Python Interviews
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Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun
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Сеть:📈 Аналитический обзор Telegram-канала Python Interviews
Канал Python Interviews (@pythoninterviews) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 28 841 подписчиков, занимая 4 609 место в категории Технологии и приложения и 14 423 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 28 841 подписчиков.
Согласно последним данным от 27 июля, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 96, а за последние 24 часа — 11, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 2.48%. В первые 24 часа после публикации контент обычно набирает 0.57% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 715 просмотров. В течение первых суток публикация набирает 163 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 2.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как |--, link:-, learning, sql, analytic.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free
For collaborations: @coderfun”
Благодаря высокой частоте обновлений (последние данные получены 28 июля, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
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Подписчики
+1124 часа
+447 дней
+9630 день
Архив постов
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝟰 𝗙𝘂𝗹𝗹‑𝗦𝘁𝗮𝗰𝗸 𝗖𝗼𝗱𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗧𝗵𝗮𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗟𝗲𝘃𝗲𝗹‑𝗨𝗽 𝗬𝗼𝘂𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼😍
Why Your Portfolio Matters in Tech🧑🎓
In 2025, having just a resume isn’t enough. Employers want to see proof of your skills — real projects that demonstrate your coding abilities, problem-solving, and understanding of end-to-end development📊✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3IMuFQu
All The Best 🎊
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Repost from Python for Data Analysts
🚀𝗧𝗼𝗽 𝟯 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲-𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟱😍
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✅ Future You Will Thank You!
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𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:
df = pd.read_csv('your_dataset.csv')
𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Like for more ❤️
ENJOY LEARNING 👍👍
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝟲 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗠𝗼𝘀𝘁 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀😍
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Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careers✅️
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
🎓𝟱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿! 🚀
Upgrade your skills and earn industry-recognized certificates — 100% FREE!
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28 841
How do analysts use SQL in a company?
SQL is every data analyst’s superpower! Here's how they use it in the real world:
Extract Data
Pull data from multiple tables to answer business questions.
Example:
SELECT name, revenue FROM sales WHERE region = 'North America';28 841
Repost from Python for Data Analysts
𝟯 𝗙𝗿𝗲𝗲 𝗦𝗤𝗟 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂 𝗮 𝗤𝘂𝗲𝗿𝘆 𝗣𝗿𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Still stuck Googling “What is SQL?” every time you start a new project?💵
You’re not alone. Many beginners bounce between tutorials without ever feeling confident writing SQL queries on their own.👨💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4f1F6LU
Let’s dive into the ones that are actually worth your time✅️
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Top 10 Python Concepts
Variables & Data Types
Understand integers, floats, strings, booleans, lists, tuples, sets, and dictionaries.
Control Flow (if, else, elif)
Write logic-based programs using conditional statements.
Loops (for & while)
Automate tasks and iterate over data efficiently.
Functions
Build reusable code blocks with def, understand parameters, return values, and scope.
List Comprehensions
Create and transform lists concisely:
[x*2 for x in range(10) if x % 2 == 0]
Modules & Packages
Import built-in, third-party, or custom modules to structure your code.
Exception Handling
Handle errors using try, except, finally for robust programs.
Object-Oriented Programming (OOP)
Learn classes, objects, inheritance, encapsulation, and polymorphism.
File Handling
Open, read, write, and manage files using open(), read(), write().
Working with Libraries
Use powerful libraries like:
- NumPy for numerical operations
- Pandas for data analysis
- Matplotlib/Seaborn for visualization
- Requests for API calls
- JSON for data parsing
#python
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝟱 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 – 𝗪𝗶𝘁𝗵 𝗙𝘂𝗹𝗹 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀!😍
Are you ready to build real-world tech projects that don’t just look good on your resume, but actually teach you practical, job-ready skills?🧑💻📌
Here’s a curated list of 5 high-value development tutorials — covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learning✨️💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3UtCSLO
They’re real, portfolio-worthy projects you can start today✅️
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝗪𝗮𝗻𝘁 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗧𝗵𝗮𝘁 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗔𝗿𝗲 𝗛𝗶𝗿𝗶𝗻𝗴 𝗙𝗼𝗿?😍
If you’re looking to land a job in tech or simply want to upskill without spending money, this is your golden chance✨️📌
We’ve handpicked 5 YouTube channels that teach 5 in-demand tech skills for FREE. These skills are widely sought after by employers in 2025 — from startups to top MNCs🧑💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/46n3hCs
Here’s your roadmap — pick one, stay consistent, and grow daily✅️
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Learn Django Easily 🤩
Here's all you need to get started 🙌
1. Introduction to Django
- What is Django?
- Setting up the Development Environment
2. Django Basics
- Django Project Structure
- Apps in Django
- Settings and Configuration
3. Models
- Creating Models
- Migrations
- Model Relationships
4. Views
- Function-Based Views
- Class-Based Views
- Generic Views
5. Templates
- Template Syntax
- Template Inheritance
- Template Tags and Filters
6. Forms
- Creating Forms
- Form Validation
- Model Forms
7. URLs and Routing
- URLconf
- Named URL Patterns
- URL Namespaces
8. Django ORM
- Querying the Database
- QuerySets
- Aggregations
9. Authentication and Authorization
- User Authentication
- Permission and Groups
- Django's Built-in User Model
10. Static Files and Media
- Serving Static Files
- File Uploads
- Managing Media Files
11. Middleware
- Using Middleware
- Creating Custom Middleware
12. REST Framework
- Django REST Framework (DRF)
- Serializers
- ViewSets and Routers
13. Testing
- Writing Tests
- Testing Models, Views, and Forms
- Test Coverage
14. Internationalization and Localization
- Translating Strings
- Time Zones
15. Security
- Securing Django Applications
- CSRF Protection
- XSS Protection
16. Deployment
- Deploying with WSGI and ASGI
- Using Gunicorn
- Deploying to Heroku, AWS, etc.
17. Optimization
- Database Optimization
- Caching Strategies
- Profiling and Performance Monitoring
18. Best Practices
- Code Structure
- DRY Principle
- Reusable Apps
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
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🚀 𝟳 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 😍
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💼 Perfect for students, freshers & working professionals
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Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝟲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 😍
A power-packed selection of 100% free, certified courses from top institutions:
- Data Analytics – Cisco
- Digital Marketing – Google
- Python for AI – IBM/edX
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- Generative AI – Google Cloud
- Machine Learning – Harvard
𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/3FcwrZK
Master in‑demand tech skills with these 6 certified, top-tier free courses
28 841
Repost from Python for Data Analysts
𝟰 𝗠𝘂𝘀𝘁-𝗪𝗮𝘁𝗰𝗵 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
If you’re starting your data analytics journey, these 4 YouTube courses are pure gold — and the best part? 💻🤩
They’re completely free💥💯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44DvNP1
Each course can help you build the right foundation for a successful tech career✅️
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to break into data science in 2025—without spending a single rupee?💰👨💻
You’re in luck! Microsoft is offering powerful, beginner-friendly resources that teach you everything from Python fundamentals to AI and data analytics—for free🤩✔️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42vCIrb
Level up your career in the booming field of data✅️
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Top 10 Python functions that are commonly used in data analysis
import pandas as pd: This function is used to import the Pandas library, which is essential for data manipulation and analysis.
read_csv(): This function from Pandas is used to read data from CSV files into a DataFrame, a primary data structure for data analysis.
head(): It allows you to quickly preview the first few rows of a DataFrame to understand its structure.
describe(): This function provides summary statistics of the numeric columns in a DataFrame, such as mean, standard deviation, and percentiles.
groupby(): It's used to group data by one or more columns, enabling aggregation and analysis within those groups.
pivot_table(): This function helps in creating pivot tables, allowing you to summarize and reshape data for analysis.
fillna(): Useful for filling missing values in a DataFrame with a specified value or a calculated one (e.g., mean or median).
apply(): This function is used to apply custom functions to DataFrame columns or rows, which is handy for data transformation.
plot(): It's part of the Matplotlib library and is used for creating various data visualizations, such as line plots, bar charts, and scatter plots.
merge(): This function is used for combining two or more DataFrames based on a common column or index, which is crucial for joining datasets during analysis.
These functions are essential tools for any data analyst working with Python for data analysis tasks.
Hope it helps :)
28 841
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝗖𝗿𝗮𝗰𝗸 𝗙𝗔𝗔𝗡𝗚 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
If you’re serious about cracking top tech interviews — from FAANG to startups — this is the roadmap you can’t afford to miss🎊
Thousands have used it to land roles at Google, Amazon, Microsoft, and more — completely free🤩📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TJlpyW
Your dream job might just start here.✅️
