How do I paginate dicts in Python using NumPy?

In Python, you can paginate dictionaries using NumPy to efficiently split large datasets into manageable chunks. Below is a simple example that demonstrates how to achieve this.

import numpy as np def paginate_dict(data, items_per_page): items = list(data.items()) total_pages = int(np.ceil(len(items) / items_per_page)) for page in range(total_pages): start = page * items_per_page end = start + items_per_page yield dict(items[start:end]) # Example usage my_dict = {i: f"Item {i}" for i in range(1, 101)} # A dictionary with 100 items for page in paginate_dict(my_dict, 10): # Paginate with 10 items per page print(page)

pagination Python NumPy dictionaries data management

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?