Python plays Twister: 8 absurd examples of complex code

Programming is an art of balancing simplicity and complexity. But there is a peculiar attraction to complexity that we as developers often fall into. Let's dive into this complexity trap, look at some common tasks and see how they can be overcomplicated in non-optimal ways.

Task 1: Addition of two numbers

The easy way:

def add(x, y):
    return x + y

The complexity trap:

def add(x, y):
    if y == 0:
        return x
    else:
        return add(x ^ y, (x & y) << 1)

In the second example, the developer is trying to implement a bitwise addition algorithm. It's a fascinating concept, but it's overkill for this basic task and can lead to problems like infinite loops when dealing with negative numbers or floating point numbers.

Task 2: Finding the maximum value in a list

The easy way:

def find_max(list):
    return max(list)

The complexity trap:

def find_max(list):
    max_val = list[0]
    for i in range(len(list)):
        for j in range(i, len(list)):
            if list[j] > max_val:
                max_val = list[j]
    return max_val

In the second example, the developer unnecessarily iterates over the list several times. It is a wasteful operation that increases the time complexity to O(n^2), which is suboptimal for a task that can be solved in O(n).

Task 3: Inversion of a string

The easy way:

def reverse_string(string):
    return string[::-1]

The complexity trap:

def reverse_string(string):
    reversed_string = ""
    index = len(string) - 1
    while index >= 0:
        reversed_string += string[index]
        index -= 1
    return reversed_string

Although the complex way is not necessarily wrong, it is certainly less efficient and more error-prone than using Python's built-in slicing feature. For example, off-by-one errors are a common bug in the manual implementation.

Task 4: Convert a string to lowercase letters

Simple type:

def to_lower_case(string):
    return string.lower()

Complexity Trap:

def to_lower_case(string):
    result = ""
    for char in string:
        ascii_value = ord(char)
        if 65 <= ascii_value <= 90:
            result += chr(ascii_value + 32)
        else:
            result += char
    return result

In this more complicated example, the ASCII value of each character is unnecessarily checked and manually converted to lowercase, even though Python has a built-in function for this.

Task 5: Check if a number is even

Simple type:

def is_even(n):
    return n % 2 == 0

Complexity Trap:

def is_even(n):
    binary_repr = bin(n)
    return binary_repr[-1] == '0'

Here the binary representation of the number is used to check if it is even. While this is technically correct (even numbers have a 0 in the last digit in binary representation), it is unnecessarily complex and less readable than using the modulo operator.

Task 6: Sort a list

Simple type:

def sort_list(list):
    return sorted(list)

Complexity Trap:

def sort_list(list):
    for i in range(len(list)):
        for j in range(len(list) - 1):
            if list[j] > list[j + 1]:
                list[j], list[j + 1] = list[j + 1], list[j]
    return list

Here the developer implements the bubble sort algorithm manually, even though Python has a built-in function for it. This increases the complexity and the chance of errors.

Task 7: Find an element in a list

Simple type:

def find_element(list, element):
    return element in list

Complexity Trap:

def find_element(list, element):
    try:
        list.index(element)
        return True
    except ValueError:
        return False

Instead of simply using the in operator, this code tries to find the index of the element and catches an error if the element is not found. This is unnecessarily complicated and results in slower code.

Task 8: Checking whether a string is a palindrome

Simple type:

def is_palindrome(string):
    return string == string[::-1]

Complexity Trap:

def is_palindrome(string):
    for i in range(len(string) // 2):
        if string[i] != string[len(string) - i - 1]:
            return False
    return True

In this case, a manual iteration process is used to compare each pair of characters instead of using Python's built-in ability to slice and flip strings. This results in more complex and less efficient code.

Lessons from the trap

Overcomplicating tasks doesn't make you a better programmer. Instead, it can make your code harder to understand, harder to maintain, and more prone to bugs. Here are some takeaways that can help you avoid the complexity trap:

  1. Know your tools: Modern programming languages have a large number of built-in Features and Libraries, which are designed to make your life easier. Use them.
  2. Keep it simple: The best solution is often the simplest. Avoid unnecessary loops, recursions or bit operations if a straightforward solution is available.
  3. Readability counts: Code is read more often than it is written. Make sure your code is clean and easy to understand.
  4. Test your code: Testing always use your code, especially if you have a complex algorithm try out. This helps to detect bugs and performance problems at an early stage.
  5. Contact an expert for a Python trainingOften the experience of experts helps to accelerate one's own learning curve.

Remember, the goal of coding is not to create complex algorithms that only you can understand, but to solve problems in the most efficient and maintainable way. Be aware of the complexity trap and strive for simplicity and clarity in your code.

"
"
Elizaveta Ezhergina Avatar

Latest articles