Do Python Developers Want Static Typing?

Python is a popular programming language known for its ease of use and flexibility. One of the key features of Python is its dynamic typing, which allows developers to change the datatype of a variable at any point in their code.

However, there is an ongoing debate among Python developers about the benefits and drawbacks of static typing.

There are two types of programming languages:

• Static typed
• Dynamic type

Statically typed and dynamically typed languages refer to the way a programming language handles type checking or the process of verifying that variables and functions are used with the correct datatypes.

Static Typed

In a statically typed language, the type of a variable or function must be explicitly declared before it is used. For example, in the programming language Java, a variable would be declared with a specific datatype, such as:

int age = 25;

In this case, the variable age is declared as an integer, and the compiler will check that it is only used in ways that are appropriate for integers.

This can help to catch errors early on, as the compiler will flag any instances where a variable of the wrong type is used.

Dynamic Typed 

On the other hand, in a dynamically typed language, the type of a variable or function is determined at runtime, and the developer does not need to explicitly declare it.

In Python, for example, a variable can be assigned a value of any datatype, such as:

age = 25

In this case, the interpreter will determine that the variable age is an integer based on the value assigned to it, and will check that it is used in ways that are appropriate for integers.

Some developers argue that static typing can make code more readable, as it clearly indicates the expected datatypes for variables and functions.

On the other hand, many Python developers argue that dynamic typing is one of the language’s greatest strengths. It allows for more flexibility in code, as well as faster development times since developers do not need to spend as much time declaring variable types. 

Additionally, many argue that the use of type annotations in Python is sufficient for catching errors and making code readable. Now, one can ask what is type annotations. For this check the below section.

Type Annotations

Type annotation is a way to provide information or hints about the expected datatypes of variables, function arguments, and return values without enforcing them. It can be used using PEP 3107 and PEP 484. There are two types of annotations:

• Variable Annotations
• Function Annotations

Note: Type annotations do not affect the runtime behavior of the code, but these annotations/hints can be used to write cleaner and more robust code.

Before diving into the details, let us first illustrate an example highlighting the advantages of using type annotations.

Variable Annotations

Variable annotations are a feature in Python that allows developers to specify the expected type of a variable at the point of its declaration. This is done by adding a type hint after the variable name, separated by a colon.

Here, is an example of using variable annotations:

age: int = 25
age = age + 1

In the above code snippet, age: int is an annotation that tells that the variable age is of int datatype.

For example, if we try to assign a string value to age, the type checker will throw an error.

age: int = 25
age = "thirty"  # this will raise a TypeError
​

Note: A great example of using variable annotations can be found when dealing with large datasets and data manipulation. Consider the following code snippet:

data = [("Alice", 25), ("Bob", 32), ("Charlie", 28), ("David", 35)]
ages = [age for name, age in data]
​

Here, the variable ages are created by extracting the ages from the data list. Without variable annotations, it can be difficult to understand the type of data that is being stored in ages.

With variable annotations, we can specify the type of data that is expected to be stored in a variable. For example,

from typing import List, Tuple
data: List[Tuple[str, int]] = [("Alice", 25), ("Bob", 32), ("Charlie", 28), ("David", 35)]
ages: List[int] = [age for name, age in data]
print(ages)

Click here to run the code in the Online Python editor

Now, it is clear that data is a list of tuples where each tuple contains a string and an integer and ages is a list of integers. This makes it easier for other developers to understand the code and catch potential errors.

In addition, this can also help with code completion and analysis in IDEs, making it easier to navigate and understand the codebase.

Function annotations

Function annotations are a feature in Python that allows developers to specify the expected types of a function’s arguments and return value. This is done by adding type hints in the function’s signature, before the colon which separates the arguments and the return value.

For example:

add_numbers(a:int,b:int)->int:
   return a+b

In this example, the function add_numbers takes two integers as arguments a and b, and returns an integer. Without the type annotations, it would be unclear what datatypes the function is expecting and returning.

Note: In the above example string can still pass float — the annotations are just hints.

One example of where type annotations can be beneficial is when working with a large codebase with many collaborators.

Without type annotations, it can be difficult to understand the expected datatypes of function arguments and return values just by reading the code. This can lead to subtle bugs and make it harder to maintain the code.

With type annotations, the expected types are clearly specified, making it easier for other developers to understand the code and catch potential errors.

Additionally, these annotations can be checked by third-party libraries such as mypy which can check your codebase for type errors and provide suggestions to fix them.

Python Type Checker

mypy is a static type checker for Python that can be used to find type errors in your codebase. It will output any type errors it finds in your code and provide suggestions on how to fix them. 

To use mypy, you will first need to install it by running the following command line.

$ pip install mypy

Here is a simple example script using mypy:

def add(a: int, b: int) -> int:
   return a + b
​
result = add(2, "3")
print(result)

You can check the output of the above script with mypy by running the following command:

mypy script.py

It will give an error message, as shown in the below output:

This error message is indicating that the function add expects two arguments of type int but the script passed an int and a string. You can observe that mypy helps to find these type errors before the code runs and helps catch bugs early.

Now, let’s pass an integer value to both arguments in the add() function, as shown below:

def add(a: int, b: int) -> int:
   return a + b
​
result = add(2, 3)
print(result)

Let’s, run the script again:

mypy script.py

Output

We can notice in the above output, mypy has not produced any error because now there is no type error in the script.

Conclusion:

Overall, whether or not Python developers want static typing is a matter of personal preference and is influenced by the type of projects they are working on and their own coding style. While some developers may prefer the added safety and readability of static typing, others may prefer the flexibility and faster development times of dynamic typing.

Good Luck with Your Learning !!