Implementing a Priority Queue in Python

Lists in Python are used almost everywhere. In this tutorial we will look at four ways to find the length of a Python list: by using built‑in functions, recursion, and a loop. Knowing the length of a list is most often required to iterate through it and perform various operations on it. len() function len() is a built‑in Python function for finding the length of a list. It takes one argument—the list itself—and returns an integer equal to the list’s length. The same function also works with other iterable objects, such as strings. Country_list = ["The United States of America", "Cyprus", "Netherlands", "Germany"] count = len(Country_list) print("There are", count, "countries") Output: There are 4 countries Finding the Length of a List with a Loop You can determine a list’s length in Python with a for loop. The idea is to traverse the entire list while incrementing a counter by  1 on each iteration. Let’s wrap this in a separate function: def list_length(list): counter = 0 for i in list: counter = counter + 1 return counter Country_list = ["The United States of America", "Cyprus", "Netherlands", "Germany", "Japan"] count = list_length(Country_list) print("There are", count, "countries") Output: There are 5 countries Finding the Length of a List with Recursion The same task can be solved with recursion: def list_length_recursive(list): if not list: return 0 return 1 + list_length_recursive(list[1:]) Country_list = ["The United States of America", "Cyprus", "Netherlands","Germany", "Japan", "Poland"] count = list_length_recursive(Country_list) print("There are", count, "countries") Output: There are 6 countries How it works. The function list_length_recursive() receives a list as input. If the list is empty, it returns 0—the length of an empty list. Otherwise it calls itself recursively with the argument list[1:], a slice of the original list starting from index 1 (i.e., the list without the element at index 0). The result of that call is added to 1. With each recursive step the returned value grows by one while the list shrinks by one element. length_hint() function The length_hint() function lives in the operator module. That module contains functions analogous to Python’s internal operators: addition, subtraction, comparison, and so on. length_hint() returns the length of iterable objects such as strings, tuples, dictionaries, and lists. It works similarly to len(): from operator import length_hint Country_list = ["The United States of America", "Cyprus", "Netherlands","Germany", "Japan", "Poland", "Sweden"] count = length_hint(Country_list) print("There are", count, "countries") Output: There are 7 countries Note that length_hint() must be imported before use. Conclusion In this guide we covered four ways to determine the length of a list in Python. Under equal conditions the most efficient method is len(). The other approaches are justified mainly when you are implementing custom classes similar to list.

In any complex program, it’s crucial to organize the code properly: define a starting point and separate its logical components. In Python, modules can be executed on their own or imported into other modules, so a well‑designed program must detect the execution context and adjust its behavior accordingly.  Separating run‑time code from import‑time code prevents premature execution, and having a single entry point makes it easier to configure launch parameters, pass command‑line arguments, and set up tests. When all important logic is gathered in one place, adding automated tests and rolling out new features becomes much more convenient.  For exactly these reasons it is common in Python to create a dedicated function that is called only when the script is run directly. Thanks to it, the code stays clean, modular, and controllable. That function, usually named main(), is the focus of this article. All examples were executed with Python 3.10.12 on a Hostman cloud server running Ubuntu 22.04. Each script was placed in a separate .py file (e.g., script.py) and started with: python script.py The scripts are written so they can be run just as easily in any online Python compiler for quick demonstrations. What Is the main() Function in Python The simplest Python code might look like: print("Hello, world!")  # direct execution Or a script might execute statements in sequence at file level: print("Hello, world!")       # action #1 print("How are you, world?") # action #2 print("Good‑bye, world...")  # action #3 That trivial arrangement works only for the simplest scripts. As a program grows, the logic quickly becomes tangled and demands re‑organization: # function containing the program’s main logic (entry point) def main():     print("Hello, world!") # launch the main logic if __name__ == "__main__":     main()                    # call the function with the main logic With more actions the code might look like: def main(): print("Hello, world!") print("How are you, world?") print("Good‑bye, world...") if __name__ == "__main__": main() This implementation has several important aspects, discussed below. The main() Function The core program logic lives inside a separate function. Although the name can be anything, developers usually choose main, mirroring C, C++, Java, and other languages.  Both helper code and the main logic are encapsulated: nothing sits “naked” at file scope. # greeting helper def greet(name): print(f"Hello, {name}!") # program logic def main(): name = input("Enter your name: ") greet(name) # launch the program if __name__ == "__main__": main() Thus main() acts as the entry point just as in many other languages. The if __name__ == "__main__" Check Before calling main() comes the somewhat odd construct if __name__ == "__main__":.  Its purpose is to split running from importing logic: If the script runs directly, the code inside the if block executes. If the script is imported, the block is skipped. Inside that block, you can put any code—not only the main() call: if __name__ == "__main__":     print("Any code can live here, not only main()") __name__ is one of Python’s built‑in “dunder” (double‑underscore) variables, often called magic or special. All dunder objects are defined and used internally by Python, but regular users can read them too. Depending on the context, __name__ holds: "__main__" when the module runs as a standalone script. The module’s own name when it is imported elsewhere. This lets a module discover its execution context. Advantages of Using  main() Organization Helper functions and classes, as well as the main function, are wrapped separately, making them easy to find and read. Global code is minimal—only initialization stays at file scope: def process_data(data): return [d * 2 for d in data] def main(): raw = [1, 2, 3, 4] result = process_data(raw) print("Result:", result) if __name__ == "__main__": main() A consistent style means no data manipulation happens at the file level. Even in a large script you can quickly locate the start of execution and any auxiliary sections. Isolation When code is written directly at the module level, every temporary variable, file handle, or connection lives in the global namespace, which can be painful for debugging and testing. Importing such a module pollutes the importer’s globals: # executes immediately on import values = [2, 4, 6] doubles = [] for v in values: doubles.append(v * 2) print("Doubled values:", doubles) With main() everything is local; when the function returns, its variables vanish: def double_list(items): return [x * 2 for x in items] # create a new list with doubled elements def main(): values = [2, 4, 6] result = double_list(values) print("Doubled values:", result) if __name__ == "__main__": main() That’s invaluable for unit testing, where you might run specific functions (including  main()) without triggering the whole program. Safety Without the __name__ check, top‑level code runs even on import—usually undesirable and potentially harmful. some.py: print("This code will execute even on import!") def useful_function(): return 42 main.py: import some print("The logic of the imported module executed itself...") Console: This code will execute even on import! 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.

A static method in Python is bound to the class itself rather than any instance of that class. So, you can call it without first creating an object and without access to instance data (self).  To create a static method we need to use a decorator, specifically @staticmethod. It will tell Python to call the method on the class rather than an instance. Static methods are excellent for utility or helper functions that are logically connected to the class but don't need to access any of its properties.  When To Use & Not to Use a Python Static Method Static methods are frequently used in real-world code for tasks like input validation, data formatting, and calculations—especially when that logic naturally belongs with a class but doesn't need its state. Here's an example from a User class that checks email format: class User: @staticmethod def is_valid_email(email): return "@" in email and "." in email This method doesn't depend on any part of the User instance, but conceptually belongs in the class. It can be used anywhere as User.is_valid_email(email), keeping your code cleaner and more organized. If the logic requires access to or modification of instance attributes or class-level data, avoid using a static method as it won't help here. For instance, if you are working with user settings or need to monitor object creation, you will require a class method or an instance method instead. class Counter: count = 0 @classmethod def increment(cls): cls.count += 1 In this example, using a static method would prevent access to cls.count, making it useless for this kind of task. Python Static Method vs Class Method Though they look similar, class and static methods in Python have different uses; so, let's now quickly review their differences. Defined inside a class, a class method is connected to that class rather than an instance. Conventionally called cls, the class itself is the first parameter; so, it can access and change class-level data. Factory patterns, alternate constructors, or any activity applicable to the class as a whole and not individual instances are often implemented via class methods. Conversely, a static method is defined within a class but does not start with either self or cls parameters. It is just a regular function that “lives” inside a class but doesn’t interact with the class or its instances. For utility tasks that are conceptually related to the class but don’t depend on its state, static methods are perfect. Here's a quick breakdown of the Python class/static methods differences: Feature Class Method Static Method Binding Bound to the class Not bound to class or instance First parameter cls (class itself) None (no self or cls) Access to class/instance data Yes No Common use cases Factory methods, class-level behavior Utility/helper functions Decorator @classmethod @staticmethod Python Static Method vs Regular Functions You might ask: why not just define a function outside the class instead of using a static method? The answer is structure. A static method keeps related logic grouped within the class, even if it doesn't interact with the class or its instances. # Regular function def is_even(n): return n % 2 == 0 # Static method inside a class class NumberUtils: @staticmethod def is_even(n): return n % 2 == 0 Both functions do the same thing, but placing is_even inside NumberUtils helps keep utility logic organized and easier to find later. Let’s proceed to the hands-on Python static method examples. Example #1 Imagine that we have a MathUtils class that contains a static method for calculating the factorial: class MathUtils: @staticmethod def factorial(n): if n == 0: return 1 else: return n * MathUtils.factorial(n-1) Next, let's enter: print(MathUtils.factorial(5))120 We get the factorial of 5, which is 120. Here, the factorial static method does not use any attributes of the class instance, only the input argument n. And we called it using the MathUtils.factorial(n) syntax without creating an instance of the MathUtils class. In Python, static methods apply not only in classes but also in modules and packages. The @staticmethod decorator marks a function you define inside a class if it does not interact with instance-specific data. The function exists on its own; it is related to the class logically but is independent of its internal state. Managed solution for Backend development Example #2 Let's say we're working with a StringUtils module with a static method for checking if a string is a palindrome. The code will be: def is_palindrome(string):    return string == string[::-1] This function doesn't rely on any instance-specific data — it simply performs a check on the input. That makes it a good candidate for a static method. To organize it within a class and signal that it doesn't depend on the class state, we can use the @staticmethod decorator like this: class StringUtils:    @staticmethod    def is_palindrome(string):       return string == string[::-1] Let's enter for verification: print(StringUtils.is_palindrome("deed"))True print(StringUtils.is_palindrome("deer"))False That's correct, the first word is a palindrome, so the interpreter outputs True, but the second word is not, and we get False. So, we can call the is_palindrome method through the StringUtils class using the StringUtils.is_palindrome(string) syntax instead of importing the is_palindrome function and calling it directly. - Python static method and class instance also differ in that the static cannot affect the state of an instance. Since they do not have access to the instance, they cannot alter attribute values, which makes sense. Instance methods are how one may modify the instance state of a class. Example #3 Let's look at another example. Suppose we have a Person class that has an age attribute and a static is_adult method that checks the value against the age of majority: class Person:    def __init__(self, age):        self.age = age    @staticmethod    def is_adult(age):       return age >= 21 Next, let's create an age variable with a value of 24, call the is_adult static method from the Person class with this value and store its result in the is_adult variable, like this: age = 24is_adult = Person.is_adult(age) Now to test this, let's enter: print(is_adult)True Since the age matches the condition specified in the static method, we get True. In the example, the is_adult static method serves as an auxiliary tool—a helper function—accepting the age argument but without access to the age attribute of the Person class instance. Conclusion Static methods improve code readability and make it possible to reuse it. They are also more convenient when compared to standard Python functions. Static methods are convenient as, unlike functions, they do not call for a separate import. Therefore, applying Python class static methods can help you streamline and work with your code greatly. And, as you've probably seen from the examples above, they are quite easy to master. On our app platform you can find Python applications, such as Celery, Django, FastAPI and Flask.