ch
Feedback

不要被骗子欺骗!Telemetrio 会找到并标记这些频道 👉 如果想查看标记,请订阅

Python learning

Python learning

前往频道在 Telegram

Learn python🔥

显示更多

📈 Telegram 频道 Python learning 的分析概览

频道 Python learning (@python3learning) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 22 281 名订阅者,在 教育 类别中位列第 9 010,并在 印度 地区排名第 18 227 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 22 281 名订阅者。

根据 25 十月, 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 -287,过去 24 小时变化为 -7,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 7.78%。内容发布后 24 小时内通常能获得 1.95% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 0 次浏览,首日通常累积 434 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 0。

📝 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🔥”

凭借高频更新(最新数据采集于 26 十月, 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 教育 类别中的关键影响点。

22 281
订阅者
-724 小时
-737 天
-28730 天
帖子存档
Useful Excel Shortcuts: ● Ctrl + A: Select all contents of the page. ● Ctrl + B: Bold highlighted selection. ● Ctrl + C: Copy selected text. ● Ctrl + X: Cut selected text. ● Ctrl + P: Open the print window. ● Ctrl + F: Open find box. ●Ctrl + I: Italic highlighted selection. ● Ctrl + K: Insert link (Shortcut Keys and their Functions). ● Ctrl + U: Underline highlighted selection. ● Ctrl + V: Paste. ● Ctrl + Y: Redo the last action performed. ● Ctrl + Z: Undo last action. ● Ctrl + L: Aligns the line or selected text to the left of the screen. ● Ctrl + E: Aligns the line or selected text to the center of the screen. ● Ctrl + R: Aligns the line or selected text to the right of the screen. ● Ctrl + M: Indent the paragraph. ● Ctrl + Shift + F: Change the font. ● Ctrl + Shift + >: Increase selected font +1pts up to 12pt and then increases font +2pts. ● Ctrl + ]: Increase selected font +1pts. ● Ctrl + Shift + <: Decrease selected font -1pts if 12pt or lower, if above 12 decreases font by +2pt. ● Ctrl + [: Decrease selected font -1pts. ● Ctrl + Shift + *: View or hide non printing characters. ● Ctrl + L: Moves one word to the left. ● Ctrl + R: Moves one word to the right. ● Ctrl + : Moves to the beginning of the line or paragraph. ● Ctrl + : Moves to the end of the paragraph. ● Ctrl + Del: Deletes word to right of cursor. ● Ctrl + Backspace: Deletes word to left of cursor. ● Ctrl + End: Moves the cursor to the end of the document. ● Ctrl + Home: Moves the cursor to the beginning of the document. ● Ctrl + Spacebar: Reset highlighted text to the default font. ● Ctrl + 1: Single-space lines. ● Ctrl + 2: Double-space lines. ● Ctrl + 5: 1.5-line spacing. ● Ctrl + Alt + 1: Changes text to heading 1. ● Ctrl + Alt + 2: Changes text to heading 2. ● Ctrl + Alt + 3: Changes text to heading 3. ● Alt + Shift + D: Insert the current date. ● Alt + Shift + T: Insert the current time. ◉ Alt + F: File menu options in current program. ◉ Alt + E: Edit options in current program. ◉ F1: Universal Help in almost every Windows program. ◉ Ctrl + A: Select all text. ◉ Ctrl + X: Cut ◉ Shift + Del: Cut selected item. ◉ Ctrl + C: Copy selected item. ◉ Ctrl + Ins: Copy selected item. ◉ Ctrl + V: Paste selected item. ◉ Shift + Ins: Paste. ◉ Home: Goes to beginning of current line. ◉ Ctrl + Home: Goes to beginning of document. ◉ End: Goes to end of current line. ◉ Ctrl + End: Goes to end of document. ◉ Shift + Home: Highlights from current position to beginning of line. ◉ Shift + End: Highlights from current position to end of line. ◉ Ctrl + Left arrow: Moves one word to the left at a time. ◉ Ctrl + Right arrow: Moves one word to the right at a time. Don't Forget To React ❤️!

Top 10 Python Libraries for Data Science 🐍 NumPy – Fast array operations and numerical computing 📊 Pandas – Data manipulation using DataFrames 📈 Matplotlib – Plotting and basic data visualization 🎨 Seaborn – Statistical plots built on Matplotlib 🧠 Scikit-learn – Machine learning models and tools 🤖 TensorFlow – Deep learning library by Google 🔥 PyTorch – Flexible deep learning by Facebook 📉 Statsmodels – Statistical tests and data exploration 🚀 XGBoost – Powerful boosting algorithm for structured data 🌐 Plotly – Interactive and web-ready visualizations React ❤️ for more

Top 10 Python Libraries for Data Science 🐍 NumPy – Fast array operations and numerical computing 📊 Pandas – Data manipulation using DataFrames 📈 Matplotlib – Plotting and basic data visualization 🎨 Seaborn – Statistical plots built on Matplotlib 🧠 Scikit-learn – Machine learning models and tools 🤖 TensorFlow – Deep learning library by Google 🔥 PyTorch – Flexible deep learning by Facebook 📉 Statsmodels – Statistical tests and data exploration 🚀 XGBoost – Powerful boosting algorithm for structured data 🌐 Plotly – Interactive and web-ready visualizations React ❤️ for more

JEE Mains results are out! No matter what your score is — take a deep breath. This isn’t the end of the road. In fact, it cou
JEE Mains results are out! No matter what your score is — take a deep breath. This isn’t the end of the road. In fact, it could be the beginning of something better. There are great colleges that don’t rely on JEE scores. Scaler School of Technology is one of them — and it’s built for students who are serious about tech. ✅ No Physics, No Chemistry -just Math + Logical Reasoning If tech is your thing, you should 100% give NSET a shot. 📌 Applications are closing very soon. 📍Use this coupon code for 50% Discount: "SCALER500" 🔗 Apply here: https://bit.ly/4j6oOmo Don’t sit this one out — your future in tech could start right here.

Free Data Science Webinar with Tools & Certification by Guvi & HCL! 🤖📊 Only 357 seats left! 😳 Register now (100% Free)👇 https://link.guvi.in/pythoncodes01982

Don't Forget To React ❤️

AWS NOTES 📚⚡️

Don't Forget To React ❤️

Stanford’s Machine Learning - by Andrew Ng A complete lecture notes of 227 pages. Available Free.

FREE ML/Ai MASTER CLASSS WITH FREE CERTIFICATION 🎓🧑‍🎓 Link 👉 https://forms.gle/HJCXb1sUUcY2kEE3A
FREE ML/Ai MASTER CLASSS WITH FREE CERTIFICATION 🎓🧑‍🎓 Link 👉 https://forms.gle/HJCXb1sUUcY2kEE3A

Git Commands 🛠 git init – Initialize a new Git repository 📥 git clone <repo> – Clone a repository 📊 git status – Check the status of your repository ➕ git add <file> – Add a file to the staging area 📝 git commit -m "message" – Commit changes with a message 🚀 git push – Push changes to a remote repository ⬇️ git pull – Fetch and merge changes from a remote repository Branching 📌 git branch – List all branches 🌱 git branch <name> – Create a new branch 🔄 git checkout <branch> – Switch to a branch 🔗 git merge <branch> – Merge a branch into the current branch ⚡️ git rebase <branch> – Apply commits on top of another branch Undo & Fix Mistakes ⏪ git reset --soft HEAD~1 – Undo the last commit but keep changes ❌ git reset --hard HEAD~1 – Undo the last commit and discard changes 🔄 git revert <commit> – Create a new commit that undoes a specific commit Logs & History 📖 git log – Show commit history 🌐 git log --oneline --graph --all – View commit history in a simple graph Stashing 📥 git stash – Save changes without committing 🎭 git stash pop – Apply stashed changes and remove them from stash Remote & Collaboration 🌍 git remote -v – View remote repositories 📡 git fetch – Fetch changes without merging 🕵️ git diff – Compare changes Don’t forget to react ❤️ if you’d like to see more content like this!

Free Data Science Webinar with Certification by HCL & Guvi! 👨🏻‍💻 Only 500 seats left! 😳 Hurry up! ⏳ Register now (FREE)! 👇 https://link.guvi.in/pythoncodes01899

This Python Data Type Cheatsheet provides a comparison of five key data structures: 1. String (str) Immutable (cannot be chan
This Python Data Type Cheatsheet provides a comparison of five key data structures: 1. String (str) Immutable (cannot be changed) Ordered and indexed Allows duplicate characters Example: my_str = "Hello" 2. List (list) Mutable (can be changed) Ordered and indexed Allows duplicate elements Example: my_list = ["Hello", "World"] Can store different data types 3. Tuple (tuple) Immutable Ordered and indexed Allows duplicates Example: my_tuple = ("Hello", "World") Can store different data types 4. Set (set) Mutable Unordered Does not allow duplicates Example: my_set = {"Hello", "World"} Can store int, str, tuple, but not list, set, or dict 5. Dictionary (dict) Mutable Unordered Does not allow duplicate keys Example: my_dict = {"greeting": "Hello", "salutation": "World"} Keys can be int, str, tuple, but not list, set, or dict Values can be any data type This cheatsheet helps in understanding the properties and use cases of each data type in Python.

Data Analyst Notes 📝 Don't Forget To React ❤️

The <input> tag in HTML is used to collect user input in web forms. Here are some key points about it: Versatile Usage – Supports multiple input types like text, password, number, email, date, etc. User Interaction – Enables users to enter or select data for forms. Customizable – Can include attributes like placeholder, required, disabled, and readonly to control behavior. Form Submission – Works with the <form> tag to send user data to a server. Validation Support – Helps validate input using attributes like maxlength, pattern, min, and max. Enhanced UX – Improves usability with features like auto-focus, default values, and autocomplete. For More Informative Contents. Don't Forget To React ❤️

Full-Stack Web Developer Roadmap🚀🔥 ✅ Day 1-25: HTML, CSS, JS. ✅ Day 26-40: React JS. ✅ Day 41-55: Node JS & Express. ✅ Day 56-70: MongoDB. ✅ Day 71-90: Build Projects. For More Informative Contents. Don't Forget To React ❤️

Free Data Science Training with Certification by HCL & Guvi!👨🏻‍💻 Only 500 seats left! Hurry up! ⏳ Register now (FREE) 👇 https://link.guvi.in/pythonlearning01871 Join us on WhatsApp: https://chat.whatsapp.com/JzziLVkrmjKGRimur9Zg9X

📍 Python Roadmap for Beginners |_ 🐍 Introduction to Python |_ 📦 Modules, Comments, & Pip |_ 🔢 Variables, Data Types, Keywords & Operators |_ 📊 Python Data Types in Detail |_ 🔁 Flow Control in Python |_ 🔄 Loops in Python |_ 📝 String Operations (Advanced) |_ 🏗 Functions in Python |_ 📂 File Handling in Python |_ 🏛 Object-Oriented Programming (OOP) |_ ⚠️ Exception Handling ]