Python Interviews
Ir al canal en Telegram
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
Mostrar más
Red:📈 Análisis del canal de Telegram Python Interviews
El canal Python Interviews (@pythoninterviews) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 28 854 suscriptores, ocupando la posición 4 620 en la categoría Tecnologías y Aplicaciones y el puesto 14 448 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 28 854 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 102, 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 2.55%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.66% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 735 visualizaciones. En el primer día suele acumular 191 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como |--, link:-, learning, sql, analytic.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”
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.
28 854
Suscriptores
+224 horas
+367 días
+10230 días
Archivo de publicaciones
28 854
Why should you learn Python?
- simple yet powerful
- versatile
- clean
- beginner friendly
- tremendous job opportunities
- high demand
- readability
- awesome community
- awesome ecosystem
- web development
- machine learning
28 854
ChatGPT can write code faster and seemingly better than many programmers.
So will it replace software engineers anytime soon?
The answer is No.
Here are 4 reasons why: 👇
1) Currently, when doing programming tasks, ChatGPT outputs code. And is everybody able to grok, manipulate, and use code? No—only software engineers are. ChatGPT's current coding-related outputs are useless to the general population and need to be handled by SWEs.
2) ChatGPT has been proven to sometimes give incorrect answers, including buggy code. No sound business will risk getting rid of their SWEs in favor of an AI that can provably write buggy software.
3) ChatGPT currently struggles to successfully debug buggy code, even in simple, self-contained code blocks. We can imagine that this will remain especially true in large, complex codebases. You can't get rid of SWEs if you need them to debug your AI's code.
4) To build complex applications with ChatGPT, you need to give it complex prompts that inherently require some technical knowledge as well as "prompt engineering" prowess. Right now, SWEs are the best-equipped people to write these prompts.
Instead of replacing software engineers, ChatGPT will serve as an amazing quality-of-life-improvement tool for them, helping them perform certain programming tasks much faster.
If you're a SWE, you don't need to worry about ChatGPT—for now. (Credits: Unknown)
28 854
Data types are foundational in computing, and it's essential to understand them to work effectively in any programming environment.
Let's take a dive into the top ten commonly used data types:
1. Integer (int):
- Represents whole numbers.
- Examples: -2, -1, 0, 1, 2, 3
2. Floating Point (float/double):
- Represents numbers with decimals.
- Examples: -2.5, 0.0, 3.14
3. Character (char):
- Represents single characters.
- Examples: 'A', 'b', '1', '%'
4. String:
- Represents sequences of characters, basically text.
- Examples: "Hello", "ChatGPT", "1234"
5. Boolean (bool):
- Represents true or false values.
- Examples: True, False
6. Array:
- Represents a collection of elements, often of the same type.
- Examples: [1, 2, 3], ["apple", "banana", "cherry"]
7. Object:
- Used in object-oriented programming, represents a combination of data and methods to manipulate the data.
- Examples: A
Car object might have data like color and speed and methods like drive() and park().
8. Date & Time:
- Represents date and time values.
- Examples: 23-10-2023, 12:30:45
9. Byte & Binary:
- Represents raw binary data.
- Examples: 01010101 (Byte), 101000111011 (Binary)
10. Enum:
- Represents a set of named constants.
- Examples: Days of the week (Monday, Tuesday...), Colors (Red, Blue, Green)28 854
6 Websites To Practice Faster Typing Online...
⌨️ monkeytype.com
⌨️ keybr.com
⌨️ typings.gg
⌨️ farzher.com
⌨️ typingbolt.com
⌨️ typrx.com
28 854
Web Scraping in Python
🔸Scrapy is an open source and collaborative framework for extracting the data you need from websites.
It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format. Unlike BeautifulSoup, which you may have heard of, Scrapy is a tool specifically created for downloading, cleaning and saving data from the web and will help you end-to-end; whereas BeautifulSoup is a smaller package which will only help you get information out of webpages.
⚙️Installation
pip install scrapy
🔗Homepage
🔗GitHub
🔗[Tutorial] Making Web Crawlers Using Scrapy for Python
#scrapy #web28 854
Visualize code execution
Have you ever had a hard time understanding what is going on in your code? Python Tutor's online coding environment allows you to write code and visualize frame-by-frame how it gets executed by the computer. Besides Python, It's also supports Java, C/C++, JavaScript and Ruby.
Just pick a language, write some code, press the "Visualize Execution" button and you’ll be redirected to a page, where all the magic happens. 💫
🔗Python Tutor's homepage
#tools
28 854
HTTP Requests
🔸Requests is an elegant and simple HTTP library for Python. It allows you to send HTTP/1.1 requests extremely easy, so you can focus on interacting with services and consuming data in your application.
⚙️Installation
pip install requests
The most-commonly-used HTTP methods are GET and POST:
requests.get(url)
requests.post(url, data=somedictdata)
Each of there functions returns a Response. It's a really powerful object for inspecting the results of the request. We can get all the information we need from this object like text, status code or encoding.
🔗Docs
🔗Python’s Requests Library (Guide)
#requests28 854
What are Python Modules?
Python modules are files containing Python code.
A module can define functions, classes and variables. A module can also include runnable code. Grouping related code into a module makes the code easier to understand and use.
28 854
Comparison, Logical, and Membership Operators in Python | Python for Beginners
