Python Projects & Resources

Ir al canal en Telegram

Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun

Red:Data Analytics India5 348 Tecnologías y Aplicaciones2 040...

📈 Análisis del canal de Telegram Python Projects & Resources

El canal Python Projects & Resources (@pythondevelopersindia) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 63 102 suscriptores, ocupando la posición 2 040 en la categoría Tecnologías y Aplicaciones y el puesto 5 348 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 63 102 suscriptores.

Según los últimos datos del 30 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 413, y en las últimas 24 horas de 2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.25%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.42% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 3 310 visualizaciones. En el primer día suele acumular 894 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 10.
Intereses temáticos: El contenido se centra en temas clave como learning, object, module, string, loop.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Python Programming 🇮🇳 Download Free Books & Courses to master Python Programming - ✅ Free Courses - ✅ Projects - ✅ Pdfs - ✅ Bootcamps - ✅ Notes Admin: @Coderfun”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 31 julio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

63 102

Suscriptores

+224 horas

+927 días

+41330 días

3 310

Visitas de la publicación

~ 89424 horas

~ 1 30148 horas

5.25%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Carga de datos en curso...

Canales Similares

79.2K

Web Development

77.0K

Data Science & Machine Learning

56.2K

40.9K

Python Projects & Free Books

29.4K

Programing Resources: Python - Java - R - SQL - Javascript - C++

Más canales

Nube de Etiquetas

Menciones Entrantes y Salientes

---

Atraer Suscriptores

julio '26

+479

en 9 canales

junio '26

+363

en 6 canales

Get PRO

mayo '26

+479

en 8 canales

Get PRO

abril '26

+396

en 9 canales

Get PRO

marzo '26

+261

en 6 canales

Get PRO

febrero '26

+781

en 10 canales

Get PRO

enero '26

+936

en 10 canales

Get PRO

diciembre '25

+882

en 2 canales

Get PRO

noviembre '25

+1 113

en 6 canales

Get PRO

octubre '25

+966

en 9 canales

Get PRO

septiembre '25

+1 206

en 14 canales

Get PRO

agosto '25

+1 294

en 30 canales

Get PRO

julio '25

+1 200

en 33 canales

Get PRO

junio '25

+1 458

en 36 canales

Get PRO

mayo '25

+3 544

en 33 canales

Get PRO

abril '25

+5 846

en 33 canales

Get PRO

marzo '25

+2 109

en 34 canales

Get PRO

febrero '25

+1 437

en 26 canales

Get PRO

enero '25

+1 887

en 37 canales

Get PRO

diciembre '24

+824

en 13 canales

Get PRO

noviembre '24

+1 470

en 16 canales

Get PRO

octubre '24

+1 518

en 18 canales

Get PRO

septiembre '24

+1 933

en 12 canales

Get PRO

agosto '24

+2 152

en 9 canales

Get PRO

julio '24

+5 254

en 15 canales

Get PRO

junio '24

+3 642

en 13 canales

Get PRO

mayo '24

+1 910

en 17 canales

Get PRO

abril '24

+3 971

en 15 canales

Get PRO

marzo '24

+2 368

en 11 canales

Get PRO

febrero '24

+4 090

en 5 canales

Get PRO

enero '24

+2 023

en 3 canales

Get PRO

diciembre '23

+2 242

en 5 canales

Get PRO

noviembre '23

+670

en 10 canales

Get PRO

octubre '23

+298

en 3 canales

Get PRO

septiembre '23

+512

en 0 canales

Get PRO

agosto '23

+776

en 0 canales

Get PRO

julio '23

+866

en 0 canales

Get PRO

junio '23

+43

en 0 canales

Get PRO

mayo '23

+226

en 0 canales

Get PRO

abril '23

+269

en 0 canales

Get PRO

marzo '23

+188

en 0 canales

Get PRO

febrero '23

+85

en 0 canales

Get PRO

enero '23

+256

en 0 canales

Get PRO

diciembre '22

+171

en 0 canales

Get PRO

noviembre '22

+230

en 0 canales

Get PRO

octubre '22

+213

en 0 canales

Get PRO

septiembre '22

+205

en 0 canales

Get PRO

agosto '22

+217

en 0 canales

Get PRO

julio '22

+187

en 0 canales

Get PRO

junio '22

+281

en 0 canales

Get PRO

mayo '22

+361

en 0 canales

Get PRO

abril '22

+92

en 0 canales

Get PRO

marzo '22

+241

en 0 canales

Get PRO

febrero '22

+280

en 0 canales

Get PRO

enero '22

+370

en 0 canales

Get PRO

diciembre '21

+96

en 0 canales

Get PRO

noviembre '21

+55

en 0 canales

Get PRO

octubre '21

+89

en 0 canales

Get PRO

septiembre '21

+711

en 0 canales

Fecha	Crecimiento de Suscriptores	Menciones	Canales
31 julio	+10
30 julio	+6
29 julio	+22
28 julio	+31
27 julio	+15
26 julio	+10
25 julio	+13
24 julio	+10
23 julio	+54
22 julio	+8
21 julio	+27
20 julio	+24
19 julio	+2
18 julio	+12
17 julio	+13
16 julio	+9
15 julio	+30
14 julio	+3
13 julio	+2
12 julio	+13
11 julio	+9
10 julio	+12
09 julio	+29
08 julio	+23
07 julio	+3
06 julio	+17
05 julio	+13
04 julio	+12
03 julio	+4
02 julio	+25
01 julio	+18

Publicaciones del Canal

✅ 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. 💬 Tap ❤️ if this helped you!

2	You already know Python. That’s 20% of an AI career. Here’s the other 80%. ML with Scikit-learn & XGBoost → Deep Learning with PyTorch → LLMs, RAG & AI Agents → Deployment with Docker. That’s the exact roadmap of the Certification in AI & ML - Vishlesan i-Hub, IIT Patna. ✅ 9 Months \| Online \| IIT faculty & industry mentors ✅ Ship deployed projects: churn predictor, image classifier + capstone ✅ Placement support through Masai's network of 5000+ companies Your Python already clears half the entry bar. The rest is a ₹99 test this Sunday. 🗓 2nd August - slot booking closing soon 🔗 https://tinyurl.com/DS-29JUL-009	1 178
3	Last 6 Hours Remaining! Before the application closes for E&ICT IIT Roorkee AI & ML Program. Don't miss out on the chance to: • Learn live from IIT professors & industry experts • Build real AI projects • Get Placement Support from Masai. Register NOW	2 347
4	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/DS-26Jul-009	2 609
5	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	2 454
6	✔ 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	2 530
7	✅ 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())	2 264
8	Final 6 Hours Left! To register for TiHAN IIT Hyderabad's AI & ML Program. Don't miss your chance to: • Learn from India's best scientists at TiHAN, IIT Professors and industry experts • Direct Interview at TiHAN IIT Hyderabad with 9+ CGPA Register before the Admission Closes!	3 271
9	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	3 775
10	🔰 Python List Methods	5 189
11	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. 💬 Tap ❤️ if this helped you learn Python faster!	6 138
12	Useful AI channels on WhatsApp 🤖 Artificial Intelligence: https://whatsapp.com/channel/0029VbBDFBI9Gv7NCbFdkg36 Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L AI Tricks: https://whatsapp.com/channel/0029Vb6xxJGGk1FnoCYE660N AI Discovery: https://whatsapp.com/channel/0029VbBHlc7H5JLuv8L9d72T AI Magic: https://whatsapp.com/channel/0029VbBA1z1JuyAH7BNeT43b OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o Tech News: https://whatsapp.com/channel/0029VbBo9qY1t90emAy5P62s ChatGPT for Education: https://whatsapp.com/channel/0029Vb6r21H9hXFFoxvWR32C ChatGPT Tips: https://whatsapp.com/channel/0029Vb6ZoSzBA1f3paReKB3B AI for Leaders: https://whatsapp.com/channel/0029VbB9LO872WTwyqNlB63R AI For Business: https://whatsapp.com/channel/0029VbBn5bn0rGiLOhM3vi1v AI For Teachers: https://whatsapp.com/channel/0029Vb7LGgLCRs1mp86TH614 How to AI: https://whatsapp.com/channel/0029VbBHQZM7z4khHBTVtI0Q AI For Students: https://whatsapp.com/channel/0029VbBIV47I7Be9BZMAJq3s Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U ChatGPT: https://whatsapp.com/channel/0029Vb6R8PI6WaKwRzLKKI0r Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w Finance & AI: https://whatsapp.com/channel/0029Vax0HTt7Noa40kNI2B1P Google Facts: https://whatsapp.com/channel/0029VbBnkGm6LwHriVjB5I04 Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t AI Discovery: https://whatsapp.com/channel/0029VbBHlc7H5JLuv8L9d72T AI News: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O Jobs: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226 Double Tap ❤️ for more	5 043
13	6 Python Free Certifications 🔥 To get hired quickly ! Python for Beginners - https://learn.microsoft.com/en-us/shows/intro-to-python-development/ Programming with Python 3. X https://www.simplilearn.com/free-python-programming-course-skillup Advanced Python - https://www.codecademy.com/learn/learn-advanced-python AI Python for Beginners - https://www.deeplearning.ai/short-courses/ai-python-for-beginners/ Python Libraries for Data Science - https://www.simplilearn.com/learn-python-libraries-free-course-skillup Data Analysis with Python - https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course	4 043
14	📱 Understanding Machine learning algorithms	5 510
15	𝗛𝗼𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗮𝘀𝘁 (𝗘𝘃𝗲𝗻 𝗜𝗳 𝗬𝗼𝘂'𝘃𝗲 𝗡𝗲𝘃𝗲𝗿 𝗖𝗼𝗱𝗲𝗱 𝗕𝗲𝗳𝗼𝗿𝗲!)🐍🚀 Python is everywhere—web dev, data science, automation, AI… But where should YOU start if you're a beginner? Don’t worry. Here’s a 6-step roadmap to master Python the smart way (no fluff, just action)👇 🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics (Don’t Skip This!) ✅ Variables, data types (int, float, string, bool) ✅ Loops (for, while), conditionals (if/else) ✅ Functions and user input Start with: Python.org Docs YouTube: Programming with Mosh / CodeWithHarry Platforms: W3Schools / SoloLearn / FreeCodeCamp Spend a week here. Practice > Theory. 🔹 𝗦𝘁𝗲𝗽 𝟮: Automate Boring Stuff (It’s Fun + Useful!) ✅ Rename files in bulk ✅ Auto-fill forms ✅ Web scraping with BeautifulSoup or Selenium Read: “Automate the Boring Stuff with Python” It’s beginner-friendly and practical! 🔹 𝗦𝘁𝗲𝗽 𝟯: Build Mini Projects (Your Confidence Booster) ✅ Calculator app ✅ Dice roll simulator ✅ Password generator ✅ Number guessing game These small projects teach logic, problem-solving, and syntax in action. 🔹 𝗦𝘁𝗲𝗽 𝟰: Dive Into Libraries (Python’s Superpower) ✅ Pandas and NumPy – for data ✅ Matplotlib – for visualizations ✅ Requests – for APIs ✅ Tkinter – for GUI apps ✅ Flask – for web apps Libraries are what make Python powerful. Learn one at a time with a mini project. 🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev) ✅ Track your code with Git ✅ Upload projects to GitHub ✅ Write clear README files ✅ Contribute to open source repos Your GitHub profile = Your online CV. Keep it active! 🔹 𝗦𝘁𝗲𝗽 𝟲: Build a Capstone Project (Level-Up!) ✅ A weather dashboard (API + Flask) ✅ A personal expense tracker ✅ A web scraper that sends email alerts ✅ A basic portfolio website in Python + Flask Pick something that solves a real problem—bonus if it helps you in daily life! 🎯 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 = 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 You don’t need to memorize code. Understand the logic. Google is your best friend. Practice is your real teacher. Python Resources: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a ENJOY LEARNING 👍👍	5 381
16	+4 🔰 Python Lambda Function: Quick Guide. Lambda function is very powerful feature in python and it comes very handy when you are working with filter, map and reduce. In this post I shared some examples of lambda function for your better understanding.	5 057
17	Clean code advice for Python: Do not add redundant context. Avoid adding unnecessary data to variable names, especially when working with classes. Example: This is bad: class Person: def __init__(self, person_first_name, person_last_name, person_age): self.person_first_name = person_first_name self.person_last_name = person_last_name self.person_age = person_age This is good: class Person: def __init__(self, first_name, last_name, age): self.first_name = first_name self.last_name = last_name self.age = age	4 535
18	Cheat sheet on the basics of Python: 🐍📚 basic syntax and language rules 📝 scalar types — basic data types (int, float, bool, str, NoneType) 🔢 datetime — working with date and time 📅⏰ data structures — Python data structures (list, tuple, dict, set) 🗄 list — mutable lists for storing data collections 📋 tuple — immutable sequences of values 🔒 dict (hash map) — storing data in a key-value format 🗝 set — unique elements without order 🔘 slicing — obtaining parts of sequences through indices and step ✂️ module/library — connecting modules and libraries 🔌 help functions — using help() and dir() to explore the Python API 🛠 #Python #Coding #DataScience #Programming #Tech #DevCommunity	4 901
19	Python Interview Questions with Answers Part-1: ☑️ 1. What is Python and why is it popular for data analysis? Python is a high-level, interpreted programming language known for simplicity and readability. It’s popular in data analysis due to its rich ecosystem of libraries like Pandas, NumPy, and Matplotlib that simplify data manipulation, analysis, and visualization. 2. Differentiate between lists, tuples, and sets in Python. ⦁ List: Mutable, ordered, allows duplicates. ⦁ Tuple: Immutable, ordered, allows duplicates. ⦁ Set: Mutable, unordered, no duplicates. 3. How do you handle missing data in a dataset? Common methods: removing rows/columns with missing values, filling with mean/median/mode, or using interpolation. Libraries like Pandas provide .dropna(), .fillna() functions to do this easily. 4. What are list comprehensions and how are they useful? Concise syntax to create lists from iterables using a single readable line, often replacing loops for cleaner and faster code. Example: [x*2 for x in range(5)] → `` 5. Explain Pandas DataFrame and Series. ⦁ Series: 1D labeled array, like a column. ⦁ DataFrame: 2D labeled data structure with rows and columns, like a spreadsheet. 6. How do you read data from different file formats (CSV, Excel, JSON) in Python? Using Pandas: ⦁ CSV: pd.read_csv('file.csv') ⦁ Excel: pd.read_excel('file.xlsx') ⦁ JSON: pd.read_json('file.json') 7. What is the difference between Python’s append() and extend() methods? ⦁ append() adds its argument as a single element to the end of a list. ⦁ extend() iterates over its argument adding each element to the list. 8. How do you filter rows in a Pandas DataFrame? Using boolean indexing: df[df['column'] > value] filters rows where ‘column’ is greater than value. 9. Explain the use of groupby() in Pandas with an example. groupby() splits data into groups based on column(s), then you can apply aggregation. Example: df.groupby('category')['sales'].sum() gives total sales per category. 10. What are lambda functions and how are they used? Anonymous, inline functions defined with lambda keyword. Used for quick, throwaway functions without formally defining with def. Example: df['new'] = df['col'].apply(lambda x: x2) React ♥️ for Part 2	5 312
20	🚨 FREE $2 GIVEAWAY 🚨 All you can claim a FREE $2 reward in just a few minutes! 1️⃣ Open @maczobot 2️⃣ Claim your FREE $2 💸 Earn up to $10 extra with referrals. ⏳ Available for a limited time only. 👉 @maczobot	1 217

Ver todas las publicaciones