Python Basics Notes 🐍📚

https://t.iss.one/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!

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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

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"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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