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Learn Python Coding

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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📈 Analytical overview of Telegram channel Learn Python Coding

Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 40 122 subscribers, ranking 3 231 in the Technologies & Applications category and 9 549 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 122 subscribers.

According to the latest data from 31 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 153 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.05%. Within the first 24 hours after publication, content typically collects 1.08% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 824 views. Within the first day, a publication typically gains 435 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
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

Thanks to the high frequency of updates (latest data received on 01 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

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40 122
Subscribers
+724 hours
+497 days
+15330 days
Posts Archive
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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&timestep=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
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)
Now let's check the difference between shallow and deep copy:
original[2].append(5)
print(copy1)
# [1, 2, [3, 4, 5]] — nested list changed!
print(copy4)
# [1, 2, [3, 4]] — unchanged
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