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All my resources will be free and unrestricted there. My goal is to build a clean community exclusively for smart programmers, and I believe Signal is the most suitable platform for this (Signal is the second most popular app after WhatsApp in the US), making it particularly suitable for us as programmers.
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β¨ Conda | Python Tools β¨
π A cross-platform package and environment manager for Python.
π·οΈ #Python
π A cross-platform package and environment manager for Python.
π·οΈ #Python
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
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This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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The largest Arabic-speaking group for Python developers to share knowledge and help.
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Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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β¨ setuptools | Python Tools β¨
π A packaging library and build backend for Python.
π·οΈ #Python
π A packaging library and build backend for Python.
π·οΈ #Python
β¨ Quiz: Python Inner Functions: What Are They Good For? β¨
π Test inner functions, closures, nonlocal, and decorators in Python. Build confidence and learn to keep state across calls. Try the quiz now.
π·οΈ #intermediate #python
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β¨ Python Inner Functions: What Are They Good For? β¨
π Learn how to create inner functions in Python to access nonlocal names, build stateful closures, and create decorators.
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π Learn how to create inner functions in Python to access nonlocal names, build stateful closures, and create decorators.
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β¨ abstract method | Python Glossary β¨
π A method that is marked with @abstractmethod.
π·οΈ #Python
π A method that is marked with @abstractmethod.
π·οΈ #Python
β¨ dependency | Python Glossary β¨
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π·οΈ #Python
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Forwarded from Machine Learning with Python
Real Python.pdf
332 KB
Real Python - Pocket Reference (Important)
#python #py #PythonTips #programming
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#python #py #PythonTips #programming
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Tip: Efficiently Slice Iterators and Large Sequences with
Explanation:
Traditional list slicing (
Example:
βββββββββββββββ
By: @DataScience4 β¨
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 β¨
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π @DataScience4
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Clean code for resistors, logic elements, and much more.
Full customization of all elements.
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