📚 Download 10 Python books for FREE:

1. Think Python — O’Reilly
https://t.co/X7y3pX68IW

2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/

3. Data Structures
https://opendatastructures.org/ods-python.pdf

4. Data Science Handbook
https://t.co/9aOLdiAbim

5. Data Analysis
https://wesmckinney.com/book/

6. Data Science
https://t.co/TusyBWGfrx

7. Machine Learning
https://t.co/7veOwESe8q

8. Statistics
https://t.co/waWDQylQpY

9. Think Bayes
https://allendowney.github.io/ThinkBayes2/

10. Clean Code in Python — Packt
https://t.co/OHhVgF1L65

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🔥 Free IT & Dev Study Kits – 2026 Prep, No Signup

If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.

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📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪

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Python is the #1 language in AI. 🐍

But you don’t need to learn all of it. (save this) 📌

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In Python 3.15, a single asterisk replaces nested loops. 🐍✨

Previously, to flatten a list, you would write:

rows = [[1, 2], [3], [4, 5]]

flat = [x for row in rows for x in row]

Now, you can simply use:

flat = [*row for row in rows]
# [1, 2, 3, 4, 5]

A double asterisk merges dictionaries: 📦

configs = [
    {"debug": False, "timeout": 10},
    {"timeout": 30},
]

config = {**part for part in configs}
# {'debug': False, 'timeout': 30}

When keys overlap, the last value wins – this is convenient for combining settings from multiple sources. ⚙️

Unpacking removes one level of nesting. In Python 3.14, this syntax within comprehensions will raise a SyntaxError. 🚫

https://peps.python.org/pep-0798/

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🗂 500+ Cheat Sheets on Machine Learning

This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.

Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.

⛓ You can find it here
https://github.com/gurovic/MLCheatSheets

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🚀 Django 6.1: Loading related data on demand, cascading deletion handled by the database, and a new email configuration.

The release was made on August 5th. Three major changes:

1. New Field Loading Modes 📂

FETCH_PEERS retrieves data for related objects from the same QuerySet when accessing an un-loaded field. This helps combat the N+1 problem:

from django.db import models

books = Book.objects.fetch_mode(models.FETCH_PEERS)

for book in books:
    print(book.author.name)

In this example, Django executes two queries: one for the books and one for their authors.

The FETCH_RAISE mode raises an exception instead of performing an implicit query – useful for detecting unexpected database accesses.

2. Deletion at the Database Level 🗑

For ForeignKey.on_delete, the options DB_CASCADE, DB_SET_NULL, and DB_SET_DEFAULT are now available. The logic is handled directly by the database using SQL ON DELETE.

Note: DB_CASCADE does not trigger pre_delete and post_delete signals.

3. Multiple Email Backends 📧

The MAILERS configuration allows you to define different connections using a dictionary, and you can select the desired connection when sending emails using the using parameter.

Before upgrading, check your database versions: you need PostgreSQL 15+, MySQL 8.4+, MariaDB 10.11+, or SQLite 3.37+.

🔗 https://www.djangoproject.com/weblog/2026/aug/05/django-61-released/

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📝 Cheat sheet for Python:

Higher-order functions:

🔹 map() - applies a function to each element
🔹 filter() - selects elements based on a condition
🔹 reduce() - sequentially combines elements into a single value

These functions help to process collections concisely and perform data transformations.

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🚀 asyncio.Queue.shutdown() — 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. 📝

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. 🚀

We will install the spaCy library and download a pre-trained language model for working with the Russian language. 🇷🇺

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. ✅

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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