How do I paginate tuples in Python with type hints?

In Python, you can paginate tuples by slicing them into smaller chunks. This can be particularly useful when handling large datasets. Below is an example of how to implement pagination with tuples using type hints.

pagination, tuples, Python, slicing, type hints

This guide describes how to paginate tuples in Python. Learn the slicing technique to manage large collections of data effectively.

def paginate_tuples(data: tuple, page: int, page_size: int) -> tuple: """Paginate through a tuple of data.""" start_index = (page - 1) * page_size end_index = start_index + page_size return data[start_index:end_index] # Example usage: my_tuple = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j') page_number = 2 items_per_page = 3 paginated_result = paginate_tuples(my_tuple, page_number, items_per_page) print(paginated_result) # Output: ('d', 'e', 'f')

pagination tuples Python slicing type hints

Related Questions

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?