Most effective Python hacks for competitive coding | Techniculus

Python is a popular programming language for competitive coding due to its flexibility, readability, and extensive library of modules and packages. Competitive programming in python is very common. Python competitive programming  can be challenging. Whether you're a beginner or an experienced coder, mastering the most effective Python hacks can help you solve problems more quickly and efficiently in programming contests. In this article, we'll explore the top 7 Python hacks for competitive coding, with examples and explanations of each technique.

1. List comprehension

List comprehension is a concise and elegant way to create lists in Python. It allows you to create a new list by applying an expression to each item in an existing list or sequence. Here's an example:

# Generate a list of squares of the numbers from 0 to 9 squares = [i**2 for i in range(10)] # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] print(squares)

In this example, we use a list comprehension to generate a new list of squares of the numbers from 0 to 9. The expression i**2 is applied to each item in the range of numbers, and the resulting list of squares is assigned to the variable 'squares'.

2. Context managers

Context managers are a way to handle resources (such as files) in a safe and efficient manner. They allow you to automatically open and close resources when they are needed and no longer needed, respectively. Here's an example:

# Read a file using a context manager with open('myfile.txt', 'r') as f: for line in f: print(line)

In this example, we use the open() function to open a file named 'myfile.txt' for reading. The with statement creates a context manager that automatically closes the file when the block of code is finished. We then use a for loop to read each line of the file and print it to the console.

3. Decorators

Decorators are a powerful tool for modifying the behavior of functions or classes. They allow you to add functionality to existing code without modifying the code itself. Here's an example:

# Define a decorator def my_decorator(func): def wrapper(): print("Before the function is called.") func() print("After the function is called.") return wrapper # Use the decorator @my_decorator def say_hello(): print("Hello!") say_hello()

In this example, we define a decorator named my_decorator that adds a message before and after a function is called. We then use the decorator on the say_hello() function by adding the @my_decorator annotation before the function definition. When we call say_hello(), the decorator adds the extra messages around the function call.

4. Generators

Generators are a memory-efficient way to generate sequences of values. They allow you to create a sequence of values on the fly, rather than generating the entire sequence at once. Here's an example:

# Define a generator function def squares(n): for i in range(n): yield i**2 # Use the generator for x in squares(10): print(x)

In this example, we define a generator function named squares that yields the squares of the numbers from 0 to n. When we use the generator in a for loop, it generates each square value on the fly, rather than generating the entire sequence of squares at once. This can be much more memory-efficient for large sequences.

5. Monkey Patching

Monkey patching is a way to dynamically modify code at runtime. It allows you to replace or add functionality to existing objects, modules, or classes. Here's an example:

# Define a class with a method class MyClass: def my_method(self): print("Original method") # Create an instance of the class obj = MyClass() # Define a new method and monkey patch it onto the object def new_method(self): print("New method") obj.my_method = new_method # Call the new method obj.my_method()

In this example, we define a class MyClass with a method my_method(). We create an instance of the class and then define a new function new_method() that we monkey patch onto the object obj. When we call obj.my_method(), it now calls the new method we defined, rather than the original method.

6. Memoization

Memoization is a way to optimize code by caching the results of expensive function calls. It allows you to avoid recomputing the same results multiple times, which can be especially useful in recursive algorithms. Here's an example:

# Define a function to compute the nth Fibonacci number def fib(n): if n <= 1: return n return fib(n-1) + fib(n-2) # Use memoization to cache results cache = {} def fib_cached(n): if n in cache: return cache[n] if n <= 1: result = n else: result = fib_cached(n-1) + fib_cached(n-2) cache[n] = result return result # Call the cached function print(fib_cached(10))

In this example, we define a function fib() to compute the nth Fibonacci number. We then define a cached version of the function, fib_cached(), that uses a dictionary cache to store the results of expensive function calls. When we call fib_cached(10), the function uses the cached results for the smaller values of n and only computes the remaining values, making the function much faster for larger values of n.

7. Zip

Zip is a built-in Python function that allows you to iterate over multiple sequences in parallel. It creates a new sequence of tuples, where each tuple contains one element from each of the input sequences. Here's an example:

# Zip two lists and iterate over the resulting tuples names = ["Alice", "Bob", "Charlie"] ages = [25, 30, 35] for name, age in zip(names, ages): print(name, age)

In this example, we use the zip() function to combine two lists of names and ages into a sequence of tuples. We then use a for loop to iterate over the tuples and print each name and age pair to the console.

In conclusion, mastering these Python hacks can help you become a faster and more efficient coder in competitive programming. By using techniques such as list comprehension, context managers, decorators, generators, monkey patching, memoization, and zip, you can optimize your code and solve problems more quickly and elegantly.