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Python Basics Notes 🐍📚

https://t.me/pythonRe 🔗

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If you work with Python, remember a simple rule: do not modify a list while iterating over it. 🐍🛑 This can lead to unexpected results because the iterator does not track structural changes.

Here is an example that looks logical but works incorrectly: 🤔

items = [1, 2, 2, 3, 4]
for item in items:
    if item == 2:
        items.remove(item)
print(items)
# Output: [1, 2, 3, 4]

It seems that all 2s should disappear, but one remains. ❓ Why?

After removing an element, the list shifts, but the loop moves on — as a result, some values are simply skipped. 🔄🚫

How to do it correctly — iterate over a copy: ✅

for item in items[:]:
    if item == 2:
          items.remove(item)
print(items)
# Output: [1, 3, 4]

Even better — use list comprehension: 🚀

items = [x for x in items if x != 2]

Conclusion: 🏁 do not modify a collection during iteration. This can lead to skipped elements, duplication, or even errors during execution. 🛠️🚧

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The Python library itertools contains many useful functions. 🐍✨

One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻

Here's an example: 📝👇

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Cheat sheet on the basics of Python: 🐍📚

basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢

datetime — working with date and time 📅⏰

data structures — Python data structures (list, tuple, dict, set) 🗄

list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘

slicing — obtaining parts of sequences through indices and step ✂️

module/library — connecting modules and libraries 🔌

help functions — using help() and dir() to explore the Python API 🛠

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔

When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡

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How to check for the presence of subclasses in Python? 🐍🧐

Here's how you can do it:

import inspect

def has_subclasses(cls):
    return any(issubclass(sub, cls) for sub in inspect.getmembers(sys.modules[cls.__module__], inspect.isclass))

This function uses the inspect module to find all subclasses of the given class. 🛠️

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📂 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

"Introduction to Algorithms" 📘 - an outstanding university resource for everyone studying algorithms and computer science. 🎓💻

The book covers computational complexity, data structures, algorithms on graphs, dynamic programming, divide-and-conquer methods, greedy algorithms, randomized algorithms, and many mathematical foundations of modern computer science. 🧮📊🔍

What's particularly valuable here is the combination of mathematical rigor and practical algorithmic thinking. 🧠✨ This is one of those books that greatly change the approach to problem analysis, efficiency, and computing itself. 🚀🛠

An essential tool in the library of any developer and engineer working in the field of computer science. 🏗💾

https://www.cs.mcgill.ca/~akroit/math/compsci/Cormen%20Introduction%20to%20Algorithms.pdf 🔗

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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="alex@test.com")

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.

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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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