Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸš€ asyncio.Queue.shutdown() β€” shutting down handlers without special markers

Starting with Python 3.13, the asyncio.Queue can be closed using shutdown(), without the need to use None or other special values.

import asyncio

async def worker(queue):
while True:
try:
task = await queue.get()
except asyncio.QueueShutDown:
break

print(task)
queue.task_done()

async def main():
queue = asyncio.Queue()
task = asyncio.create_task(worker(queue))

await queue.put("task-1")
await queue.put("task-2")

queue.shutdown()
await queue.join()
await task

asyncio.run(main())

After shutdown(), no new items are added, and existing items are processed. When the queue is empty, get() will raise asyncio.QueueShutDown.

For a forced shutdown, there is shutdown(immediate=True), which clears the queue but may disrupt the normal behavior of join().

#Python #Asyncio #Programming #DevTips #Coding #Tech

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Extracting named entities (NER) from contract texts using spaCy. πŸ“

Manually parsing legal documents to find organizations, dates, and amounts is too time-consuming. The spaCy library allows you to automatically find and classify named entities in unstructured text. We will write a Python script that loads a language model and recognizes key entities in a contract excerpt. πŸš€

We will install the spaCy library and download a pre-trained language model for working with the Russian language. πŸ‡·πŸ‡Ί

pip install spacy && python3 -m spacy download ru_core_news_sm


The spaCy library and the Russian language model have been successfully loaded into the environment. βœ…

We will create a Python script called extract_ner.py that processes the contract text through a language pipeline and prints the identified entities. 🐍

import spacy

nlp = spacy.load("ru_core_news_sm")
text = "OOO 'Vector' undertakes to pay 500,000 rubles by October 15, 2026, in Moscow."
doc = nlp(text)

for ent in doc.ents:
print(f"{ent.text}


#spacy #python #nlp #dataScience #programming #automation

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