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Python Projects & Free Books
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Python Interview Projects & Free Courses Admin: @Coderfun
Show moreπ Analytical overview of Telegram channel Python Projects & Free Books
Channel Python Projects & Free Books (@pythonfreebootcamp) in the English language segment is an active participant. Currently, the community unites 40 940 subscribers, ranking 3 313 in the Technologies & Applications category and 9 602 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 40 940 subscribers.
According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 142 over the last 30 days and by -13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.59%. Within the first 24 hours after publication, content typically collects 0.93% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 470 views. Within the first day, a publication typically gains 380 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- Thematic interests: Content is focused on key topics such as learning, analyst, framework, link:-, structure.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βPython Interview Projects & Free Courses
Admin: @Coderfunβ
Thanks to the high frequency of updates (latest data received on 24 June, 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.
40 940
Subscribers
-1324 hours
-637 days
+14230 days
Data loading in progress...
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Channel Posts
| 2 |  If you work with Python, remember a simple rule: do not modify a list while iterating over it. ππ This can lead to unexpected results because the iterator does not track structural changes.
Here is an example that looks logical but works incorrectly: π€
items = [1, 2, 2, 3, 4]
for item in items:
Β Β Β if item == 2:
Β Β Β Β Β Β Β items.remove(item)
print(items)
# Output: [1, 2, 3, 4]
It seems that all 2s should disappear, but one remains. β Why?
After removing an element, the list shifts, but the loop moves on β as a result, some values are simply skipped. ππ«
How to do it correctly β iterate over a copy: β
for item in items[:]:
Β Β Β if item == 2:
Β Β Β Β Β Β Β Β Β items.remove(item)
print(items)
# Output: [1, 3, 4]
Even better β use list comprehension: π
items = [x for x in items if x != 2]
Conclusion: π do not modify a collection during iteration. This can lead to skipped elements, duplication, or even errors during execution. π οΈπ§
#Python #Coding #Programming #Debugging #TechTips #PythonTips | 870 |
| 3 |  Python Basics Arrays & Loops π
Essential you need to start strong πͺ
https://t.me/pythonRe π | 2 070 |
| 4 | Hey guys,
Here are some best Telegram Channels for free education in 2025
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| 5 | Essential Python Libraries to build your career in Data Science ππ
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
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5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
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7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.me/datasciencefree
Python Project Ideas: https://t.me/dsabooks/85
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Python Tutorial
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Best Data Science & Machine Learning Resources
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| 6 |  π° Python List Slicing | 0 |
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| 9 | π° Type Conversion in Python | 0 |
| 10 |  Generate Barcode using Python π | 0 |
