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Typing in Python: A Guide

Typing in Python: A Guide
Hostman Team
Technical writer
Python
18.07.2024
Reading time: 7 min

Python is one of the most popular programming languages in the world, known for its simplicity and convenience. Like C, C#, Java, and others, Python is a typed language. Typing in Python helps developers create more reliable, user-friendly programs, providing improved optimization, performance, and code security.

Each typed language was created for specific purposes, which is why programming languages differ in their typing methods. Typing in Python is both dynamic and strong.

Dynamic Typing in Python

Dynamic typing allows for determining and modifying variable types during program execution, unlike static typing, where data types are defined at compile time and cannot be changed. Dynamic typing reduces code complexity but increases the risk of errors related to incorrect data types.

x = 7
x = "Hostman"
x = [1, 2, 3]

In this example, the variable x is initialized as an integer (int), then redefined as a string (str), and later as a list (list).

Strong Typing in Python

Strong typing ensures adherence to necessary rules when interacting with different data types. Operations between different data types are usually prohibited or require explicit type conversion.

x = 7
y = "Hostman"
result = x + y

In this example, variable x is of type int, and variable y is of type str. Attempting to add variables of different types will result in a TypeError because Python enforces typing rules.

x = 7
y = 3.14
result = x + y

In this example, when adding the integer x and the float y, Python will automatically convert x to a float and perform the addition.

Although Python is a strongly typed language, it offers the flexibility of dynamic typing, allowing developers to convert data types using type conversion functions (int(), float(), etc.).

Python Data Types

Python uses built-in data types to define variable types:

  • int: Used for representing whole numbers, both positive and negative.

  • float: Numbers that can have a decimal part.

  • str: Text information (strings).

  • bool: Boolean values: True, False.

  • list: Mutable ordered collections of elements (lists).

  • tuple: Immutable ordered collections of elements (tuples).

  • dict: Key-value pairs where each key is unique (dictionaries).

  • bytes: Byte sequences, used for working with binary files.

Example of Basic Data Types

x = -10        # int
pi = 3.14      # float
name = "Anna"  # str
fruits = ["apple", "banana", "orange"]  # list
coordinates = (3, 4)  # tuple

Each of these data types has its characteristics. Python offers many built-in data types, but developers can create their own using the class keyword. Typing in Python classes is facilitated by type annotations.

Type Annotations

Type annotations allow developers to specify the expected data type of a variable, function argument, or function return value. They improve code readability without affecting program execution. Annotations are usually specified after the variable, argument, or function name, separated by a colon.

class Employee:
    def __init__(self, employee_id: str, salary: float):
        self.employee_id: str = employee_id
        self.salary: float = salary

In this example, type annotations for class attributes indicate that employee_id is expected to be a string, and salary a float.

class Circle:
    def __init__(self, radius: float):
        self.radius: float = radius

    def area(self) -> float:
        return 3.14159 * self.radius**2

Type annotations can also be applied to class methods. In this example, both the arguments and the return value are annotated as floats.

Type annotations can also be used to type functions in Python. In functions, you can annotate the function arguments, their return values, decorators, etc.

from typing import List

def find_max(numbers: List[int]) -> int:
    if not numbers:
        raise ValueError("List is empty")
    max_value: int = numbers[0]
    for num in numbers:
        if num > max_value:
            max_value = num
    return max_value

This example shows a function that takes a list annotated as int and returns the largest integer from the list, also annotated as int.

The typing Module

Besides type annotations, the built-in typing module provides additional tools for more precise and advanced typing. Here are some data structures from the typing module:

  • Any: Represents an unspecified type, used when the variable type is unknown.

  • Union: Allows specifying multiple possible types for a variable.

  • Optional: Indicates that a variable can have a specific type or be None.

from typing import Union, Optional, Any

def process_data(data: Union[int, str, float, None]) -> Optional[Any]:
    if data is None:
        return None
    if isinstance(data, int):
        return f"Processed integer: {data * 2}"
    if isinstance(data, str):
        return f"Processed string: {data.upper()}"
    if isinstance(data, float):
        return f"Processed float: {round(data, 2)}"

# Examples of usage
print(process_data(42))
print(process_data("hello"))
print(process_data(3.1415926535))
print(process_data(None))

The process_data function takes an argument data that can be an integer, string, float, or None. The function returns either the processed value or None. For example, if data is an integer, it is multiplied by 2 and returned as a string with a message.

  • TypeVar: Allows creating parameterized types.

  • Generic: Allows creating generic classes and functions.

from typing import TypeVar, Generic

T = TypeVar('T')

class Box(Generic[T]):
    def __init__(self, item: T):
        self.item = item

    def get_item(self) -> T:
        return self.item

# Example of usage
string_box = Box("Hello, World!")
int_box = Box(42)

# Retrieving items from containers
string_value: str = string_box.get_item()
int_value: int = int_box.get_item()

print("String Box Value:", string_value)
print("Int Box Value:", int_value)

In this example, a generic class Box is created that can contain objects of any type. TypeVar is used to create a generic type variable T, which is then used as the argument type for the Box class. The Box class accepts and returns items of the specified type. Containers with different data types (string and integer) are used in the example.

Type Checking Tools

Using data annotations and the typing module improves code readability and helps static checking tools identify potential type errors early in the development process. Here are some popular type-checking tools:

  • Mypy: Allows adding data annotations to code and checking their correctness, identifying potential type errors. Mypy supports various third-party libraries and frameworks, including NumPy, Django, and others.
  • Pyright: Developed to support typing in Python through the use of data annotations and type comments. Pyright integrates directly into Visual Studio Code and provides real-time data structure suggestions and autocompletion.
  • PyCharm: An integrated development environment (IDE) for Python that includes built-in support for type annotations and static type checking. PyCharm provides many tools for improving code typing, including quick fixes and automatic type annotation generation.

There are also several other tools and IDEs that offer static data structure checking capabilities and support type annotations.

These tools make it easier for developers to collaborate in teams, identifying type errors and providing code improvement suggestions.

Conclusion

Typing in Python is an important aspect of program code development. To work effectively, a developer should consider Python's strong dynamic typing, use type annotations and the typing module, remember the possibility of creating custom data structures, and not neglect type-checking tools. It's essential to understand that typing is a tool for improving code quality, and it's not always necessary to strive for absolute static typing, avoiding excessive complexity.

If you want to build a web service using Python, you can rent a cloud server at competitive prices with Hostman.

Python
18.07.2024
Reading time: 7 min

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The logic of the imported module executed itself... The safer some.py: def useful_function():     return 42 def main():     print("This code will not run on import") main() plus the __name__ check guard against accidental execution. Inside main() you can also verify user permissions or environment variables. How to Write main() in Python Remember: main() is not a language construct, just a regular function promoted to “entry point.” To ensure it runs only when the script starts directly: Tools – define helper functions with business logic. Logic – assemble them inside main() in the desired order. Check – add the if __name__ == "__main__" guard.  This template yields structured, import‑safe, test‑friendly code—excellent practice for any sizable Python project. Example Python Program Using main() # import the standard counter from collections import Counter # runs no matter how the program starts print("The text‑analysis program is active") # text‑analysis helper def analyze_text(text): words = text.split() # split text into words total = len(words) # total word count unique = len(set(words)) # unique word count avg_len = sum(len(w) for w in words) / total if total else 0 freq = Counter(words) # build frequency counter top3 = freq.most_common(3) # top three words return { 'total': total, 'unique': unique, 'avg_len': avg_len, 'top3': top3 } # program’s main logic def main(): print("Enter text (multiple lines). Press Enter on an empty line to finish:") lines = [] while True: line = input() if not line: break lines.append(line) text = ' '.join(lines) stats = analyze_text(text) print(f"\nTotal number of words: {stats['total']}") print(f"Unique words: {stats['unique']}") print(f"Average word length: {stats['avg_len']:.2f}") print("Top‑3 most frequent words:") for word, count in stats['top3']: print(f" {word!r}: {count} time(s)") # launch program if __name__ == "__main__": main() Running the script prints a prompt: Enter text (multiple lines). Press Enter on an empty line to finish: Input first line: Star cruiser Orion glided silently through the darkness of intergalactic space. Second line: Signals of unknown life‑forms flashed on the onboard sensors where the nebula glowed with a phosphorescent light. Third line: The cruiser checked the sensors, then the cruiser activated the defense system, and the cruiser returned to its course. Console output: The text‑analysis program is active Total number of words: 47 Unique words: 37 Average word length: 5.68 Top‑3 most frequent words: 'the': 7 time(s) 'cruiser': 4 time(s) 'of': 2 time(s) If you import this program (file program.py) elsewhere: import program         # importing program.py Only the code outside main() runs: The text‑analysis program is active So, a moderately complex text‑analysis utility achieves clear logic separation and context detection. When to Use main() and When Not To Use  main() (almost always appropriate) when: Medium/large scripts – significant code with non‑trivial logic, multiple functions/classes. Libraries or CLI utilities – you want parts of the module importable without side effects. Autotests – you need to test pure logic without extra boilerplate. You can skip main() when: Tiny one‑off scripts – trivial logic for a quick data tweak. Educational snippets – short examples illustrating a few syntax features. In short, if your Python program is a standalone utility or app with multiple processing stages, command‑line arguments, and external resources—introduce  main(). If it’s a small throw‑away script, omitting main() keeps things concise. Conclusion The  main() function in Python serves two critical purposes: Isolates the program’s core logic from the global namespace. Separates standalone‑execution logic from import logic. Thus, a Python file evolves from a straightforward script of sequential actions into a fully‑fledged program with an entry point, encapsulated logic, and the ability to detect its runtime environment.
14 July 2025 · 8 min to read

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