Writing Robust and Error-Free Python Code Using Pydantic

Finally! Python autocomplete that actually works (like in C# and Java) and type hinting support

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Python is a dynamically typed language which means that type checking is performed at run-time (when executed). If there is an error in the code it will be thrown at execution. Languages such as Java, C# and C are statically typed meaning type checking is performed at compile-time. In this case, the error will be thrown before the program is run.

In a statically typed language, the type of constructs cannot be changed. The compiler needs to know the types beforehand. A variable declared as an int in C for example cannot be changed to a string later.

We can do this in Python however:

myVar = 1 
myVar = "hello" #this works

This enhanced flexibility means that dynamically typed languages are slower at execution than statically typed ones. A lot of checking has to be done at run-time to figure out the type of variables and other constructs so that the program can be executed. This creates overhead.

Now that Python is the go-to language for machine learning there is an increasing use-case for developing APIs and web applications that serve machine learning models. It is a lot simpler to have a single language for creating models and wrapping them up in a user-facing application than a variety of languages.

However, for these full-stack applications, the chances of type errors increase when type checking is performed at run-time rather than compile time. This is where Python type hinting helps. It allows us to declare types of programming constructs directly in our code.

First, we look at basics of type hints.

Python type hints and code completion

To define a type hint for a function argument we can write a : (colon) followed by the type after the variable name. For non-singular types such as Lists, Dicts, Sets we need to import the typing package.

Let’s look at some code: