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✨ Learn From 2025's Most Popular Python Tutorials and Courses ✨ 📖 Pick from the best Python tutorials and courses of 2025. Revisit core skills, 3.14 updates, AI coding tools, and project walkthroughs. Kickstart your 2026! 🏷️ #basics #community #news

✨ line_profiler | Python Tools ✨ 📖 A line-by-line performance profiler for Python. 🏷️ #Python

✨ Kiro | AI Coding Tools ✨ 📖 An agentic IDE that organizes AI-assisted coding around spec-driven development. 🏷️ #Python

✨ LlamaIndex in Python: A RAG Guide With Examples ✨ 📖 Learn how to set up LlamaIndex, choose an LLM, load your data, build and persist an index, and run queries to get grounded, reliable answers with examples. 🏷️ #intermediate #ai

1. Which feature allows a function to remember values between calls without using global variables? A. Closures B. Decorators C. Generators D. Context managers Correct answer: A. 2. What is the primary purpose of a decorator in Python? A. Modify class inheritance B. Change function behavior without altering its code C. Improve execution speed D. Handle exceptions automatically Correct answer: B. 3. Which statement about Python generators is true? A. They store all values in memory B. They use return to yield multiple values C. They produce values lazily using yield D. They cannot be iterated over Correct answer: C. 4. What does the __slots__ attribute primarily optimize? A. Method resolution order B. Attribute access speed and memory usage C. Garbage collection D. Inheritance depth Correct answer: B. 5. Which data structure guarantees insertion order preservation as of Python 3.7? A. set B. tuple C. dict D. frozenset Correct answer: C. 6. What is the main advantage of using collections.defaultdict? A. Faster sorting B. Automatic key initialization C. Reduced memory footprint D. Immutable values Correct answer: B. 7. Which statement about list comprehensions is correct? A. They always execute faster than loops B. They cannot contain conditions C. They create lists eagerly D. They are equivalent to generators Correct answer: C. 8. What does the * operator do in function parameters? A. Forces keyword-only arguments B. Captures extra positional arguments C. Unpacks dictionaries D. Captures extra keyword arguments Correct answer: B. 9. What is the role of **kwargs in a function definition? A. Enforce positional arguments B. Capture extra keyword arguments C. Define default values D. Improve performance Correct answer: B. 10. Which protocol enables objects to be used in for loops? A. Context manager protocol B. Descriptor protocol C. Iterator protocol D. Numeric protocol Correct answer: C. 11. What must an object implement to be an iterator? A. __iter__ only B. __next__ only C. Both __iter__ and __next__ D. __getitem__ Correct answer: C. 12. What is the primary use of context managers? A. Memory allocation B. Automatic resource management C. Error suppression D. Parallel execution Correct answer: B. 13. Which keyword is used to define a context manager without a class? A. with B. manage C. using D. yield Correct answer: D. 14. What problem does the Global Interpreter Lock (GIL) primarily affect? A. File I/O performance B. Network latency C. CPU-bound multithreaded code D. Memory leaks Correct answer: C. 15. Which module is commonly used for parallelism that bypasses the GIL? A. threading B. asyncio C. multiprocessing D. concurrent.futures.thread Correct answer: C. 16. What distinguishes asyncio from threading? A. It uses OS-level threads B. It is based on cooperative multitasking C. It bypasses the GIL D. It is suitable only for CPU-bound tasks Correct answer: B. 17. What does the await keyword do? A. Blocks the entire program B. Pauses execution until a coroutine completes C. Starts a new thread D. Forces synchronous execution Correct answer: B. 18. Which operation is typically CPU-bound? A. Reading a file B. Waiting for a network response C. Parsing a large dataset D. Sleeping for one second Correct answer: C. 19. What is the main benefit of using functools.lru_cache? A. Improves recursion depth B. Memoizes function results C. Reduces function arguments D. Enables parallel execution Correct answer: B. 20. Which statement about Python exceptions is correct? A. They always terminate the program B. They cannot be nested C. They propagate up the call stack if unhandled D. They are only for runtime errors Correct answer: C.

📉 The bitcoin is falling, boss! We will teach Python to monitor the cryptocurrency rate and notify if the rate is above or below the threshold. We will connect the requests library and import time:

import requests
import time

We will create a function to get the BTC price in USD via the CoinGecko API:

def get_btc_price():
    url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd"
    r = requests.get(url)
    return r.json()["bitcoin"]["usd"]

Now — the main monitoring cycle. We will set a threshold and check the price every minute:

threshold = 65000  # specify your goal
while True:
    price = get_btc_price()
    print(f"BTC: ${price}")
    if price > threshold:
        print("🚀 Time to sell!")
        break
    time.sleep(60)

🔥 You can also easily adapt it for Ethereum, DOGE, or even Telegram Token — just replace bitcoin with the desired coin in the URL. 🚪 @DataScience4

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

✨ poetry-core | Python Tools ✨ 📖 A lightweight build backend for Python. 🏷️ #Python

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I rarely say this, but this is the best repository for mastering Python. The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley). In this PythonMastery.pdf, all the information is structured 👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf In the Exercises folder, all the exercises are located 👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises In the Solutions folder — the solutions 👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions 👉 @codeprogrammer

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✨ isort | Python Tools ✨ 📖 A command-line utility and library for sorting and organizing Python imports. 🏷️ #Python

✨ mypy | Python Tools ✨ 📖 A static type checker for Python. 🏷️ #Python

Tip: Efficiently Slice Iterators and Large Sequences with itertools.islice Explanation: Traditional list slicing (my_list[start:end]) creates a new list in memory containing the sliced elements. While convenient for small lists, this becomes memory-inefficient for very large lists and is impossible for pure iterators (like generators or file objects) that don't support direct indexing. itertools.islice provides a memory-optimized solution by returning an iterator that yields elements from a source iterable (list, generator, file, etc.) between specified start, stop (exclusive), and step indices, without first materializing the entire slice into a new collection. This "lazy" consumption of the source iterable is crucial for processing massive datasets, infinite sequences, or streams where only a portion is needed, preventing excessive memory usage and improving performance. It behaves syntactically similar to standard slicing but operates at the iterator level. Example:

import itertools
import sys

# A generator for a very large sequence
def generate_large_sequence(count):
    for i in range(count):
        yield f"Data_Item_{i}"

# Imagine needing to process only a small segment of 10 million items
total_items = 10**7
data_stream = generate_large_sequence(total_items)

# Get items from index 500 to 509 (inclusive)
# Using islice:
print("--- Using itertools.islice ---")
# islice(iterable, [start], stop, [step])
# Here, start=500, stop=510 (exclusive)
for item in itertools.islice(data_stream, 500, 510):
    print(item)

# Compare memory usage (conceptual, as actual list materialization would be massive)
# If you tried:
# large_list = list(generate_large_sequence(total_items)) # <-- HUGE memory consumption here!
# for item in large_list[500:510]:
#    print(item)

# islice consumes minimal memory, only holding iterator state.
# The `data_stream` generator itself only holds its current state, not the whole sequence.
print("\n`itertools.islice` memory footprint is negligible compared to creating a full list slice.")

no any words from your ━━━━━━━━━━━━━━━ By: @DataScience4 ✨

✨ Quiz: Writing DataFrame-Agnostic Python Code With Narwhals ✨ 📖 If you're a Python library developer wondering how to write DataFrame-agnostic code, the Narwhals library is the solution you're looking for. 🏷️ #advanced #data-science #python

✨ Pylint | Python Tools ✨ 📖 A static code checker for Python. 🏷️ #Python

✨ pytest | Python Tools ✨ 📖 A test runner and framework for Python. 🏷️ #Python