Validating Method Parameters in Python

When you start coding in Python programming language as a coder who is routined in some strongly typed language such as TypeScript, Java or C#, my bet is that first thing you want to enable in Python is strong typing.

At least, this was one of the first things I started doing on Python when I finally decided to go for it. It turns out, Python is a fantastic programming language and it has the best selection of scientific libraries available so far. Luckily, it is also possible to fix that final annoyance that a former TypeScript developer encounters while getting used to Python syntax.

As I described in my previous article, there is a way to introduce strong typing into your Python code and it can be done with Pydantic library.

In my last article, I showed how to define Python classes peppered with Pydantic type checking. Defining classes as Pydantic models helps you enable validation on the class constructor level. However, this alone is not enough when you really want to use strong typing in Python the way it is done in typical strongly typed languages. Of course, you also want every method to validate their input parameters!

Validating method input parameters with Pydantic

The use case is clear - if you write a method that expects an integer value as its input parameter, you want Python to type check that and immediately throw an error if anything else is fed in - a float, a string or whatever else.

Let’s check how to make it happen!

In my example, I want to write a wrapper class for Numpy library’s ndarray class. That wrapper class must encapsulate the underlying type-unsafe ndarray object and provide alternative constructors for initializing my ndarray in quite specific ways that serve particular use case - therefore I write a special constructor method to get a new instance of my MSArray class with a specially shaped ndarray object within:

@staticmethod
@validate_call
def new_shaped(item_count: int, axis_count: int, elements_per_axis_count: int):
  return MSArray(**{
    'array': np.arange(item_count).reshape(axis_count, elements_per_axis_count)
  })

Nice, isn’t it! The constructor new_shaped to create a new MSArray instance with a shaped ndarray inside needs to be a “static” class method because if it wasn’t, it would be an instance method. But you can’t possibly invoke an instance method when you are only creating your instance at this point!

And applying @validate_call annotation, Pydantic will throw an error if you try to invoke the method with wrong kinds of parameters. Bam!

Validating custom and third party objects

That’s how Pydantic’s parameter validation works for primitive data types (such as numbers and strings) but you need some additional definitions to make the validation framework work also for more complex third party objects, such as Numpy’s ndarray object et cetera.

Let’s make another constructor for our MSArray class that requires an ndarray as its input parameter:

@staticmethod
@validate_call(config=dict(arbitrary_types_allowed=True))
def new_from_ndarray(array: ndarray):
  return MSArray(**{
    'array': array
  })

Adding definition config=dict(arbitrary_types_allowed=True) to your @validate_call annotation does the trick!

By the way, when you use these custom objects as properties in your classes, remember to configure your classes to allow arbitrary types:

class MSArray(BaseModel):
  model_config = ConfigDict(arbitrary_types_allowed=True)

  array: ndarray

Have a look at my full implementation of MSArray class on GitHub. By the way, the “MS” prefix refers to Metamatic Systems which is the title of this blog :)

This is very much all I want to say to the humanity right now. Enjoy your time with Python!