Learn Python Coding

前往频道在 Telegram

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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网络:Machine Learning with Python 印度10 621 技术与应用3 510...

📈 Telegram 频道 Learn Python Coding 的分析概览

频道 Learn Python Coding (@pythonre) 英语语言赛道中的是活跃参与者。目前社区聚集了 39 128 名订阅者，在 技术与应用 类别中位列第 3 510，并在印度地区排名第 10 621 位。

📊 受众指标与增长动态

自 невідомо 创建以来，项目保持高速增长，吸引了 39 128 名订阅者。

根据 04 六月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 481，过去 24 小时变化为 16，整体触达仍然可观。

认证状态： 未认证
互动率 (ER)： 平均受众互动率为 2.64%。内容发布后 24 小时内通常能获得 1.30% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 1 032 次浏览，首日通常累积 507 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 4。
主题关注点： 内容集中在 math, harvard, oxford, supervision, waybienad 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台：
“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”

凭借高频更新（最新数据采集于 05 六月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。

39 128

订阅者

+1624 小时

+1447 天

+48130 天

1 032

帖子浏览量

~ 50724 小时

~ 66148 小时

2.64%

参与率

~ 2

每日帖子数

Ads index

beta

帖子存档

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"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨ The book discusses the implementation and analysis of basic structures: array-based lists, linked lists, hash tables, binary trees, red-black trees, heaps, sorting algorithms, graphs, and data structures for working with integers. 🔍🧮 This is a full-fledged open textbook for studying one of the fundamental topics of computer science and a good reference that's worth keeping on hand. 💻🌟 https://opendatastructures.org/ods-python.pdf 📄 👉 @PythonRe #DataStructures #Algorithms #Python #ComputerScience #OpenSource #Learning

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡 #Python #Programming #Deque #CodingTips #Tech #DevCommunity

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The Python library itertools contains many useful functions. 🐍✨ One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻 Here's an example: 📝👇 #Python #Programming #Itertools #Coding #Tech #DataScience

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Many applications require mapping strings to integers. In Python, this usually looks like:

d = {"apple": 100, "banana": 200, "cherry": 300}

If there are 1 million keys, this can consume a lot of memory — more than 100 bytes per key. Our elephant has published a new library that uses about 9 bytes per key. Yes, only 9 bytes. Usage looks like this:

from fastconstmap import ConstMap

d = {"apple": 100, "banana": 200, "cherry": 300}
m = ConstMap(d)

m["apple"]                  # -> 100
m.get_many(["banana", "cherry"])  # -> [200, 300]

It can be significantly faster (for example, up to 2 times in some cases) than the standard dictionary. It can also be serialized and deserialized to disk or network for convenient reuse. https://pypi.org/project/fastconstmap/ github: https://github.com/lemire/fastconstmap 👉 @PythonRe

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Repost from Machine Learning with Python

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Python Basics Notes 🐍📚 https://t.me/pythonRe 🔗 #Python #Coding #Programming #LearnPython #Tech #DevCommunity

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Repost from Machine Learning with Python

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t.me/+-WZeIeP8YI8wM2E6 You can join at this link! 👆👇 https://t.me/+-WZeIeP8YI8wM2E6

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𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗻𝗴𝗹𝗶𝘀𝗵. 𝗦𝗽𝗲𝗮𝗸 𝗜𝘁 𝗙𝗹𝘂𝗲𝗻𝘁𝗹𝘆! Here’s Your Ultimate Guide!

𝗜𝗻𝗽𝘂𝘁/𝗢𝘂𝘁𝗽𝘂𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- print() 
- input() 
- format()

𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀  
- int() 
- float()  
- str() 
- bool() 
- complex()  
- list() 
- tuple()
- set() 
- dict()
- frozenset()  
- bytes()
- bytearray()  
- memoryview()  

𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- abs()
- pow()  
- round()
- divmod()  
- sum()  
- min()  
- max()  

𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 & 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀  
- len()  
- sorted() 
- range() 
- zip() 
- enumerate()
- reversed() 
- all()  
- any() 

𝗧𝘆𝗽𝗲 & 𝗢𝗯𝗷𝗲𝗰𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- type()
- id()
- isinstance()  
- issubclass()

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- open()
- close()  
- read()
- write()  
- seek()
- tell()

𝗦𝘁𝗿𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- ord()
- chr()
- ascii()
- repr()

𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 
- help() 
- dir()
- eval()  
- exec() 
- hash()

𝗟𝗼𝗴𝗶𝗰𝗮𝗹 & 𝗕𝗼𝗼𝗹𝗲𝗮𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- bin()
- oct() 
- hex()
- bool()

𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗢𝗯𝗷𝗲𝗰𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀
- memoryview()
- object()
- callable()

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❔ Interview question What tools are used for error monitoring in Python services? Answer: Most often, Sentry, centralized logging, and metrics are used. Sentry collects stack traces, context, and shows the frequency of errors. It's also important to set up alerts - a sharp increase in exceptions usually signals problems after a release or a service degradation. tags: #interview https://t.me/pythonRe ✅