File Processing

File Processing Skill

This skill provides utilities and guidance for building robust file processing applications.

Purpose

Use this skill when your task involves:

• Reading and parsing CSV, JSON, or text files
• Data validation and cleaning
• File format conversions
• Batch processing of multiple files
• Generating reports from data files

Key Capabilities

1. Data Reading

• CSV parsing with header detection
• JSON file handling (single object or array)
• Text file processing line-by-line
• Error handling for malformed files

2. Data Validation

• Check for required fields
• Validate data types
• Handle missing values
• Report data quality issues

3. Data Transformation

• Filter rows based on conditions
• Calculate statistics (sum, avg, count)
• Format conversions
• Data aggregation

4. Output Generation

• Write processed data to new files
• Generate summary reports
• Create multiple output formats

Best Practices

Project Structure for File Processing

project/
├── main.py              # Entry point with CLI
├── file_reader.py       # File I/O operations
├── data_processor.py    # Core processing logic
├── validator.py         # Data validation
├── config.py            # Configuration constants
└── utils.py             # Helper functions

Error Handling Pattern

def read_file_safely(filepath):
    """Read file with proper error handling"""
    try:
        if not os.path.exists(filepath):
            raise FileNotFoundError(f"File not found: {filepath}")

        with open(filepath, 'r', encoding='utf-8') as f:
            return f.read()
    except Exception as e:
        print(f"Error reading file: {e}")
        return None

CSV Processing Template

import csv

def process_csv(input_file, output_file):
    """Process CSV with header detection"""
    with open(input_file, 'r', encoding='utf-8') as f:
        reader = csv.DictReader(f)

        processed = []
        for row in reader:
            # Transform each row
            processed_row = transform_row(row)
            processed.append(processed_row)

    # Write results
    with open(output_file, 'w', encoding='utf-8') as f:
        if processed:
            writer = csv.DictWriter(f, fieldnames=processed[0].keys())
            writer.writeheader()
            writer.writerows(processed)

JSON Processing Template

import json

def process_json(input_file, output_file):
    """Process JSON data"""
    with open(input_file, 'r', encoding='utf-8') as f:
        data = json.load(f)

    # Process data (handle both list and dict)
    processed = process_data(data)

    with open(output_file, 'w', encoding='utf-8') as f:
        json.dump(processed, f, indent=2, ensure_ascii=False)

Common Patterns

1. CLI with Argument Parsing

import argparse

def main():
    parser = argparse.ArgumentParser(description='File Processor')
    parser.add_argument('input', help='Input file path')
    parser.add_argument('output', help='Output file path')
    parser.add_argument('--format', choices=['csv', 'json'], default='csv')

    args = parser.parse_args()
    process_file(args.input, args.output, args.format)

2. Batch Processing

import glob

def process_directory(input_dir, output_dir, pattern='*.csv'):
    """Process all matching files in directory"""
    files = glob.glob(os.path.join(input_dir, pattern))

    for filepath in files:
        filename = os.path.basename(filepath)
        output_path = os.path.join(output_dir, f"processed_{filename}")
        process_file(filepath, output_path)

3. Progress Reporting

def process_with_progress(items):
    """Process items with progress feedback"""
    total = len(items)
    for i, item in enumerate(items, 1):
        process_item(item)
        print(f"Progress: {i}/{total} ({i*100//total}%)", end='\r')
    print()  # New line when complete

Tools Available

When implementing file processing tasks, you have access to:

• Read - Read file contents
• Write - Create new files
• Edit - Modify existing files
• Glob - Find files by pattern
• Bash - Run shell commands (e.g., wc -l, head)

Testing Tips

Always test your file processor with:

1. Empty files - Should handle gracefully
2. Malformed data - CSV with wrong column count, invalid JSON
3. Missing files - Should provide clear error messages
4. Large files - Consider memory usage
5. Special characters - Unicode, newlines in CSV fields

Example Task Breakdown

Task: "Create a CSV analyzer that calculates statistics"

Suggested Steps:

1. Read CSV file and detect headers
2. Parse data into structured format
3. Calculate statistics (count, sum, average) per column
4. Generate summary report
5. Write results to output file

Recommended Structure:

• csv_analyzer.py - Main program
• stats.py - Statistics calculations
• report_generator.py - Format output

References

For more complex tasks, consider:

• Python's csv module for CSV handling
• json module for JSON operations
• pathlib for cross-platform file paths
• pandas for advanced data processing (if allowed)

Source: baidu-baige/LoongFlow — distributed by TomeVault.