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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📈 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 40 881 名订阅者，在 技术与应用 类别中位列第 3 284，并在 印度 地区排名第 9 119 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 40 881 名订阅者。
根据 02 十一月, 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 278，过去 24 小时变化为 -2，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 4.94%。内容发布后 24 小时内通常能获得 0.99% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 2 021 次浏览，首日通常累积 403 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 2。
- 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 03 十一月, 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。
40 881
订阅者
-224 小时
-307 天
+27830 天
帖子存档
40 882
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
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1️⃣ Data Science
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40 882
🐍 Tip of the day for experienced Python developers
📌 Use decorators with parameters — a powerful technique for logging, control, caching, and custom checks.
Example: a logger that can set the logging level with an argument:
import functools
import logging
def log(level=logging.INFO):
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
logging.log(level, f"Call {func.__name__} with args={args}, kwargs={kwargs}")
return func(*args, **kwargs)
return wrapper
return decorator
@log(logging. DEBUG)
def compute(x, y):
return x + y
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8. Set up the user interface and trigger the main function.
• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"
40 882
7. Define the main function to run all LLMs and aggregate results.
• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.
40 882
6. Implement the LLM call function.
• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response
40 882
5. Define the models and aggregator system prompt.
• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt
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4. Initialize Together AI clients.
• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients
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3. Set up the Streamlit app and API key input.
• Creates a title for the app
• Adds a secure input field for the Together API key
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2. Import necessary libraries
• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions
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1. Install the necessary Python Libraries
Run the following commands from your terminal to install the required libraries:
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🧠 Build your own ChatGPT
Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code⬇️ step-by-step instructions ⬇️
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
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7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
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