An incredibly short book, but with a deep analysis of the internal mechanisms of Python, which we use every day. ❤️
Each chapter contains an explanation of a specific language feature, such as working with *args/**kwargs, mutable arguments, generators, decorators, context managers, enumerate/zip, exceptions, dunder methods, and other clever constructs.
Link: https://book.pythontips.com/en/latest/
👉 @DataScience4
Each chapter contains an explanation of a specific language feature, such as working with *args/**kwargs, mutable arguments, generators, decorators, context managers, enumerate/zip, exceptions, dunder methods, and other clever constructs.
Link: https://book.pythontips.com/en/latest/
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Forwarded from Machine Learning with Python
Real Python.pdf
332 KB
Real Python - Pocket Reference (Important)
#python #py #PythonTips #programming
https://t.me/CodeProgrammer🩵
#python #py #PythonTips #programming
https://t.me/CodeProgrammer
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Tip: Efficiently Slice Iterators and Large Sequences with
Explanation:
Traditional list slicing (
Example:
━━━━━━━━━━━━━━━
By: @DataScience4 ✨
itertools.isliceExplanation:
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.")
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By: @DataScience4 ✨
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I just discovered
No more dragging wires around in inconvenient GUIs.
Clean code for resistors, logic elements, and much more.
Full customization of all elements.
👉 @DataScience4
schemdraw — a Python library that turns code into neat and clear electrical schematics.No more dragging wires around in inconvenient GUIs.
Clean code for resistors, logic elements, and much more.
Full customization of all elements.
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✨ isort | Python Tools ✨
📖 A command-line utility and library for sorting and organizing Python imports.
🏷️ #Python
📖 A command-line utility and library for sorting and organizing Python imports.
🏷️ #Python
Forwarded from Machine Learning with Python
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
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
In the Exercises folder, all the exercises are located
In the Solutions folder — the solutions
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Forwarded from Machine Learning with Python
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
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📉 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
We will create a function to get the BTC price in USD via the CoinGecko API:
Now — the main monitoring cycle. We will set a threshold and check the price every minute:
🔥 You can also easily adapt it for Ethereum, DOGE, or even Telegram Token — just replace bitcoin with the desired coin in the URL.
🚪 @DataScience4
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.
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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
C. They produce values lazily using
D. They cannot be iterated over
Correct answer: C.
4. What does the
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
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
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
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
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.
B.
C. Both
D.
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
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
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
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.
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 valuesC. They produce values lazily using
yieldD. 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__ onlyB.
__next__ onlyC. 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.
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✨ 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
📖 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
❤4
✨ Kiro | AI Coding Tools ✨
📖 An agentic IDE that organizes AI-assisted coding around spec-driven development.
🏷️ #Python
📖 An agentic IDE that organizes AI-assisted coding around spec-driven development.
🏷️ #Python
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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
📖 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
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