When working with large datasets in Python, paginating tuples can help manage memory efficiently while allowing you to process content in manageable chunks. This is particularly useful when dealing with large amounts of data, as it prevents loading everything into memory at once.
You can create a simple pagination function that takes a list of tuples and a page size as inputs and returns the relevant subset of tuples for a given page. Below is an example:
def paginate_tuples(data, page_size, page_number):
start_index = (page_number - 1) * page_size
end_index = start_index + page_size
return data[start_index:end_index]
# Example usage
tuples_data = [(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E'), (6, 'F')]
page_size = 2
page_number = 2
page = paginate_tuples(tuples_data, page_size, page_number)
print(page) # Output: [(3, 'C'), (4, 'D')]
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?