Input in Python

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. 

Python operators are tools used to perform various actions with variables, as well as numerical and other values called operands—objects on which operations are performed. There are several types of Python operators: Arithmetic Comparison Assignment Identity Membership Logical Bitwise This article will examine each of them in detail and provide examples. Arithmetic Operators For addition, subtraction, multiplication, and division, we use the Python operators +, -, *, and / respectively: >>> 24 + 28 52 >>> 24 - 28 -4 >>> 24 * 28 672 >>> 24 / 28 0.8571428571428571 For exponentiation, ** is used: >>> 5 ** 2 25 >>> 5 ** 3 125 >>> 5 ** 4 625 For floor division (integer division without remainder), // is used: >>> 61 // 12 5 >>> 52 // 22 2 >>> 75 // 3 25 >>> 77 // 3 25 The % operator returns the remainder (modulo division): >>> 62 % 6 2 >>> 65 % 9 2 >>> 48 % 5 3 >>> 48 % 12 0 Comparison Operators Python has six comparison operators: >, <, >=, <=, ==, !=. Note that equality in Python is written as ==, because a single = is used for assignment. The != operator is used for "not equal to." When comparing values, Python will return True or False depending on whether the expressions are true or false. >>> 26 > 58 False >>> 26 < 58 True >>> 26 >= 26 True >>> 58 <= 57 False >>> 50 == 50 True >>> 50 != 50 False >>> 50 != 51 True Assignment Operators A single = is used for assigning values to variables: >>> b = 5 >>> variants = 20 Python also provides convenient shorthand operators that combine arithmetic operations with assignment: +=, -=, *=, /=, //=, %=. For example: >>> cars = 5 >>> cars += 7 >>> cars 12 This is equivalent to: >>> cars = cars + 7 >>> cars 12 The first version is more compact. Other assignment operators work similarly: >>> train = 11 >>> train -= 2 >>> train 9 >>> moto = 3 >>> moto *= 7 >>> moto 21 >>> plain = 8 >>> plain /= 4 >>> plain 2.0 Notice that in the last case, the result is a floating-point number (float), not an integer (int). Identity Operators Python has two identity operators: is and is not. The results are True or False, similar to comparison operators. >>> 55 is 55 True >>> 55 is 56 False >>> 55 is not 55 False >>> 55 is not 56 True >>> 55 is '55' False >>> '55' is "55" True In the last two examples: 55 is '55' returned False because an integer and a string were compared. '55' is "55" returned True because both operands are strings. Python does not differentiate between single and double quotes, so the identity check was successful. Membership Operators There are only two membership operators in Python: in and not in. They check whether a certain value exists within a sequence. For example: >>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach') >>> 'house' in wordlist False >>> 'assistant' in wordlist True >>> 'assistant' and 'streetcar' in wordlist True In the last case, a logical operator (and) was used, which leads us to the next topic. Logical Operators Python has three logical operators: and, or, and not. and returns True only if all operands are true. It can process any number of values. Using an example from the previous section: >>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach') >>> 'assistant' and 'streetcar' in wordlist True >>> 'fraudster' and 'dancer' and 'heat' and 'blank' in wordlist True >>> 'fraudster' and 'dancer' and 'heat' and 'blank' and 'house' in wordlist False Since 'house' is not in the sequence, the result is False. These operations also work with numerical values: >>> numbers = 54 > 55 and 22 > 21 >>> print(numbers) False One of the expressions is false, and and requires all conditions to be true. or works differently: it returns True if at least one operand is true. If we replace and with or in the previous example, we get: >>> numbers = 54 > 55 or 22 > 21 >>> print(numbers) True Here, 22 > 21 is true, so the overall expression evaluates to True, even though 54 > 55 is false. not reverses logical values: >>> first = True >>> second = False >>> print(not first) False >>> print(not second) True As seen in the example, not flips True to False and vice versa. Bitwise Operators Bitwise operators are used in Python to manipulate objects at the bit level. There are five of them (shift operators belong to the same type, as they differ only in shift direction): & (AND) | (OR) ^ (XOR) ~ (NOT) << and >> (shift operators) Bitwise operators are based on Boolean logic principles and work as follows: & (AND) returns 1 if both operands contain 1; otherwise, it returns 0: >>> 1 & 1 1 >>> 1 & 0 0 >>> 0 & 1 0 >>> 0 & 0 0 | (OR) returns 1 if at least one operand contains 1, otherwise 0: >>> 1 | 1 1 >>> 1 | 0 1 >>> 0 | 1 1 >>> 0 | 0 0 ^ (XOR) returns 1 if the operands are different and 0 if they are the same: >>> 1 ^ 1 0 >>> 1 ^ 0 1 >>> 0 ^ 1 1 >>> 0 ^ 0 0 ~ (NOT) inverts bits, turning positive values into negative ones with a shift of one: >>> ~5 -6 >>> ~-5 4 >>> ~7 -8 >>> ~-7 6 >>> ~9 -10 >>> ~-9 8 << and >> shift bits by a specified number of positions: >>> 1 << 1 2 >>> 1 >> 1 0 To understand shifts, let’s break down values into bits: 0 = 00 1 = 01 2 = 10 Shifting 1 left by one bit gives 2, while shifting right results in 0. What happens if we shift by two positions? >>> 1 << 2 4 >>> 1 >> 2 0 1 = 001 2 = 010 4 = 100 Shifting 1 two places to the left results in 4 (100 in binary). Shifting right always results in zero because bits are discarded. For more details, refer to our article on bitwise operators. Difference Between Operators and Functions You may have noticed that we have included no functions in this overview. The confusion between operators and functions arises because both perform similar actions—transforming objects. However: Functions are broader and can operate on strings, entire blocks of code, and more. Operators work only with individual values and variables. In Python, a function can consist of a block of operators, but operators can never contain functions.

In this article, we will explain the features of while loops in Python and address common issues that beginners often encounter. What is the while Statement and How Does It Work? Looping constructs in programming languages are used to repeat certain blocks of code until a specified condition is met. One such construct in Python is the while loop.  Here's an example of a while loop in Python: number = 0 while number < 3: print('The condition is true, the variable number is less than three and is now', number, ', so the loop continues to execute.') number += 1 print('We have exited the loop because the value of number became', number, ', and the condition is no longer true.') input('Press Enter to exit') When we run this program, we get the following output: The condition is true, the variable number is less than three and is now 0, so the loop continues to execute. ... We have exited the loop because the value of number became 3, and the condition is no longer true. Here's a snippet of a more realistic program: while score1 < 5 and score2 < 5: print('The game continues because no one has scored 5 points yet.') If we translate this Python statement into plain English (which can be useful for beginners), it reads:  "As long as the number of points of the first player (score1) or the number of points of the second player (score2) is less than 5, display the message: ‘The game continues because no one has scored 5 points yet.’" The Python interpreter evaluates the while condition (score1 < 5 and score2 < 5) as True, meaning the loop continues until it becomes False (i.e., when one of the players scores 5 points, ending the game). Let's look at a simple dice game program and analyze how it works: import random score1 = 0 score2 = 0 die1 = 0 die2 = 0 input('Player 1 rolls the dice, press Enter!\n') while score1 < 5 and score2 < 5: die1 = random.randint(1, 6) print(die1, '\n') input('Now Player 2 rolls the dice, press Enter!\n') die2 = random.randint(1, 6) print(die2, '\n') if die1 > die2: score1 += 1 elif die1 < die2: score2 += 1 else: print('It’s a tie!\n') print('Score', score1, ':', score2, '\n') input('Press Enter!\n') if score1 > score2: input('Player 1 wins! Press Enter to exit') else: input('Player 2 wins! Press Enter to exit') In this program: We use the random module to generate random values for the dice rolls. We declare variables to track each player’s score and dice roll results. The while loop ensures the game continues until one of the players reaches 5 points. The logical and operator ensures the loop stops when either score1 or score2 reaches 5. This program has one minor flaw: in theory, the random number generator might produce a large number of ties, requiring players to press Enter repeatedly. This could create a situation similar to an infinite loop, so we need to modify the program to limit the number of iterations. One possible solution is to introduce an additional variable that tracks the number of rounds played. We will also discuss methods for dealing with actual infinite loops in the next section. Handling Infinite Loops Infinite loops are one of the biggest challenges programmers face. When a program gets stuck in an infinite loop, it can cause memory overflow or excessive CPU usage, leading to application crashes or even system freezes. To understand what an infinite loop is, let's look at this simple Python program: number = 1 while number > 0: print(number) number += 1 Run this code in the Python interpreter and observe the result. The program will keep printing numbers indefinitely (or until your computer runs out of memory). Now, let's kill the program by closing the IDE and analyze what happened: number = 1 We initialize a variable number and set it to 1. while number > 0: print(number) number += 1 We create a loop that keeps running as long as number is greater than 0. Since number is always increasing, the condition will never become false, meaning the loop will never stop. Another way to create an infinite loop is: number = 1 while number == 1: print(number) While intentionally creating infinite loops is sometimes useful (e.g., in game loops or servers), they usually happen by accident because programmers overlook certain conditions. Consider this program, where we try to visit as many cities as possible before running out of fuel: import random fuel = 30 cities = 0 consumption = random.randint(1, 10) while fuel != 0: cities += 1 fuel -= consumption consumption = random.randint(1, 10) print('You have traveled through', cities, 'cities') input('Out of fuel! Press Enter to exit the car.') At first glance, this seems correct. However, when we run the program, we notice something strange: You have traveled through 298 cities You have traveled through 299 cities You have traveled through 300 cities ... The program never stops, even though we expected it to end when the fuel reached zero. To find the issue, let's modify the print statement to track the remaining fuel: print('You have traveled through', cities, 'cities, and you have', fuel, 'liters of fuel left.') Now we get: You have traveled through 6 cities, and you have 5 liters of fuel left. You have traveled through 7 cities, and you have -4 liters of fuel left. You have traveled through 8 cities, and you have -8 liters of fuel left. The problem is in the condition while fuel != 0. The fuel never becomes exactly 0; instead, it skips to a negative value. If, by pure chance, fuel had become 0, the loop would have ended, but that’s not guaranteed. Since fuel was never exactly 0, the condition remained true, and the program continued running forever. We just need to change one symbol in the condition: import random fuel = 30 cities = 0 consumption = random.randint(1, 10) while fuel > 0: cities += 1 fuel -= consumption consumption = random.randint(1, 10) print('You have traveled through', cities, 'cities') input('Out of fuel! Press Enter to exit the car.') Now, the condition while fuel > 0 ensures the loop only runs while there is fuel left. You have traveled through 5 cities Out of fuel! Press Enter to exit the car. The loop correctly stops when the fuel runs out, and the program moves to the next block of code. Writing a Program with while and Conditional Branching Conditional branching in Python is implemented using if-elif-else statements. When combined with loops, they allow for highly flexible and dynamic programs. Let’s write a simple sports simulator. Imagine a hockey final between The Montreal Canadiens and The Maple Leafs. The main game time ended in a draw, so the match continues until the first goal is scored: import random goal1 = 0 goal2 = 0 time = 1 while goal1 == 0 and goal2 == 0: attack = random.randint(1, 2) if attack == 1: shoot = random.randint(1, 2) if shoot == 1: keeper = random.randint(1, 2) if keeper == 1: print('The Maple Leafs’ goalkeeper saved the puck, the match continues') time += 1 attack = random.randint(1, 2) else: print('Goal! The Montreal Canadiens wins 1:0!') goal1 += 1 else: print('The Montreal Canadiens forward missed the goal') time += 1 attack = random.randint(1, 2) else: shoot = random.randint(1, 2) if shoot == 1: keeper = random.randint(1, 2) if keeper == 1: print('The Montreal Canadiens’ goalkeeper saved the puck, the match continues') time += 1 attack = random.randint(1, 2) else: print('Goal! The Maple Leafs wins 0:1!') goal2 += 1 else: print('The Maple Leafs forward missed the goal') time += 1 attack = random.randint(1, 2) print('The match ended in the', time, 'minute of overtime.') input() The while loop ensures that the game continues until one of the teams scores. Theoretically, this loop could run infinitely, but the probability of a random number generator never selecting a goal is quite low. However, it's always a good idea to limit the number of iterations. Can you think of how to do that? (Hint: Add a condition to limit the variable time to a maximum number of minutes.) In our simulation, the first overtime was quite intense—both teams had four goal attempts before The Maple Leafs finally scored: The Montreal Canadiens forward missed the goal The Montreal Canadiens’ goalkeeper saved the puck, the match continues The Maple Leafs’ goalkeeper saved the puck, the match continues The Montreal Canadiens forward missed the goal The Maple Leafs forward missed the goal The Maple Leafs’ goalkeeper saved the puck, the match continues The Montreal Canadiens’ goalkeeper saved the puck, the match continues The Montreal Canadiens’ goalkeeper saved the puck, the match continues Goal! The Maple Leafs wins 0:1! The match ended in the 9th minute of overtime. This is similar to how sports simulators and manager games work—except that real games take many more factors into account. However, the core logic of all complex programs follows the same principles: they are built using loops, conditions, and interactive elements like the input() function. Loop Control Statements: break and continue The while loop has two powerful assistants: break immediately exits the loop when a condition is met. continue skips an iteration but allows the loop to continue. These statements greatly enhance a program’s structure. Here’s an elegant way to handle user input errors without crashing the program: while True: try: number = int(input('Enter a number: ')) break except ValueError: print('Invalid input, please enter a valid number.') continue print('Thank you!') If the user enters a valid number, break stops the loop. If the user enters text or symbols, the except block catches the error, prints a message, and continue restarts the loop. This way, the program does not crash but patiently waits for valid input.