High Quality Type-Safe Code With Python

If you are experienced in Java or TypeScript and then start coding in Python language, my bet is that the first thing that will be making you annoyed is the lack of strong typing!

There is a reason why TypeScript language has become immensely popular in recent years. And the reason is - surprise - the strong typing that it provides! If this wasn’t the reason, everybody would be using just JavaScript - it is not to be forgotten that TypeScript is really just a hack on top of the actual underlying programming language beneath, which is JavaScript. All TypeScript code is transpiled into JavaScript code before its actual execution.

Why lack of strong typing in Python sucks

If you never really wrote code in any other language than Python, you may not even be aware what sucks with Python. But if you arrive at the planet Python from the planet TypeScript, you will immediatelly realize the problem.

Your functions may perfectly work in your unit test bench but when you use them in your execution code, nothing works anymore and everything just blows up. This is because in the laborary environment of your unit tests, you are at least passing the right kind of objects as arguments to your functions. But when you are writing the actual execution code, there is nothing to prevent you from passing wrong type of objects as parameters to your functions.

It is commonplace in Python code that an object you have at hand isn’t actually the kind of an object you believe it is. This makes you pass objects to functions that aren’t expecting them and refer to object properties that do not exist.

And you will be spending a lot of time debugging your code!

In TypeScript, this problem rarely occurs because you can’t make these kinds of mistakes. Firstly, the editor will already warn you about the mistakes when you are only starting to write them, and secondly, even if you ignore this warning, the code won’t even compile so you won’t be running it before it’s right.

None of these gatekeepers really work in Python. Well, there is a feature called hints in Python, but in my honest opinion, it doesn’t really work that well. Because it does just what it promises. It may give your editor hints about what kinds of objects should be passed as arguments but it won’t actually prevent you from typing and running erratic code.

But no worries, there is a fix. Let’s have a look at it!

Fix Python with Pydantic library

The good news is that you actually can achieve about the same level of type safety in Python code as you natively get in TypeScript, if you use Pydantic. Let’s use Pydantic now and define a user that has many addresses:

from typing import List, Optional
from pydantic import BaseModel, StrictStr, StrictInt

class Address(BaseModel):
  street_name: StrictStr
  street_number: StrictInt
  postal_code: StringStr
  country: StrictStr

class User(BaseModel):
  id: StrictInt
  name: StrictStr
  addresses: List[Address] = []

And now whe are starting to get here some robust schema definitions, familiar from TypeScript language! Let me instantiate a user with some addresses:

user = User(**{
  'id': 124,
  'name': 'Jon Doe',
  'addresses': [{
    'street_name': 'Sesam Street',
    'street_number': 1,
    'postal_code': 54321,
    'country': 'Everland'
  },
  {
    'street_name': 'Vinegar Valley',
    'street_number': 12,
    'postal_code': 123456,
    'country': 'Neverland'
  }]
})

Now, this code executes because it correctly instantiates the structure of a user. But if you don’t have the constructor object right, your code fails.

It’s a list of strings, but is it really?

Let me show another example. You are writing code that utilizies a certain external library. You believe that your function is getting a list of strings when invoking some function from the library. But is your assumption correct?

You will be better off debugging your code if your functions cannot be accessed at all by wrong kinds of objects. You are fortunate if you can be sure that you really have a list of strings when you think that you have a list of strings - because the execution would never even start on that code otherwise. So let’s have a look at my implementation of a wrapper that ensures that you really have a list of strings when you believe you have a list of strings:

from typing import List
from pydantic import BaseModel, StrictStr

class StringList(BaseModel):
  items: List[StrictStr]

  @staticmethod
  def new(strings: list[str]):
    return StringList(**{
      'items': strings
    })

def test_instantiation():
  assert(type(StringList.new(['list', 'of', 'words']))) == StringList

Using these kinds of encapsulations in your code can really boost your productivity and make debugging easier when you can be sure that the objects that your code handles actually are the kinds of objects your code assumes them to be. And if not, your gatekeeping constructor functions will alarm you about the case, so you won’t be spending your time debugging places thar aren’t the problem.

In contrast to a common belief, it’s a sign for good quality, not bad quality, when you are actually getting stuff done!

But it’s only runtime checking

Pydantic provides robust TypeScript-like schema validation and checking but it only works in runtime. Unlike with TypeScript code, your editor won’t still be nagging in the first place when you are actually writing the mistake. But is this really so bad? There are two sides also on this coin!

Namely, TypeScript’s compile-time type checking does come with its own problems. It may prevent you from writing faulty code, but it won’t be helping you in production environment when your system receives and serializes objects from external interfaces, HTTP requests, database queries or files. If those objects aren’t what expected, coding-time or compile-time type-checking won’t help you at all. It helped you write your code correctly for the ideal case and it even helped you write your tests right, but when your code is in production and receiving objects over HTTP requests it won’t be checking anything when it gets a mismatching one - a property that your schema defined as obligatory may still be missing and enter into your function crashing it from inside, because of the lack of runtime type checking. And the same may happen as a result of a database query or when the system serializes an object from the disk.

On the contrary, runtime type checking that you get with Pydantic, prevents erratic and malformed objects from entering your system when it is in production. It will be easier to debug what is wrong in your production system when you have robust runtime gatekeepers on your instances - you will immediately see from the error logs that the error was caused because a HTTP request object didn’t match the expectation. This saves you from a lot of debugging effort.

Of course you can also add runtime validation in TypeScript (for example with Yup library) but it’s then always manual extra effort on top of TypeScript’s native compile-time type checking. Can’t have one without the other!

I’ll be back coding in Python, you can be sure about that. Cheers!