Python Result Type

A functional programming Result type for Python, inspired by Rust's Result<T, E> and similar to PyMonad's Either, but with more intuitive naming (Success/Failure instead of Right/Left).

🚀 Features

• Intuitive API: Success and Failure instead of cryptic Right/Left
• Rust-like Aliases: Optional Ok and Err aliases for Rust developers
• Type Safe: Full generic type support with Result[T, E]
• Chainable Operations: Use .then() method or >> operator for clean chaining
• Exception Safety: Automatic exception handling in chained operations
• Async/Await Support: Full async support with AsyncResult wrapper
• Zero Dependencies: Pure Python with no external dependencies
• Comprehensive: Includes helper functions and decorators for common patterns
• Well Tested: 100% test coverage with extensive edge case testing

📦 Installation

pip install python-result-type

🎯 Quick Start

Basic Usage

from result_type import Success, Failure, Result

def divide(a: float, b: float) -> Result[float, str]:
    if b == 0:
        return Failure("Division by zero")
    return Success(a / b)

# Success case
result = divide(10, 2)
if result.is_success():
    print(f"Result: {result.value}")  # Result: 5.0
else:
    print(f"Error: {result.error}")

# Failure case  
result = divide(10, 0)
if result.is_failure():
    print(f"Error: {result.error}")  # Error: Division by zero

Chaining Operations

Chain operations that can fail using .then() or the >> operator:

from result_type import Success, Failure

def divide(a: float, b: float) -> Result[float, str]:
    if b == 0:
        return Failure("Division by zero")
    return Success(a / b)

def multiply_by_2(x: float) -> Result[float, str]:
    return Success(x * 2)

def subtract_1(x: float) -> Result[float, str]:
    if x < 1:
        return Failure("Result would be negative")
    return Success(x - 1)

# Method 1: Using .then() method
result = (
    divide(10, 2)
    .then(multiply_by_2)
    .then(subtract_1)
    .map(lambda x: x + 5)
)

# Method 2: Using >> operator (cleaner syntax)
result = divide(10, 2) >> multiply_by_2 >> subtract_1

# Method 3: Mixed approach
result = (
    divide(10, 2)
    >> multiply_by_2
    .map(lambda x: x + 10)  # Transform without failure
    >> subtract_1
)

if result.is_success():
    print(f"Final result: {result.value}")
else:
    print(f"Error occurred: {result.error}")

Safe Function Calls

Automatically handle exceptions with safe_call:

from result_type import safe_call

# Wrap risky function calls
result = safe_call(
    lambda: 10 / 0,
    "Math operation failed"
)

if result.is_failure():
    print(result.error)  # "Math operation failed: division by zero"

# Use as decorator
from result_type import safe_call_decorator

@safe_call_decorator("Database error")
def risky_database_operation():
    # Some operation that might throw
    return fetch_user_from_db()

result = risky_database_operation()

Rust-like Aliases (Ok/Err)

For developers familiar with Rust's Result<T, E>, this library provides Ok and Err aliases for Success and Failure:

from result_type import Ok, Err, ok, err

def divide_rust_style(a: float, b: float):
    if b == 0:
        return Err("Division by zero")
    return Ok(a / b)

# Usage is identical to Success/Failure
result = divide_rust_style(10, 2)
if result.is_success():
    print(f"Result: {result.value}")  # Result: 5.0

# Chain operations using Rust-style
def multiply_by_2(x):
    return Ok(x * 2)

def validate_positive(x):
    if x > 0:
        return Ok(x)
    return Err("Must be positive")

result = Ok(5) >> multiply_by_2 >> validate_positive
if result.is_success():
    print(f"Final: {result.value}")  # Final: 10

# Helper functions with Rust naming
success_result = ok(42)      # Same as Success(42)
error_result = err("oops")   # Same as Failure("oops")

# Mix and match - they're the same types!
mixed_result = Ok(10) >> (lambda x: Success(x * 2))  # Works perfectly
print(Ok(42) == Success(42))  # True

📚 Complete API Reference

Core Types

Result[T, E]

Abstract base class representing either success or failure.

Methods:

• is_success() -> bool - Check if result is Success
• is_failure() -> bool - Check if result is Failure
• then(func: Callable[[T], Result[U, E]]) -> Result[U, E] - Chain operations
• map(func: Callable[[T], U]) -> Result[U, E] - Transform success value
• map_error(func: Callable[[E], F]) -> Result[T, F] - Transform error value
• unwrap() -> T - Extract value or raise exception
• unwrap_or(default: T) -> T - Extract value or return default
• unwrap_or_else(func: Callable[[E], T]) -> T - Extract value or compute from error

Success[T]

Represents successful result containing a value.

success_result = Success(42)
print(success_result.value)  # 42
print(success_result.is_success())  # True

Failure[E]

Represents failed result containing an error.

failure_result = Failure("Something went wrong")
print(failure_result.error)  # "Something went wrong"
print(failure_result.is_failure())  # True

Rust-like Aliases

Ok and Err

Type aliases for Success and Failure respectively.

from result_type import Ok, Err

# These are identical to Success/Failure
ok_result = Ok(42)          # Same as Success(42)
err_result = Err("error")   # Same as Failure("error")

ok(value: T) -> Success[T] and err(error: E) -> Failure[E]

Helper functions with Rust-style naming.

from result_type import ok, err

result = ok(42)        # Same as success(42) or Success(42)
error = err("oops")    # Same as failure("oops") or Failure("oops")

Helper Functions

success(value: T) -> Success[T]

Create a Success result.

from result_type import success
result = success(42)  # Same as Success(42)

failure(error: E) -> Failure[E]

Create a Failure result.

from result_type import failure
result = failure("error")  # Same as Failure("error")

safe_call(func: Callable[[], T], error_msg: str = None) -> Result[T, str]

Safely call a function that might raise exceptions.

from result_type import safe_call

result = safe_call(lambda: risky_operation())
if result.is_failure():
    print(f"Operation failed: {result.error}")

safe_call_decorator(error_msg: str = None)

Decorator version of safe_call.

from result_type import safe_call_decorator

@safe_call_decorator("API call failed")
def call_external_api():
    return requests.get("https://api.example.com").json()

result = call_external_api()  # Returns Result[dict, str]

🔄 Chaining Operations

Error Propagation

When chaining operations, errors automatically propagate:

result = (
    Success(10)
    >> (lambda x: Failure("Something went wrong"))  # This fails
    >> (lambda x: Success(x * 2))  # This won't execute
    >> (lambda x: Success(x + 1))  # Neither will this
)

print(result.error)  # "Something went wrong"

Exception Handling in Chains

Exceptions in chained operations are automatically converted to Failure:

def risky_operation(x: int) -> Result[int, str]:
    return Success(x / 0)  # This will raise ZeroDivisionError

result = Success(10) >> risky_operation

print(result.is_failure())  # True  
print(type(result.error))   # <class 'ZeroDivisionError'>

🔄 Async/Await Support

The library includes full async/await support for modern Python applications with the AsyncResult wrapper:

Basic Async Usage

import asyncio
from result_type import Success, Failure, Result
from result_type.async_result import AsyncResult, async_safe_call

async def fetch_user(user_id: int) -> Result[dict, str]:
    await asyncio.sleep(0.1)  # Simulate API call
    if user_id <= 0:
        return Failure("Invalid user ID")
    return Success({"id": user_id, "name": f"User {user_id}"})

# Use async operations
result = await fetch_user(123)
if result.is_success():
    print(f"Found user: {result.value}")

Async Chaining

Chain async and sync operations seamlessly:

async def fetch_user_posts(user: dict) -> Result[list, str]:
    await asyncio.sleep(0.1)
    return Success([f"Post {i}" for i in range(3)])

def format_summary(posts: list) -> Result[str, str]:
    return Success(f"User has {len(posts)} posts")

# Chain async and sync operations
pipeline = (AsyncResult(fetch_user(123))
           .then_async(fetch_user_posts)    # Async operation
           .then_sync(format_summary))      # Sync operation

result = await pipeline.resolve()
if result.is_success():
    print(result.value)  # "User has 3 posts"

Async Safe Calls

Handle async exceptions safely:

from result_type.async_result import async_safe_call, async_safe_call_decorator

# Function approach
async def risky_api_call():
    # Might raise an exception
    return await some_external_api()

result = await async_safe_call(risky_api_call, "API Error")

# Decorator approach
@async_safe_call_decorator("Database Error")
async def database_operation():
    return await db.fetch_data()

result = await database_operation()  # Returns Result[Any, str]

Gathering Multiple Async Results

Process multiple async operations concurrently:

from result_type.async_result import gather_results

async def fetch_data(source: str) -> Result[str, str]:
    await asyncio.sleep(0.1)
    return Success(f"Data from {source}")

# Gather results - stops at first failure
async_operations = [
    AsyncResult(fetch_data("source1")),
    AsyncResult(fetch_data("source2")),
    AsyncResult(fetch_data("source3")),
]

combined = await gather_results(*async_operations)
if combined.is_success():
    print(combined.value)  # ["Data from source1", "Data from source2", "Data from source3"]

Converting Regular Awaitables

Convert any awaitable to an AsyncResult:

from result_type.async_result import from_awaitable

async def regular_async_function():
    return {"data": "success"}

# Convert to AsyncResult with error handling
async_result = await from_awaitable(regular_async_function(), "Operation failed")
result = await async_result.resolve()

🆚 Comparison with Alternatives

vs PyMonad Either

# PyMonad Either (less intuitive)
from pymonad.either import Left, Right

result = Right(42)  # Success
result = Left("error")  # Failure

# This library (more readable)
from result_type import Success, Failure

result = Success(42)  # Clear success intent
result = Failure("error")  # Clear failure intent

vs Exception Handling

# Traditional exception handling
try:
    result = risky_operation()
    result = transform(result)
    result = another_transform(result)
except Exception as e:
    handle_error(e)

# With Result type
result = (
    safe_call(risky_operation)
    >> safe_transform
    >> safe_another_transform
)

if result.is_failure():
    handle_error(result.error)

🧪 Real-World Examples

Database Operations

from result_type import Result, Success, Failure

def fetch_user(user_id: str) -> Result[dict, str]:
    try:
        user = database.users.find_one({"_id": user_id})
        if not user:
            return Failure("User not found")
        return Success(user)
    except Exception as e:
        return Failure(f"Database error: {e}")

def validate_user(user: dict) -> Result[dict, str]:
    if not user.get("is_active"):
        return Failure("User is inactive")
    return Success(user)

def get_user_permissions(user: dict) -> Result[list, str]:
    permissions = user.get("permissions", [])
    if not permissions:
        return Failure("User has no permissions")
    return Success(permissions)

# Chain the operations
result = (
    fetch_user("user123")
    >> validate_user
    >> get_user_permissions
)

if result.is_success():
    print(f"User permissions: {result.value}")
else:
    print(f"Failed to get permissions: {result.error}")

API Calls

import requests
from result_type import safe_call, Result, Success, Failure

def fetch_weather(city: str) -> Result[dict, str]:
    return safe_call(
        lambda: requests.get(f"http://api.weather.com/{city}").json(),
        f"Failed to fetch weather for {city}"
    )

def extract_temperature(weather_data: dict) -> Result[float, str]:
    try:
        temp = weather_data["current"]["temperature"]
        return Success(float(temp))
    except (KeyError, ValueError, TypeError) as e:
        return Failure(f"Invalid weather data: {e}")

def celsius_to_fahrenheit(celsius: float) -> Result[float, str]:
    return Success(celsius * 9/5 + 32)

# Chain API call and transformations
result = (
    fetch_weather("London")
    >> extract_temperature
    >> celsius_to_fahrenheit
)

if result.is_success():
    print(f"Temperature in Fahrenheit: {result.value}")
else:
    print(f"Error: {result.error}")

File Operations

from pathlib import Path
from result_type import safe_call, Result

def read_config_file(path: str) -> Result[dict, str]:
    def _read_and_parse():
        content = Path(path).read_text()
        return json.loads(content)
    
    return safe_call(_read_and_parse, f"Failed to read config from {path}")

def validate_config(config: dict) -> Result[dict, str]:
    required_fields = ["api_key", "database_url", "port"]
    missing = [field for field in required_fields if field not in config]
    
    if missing:
        return Failure(f"Missing required fields: {missing}")
    return Success(config)

def start_application(config: dict) -> Result[str, str]:
    # Application startup logic here
    return Success(f"Application started on port {config['port']}")

# Chain configuration loading and validation
result = (
    read_config_file("config.json")
    >> validate_config
    >> start_application
)

if result.is_success():
    print(result.value)  # "Application started on port 8080"
else:
    print(f"Startup failed: {result.error}")

🧪 Testing

# Install development dependencies
pip install python-result-type[dev]

# Run tests
pytest

# Run tests with coverage
pytest --cov=result_type --cov-report=html

# Run type checking
mypy result_type

# Format code
black result_type tests

📄 Type Safety

This library is fully typed and compatible with mypy:

from result_type import Result

def typed_operation(x: int) -> Result[str, str]:
    if x < 0:
        return Failure("Negative numbers not allowed")
    return Success(str(x))

# mypy will catch type errors
result: Result[str, str] = typed_operation(42)

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details.

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

📜 License

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

🙏 Acknowledgments

• Inspired by Rust's Result type
• Similar concepts from PyMonad
• Functional programming patterns from Haskell's Either type

📈 Changelog

1.2.1

• Fixed: Mypy warning "Result[T, E]" has no attribute "error" when accessing result.error
• Fixed: Mypy warning "Result[T, E]" has no attribute "value" when accessing result.value
• Enhanced: Added value and error properties to the abstract Result class for better type checking
• Improved: Better mypy compatibility and type safety throughout the codebase
• Maintained: Full backward compatibility - no breaking changes to existing code

1.2.0

• New: Rust-like aliases Ok and Err for Success and Failure
• New: Helper functions ok() and err() with Rust-style naming
• Enhanced: Full compatibility between Rust aliases and original naming
• Improved: Better developer experience for Rust developers
• Added: Comprehensive tests and examples for Rust-style usage

1.1.0

• New: Full async/await support with AsyncResult wrapper
• New: async_safe_call and async_safe_call_decorator for async exception handling
• New: Async chaining operations with .then_async() and .then_sync()
• New: gather_results() for concurrent async operations
• New: from_awaitable() to convert regular awaitables to AsyncResult
• Improved: Enhanced type annotations for better IDE support
• Enhanced: Better error messages and stack traces

1.0.0

• Initial release
• Core Result, Success, and Failure types
• Chaining with .then() and >> operator
• Helper functions and decorators
• Comprehensive test suite
• Full type annotations