transmog 0.1.0

An ETL/ELT transformation library for converting nested JSON structures into flat, tabular formats

Transmogrify

A Python library for transforming complex nested JSON data into flat, structured formats.

Features

• Flatten deeply nested JSON/dict structures with customizable delimiter options
• Transform values during processing with custom functions
• Native Formats: output to PyArrow Tables, Python dictionaries, or JSON objects
• Bytes Output: serialize directly to Parquet, CSV, or JSON bytes
• File Export: write to various file formats (JSON, CSV, Parquet)
• Recover from errors in malformed data with customizable strategies
• Optimize for performance with optional dependencies
• Stream large datasets efficiently
• Deterministic ID generation for data consistency across processing runs

Installation

pip install transmogrify

For minimal installation without optional dependencies:

pip install transmogrify[minimal]

For development installation:

pip install transmogrify[dev]

See the installation guide for more details.

Quick Example

import transmogrify as tm

# Sample nested data
data = {
    "user": {
        "id": 1,
        "name": "John Doe",
        "contact": {
            "email": "[e-mail]"
        },
        "orders": [
            {"id": 101, "amount": 99.99},
            {"id": 102, "amount": 45.50}
        ]
    }
}

# Process the data
processor = tm.Processor()
result = processor.process(data)

# Native data structure output
tables = result.to_dict()                # Get all tables as Python dictionaries
pa_tables = result.to_pyarrow_tables()   # Get as PyArrow Tables

# Access the data in memory
main_table = tables["main"]              # Main table as Python dict
orders = tables["user_orders"]           # Child table as Python dict

# Bytes output for direct writing
json_bytes = result.to_json_bytes(indent=2)  # Get all tables as JSON bytes
csv_bytes = result.to_csv_bytes()        # Get all tables as CSV bytes
parquet_bytes = result.to_parquet_bytes()    # Get all tables as Parquet bytes

# Direct write to files
with open("main_table.json", "wb") as f:
    f.write(json_bytes["main"])

# Or use PyArrow tables directly
pa_table = pa_tables["main"]       # Work with PyArrow Table directly
print(f"Table has {pa_table.num_rows} rows and {pa_table.num_columns} columns")

# File output (still supported)
result.write_all_json("output_dir/json")
result.write_all_csv("output_dir/csv")
result.write_all_parquet("output_dir/parquet")

Deterministic ID Generation

Transmogrify can now ensure consistent IDs for records across multiple processing runs:

# Configure deterministic IDs based on specific fields
processor = tm.Processor(
    deterministic_id_fields={
        "": "id",                     # Root level uses "id" field
        "user_orders": "id"           # Order records use "id" field
    }
)

# Process the data - IDs will be consistent across runs
result = processor.process(data)

# For complex ID generation logic, use a custom function
def custom_id_generator(record):
    # Generate custom ID based on record contents
    if "id" in record:
        return f"CUSTOM-{record['id']}"
    return str(uuid.uuid4())  # Fallback

processor = tm.Processor(id_generation_strategy=custom_id_generator)

See the deterministic IDs guide for more information.

Output Format Options

Transmogrify provides three main categories of output formats:

1. Native Data Structures - Python objects like dictionaries and PyArrow Tables

   result.to_dict()              # Python dictionaries
   result.to_json_objects()      # JSON-serializable Python objects
   result.to_pyarrow_tables()    # PyArrow Tables
   

2. Bytes Serialization - Raw bytes in JSON, CSV, or Parquet format

   result.to_json_bytes()        # JSON bytes
   result.to_csv_bytes()         # CSV bytes
   result.to_parquet_bytes()     # Parquet bytes
   

3. File Output - Direct writing to files in different formats

   result.write_all_json()       # Write to JSON files
   result.write_all_csv()        # Write to CSV files
   result.write_all_parquet()    # Write to Parquet files
   

Documentation

• Installation Guide
• Getting Started
• Output Formats
• In-Memory Processing
• Deterministic IDs
• API Reference
• Examples

Use Cases

• Data ETL pipelines
• API response processing
• JSON/CSV conversion
• Preparing nested data for tabular analysis
• Data normalization and standardization
• Integration with data processing frameworks
• In-memory data transformation
• Cloud-based serverless processing
• Incremental data processing with consistent IDs

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please make sure to update tests as appropriate.

License

Distributed under the MIT License. See LICENSE for more information.