Does python have strict typing?
Type hints, variable annotation, and forcing runtime type checks — everything you need to know. Show Python is a dynamically typed language — I’m sure you know that. This makes it easy and fun for beginners, as there’s no need to think about data types. Still, static typing has some benefits. Today we’ll explore how to make Python as statically typed as possible. So, what’s the deal with dynamically typed languages? In a nutshell, it means there’s no code compilation, so the Python interpreter performs type checking as code runs. As a result, variable types are allowed to change throughout the application. It may sound like an advantage, but it can lead to strange and hard-to-track errors as the code base gets larger. On the other hand, statically typed languages perform type checks upon compilation (think C or Java). Further, they give you some feeling of safety, as you can immediately tell the type of parameter going into each function. Just a quick disclaimer before we start — Python will always be a dynamically typed language. There’s nothing you can do to make it static as Java or C. PEP 484 introduced type hints, so we’ll have to work with that. The article is structured as follows:
Type hintsType hints are just that — hints. They show you which data type is expected, but nothing is stopping you from ignoring them. We’ll later explore how to force type checking by the Python interpreter, but let’s cover the basics first. Let’s make an example without type hints. Below is a function designed to add two numbers and return the sum. Python being Python, we don’t have to specify data types for parameters nor for the return value:
The printed results from the function calls are 15, 15.3, and BobMark, respectively. To introduce the type hints, we have to do the following:
Here’s how to add type hints to our
The function is a bit clearer to look at now, but we can still pass any data types — indicating that hints are just hints, not an obligation. Here are type hint recommendations, according to PEP 8:
We’ll see how to force type checks later. Variable annotationsWe can also provide type hints for variables. Once again, these are just hints and do not affect how the code runs. Here’s a quick example of how to add type hints to variables:
Nothing is stopping you from assigning a value of 32.95 to the variable Denoting more advanced data typesEverything we covered thus far is great, but what if you want to declare a list of strings or a dictionary where both keys and values are strings? That’s where the Let’s now use the
A lot more verbose, but feels safer. Further, let’s see how to make a function parameter to be a list of integers. It’s pretty straightforward:
You now know how to use type hints to declare a variable of any data type, but how to force type checks on runtime? Let’s explore that next. Forcing type checking on runtimeI’m not aware of any methods of forcing type
checks in the Notebook environment — so we’ll switch to scripts. I’ve made a file called Before proceeding, we’ll have to install a library called We are now good to go. The
From the Terminal, execute the script in the traditional way — by writing
The code
will run without throwing an error. This is equivalent to what we did earlier in the Notebook. But we don’t want that. With As you can see, the program crashed. Just the behavior we wanted, as the function expects two integers, but we didn’t provide that data types in the function calls. And that’s all you should know for starting out. Let’s wrap things up in the next section. Parting wordsLet’s end this article with a couple of pros and cons of using type hints. Pros:
Cons:
To conclude — type hints are not required but are good practice, especially when working on large codebases — even if you decide not to use What are your thoughts on type hints? Do you always use them, or just when the codebase gets larger? Let me know in the comment section below. Does Python have type safety?A Python program can store a double value in a variable m; later, it can store a string, an array, or anything else int m. The type of an expression is not a syntactic property, as it is in Java. But Python does try for type safety by performing type checks as runtime. Thus, Python is strongly typed.
Does Python have static typing?Python will always remain a dynamically typed language. However, PEP 484 introduced type hints, which make it possible to also do static type checking of Python code. Unlike how types work in most other statically typed languages, type hints by themselves don't cause Python to enforce types.
Is Python strong on type binding?Python is strongly, dynamically typed. Strong typing means that the type of a value doesn't change in unexpected ways. A string containing only digits doesn't magically become a number, as may happen in Perl. Every change of type requires an explicit conversion.
Why Python is loosely typed system?Example: initializing a string value to an int variable is not allowed and the program will not compile. But Python is a dynamically typed language. It doesn't know about the type of the variable until the code is run. So declaration is of no use.
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