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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Network:📈 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 40 060 subscribers, ranking 3 238 in the Technologies & Applications category and 9 700 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 060 subscribers.
According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 182 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.93%. Within the first 24 hours after publication, content typically collects 1.12% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 172 views. Within the first day, a publication typically gains 447 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- 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 28 August, 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.
40 060
Subscribers
-1024 hours
-397 days
+18230 days
Posts Archive
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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40 055
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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🤔 Do I need deep math first?
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🧭 How do I avoid wasting time learning the wrong things?
🚀 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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40 055
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