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Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun
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Сітка:📈 Аналітичний огляд Telegram-каналу Python Projects & Resources
Канал Python Projects & Resources (@pythondevelopersindia) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 63 404 підписників, посідаючи 2 021 місце в категорії Технології та додатки та 5 201 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 63 404 підписників.
За останніми даними від 02 вересня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 295, а за останні 24 години на 0, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.18%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.38% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 651 переглядів. Протягом першої доби публікація в середньому набирає 874 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, object, module, string, loop.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Perfect channel to learn Python Programming 🇮🇳
Download Free Books & Courses to master Python Programming
- ✅ Free Courses
- ✅ Projects
- ✅ Pdfs
- ✅ Bootcamps
- ✅ Notes
Admin: @Coderfun”
Завдяки високій частоті оновлень (останні дані отримано 03 вересня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
63 404
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| Дата | Залучення підписників | Згадування | Канали | |
| 03 вересня | +5 | |||
| 02 вересня | 0 | |||
| 01 вересня | +6 |
Дописи каналу
📊 Pandas Cheatsheet Every Data Analyst Should Save
Pandas is one of the most important tools for data analysis. Master these core operations to work faster and more efficiently:
🔹 Read & Inspect Data
head(), shape, dtypes, describe()
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Extract relevant rows and columns with ease.
🔹 Row Selection
Use loc[] (labels) and iloc[] (positions).
🔹 Handle Missing Values
isnull(), dropna(), fillna()
🔹 Group & Aggregate
Summarize data using groupby() and aggregation functions.
🔹 Merge & Join Data
Combine datasets with merge() using different join types.
💡 Key Insight :
Strong Pandas skills help transform raw data into actionable insights faster and more effectively.
🚀 Whether you're a beginner or an experienced analyst, mastering these fundamentals is essential for data analytics success.
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| 3 | Today, lets understand Machine Learning in simplest way possible
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”
Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.
That’s what machine learning does — but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
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| 5 | Google Free Certificate Courses — No Fees, No Experience Needed
Google offers genuinely free certificate courses across three platforms: Digital Garage, Skillshop, and Cloud Skills Boost.
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If you need more such type of content then do let me know by responding to this message. | 4 556 |
| 6 | ⏳ Your Python already clears half the bar.
The other half is a 60-min aptitude test - tomorrow.
Certification in AI & ML - Vishlesan i-Hub, IIT Patna ML → PyTorch → LLMs, RAG & Agents → Docker deployment ₹99 · Sunday · one attempt
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| 7 | ✅ Python Exception Handling! 🐍✨
Exception handling allows your program to handle errors gracefully instead of crashing unexpectedly.
num = 10
print(num / 0)
❌ Output → ZeroDivisionError
💡 Without exception handling, the program stops immediately when an error occurs.
1. Basic Syntax:
› Use try and except to handle errors.
try:
num = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero")
✔ Output → Cannot divide by zero
2. Catch Any Exception:
Use Exception to handle all types of errors.
try:
number = int("Hello")
except Exception:
print("Something went wrong")
✔ Output → Something went wrong
3. Catch Multiple Exceptions:
try:
num = int(input("Enter a number: "))
print(10 / num)
except ValueError:
print("Invalid number")
except ZeroDivisionError:
print("Cannot divide by zero")
💡 Different errors can be handled separately.
4. Using else:
The else block runs only if no exception occurs.
try:
num = 10 / 2
except ZeroDivisionError:
print("Error")
else:
print("Division Successful")
✔ Output → Division Successful
5. Using finally:
The finally block always executes, whether an exception occurs or not.
try:
print(10 / 2)
except ZeroDivisionError:
print("Error")
finally:
print("Program Finished")
✔ Output →
5.0
Program Finished
💡 Commonly used to close files or database connections.
6. Using raise:
Manually raise an exception.
age = -5
if age < 0:
raise ValueError("Age cannot be negative")
✔ Output → ValueError: Age cannot be negative
7. Get the Error Message:
try:
print(10 / 0)
except Exception as e:
print(e)
✔ Output → division by zero
💡 e stores the actual error message.
8. Nested Exception Handling:
try:
try:
print(10 / 0)
except ZeroDivisionError:
print("Inner Exception")
except:
print("Outer Exception")
✔ Output → Inner Exception
9. Common Python Exceptions:
✔ ZeroDivisionError → Dividing by zero: 10 / 0
✔ ValueError → Invalid value: int("Hello")
✔ TypeError → Invalid data type: 10 + "20"
✔ IndexError → Invalid list index:
nums = [1, 2]
print(nums[5])
✔ KeyError → Missing dictionary key:
student = {"name": "Alex"}
print(student["age"])
✔ FileNotFoundError → File doesn't exist: open("data.txt")
10. Practice Examples:
✔ Handle invalid input
try:
age = int(input("Enter age: "))
print(age)
except ValueError:
print("Please enter a valid number")
✔ Handle list index error
try:
nums = [10, 20]
print(nums[5])
except IndexError:
print("Index out of range")
✔ Handle dictionary key error
try:
student = {"name": "Alex"}
print(student["age"])
except KeyError:
print("Key not found")
💡 Exception handling makes your programs more reliable by preventing unexpected crashes and providing meaningful error messages.
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| 8 |  You already know Python.
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| 10 |  You already know Python.
Now learn how companies actually use it for AI.
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🗓 Entrance Exam: 19th July
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| 11 | List of Python Project Ideas💡👨🏻💻🐍 -
Beginner Projects
🔹 Calculator
🔹 To-Do List
🔹 Number Guessing Game
🔹 Basic Web Scraper
🔹 Password Generator
🔹 Flashcard Quizzer
🔹 Simple Chatbot
🔹 Weather App
🔹 Unit Converter
🔹 Rock-Paper-Scissors Game
Intermediate Projects
🔸 Personal Diary
🔸 Web Scraping Tool
🔸 Expense Tracker
🔸 Flask Blog
🔸 Image Gallery
🔸 Chat Application
🔸 API Wrapper
🔸 Markdown to HTML Converter
🔸 Command-Line Pomodoro Timer
🔸 Basic Game with Pygame
Advanced Projects
🔺 Social Media Dashboard
🔺 Machine Learning Model
🔺 Data Visualization Tool
🔺 Portfolio Website
🔺 Blockchain Simulation
🔺 Chatbot with NLP
🔺 Multi-user Blog Platform
🔺 Automated Web Tester
🔺 File Organizer | 4 254 |
| 12 | ✔ Print all values
print(student.values())
✔ Add a new key
student["country"] = "India"
print(student)
✔ Update a value
student["age"] = 23
print(student)
💡 Dictionaries are one of the most powerful data structures in Python and are widely used to store structured data like JSON, APIs, and database records.
💬 Tap ❤️ if this helped you learn Python faster!
-----
1.32 ₽ · /balance_help | 4 108 |
| 13 | ✅ Python Dictionaries! 🐍✨
Dictionaries are used to store data in key-value pairs. They are ordered, mutable, and do not allow duplicate keys.
student = {
"name": "Alex",
"age": 22,
"city": "Mumbai"
}
1. Basic Syntax:
› Dictionaries use curly braces {}.
› Each item consists of a key: value pair.
person = {
"name": "John",
"age": 25
}
💡 Keys must be unique, but values can be duplicated.
2. Access Dictionary Values:
Access values using their keys.
student = {
"name": "Alex",
"age": 22
}
print(student["name"])
print(student["age"])
✔ Output
Alex
22
3. Using get() Method:
Safely access a value without getting an error if the key doesn't exist.
student = {
"name": "Alex",
"age": 22
}
print(student.get("name"))
✔ Output
Alex
💡 If the key doesn't exist, get() returns None by default.
4. Change Dictionary Values:
student = {
"name": "Alex",
"age": 22
}
student["age"] = 23
print(student)
✔ Output
{'name': 'Alex', 'age': 23}
5. Add New Items:
student = {
"name": "Alex"
}
student["city"] = "Mumbai"
print(student)
✔ Output
{'name': 'Alex', 'city': 'Mumbai'}
6. Remove Items:
Using pop()
student.pop("age")
Using del
del student["city"]
Remove all items
student.clear()
7. Dictionary Length:
student = {
"name": "Alex",
"age": 22
}
print(len(student))
✔ Output
2
8. Loop Through a Dictionary:
Loop through keys
for key in student:
print(key)
✔ Output
name
age
Loop through values
for value in student.values():
print(value)
✔ Output
Alex
22
Loop through key-value pairs
for key, value in student.items():
print(key, value)
✔ Output
name Alex
age 22
9. Check if a Key Exists:
student = {
"name": "Alex",
"age": 22
}
print("name" in student)
✔ Output
True
10. Common Dictionary Methods:
✔ keys() → Returns all keys
print(student.keys())
✔ values() → Returns all values
print(student.values())
✔ items() → Returns key-value pairs
print(student.items())
✔ update() → Updates dictionary
student.update({"age": 24})
✔ Output
{'name': 'Alex', 'age': 24}
11. Nested Dictionaries:
students = {
"student1": {
"name": "Alex",
"age": 22
},
"student2": {
"name": "John",
"age": 25
}
}
print(students["student1"]["name"])
✔ Output
Alex
12. Practice Examples:
✔ Print all keys
student = {
"name": "Alex",
"age": 22
}
print(student.keys()) | 3 783 |
| 14 | Final 6 Hours Left!
To register for TiHAN IIT Hyderabad's AI & ML Program.
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Register before the Admission Closes! | 4 225 |
| 15 |  You already know Python.
Now learn how companies actually use it for AI.
Applications are open for TiHAN IIT Hyderabad's AI & ML Program.
✅ Learn from TiHAN scientists, IIT professors & industry experts
✅ Build projects from Flipkart & Mamaearth
✅ Assured interview at TiHAN IIT Hyderabad with 9+ CGPA
✅ Placement support across 5000+ companies through Masai
🗓 Entrance Exam: 19th July
🔗 Register: https://tinyurl.com/datasimplifier-17jul-tihan-009 | 5 003 |
| 16 |  🔰 Python List Methods | 5 862 |
| 17 | Python Strings
Strings are used to store text data in Python. A string is a sequence of characters enclosed in single quotes or double quotes.
name = "Python"
message = 'Hello World'
1. Basic Syntax
Strings can be created using single or double quotes.
name = "Alex"
city = 'Mumbai'
Both are valid strings.
2. Access Characters using Indexing
Each character has an index starting from 0.
text = "Python"
print(text[0])
print(text[3])
Output:
P
h
Negative indexing starts from the end.
print(text[-1])
Output:
n
3. String Slicing
Extract part of a string using slicing.
text = "Python"
print(text[0:3])
print(text[2:6])
Output:
Pyt
thon
4. String Length
Use len() to find the number of characters.
text = "Python"
print(len(text))
Output:
6
5. Convert Case
text = "Python Programming"
print(text.upper())
print(text.lower())
print(text.title())
Output:
PYTHON PROGRAMMING
python programming
Python Programming
6. Remove Spaces
Use strip() to remove leading and trailing spaces.
text = " Python "
print(text.strip())
Output:
Python
7. Replace Text
text = "I love Java"
print(text.replace("Java", "Python"))
Output:
I love Python
8. Split a String
Convert a string into a list.
text = "Python SQL Excel"
print(text.split())
Output:
['Python', 'SQL', 'Excel']
9. Join Strings
Join list elements into a single string.
words = ["Python", "SQL", "Excel"]
print(" | ".join(words))
Output:
Python | SQL | Excel
10. Check String Methods
text = "Python"
print(text.startswith("Py"))
print(text.endswith("on"))
print("th" in text)
Output:
True
True
True
11. String Concatenation
Combine multiple strings using +.
first = "Hello"
second = "World"
print(first + " " + second)
Output:
Hello World
12. f-Strings Recommended
The easiest way to format strings.
name = "Alex"
age = 25
print(f"My name is {name} and I am {age} years old.")
Output:
My name is Alex and I am 25 years old.
Note: f-Strings are faster and more readable than string concatenation.
13. Practice Examples
Reverse a string
text = "Python"
print(text[::-1])
Output:
nohtyP
Count occurrences
text = "banana"
print(text.count("a"))
Output:
3
Find character position
text = "Python"
print(text.find("t"))
Output:
2
Check if string contains a word
text = "I am learning Python"
print("Python" in text)
Output:
True
Note: Strings are one of the most frequently used data types in Python, especially in web development, automation, and data analysis.
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