You are an expert Python developer tasked with refactoring code. Your goal is to improve readability, maintainability, and performance. Follow these steps meticulously:
**1. Understand the Goal:** The `process_data` function reads a CSV-like file, parses lines into dictionaries, filters data based on a numeric value, sorts it, and calculates a sum.
**2. Identify Areas for Improvement (Think step-by-step):**
* **Readability:** Can parsing logic be clearer? Can intermediate variables be more descriptive? Is error handling explicit?
* **Performance:** Are there redundant iterations? Can sorting or filtering be optimized? Consider list comprehensions vs. generator expressions where appropriate. Avoid unnecessary data structure creation.
* **Maintainability:** Break down complex operations into smaller, single-responsibility helper functions. Use constants for magic numbers. Add type hints.
* **Standard Practices:** Incorporate Pythonic idioms.
**3. Propose Specific Changes (Refactoring Plan):**
* Introduce `csv.reader` for robust parsing.
* Define a separate `_parse_line` helper function for clearer error handling and parsing of individual lines. Return `None` for invalid lines.
* Use more descriptive variable names.
* Combine filtering and sorting where feasible, or clarify their order.
* Consider generator expressions for summing to reduce memory footprint for very large datasets if `filtered_data` itself isn't needed as a full list. (In this specific case, `filtered_data` is used as a list, so a separate sum is fine, but note the thought process).
* Add type hints to function signatures and variables.
* Convert `print` statements in error handling to `logging` for better production readiness.
**4. Refactor the Code:** Apply the proposed changes to the provided Python codeblock.
**Original Code:**
```python
import os
def process_data(file_path):
data = []
with open(file_path, 'r') as f:
for line in f:
parts = line.strip().split(',')
if len(parts) == 2:
try:
name = parts[0]
value = int(parts[1])
data.append({'name': name, 'value': value})
except ValueError:
print(f"Skipping invalid line: {line.strip()}")
# Sort data by value and then filter
sorted_data = sorted(data, key=lambda x: x['value'])
filtered_data = [item for item in sorted_data if item['value'] > 10]
# Calculate sum of values
total_sum = sum(item['value'] for item in filtered_data)
return filtered_data, total_sum
# Example usage
# file = 'data.txt'
# if not os.path.exists(file):
# with open(file, 'w') as f:
# f.write('apple,5\nbanana,12\norange,7\n IndexError,abc\ngrape,20\n')
#
# result, s = process_data(file)
# print(result, s)
```
**Refactored Code:** (Provide only the refactored code block, no additional comments or explanation outside the code itself.)
Structured, task-focused, reduced hallucinations