📂 Reminder about Python map()!

map() — a built-in function that applies the specified function to each element of an iterable object (list, tuple, set, etc.).

The picture shows the basic syntax, an example of use with lambda, and a typical case — data transformation without a manual for loop.

Save it to quickly remember the syntax!

🐍💻🗺️ #Python #Coding #Programming #LearnToCode #DevTips #Tech

If you're working with data pipelines, these repositories are very useful: 🚀📊

ibis: A Python API that allows you to write queries once and run them on different data backends, such as DuckDB, BigQuery, and Snowflake. 🐍🔗
https://github.com/ibis-project/ibis

pygwalker: Instantly turns a DataFrame into an interactive UI for visual data exploration. 📈🖥️
https://github.com/Kanaries/pygwalker

katana: A fast and scalable web crawler, often used for security testing and large-scale data collection/search. 🕷️🔒
https://github.com/projectdiscovery/katana

#dataengineering #python #opensource #devtools #dataviz #security

Why is enumerate() used in Python? 🤔🐍

It allows you to simultaneously obtain the value of an element and its index when iterating through a list. 📊✨

This is more convenient and more readable than manually working with a counter. ✅🚀

for i, item in enumerate(items):
    print(i, item)

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Data validation with Pydantic! 🐍✨

In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:

if not isinstance(age, int):
    raise ValueError("age must be an int")

But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks.

For such tasks, Pydantic is often used. Installation:

pip install pydantic
pip install "pydantic[email]"

Create a model:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

Now the data is validated automatically:

user = User(
    name="Alex",
    age="30"
)

print(user.age)
print(type(user.age))

The result:
30
<class 'int'>

Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:

User(
    name="Alex",
    age="test"
)

This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside.

A common production case is checking email:

from pydantic import BaseModel, EmailStr

class User(BaseModel):
    email: EmailStr

User(email="[email protected]")

If the email is invalid, Pydantic will throw a ValidationError. You can set default values:

from pydantic import BaseModel

class Config(BaseModel):
    host: str = "localhost"
    port: int = 5432

And allow None:

from pydantic import BaseModel

class User(BaseModel):
    nickname: str | None = None

This field becomes optional. A practical example is processing an API response:

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    title: str
    price: float

data = {
    "id": "1",
    "title": "Keyboard",
    "price": "99.5"
}

product = Product(**data)

print(product)

The types will be automatically converted. For nested model structures, you can combine:

from pydantic import BaseModel

class Address(BaseModel):
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    address: Address

user = User(
    name="Alex",
    address={
        "city": "Berlin",
        "zip_code": "10115"
    }
)

print(user)

The nested object will also be validated. Serialization in Pydantic v2:

print(user.model_dump())
print(user.model_dump_json())

Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients.

For working with environment variables in Pydantic v2, a separate package is usually used:

pip install pydantic-settings

It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas.

🔥 Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable.

#Python #Pydantic #DataValidation #FastAPI #Coding #DevOps

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# Cheat sheet on high-order functions in Python:

🐍 map() - applies a function to every element of an iterable and returns an iterator with the results
🔍 filter() - filters elements based on a condition and leaves only those for which the function returns True
🔄 reduce() - successively combines all elements of an iterable into a single value
⚡ lambda functions - anonymous functions for short expressions and working with map/filter/reduce
📦 iterable objects - lists, tuples, and other collections for processing
📚 functools - a Python module that contains reduce()
🧠 functional programming - an approach to programming through functions and data processing without changing the state

```python
# Example usage
from functools import reduce

# map
squared = map(lambda x: x**2, [1, 2, 3, 4])
print(list(squared))

# filter
evens = filter(lambda x: x % 2 == 0, [1, 2, 3, 4, 5])
print(list(evens))

# reduce
total = reduce(lambda x, y: x + y, [1, 2, 3, 4])
pr

int(total)```

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❤️ Architecture Patterns — an informative repository on backend architecture in Python!

Here, they excellently demonstrate how to properly separate application logic, work with complex architecture, build a scalable backend, and maintain a codebase in an adequate state as the project grows. Instead of dry theory, the authors gradually build a full-fledged application and show how the architecture evolves as the project grows.

I'll leave a link: https://github.com/cosmicpython/book

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Why in Python it is better to check None using is 🐍

In Python, you should not write obj == None, even if sometimes it works the same ⚠️

The reason is that == calls the comparison method eq, which can be overridden in the class — and then the behavior becomes unpredictable 🎲

For example:

class Weird:
    def eq(self, other):
        return True  # always says "equal"

obj = Weird()

print(obj == None)  # True
print(obj is None)  # False

Here obj == None gives a false result due to custom logic 🤔

Instead:

obj is None

is checks the identity of the object and cannot be overridden. Since None is a singleton, such a check is always correct and predictable ✅

Conclusion: to check for None always use is None — it is the right and safe approach 🛡️

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Deep copying of objects with the copy module 🐍📦

import copy

# Original list with nested structure
original = [[1, 2, 3], [4, 5, 6]]

# 1. Shallow copy
shallow = copy.copy(original)
shallow[0][0] = 'X'
# Oh no! Both lists have changed, because the nested list wasn't copied, but passed by reference
print(f"Original after shallow: {original}") # [['X', 2, 3], [4, 5, 6]]

# Restore the data
original = [[1, 2, 3], [4, 5, 6]]

# 2. Deep copy
deep = copy.deepcopy(original)
deep[0][0] = 'X'
# Everything is fine! Only deep has changed, the original remains untouched
print(f"Original after deep:    {original}") # [[1, 2, 3], [4, 5, 6]]

The link trap in Python 🔗🕳️

When you assign a list to another variable (A = B) or make a regular slice (A = B[:]), Python doesn't physically copy the data. It simply creates a new reference to the same objects in memory. If the list contains other mutable objects (lists, dictionaries, custom classes), standard copying methods will only create a shallow copy. The copy module allows you to control this process.

— Breaking the links: The deepcopy function recursively traverses the entire data structure and creates honest, independent duplicates for each nested element. This ensures that changes in the copy will not harm the original data. 🔓🔒
— Safe state: The use of deep copying is critical when implementing design patterns (for example, Snapshot/Memento), creating game state backups, or when you pass complex configurations to functions that may modify them accidentally. 🛡️💾
— A sensible balance: It's worth remembering that deepcopy works slower and consumes more memory than shallow copying, as it spends resources on creating new objects and checking for cyclic references. Use it specifically when there are nested mutable containers within the structure. ⚖️🧠

#Python #Programming #DeepCopy #Coding #Tech #Dev

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Regular for-loops are versatile but not always optimal: they add extra interpreter overhead, which is especially noticeable on large data 🐍

In such cases, it's better to use standard Python tools, for example itertools ⚙️

For example, to get all unique pairs from a list, nested loops are not needed — just combinations():

from itertools import combinations

def get_unique_pairs(items):
    return list(combinations(items, 2))

print(get_unique_pairs(['A', 'B', 'C', 'D']))

# Output:
# [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]

Conclusion: instead of manual loops, it's better to use ready-made tools from the standard library — it's cleaner and more efficient 🚀

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🐍 Python Roadmap 2026: Finally, a comprehensive and up-to-date map for learning Python, not just a list of "figure it out yourself" links

A large Russian-language Python roadmap for 2026 has been posted on GitHub - from the first scripts to the Middle+/Senior level.

The route is compiled for modern Python:

- Python 3.13+
- free-threaded mode without GIL
- JIT
- uv instead of the hassle with pip/venv/poetry
- ruff, pyright, pytest, hypothesis
- async-first approach
- typing
- CPython inside
- web, databases, ML/AI, DevOps, and architecture

The roadmap has a logical sequence: first the environment and foundation, then idioms, OOP, types, the standard library, asynchrony, testing, CPython internals, web, databases, the AI direction, production, and architecture.

A particular plus is the practical format. At each stage, there are tasks, checklists, code examples, and free resources. This is not a motivational document, but a roadmap that you can actually follow for several months and see progress.

For beginners - a clear path without chaos.
For juniors - a way to fill in the gaps.
For those who already write in Python - a good checklist to understand where you're still struggling.

Python in 2026 is about tooling, types, async, infrastructure, AI, and production discipline. And this roadmap is exactly about such a Python.

https://github.com/justxor/pythonroamap2026

#Python #PythonRoadmap #Programming #2026 #Coding #DevOps

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5 More Must-Know Python Concepts 🐍

Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit. 🛠️

Read: https://www.kdnuggets.com/5-more-must-know-python-concepts 🔗

#Python #Programming #Coding #Developer #TechTips #LearnPython

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✍️ Pyneng — a large base for Python and network automation!

Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.

📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/

#Python #NetworkAutomation #Pyneng #LearnPython #DevOps #TechEducation

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When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle.

In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.

The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.

The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.

It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.

pip install python-markdownify

If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.

If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.

➡️ Link to GitHub
https://github.com/matthewwithanm/python-markdownify

#python #markdown #html #coding #devtools #opensource

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Advice for Python, UV, and Docker 🐍🐳

Sometimes dependencies are better installed separately from the code — this noticeably speeds up the compilation of Docker images 🚀

The idea is simple: first, we install dependencies, then we add the project 🛠

Why is this necessary:
• Docker caches layers and does not rebuild them unnecessarily ⚡️
• if only the code changes — the dependencies are taken from the cache 💾
• if the dependencies change — only the corresponding layer is rebuilt 🔁
• without this, any minor change triggers a full reinstallation 🔄

Example:

RUN --mount=type=cache,target=/root/.cache/uv  --mount=type=bind,source=uv.lock,target=uv.lock  --mount=type=bind,source=pyproject.toml,target=pyproject.toml  uv sync --locked --no-install-project

COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv  uv sync --locked

#Python #Docker #DevOps #UV #SoftwareEngineering #TechTips

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