content.

Hints

• Consider using dictionary lookups to replace repeated searches through the list.
• Extract reusable blocks of code into separate functions for clarity.
• Use pythonic constructs like enumerate and zip to simplify nested loops.

Starter code

def process_data(data):
    result = []
    for i in range(len(data)):
        temp = []
        for j in range(len(data[i]['items'])):
            item = data[i]['items'][j]
            if item['value'] > 10:
                acc = 0
                for k in range(len(item['subitems'])):
                    subitem = item['subitems'][k]
                    acc += subitem['metric']
                temp.append({'name': item['name'], 'total_metric': acc})
        result.append({'id': data[i]['id'], 'processed': temp})
    return result

Expected output

[{'id': 1, 'processed': [{'name': 'itemA', 'total_metric': 25}]}, {'id': 2, 'processed': []}]

Core concepts

code refactoringperformance optimizationcode readabilitynested data processing

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