How do I deduplicate dicts in Python in an async application?

In Python, when working with dictionaries, you may encounter situations where you need to remove duplicate dictionaries from a collection, especially in an asynchronous application. Below is an example of how to achieve this using Python's built-in functionalities, along with asynchronous programming principles.

Example of Deduplicating Dicts

import asyncio async def deduplicate_dicts(dict_list): seen = set() unique_dicts = [] for d in dict_list: # Create a hashable representation of the dictionary dict_tuple = tuple(sorted(d.items())) if dict_tuple not in seen: seen.add(dict_tuple) unique_dicts.append(d) return unique_dicts async def main(): dicts = [ {'id': 1, 'name': 'Alice'}, {'id': 2, 'name': 'Bob'}, {'id': 1, 'name': 'Alice'}, # Duplicate ] deduped = await deduplicate_dicts(dicts) print(deduped) asyncio.run(main())

Python Async Deduplicate Dictionaries Remove Duplicates Asynchronous Programming

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?