Building Your First Code Snippet

Ready to contribute to the community? This tutorial walks you through creating a professional automation script from scratch.

Prerequisites

• Completed the Getting Started tutorial
• Python 3.8+ with a virtual environment
• Understanding of basic Python (functions, file I/O)

Step 1: Identify a Task to Automate

Look for repetitive tasks in your daily workflow:

• Renaming files in bulk
• Converting between data formats
• Sending templated emails
• Processing spreadsheets
• Scraping data from websites

For this tutorial, we'll build a JSON to CSV converter.

Step 2: Write Clean Code

import json
import csv
from pathlib import Path
from typing import Optional


def json_to_csv(
    input_file: str,
    output_file: Optional[str] = None,
    delimiter: str = ",",
) -> dict:
    """
    Convert a JSON array file to CSV format.

    Args:
        input_file: Path to the JSON file (must contain a list of objects).
        output_file: Output CSV path. Defaults to same name with .csv extension.
        delimiter: CSV delimiter character.

    Returns:
        Dict with conversion stats.

    Raises:
        ValueError: If JSON doesn't contain a list of objects.
    """
    input_path = Path(input_file)
    if not input_path.exists():
        raise FileNotFoundError(f"File not found: {input_file}")

    with open(input_path, encoding="utf-8") as f:
        data = json.load(f)

    if not isinstance(data, list) or not data:
        raise ValueError("JSON must contain a non-empty array of objects")

    if output_file is None:
        output_file = str(input_path.with_suffix(".csv"))

    headers = list(data[0].keys())

    with open(output_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=headers, delimiter=delimiter)
        writer.writeheader()
        writer.writerows(data)

    return {
        "input": input_file,
        "output": output_file,
        "rows": len(data),
        "columns": len(headers),
    }

Step 3: Add Error Handling

Always handle edge cases:

• File not found
• Invalid JSON format
• Empty data arrays
• Encoding issues
• Permission errors

Step 4: Test Thoroughly

if __name__ == "__main__":
    # Test with sample data
    result = json_to_csv("sample_data.json")
    print(f"Converted: {result['rows']} rows, {result['columns']} columns")
    print(f"Output: {result['output']}")

Step 5: Publish to the Library

1. Navigate to your Dashboard
2. Click New Snippet
3. Fill in: title, description, language, category
4. Paste your code
5. Submit for community review

What You Learned

• How to structure an automation script with proper typing and docstrings
• How to handle errors gracefully
• How to test your script before publishing
• How to contribute to the vAIbecode library