In Python, you can reduce lists using various approaches. The most common method is by using the `reduce()` function from the `functools` module. This function applies a rolling computation to sequential pairs of values in a list.
Here are a few examples of how to reduce lists in Python:
from functools import reduce
# Example 1: Sum of all elements in a list
numbers = [1, 2, 3, 4, 5]
total = reduce(lambda x, y: x + y, numbers)
print("Sum:", total) # Output: Sum: 15
# Example 2: Finding the product of all elements in a list
numbers = [1, 2, 3, 4, 5]
product = reduce(lambda x, y: x * y, numbers)
print("Product:", product) # Output: Product: 120
# Example 3: Finding the maximum element in a list
numbers = [1, 3, 2, 5, 4]
maximum = reduce(lambda x, y: x if x > y else y, numbers)
print("Maximum:", maximum) # Output: Maximum: 5
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?