datamapperx 0.1.0

A reusable Python library that maps dictionary data to Python objects.

datamapperx

A complete, production-ready Python package for mapping dictionary data to Python objects (classes), inspired by AutoMapper in .NET.

Features

• Basic Mapping: Map dictionaries to standard Python objects or dataclasses.
• Custom Mapping: Rename source fields to match target attributes based on custom dictionary mappings.
• Ignore Fields: Exclude specific fields from mapping during object creation.
• Nested Mapping: Supports mapping nested dictionaries to nested Python class structures automatically using type hints.
• Type Conversion: Converts data types automatically based on type hints (e.g. str to int or float).
• Error Handling: Custom MappingError exception with informative messages for missing fields or failed conversions.
• Logging: Configured via the logging module to trace mapping execution procedures.

Installation

You can install datamapperx locally:

pip install -e .

Or build and publish it:

python -m build
twine upload dist/*

Usage Example

from datamapperx import map_to

class UserProfile:
    def __init__(self, username: str, age: int, is_active: bool = True):
        self.username = username
        self.age = age
        self.is_active = is_active

# 1. Basic Mapping
data = {"username": "john_doe", "age": 30}
user = map_to(UserProfile, data)
print(user.username, user.age)

# 2. Custom Mapping
data = {"user_name": "jane", "user_age": 25}
custom_map = {"user_name": "username", "user_age": "age"}
user = map_to(UserProfile, data, custom_map=custom_map)

# 3. Ignoring Fields
data = {"username": "admin", "age": 40, "is_active": False}
# is_active gets the default (True) instead of False because it's ignored
user = map_to(UserProfile, data, ignore=["is_active"])

# 4. Nested Mapping
class Address:
    def __init__(self, city: str):
        self.city = city
        
class UserWithAddress:
    def __init__(self, profile: UserProfile, address: Address):
        self.profile = profile
        self.address = address

data = {
    "profile": {"username": "bob", "age": 35},
    "address": {"city": "New York"}
}
user_nest = map_to(UserWithAddress, data)
print(user_nest.address.city)  # "New York"

MLOps & CI/CD

datamapperx follows software engineering and MLOps best practices:

• CI/CD Pipeline: GitHub Actions are configured in .github/workflows/ci.yml. It runs pytest automatically on PRs and commits to the main branch.
• Testing: Using pytest for all modules (tests/test_mapper.py). To run locally, simply run pytest.
• Packaging: Standardized layout with pyproject.toml, setup.py, and src/datamapperx for safe package resolution and module exporting.
• Logging Control: Uses standard Python logging. This allows granular verbosity control dynamically in your production systems.