Here's a small fact about Python 🐍

The := operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus 🦭

It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.

For example:

while (line := input("Say something: ")) != "quit":
    print(f"You said: {line}")

Without it, you would have to retrieve the value separately using input(), and then check it.

Have you ever used the := operator in your code?

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Do not violate the Single Responsibility Principle 🎯

A function should do one thing, and do it well.

This function does too much:

def calculate_final_total(
    price: float,
    quantity: int,
    discount_rate: float,
    tax_rate: float
) -> float:
    # Calculate the subtotal
    subtotal = price * quantity

    # Apply the discount
    discounted_amount = subtotal * (1 - discount_rate)

    # Calculate the tax
    final_total = discounted_amount * (1 + tax_rate)

    return final_total

The problem here is that the calculation of the subtotal, discount, and tax are all combined into one function. Any change to one of these steps can affect the entire calculation.

It's better to break down the logic into smaller, more specialized functions:

def calculate_subtotal(price: float, quantity: int) -> float:
    return price * quantity

def apply_discount(subtotal: float, discount: float) -> float:
    return subtotal * (1 - discount)

def calculate_tax(amount: float, tax_rate: float) -> float:
    return amount * (1 + tax_rate)

This is much better. ✅

Smaller functions with a single task are easier to test with unit tests because they have fewer dependencies and require less mocking.

Furthermore, isolated components are easier to reuse in different parts of the application or pipeline without bringing in unnecessary dependencies.

Therefore, keep your functions simple and focused.

One function – one responsibility. 📝

#Python #Coding #SoftwareDevelopment #CleanCode #Programming #BestPractices

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The tell() function in Python 🐍

The tell() function returns the current position of the file pointer within the data stream. It is most often used when working with files. 📂

The function does not accept any arguments and returns an integer – the position in bytes from the beginning of the stream. 🔢

with open("file.txt", "rb") as f:
    print(f.tell())

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📌 Marking Deprecated Code in Python: deprecated()

In large projects, old functions are not always immediately removed. They are often left for compatibility, but it's important to warn developers that they should no longer be used.

Previously,

warnings.warn()

was often used for this purpose:

import warnings

def old_api():
    warnings.warn(
        "Use new_api()",
        DeprecationWarning
    )

In Python 3.13, a

deprecated()

decorator has been introduced:

from warnings import deprecated

@deprecated("Use new_api()")
def old_api():
    return "old"

Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools.

This also works for classes:

@deprecated("Use NewClient")
class OldClient:
    pass

This is convenient for libraries, SDKs, and large projects where the API is gradually changing.

#Python #Python313 #Deprecated #Coding #Programming #SoftwareDevelopment

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👩‍💻 heapq.merge(): Combining sorted data!

If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again. heapq.merge() combines such sources into a single, ordered iterator.

In this guide:
• We will combine multiple sorted sequences;
• We will explore lazy processing of large data sources;
• We will configure comparison using the key argument;
• We will combine data sorted in reverse order.

This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.

#Python #heapq #DataProcessing #CodingTips #Programming #Algorithms

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Using pickletools.dis() to analyze serialized data.

🐍 pickle doesn't just store a snapshot of an object; it stores a sequence of instructions for its subsequent reconstruction.

Normally, we only see the result of serialization:

import pickle

payload = pickle.dumps(
    {"name": "Alex", "roles": ["admin", "user"]}
)

print(len(payload))

The standard library includes pickletools.dis(), which disassembles the pickle stream and shows its instructions in a readable format.

import pickletools

pickletools.dis(payload)

The output shows the creation of a dictionary, strings, a list, and the operations used to assemble the final object.

EMPTY_DICT
SHORT_BINUNICODE 'name'
SHORT_BINUNICODE 'Alex'
SHORT_BINUNICODE 'roles'
EMPTY_LIST

This is useful when debugging your own classes: you can check which global objects and reconstruction mechanisms are included in the serialization.

class User:
    def init(self, name):
        self.name = name

payload = pickle.dumps(User("Alex"))

pickletools.dis(payload)

For further analysis, there's pickletools.optimize(): it removes some unused operations from the pickle stream without changing the object being reconstructed.

optimized = pickletools.optimize(payload)

assert pickle.loads(optimized).name == "Alex"

⚠️ pickletools is designed for analyzing pickle streams, not for safely reading untrusted data. You should still not pass unknown pickle data to pickle.loads().

🔥 pickletools.dis() allows you to peek inside the serialization and see the instructions from which pickle reconstructs the object.

#Python #Pickle #DataScience #Debugging #Coding #DevTools

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Save this to your favorites, as the list is updated regularly.

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Python Lists: A Quick Reference 🐍

*   Definition: Lists are ordered, mutable collections that can contain various data types and duplicates.
*   Creating Lists: Creating empty, numerical, mixed, nested lists, and lists with duplicates.
*   Accessing List Elements: Accessing elements using positive and negative indexing, as well as slicing with

start:end:step

.
*   Common Operations: Adding, inserting, concatenating, deleting, clearing, searching, sorting, and reversing lists.
*   List Methods:

append()

,

insert()

,

extend()

,

remove()

,

pop()

,

clear()

,

index()

,

count()

,

sort()

, and

reverse()

.
*   List Comprehension: Quickly creating lists using expressions and conditions.
*   List vs. Tuple: Differences between mutable lists and immutable tuples.
*   Important Points: Specifics of indexing, slicing, data types, and working with lists.

#Python #Coding #Programming #DataStructures #PythonTips #LearnPython

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Python is the #1 language in AI. 🐍

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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/

#Python #Python315 #Programming #CodingTips #TechNews #Developer

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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.

#Python #Programming #Coding #HigherOrderFunctions #DataProcessing #TechTips

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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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