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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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Learn Python Coding (@pythonre) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 879 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 292-o'rinni va Hindiston mintaqasida 9 353-o'rinni egallagan.

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  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 3.98% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.48% ini tashkil etuvchi reaksiyalarni to‘playdi.
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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”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 28 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

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📂 Reminder about Python map()! map() — a built-in function that applies the specified function to each element of an iterabl
📂 Reminder about Python map()! map() — a built-in function that applies the specified function to each element of an iterable object (list, tuple, set, etc.). The picture shows the basic syntax, an example of use with lambda, and a typical case — data transformation without a manual for loop. Save it to quickly remember the syntax! 🐍💻🗺️ #Python #Coding #Programming #LearnToCode #DevTips #Tech

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"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨ The book
"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 th
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,
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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Python Basics Notes 🐍📚 https://t.me/pythonRe 🔗 #Python #Coding #Programming #LearnPython #Tech #DevCommunity

🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸 Join our channel today for free! Tomorrow it will cost 500$! https://t
🙏💸 500$ 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()