Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills. Admin: @HusseinSheikho || @Hussein_Sheikho
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شبکه:📈 تحلیل کانال تلگرام Learn Python Coding
کانال Learn Python Coding (@pythonre) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 122 مشترک است و جایگاه 3 231 را در دسته فناوری و برنامهها و رتبه 9 549 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 122 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 31 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 153 و در ۲۴ ساعت گذشته برابر 7 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.05% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.08% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 824 بازدید دریافت میکند. در اولین روز معمولاً 435 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 2 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند math, harvard, oxford, supervision, waybienad تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 01 سپتامبر, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
40 122
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Repost from Machine Learning with Python
Visualization of Python objects and references
Many beginner Python developers face confusion when working with mutability and references between variables. It is especially difficult to understand during debugging of complex data structures when it is unclear how exactly they are connected. ⌨️
So here is memory_graph — an open-source tool for visualizing Python objects and references. It shows the data structure, call stack, and connections between variables.
👉 Link: https://github.com/bterwijn/memory_graph?tab=readme-ov-file
It also supports working with recursion and structures such as binary trees or linked lists.
Works in VS Code, Jupyter, PyCharm, and is available online without installation.
😁 more: https://memory-graph.com/#breakpoints=8&continues=1×tep=1.0&play
👉 https://t.me/CodeProgrammer
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4 ways to copy a list in Python
In Python, there are several ways to make a copy of a list. But it is important to understand the difference between a shallow copy and a deep copy.
original = [1, 2, [3, 4]]
# 1. Slice (shallow copy)
copy1 = original[:]
# 2. .copy() method (shallow copy)
copy2 = original.copy()
# 3. Using list() (shallow copy)
copy3 = list(original)
# 4. deepcopy (deep copy)
import copy
copy4 = copy.deepcopy(original)original[2].append(5)
print(copy1)
# [1, 2, [3, 4, 5]] — nested list changed!
print(copy4)
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