Modern Techniques for Type Dispatch in Python Functions
Tags: python, programming, dispatching, tutorial
Published: 2023-10-03 13:03:15

## Introduction
In programming, particularly in object-oriented languages like Python, the necessity to perform different actions based on the type of an object is quite common. This concept is called "**Type Dispatching**." In this article, let’s walk through the evolution of strategies for type dispatch in functions with an eye on maintainability and robustness, considering an example that pertains to drawing shapes.

## Traditional Approach

Traditionally, one might employ a series of conditional checks using **isinstance()** to determine an object’s type and execute type-specific logic accordingly. This approach, while straightforward, tends to generate unwieldy code as more types are added.

Example:

```
def draw(shape):
    if isinstance(shape, Rectangle): 
        draw_rectangle(shape)
    elif isinstance(shape, Circle):
        draw_circle(shape)
    elif isinstance(shape, Polygon):
        draw_polygon(shape)
    #...etc
```

## Dictionary Dispatching

To mitigate the drawbacks of a lengthy **if-elif-else** chain, developers often leverage a dispatch dictionary where object types are keys and associated functions are values.

Example:

```
def draw(shape): 
    draw_dispatcher = {
        Rectangle: draw_rectangle,
        Circle: draw_circle,
        Polygon: draw_polygon  # etc...
    }
    try: 
        draw_dispatcher[type(shape)](shape)
    except KeyError:
        raise TypeError("Shape not recognized")
```

## Introspective Lookup

Another technique involves introspection, utilizing naming conventions and the built-in **globals()** function to dynamically look up and invoke the appropriate draw function based on shape type.

Example:

```
def draw_polygon(shape): ...
def draw_circle(shape): ...

def draw(shape):
    shape_type = type(shape).__name__.lower()
    shape_name = f"draw_{shape_type}"
    try:
        get_shape_method_object = globals()[shape_name]
    except KeyError:
        raise TypeError("Function not found")
    else:
        get_shape_method_object(shape)
```
This method reduces explicit mappings between types and functions but is fragile with respect to refactoring and naming consistency.

## Leveraging @singledispatch

Python provides a powerful and clean mechanism via the **functools.singledispatch** decorator, allowing the registration of multiple specialized functions to handle different types for a generic function. This approach is particularly robust and clean.

Example:

```
from functools import singledispatch

@singledispatch
def draw(shape):
    raise TypeError("Shape not recognized")

@draw.register(Rectangle)
def _(rect):
    # Draw rectangle...

@draw.register(Circle)
def _(circ):
    # Draw circle...

@draw.register(Polygon)
def _(poly):
    # Draw polygon...
```
In this technique, the @singledispatch decorator designates a generic function (draw()). Subsequent specialized functions are then registered to handle specific types.

## Conclusion
From the traditional method of chaining if-elif-else checks to the modern approach using @singledispatch, Python offers various strategies for function dispatch based on type. While each method has its place depending on specific use cases and requirements, @singledispatch emerges as a clear winner in terms of cleanliness and maintainability, especially when dealing with a growing set of types and associated functions.

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