Python Projects & Free Books
Python Interview Projects & Free Courses Admin: @Coderfun
Mostrar más📈 Análisis del canal de Telegram Python Projects & Free Books
El canal Python Projects & Free Books (@pythonfreebootcamp) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 40 879 suscriptores, ocupando la posición 3 283 en la categoría Tecnologías y Aplicaciones y el puesto 9 515 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 40 879 suscriptores.
Según los últimos datos del 13 julio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -111, y en las últimas 24 horas de -7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.84%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.83% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 571 visualizaciones. En el primer día suele acumular 341 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 learning, analyst, framework, link:-, structure.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Python Interview Projects & Free Courses
Admin: @Coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 14 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.
Carga de datos en curso...
| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 14 julio | 0 | |||
| 13 julio | 0 | |||
| 12 julio | +7 | |||
| 11 julio | +1 | |||
| 10 julio | +2 | |||
| 09 julio | +2 | |||
| 08 julio | +8 | |||
| 07 julio | 0 | |||
| 06 julio | +14 | |||
| 05 julio | +2 | |||
| 04 julio | +9 | |||
| 03 julio | +2 | |||
| 02 julio | +10 | |||
| 01 julio | +7 |
| 2 | 📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀
✅ 100% FREE learning opportunities
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🚀 Start learning today. Build your analytics foundation. Earn free certifications. Move one step closer to your Data Analyst career. | 694 |
| 3 | 15 Best Project Ideas for Python : 🐍
🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter
🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator
🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis | 869 |
| 4 | 7 GitHub repos to master AI engineering in 2026 👇
1/ Awesome Artificial Intelligence:
https://github.com/owainlewis/awesome-artificial-intelligence
2/ Awesome LLM Apps:
https://github.com/Shubhamsaboo/awesome-llm-apps
3/ 100 Days of ML Code:
https://github.com/avik-jain/100-Days-of-ML-Code
4/ System Prompts and AI Tools:
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools
5/ AI Agents for Beginners:
https://github.com/microsoft/ai-agents-for-beginners
6/ Microsoft Gen AI for Beginners:
https://github.com/microsoft/ai-for-beginners
7/ Learn Agentic AI:
https://github.com/panaversity/learn-agentic-ai | 1 316 |
| 5 | 🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.
2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.
3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.
4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.
5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.
6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.
7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.
8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.
9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.
I have curated the best interview resources to crack Python Interviews 👇👇
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#Python | 1 552 |
| 6 | 🔰 Python functions | 1 879 |
| 7 | If you work with Python, remember a simple rule: do not modify a list while iterating over it. 🐍🛑 This can lead to unexpected results because the iterator does not track structural changes.
Here is an example that looks logical but works incorrectly: 🤔
items = [1, 2, 2, 3, 4]
for item in items:
if item == 2:
items.remove(item)
print(items)
# Output: [1, 2, 3, 4]
It seems that all 2s should disappear, but one remains. ❓ Why?
After removing an element, the list shifts, but the loop moves on — as a result, some values are simply skipped. 🔄🚫
How to do it correctly — iterate over a copy: ✅
for item in items[:]:
if item == 2:
items.remove(item)
print(items)
# Output: [1, 3, 4]
Even better — use list comprehension: 🚀
items = [x for x in items if x != 2]
Conclusion: 🏁 do not modify a collection during iteration. This can lead to skipped elements, duplication, or even errors during execution. 🛠️🚧
#Python #Coding #Programming #Debugging #TechTips #PythonTips | 2 273 |
| 8 | Python Basics Arrays & Loops 🐍
Essential you need to start strong 💪
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| 10 | Essential Python Libraries to build your career in Data Science 📊👇
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.me/datasciencefree
Python Project Ideas: https://t.me/dsabooks/85
Best Resources to learn Python & Data Science 👇👇
Python Tutorial
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Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
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ENJOY LEARNING👍👍 | 0 |
