Python learning
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π Analytical overview of Telegram channel Python learning
Channel Python learning (@python3learning) in the English language segment is an active participant. Currently, the community unites 22 882 subscribers, ranking 8 592 in the Education category and 18 283 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 22 882 subscribers.
According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -259 over the last 30 days and by -11 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.20%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn pythonπ₯β
Thanks to the high frequency of updates (latest data received on 26 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 Education category.
22 882
Subscribers
-1124 hours
-667 days
-25930 days
Posts Archive
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Top 10 Python Libraries for Data Science
π NumPy β Fast array operations and numerical computing
π Pandas β Data manipulation using DataFrames
π Matplotlib β Plotting and basic data visualization
π¨ Seaborn β Statistical plots built on Matplotlib
π§ Scikit-learn β Machine learning models and tools
π€ TensorFlow β Deep learning library by Google
π₯ PyTorch β Flexible deep learning by Facebook
π Statsmodels β Statistical tests and data exploration
π XGBoost β Powerful boosting algorithm for structured data
π Plotly β Interactive and web-ready visualizations
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Stanfordβs Machine Learning - by Andrew Ng
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Git Commands
π git init β Initialize a new Git repository
π₯ git clone <repo> β Clone a repository
π git status β Check the status of your repository
β git add <file> β Add a file to the staging area
π git commit -m "message" β Commit changes with a message
π git push β Push changes to a remote repository
β¬οΈ git pull β Fetch and merge changes from a remote repository
Branching
π git branch β List all branches
π± git branch <name> β Create a new branch
π git checkout <branch> β Switch to a branch
π git merge <branch> β Merge a branch into the current branch
β‘οΈ git rebase <branch> β Apply commits on top of another branch
Undo & Fix Mistakes
βͺ git reset --soft HEAD~1 β Undo the last commit but keep changes
β git reset --hard HEAD~1 β Undo the last commit and discard changes
π git revert <commit> β Create a new commit that undoes a specific commit
Logs & History
π git log β Show commit history
π git log --oneline --graph --all β View commit history in a simple graph
Stashing
π₯ git stash β Save changes without committing
π git stash pop β Apply stashed changes and remove them from stash
Remote & Collaboration
π git remote -v β View remote repositories
π‘ git fetch β Fetch changes without merging
π΅οΈ git diff β Compare changes
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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.
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The <input> tag in HTML is used to collect user input in web forms. Here are some key points about it:
Versatile Usage β Supports multiple input types like text, password, number, email, date, etc.
User Interaction β Enables users to enter or select data for forms.
Customizable β Can include attributes like placeholder, required, disabled, and readonly to control behavior.
Form Submission β Works with the <form> tag to send user data to a server.
Validation Support β Helps validate input using attributes like maxlength, pattern, min, and max.
Enhanced UX β Improves usability with features like auto-focus, default values, and autocomplete.
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π Python Roadmap for Beginners
|_ π Introduction to Python
|_ π¦ Modules, Comments, & Pip
|_ π’ Variables, Data Types, Keywords & Operators
|_ π Python Data Types in Detail
|_ π Flow Control in Python
|_ π Loops in Python
|_ π String Operations (Advanced)
|_ π Functions in Python
|_ π File Handling in Python
|_ π Object-Oriented Programming (OOP)
|_ β οΈ Exception Handling
]
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