All sorts of programming require you to master the language that helps interpret between the system and the person. Your ideas can come to life using type systems that help in different areas of the programming. These type systems are there to formalize the categories of objects that they can work with and how each category may be treated. If you need help, you can try making an account on Pluralsight vs Udemy to get better at programming. Static type checking in Python is the opposite of dynamic type checking. Running the program isn’t necessary while carrying out static type checks. Most static type checks including C and Java help compile the program for you It’s usually a lot faster for someone to end up writing a new code in a dynamically-typed language. Python is that language and it’s easy because you don’t have to type out all the type declarations. However, when your codebase starts to get large, you inevitably run into several runtime bugs that would not have occurred if you chose static typing.

How to Use Static Type Checking?

While working with nested lists and dictionaries, a lot of programmer’s face problems. Python has been built and updated to its newest 3.6 version to help the programmer with more complex data types. You can use the system to import the name of the complex data type from the typing module. From there on, the data is passed on in nested types in brackets. Using static type checking in Python (tutorial on Pluralsight with greater depth) actually proves to be more useful than dynamic that way. Python functions are flexible enough to handle the different types of work of data. In simple words, you check for bugs in the system throughout the typing of the code. You also enable data checker which is a syntax that declares different types whether true or false. You can enforce type checking by two different methods.

• Either you download an open-source mypy type checker. You can use it by running it as a part of your unit tests.
• Or, you can use PyCharm. It has built-in type checking that gives you flexibility and saves your overall time while carrying out the process of.

Static type checking has phenomenal results if you just know your way around it. You need to make sure that every time some error occurs, you end up resolving it using the type checker. It’s okay to have a few mistakes because anyone can mistake a code for another while typing out Another of the neat things in Python is that you can easily mix a code without any type declarations but using static type checking is beneficial as it can tell whether the declaration is correct or not.

Conclusion:

If you face problems with Data Types or feel the need to revise on Python, we recommend you sign up on Pluralsight and get those lessons. They can refresh your memory on a lot of the concepts and help you become a great programmer.