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 40 049 مشترک است و جایگاه 3 238 را در دسته فناوری و برنامهها و رتبه 9 700 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 049 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 26 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 182 و در ۲۴ ساعت گذشته برابر -10 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.93% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.12% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 1 172 بازدید دریافت میکند. در اولین روز معمولاً 447 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 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 27 اوت, 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.
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40 055
Safe rounding of numbers with math.fsum
import math
# Initial list with fractions
values = [0.1] * 10
# 1. Regular summation via sum()
print(f"Standard sum(): {sum(values)}") # 0.9999999999999999
# 2. Exact summation via math.fsum()
print(f"Exact math.fsum(): {math.fsum(values)}") # 1.0
40 055
Convert PDF to structured JSON — in a couple of lines and without hassle! 📄✨
Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. 🚀
First, let's connect the necessary libraries and API key:
import os
from PyPDF2 import PdfReader
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))reader = PdfReader("document.pdf")
text = "
".join(page.extract_text() for page in reader.pages)response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": (
"You are a PDF parser. Return a JSON with the fields: title, author, date, sections. "
"Each section is an object with name and summary."
)},
{"role": "user", "content": text}
]
)structured = response.choices[0].message.content.strip()
print(structured)40 055
Shuffling without repetitions:
import random
# Initial list of candidates or prizes
participants = ["Alexey", "Maria", "Ivan", "Olga", "Dmitry"]
# 1. Selecting 3 unique winners (sample without replacement)
winners = random.sample(participants, k=3)
print(f"Winners: {winners}")
# The result is different each time, but there will be no repetitions within the list of winners!
# 2. Shuffling an entire string (creating an anagram)
word = "python"
shuffled_word = "".join(random.sample(word, len(word)))
print(f"Anagram: {shuffled_word}")
# 3. Important difference: random.choices allows repetitions
print(f"With repetitions: {random.choices(participants, k=3)}")random.choice() in a loop. But this approach requires manually ensuring that the same element is not selected twice. The random.sample function takes on this routine.
— Guarantee of uniqueness: The main property of random.sample is "without replacement". The extracted element no longer participates in the next selection cycle, which completely eliminates duplicates in the resulting list.
— Safety of the original: The function does not modify the original list (unlike random.shuffle()), but creates a completely new array with the results. This allows the structure of the original data to remain intact.
— Strict control of size: If you pass a parameter k (the number of elements) that exceeds the length of the original list, Python will not start duplicating elements and will immediately throw an ValueError error. This protects the program logic from incorrect data.
#Python #Random #Coding #NoRepetition #DataScience #UniqueSets
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Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.
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Many people interested in AI Engineering are asking the same questions:
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🚀 How do I go from learning to becoming hireable?
If you’re interested in AI Engineering but unsure how to approach it, this livestream is for you.
What you’ll learn
✦ What AI Engineering really is
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40 055
A 14-day tutorial where you build a Python code-agent CLI in the style of Claude Code from scratch and simultaneously understand how the Agent Harness actually works. 🛠️🤖
In the end, you don't just call a ready-made agent via the API, but you understand the components that make up a Claude Code-like tool. 🧠⚙️
https://github.com/bozhouDev/14days-build-claude-code-cli/blob/main/README.en.md
#Python #AI #ClaudeCode #CLI #CodingTutorial #Tech
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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