TypeDict

A dictionary that uses types as keys, automatically converting values to their types.

Features

• Type-based keys: Automatically converts any key to its type
• Full dict compatibility: Supports all standard dictionary methods
• Type safety: Full type hints and mypy support
• Modern Python: Requires Python 3.11+
• Comprehensive testing: 100% test coverage

Installation

pip install typedict

Quick Start

from typedict import TypeDict

# Create a TypeDict
td = TypeDict()

# Set values - keys are automatically converted to their types
td[42] = "hello"        # Key becomes int
td["world"] = 123       # Key becomes str
td[3.14] = "pi"         # Key becomes float

# Access values using either the original key or the type
print(td[int])          # "hello"
print(td[42])           # "hello" (same as above)
print(td[str])          # 123
print(td["test"])       # 123 (same as above)

# Type keys overwrite value keys
td[int] = "type_key"
print(td[42])           # "type_key" (now uses the type key)

Use Cases

Configuration by Type

from typedict import TypeDict

# Store configuration values by type
config = TypeDict()
config[42] = "port"
config["localhost"] = "host"
config[True] = "debug_mode"

# Access configuration
port = config[int]      # "port"
host = config[str]      # "localhost"
debug = config[bool]    # "debug_mode"

Type-based Caching

from typedict import TypeDict
import time

cache = TypeDict()

def expensive_operation(data_type):
    if data_type in cache:
        return cache[data_type]
    
    # Simulate expensive operation
    time.sleep(1)
    result = f"processed_{data_type.__name__}"
    cache[data_type] = result
    return result

# First call - expensive
result1 = expensive_operation(int)    # Takes 1 second

# Second call - cached
result2 = expensive_operation(int)   # Instant!

Serialization by Type

from typedict import TypeDict
import json

serializers = TypeDict()
serializers[int] = lambda x: str(x)
serializers[str] = lambda x: f'"{x}"'
serializers[list] = lambda x: json.dumps(x)

def serialize(data):
    data_type = type(data)
    serializer = serializers.get(data_type, str)
    return serializer(data)

print(serialize(42))           # "42"
print(serialize("hello"))      # '"hello"'
print(serialize([1, 2, 3]))    # "[1, 2, 3]"

API Reference

TypeDict

A dictionary that uses types as keys.

Methods

• __getitem__(key): Get value for key (converted to type)
• __setitem__(key, value): Set value for key (converted to type)
• __delitem__(key): Delete key (converted to type)
• __contains__(key): Check if key exists (converted to type)
• get(key, default=None): Get value with default
• pop(key, default=None): Remove and return value
• setdefault(key, default=None): Set default if key doesn't exist
• update(other, **kwargs): Update from dict or kwargs
• keys(): Return view of type keys
• values(): Return view of values
• items(): Return view of (type, value) pairs
• get_types(): Return list of all types used as keys
• has_type(type_key): Check if type exists as key

Special Behavior

• Type conversion: All keys are automatically converted to their types
• Type precedence: Using a type directly as a key overwrites any value keys of that type
• Collision handling: Multiple values of the same type overwrite each other (last wins)

Examples

Basic Operations

from typedict import TypeDict

td = TypeDict()

# Set values
td[42] = "integer"
td["hello"] = "string"
td[3.14] = "float"
td[True] = "boolean"

# Access values
print(td[int])      # "integer"
print(td[str])      # "string"
print(td[float])    # "float"
print(td[bool])     # "boolean"

# Type keys overwrite value keys
td[int] = "type_key"
print(td[42])       # "type_key" (not "integer")

Dictionary Methods

from typedict import TypeDict

td = TypeDict()
td[42] = "hello"
td["world"] = 123

# Standard dict methods work
print(len(td))              # 2
print(int in td)            # True
print(td.get(str, "default"))  # 123
print(td.pop(int))          # "hello"
print(td.get(int, "missing"))  # "missing"

# Type-specific methods
print(td.get_types())       # [<class 'str'>]
print(td.has_type(int))     # False
print(td.has_type(str))     # True

Edge Cases

from typedict import TypeDict

td = TypeDict()

# None handling
td[None] = "none_value"
print(td[type(None)])  # "none_value"

# Custom classes
class MyClass:
    pass

instance = MyClass()
td[instance] = "custom"
print(td[MyClass])      # "custom"

# Type vs value collision
td[42] = "value_key"
td[int] = "type_key"
print(td[42])           # "type_key" (type key wins)

Development

Setup

git clone https://github.com/eddiethedean/typedict.git
cd typedict
pip install -e .[dev]

Running Tests

pytest

Code Quality

ruff check src tests
mypy src

Pre-commit Hooks

pre-commit install
pre-commit run --all-files

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

0.1.0 (2024-01-XX)

• BREAKING: Minimum Python version is now 3.11
• NEW: Complete rewrite with modern type hints
• NEW: Added all standard dict methods (get, pop, setdefault, etc.)
• NEW: Added type-specific methods (get_types, has_type)
• NEW: Comprehensive test suite with 97% coverage
• NEW: GitHub Actions CI/CD pipeline
• NEW: Modern build system with pyproject.toml
• NEW: Pre-commit hooks and code quality tools
• IMPROVED: Better error handling and edge case support
• IMPROVED: Enhanced documentation and examples

0.0.3 (Previous)

• Initial release with basic functionality