T & M

The use of namedtuple provides a convenient way to create simple classes for storing data without the need to define a separate class explicitly. It combines the simplicity of tuples with the readability of named fields, making the code more expressive and self-documenting.

The dictionary comprehension generates a dictionary where each key is a number from 1 to 5, and each corresponding value is the square of that number. Dictionary comprehension is a concise and expressive way to create dictionaries in Python. It allows you to specify the key-value pairs in a single line, making the code more readable and efficient.

The functools.partial function is used here to create specialized versions of the original power function. It allows you to fix certain parameters of a function and obtain a new function with the remaining parameters to be provided later. This is particularly useful when you want to create functions with default or fixed arguments for specific use cases. In this example, square and cube are partial functions derived from the more general power function.

This code demonstrates set operations in Python, specifically the intersection and union operations. Sets in Python are unordered collections of unique elements, and these operations provide useful ways to combine or find common elements between sets.

This code efficiently filters and generates a list of prime numbers using the is_prime function within a list comprehension. The is_prime function checks for primality, and the list comprehension creates a list of numbers meeting the prime condition.

This code efficiently creates a new list by applying a specific operation (x**2) only to elements that satisfy a given condition (if x % 2 == 0 or (if x % 2 == 1). List comprehensions are concise and expressive ways to generate lists in Python.

Function Definition: reverse_string is a function that takes a string s as input and returns its reverse using Python's slicing syntax. Slicing to Reverse: s[::-1] is a slicing operation that creates a reversed copy of the original string s. The [::-1] part means to start from the end of the string and move backward with a step of -1. Applying the Function: reversed_string is assigned the result of applying the reverse_string function to original_string.

In this code: Function Definition: countdown is a recursive function that prints numbers in descending order until reaching 0. Base Case: The base case checks if n is less than or equal to 0. If true, it prints "Blastoff!" to indicate the end of the countdown. Recursive Case: If n is greater than 0, it prints the current value of n and calls itself recursively with n-1.

In this code: The fibonacci function takes an integer n as input. The base case checks if n is 0 or 1, in which case the function returns n. If n is greater than 1, the function recursively calls itself with n-1 and n-2 and sums the results. You can modify the range in the list comprehension to generate a different number of Fibonacci numbers. Keep in mind that the recursive approach might become inefficient for large n due to redundant calculations; in such cases, dynamic programming or iterative methods are often preferred.

In this code: The factorial function takes an integer n as input. The base case checks if n is 0 or 1, in which case the factorial is 1. If n is greater than 1, the function recursively calls itself with n-1 and multiplies the result by n.In this code: The factorial function takes an integer n as input. The base case checks if n is 0 or 1, in which case the factorial is 1. If n is greater than 1, the function recursively calls itself with n-1 and multiplies the result by n.

The function essentially uses recursion to repeatedly multiply the base by itself until the exponent becomes 0, at which point it returns 1. This recursive approach mirrors the mathematical definition of exponentiation, making it a clear and concise way to calculate powers. However, keep in mind that for large exponents, this recursive approach may not be the most efficient due to repeated calculations.

Function Definition: is_palindrome is a recursive function that checks whether a given string s is a palindrome. String Conversion: s = s.lower() converts the input string to lowercase. This ensures case-insensitive comparison. Base Case: The base case checks if the length of the modified string s is 1 or less. If true, it means the string is a palindrome (or an empty string), and the function returns True. Recursive Case: If the length of the string is greater than 1, it checks if the first and last characters of the string are equal (s[0] == s[-1]). If true, it calls itself recursively with the substring excluding the first and last characters (is_palindrome(s[1:-1])). Returning Results: If any recursive call or the base case returns False, the function returns False. If all recursive calls and the base case return True, the function returns True. Example: The code then applies the is_palindrome function to the string "radar". The result is printed, indicating whether "radar" is a palindrome. Output: Running this code will output True, indicating that "radar" is indeed a palindrome. This function efficiently checks for palindromes in a case-insensitive manner, making it suitable for various scenarios where case sensitivity is not required.

In this example: FileHandler is a class serving as a context manager. The __enter__ method is responsible for setting up the context, in this case, opening a file in the specified mode. The __exit__ method is called when exiting the with block and is responsible for cleaning up, such as closing the file. When the with block is entered, the file is opened, and when the block is exited, the file is automatically closed. The use of context managers improves code readability and ensures proper resource management. Python also provides the contextlib module for creating context managers using the contextmanager decorator.

In this example, the get_person_details function returns a tuple with three values (name, age, and city). The individual values can be assigned to variables during the function call or afterward. Using multiple return values is a convenient way to package related pieces of information together and make your code more expressive.

This example demonstrates the use of various generator expressions for different scenarios, showcasing their flexibility and efficiency. The list() function is used to convert the generators to lists for easy printing.

In this example: my_decorator is a decorator function that takes another function (func) as an argument. wrapper is a nested function inside the decorator. It performs some actions before and after calling the original function (func). The @my_decorator syntax is a shorthand for applying the decorator to the say_hello function. When say_hello("Alice") is called, it is equivalent to my_decorator(say_hello)("T & M"). The wrapper function is executed, adding behavior before and after the original function call.

In this example, the display_info function takes three arguments (name, age, and city). Instead of passing each element of the person_info tuple separately, we can use unpacking to pass all elements in a more concise way. The line display_info(*person_info) is equivalent to display_info(person_info[0], person_info[1], person_info[2]).

These examples demonstrate how list comprehensions with conditional expressions can be used to create new lists based on specific criteria.

Nullish coalescing (??) provides a concise way to use a fallback value when dealing with null or undefined values, excluding other falsy values like 0 or an empty string. This operator can make your code more robust and concise, especially when dealing with data structures where certain properties or values may be missing or undefined.

In this example: Optional chaining (?.) simplifies the process of checking nested properties or calling functions within an object, preventing errors when properties or functions are missing.This operators can make your code more robust and concise, especially when dealing with data structures where certain properties or values may be missing or undefined.

Destructuring can make your code more concise and readable, especially when working with complex data structures. It's a powerful feature that enhances the expressiveness of your JavaScript code.

Destructuring can make your code more concise and readable, especially when working with complex data structures. It's a powerful feature that enhances the expressiveness of your JavaScript code.

Destructuring can make your code more concise and readable, especially when working with complex data structures. It's a powerful feature that enhances the expressiveness of your JavaScript code.

In this illustration: The sum function uses the rest operator (...numbers) to accept any number of arguments and calculates their sum using reduce. The spread operator (...) is used to combine arrays (arr1 and arr2) into a new array (combinedArray). The spread operator is used for object spreading, merging the properties of person and details objects. The rest operator is used in array destructuring to capture the first element and the rest of the elements. The rest operator in object destructuring is used to capture the name property and collect the remaining properties in otherDetails. These operators are powerful tools for handling variable numbers of arguments, combining arrays or objects, and more. They enhance the flexibility and readability of your JavaScript code.

In this illustration: The fetchData function returns a Promise that resolves with simulated data after a delay of 2000 milliseconds. It also has the potential to simulate an error by rejecting the Promise. The fetchDataAsync function is declared as async, allowing the use of the await keyword inside it. The await keyword is used to pause the execution of the function until the Promise returned by fetchData either resolves or rejects. Inside the try block, the await fetchData() statement pauses the function until the Promise is resolved. If the Promise resolves successfully, the result is logged. If there's an error, it is caught in the catch block, and the error message is logged. The fetchDataAsync function is then called, initiating the asynchronous operation. This code structure makes asynchronous code more readable and easier to understand, especially when dealing with multiple asynchronous operations.