⌨️ Asynchronous code

Asynchronous code is an approach to writing code that allows multiple tasks to be performed simultaneously within a single process. This is achieved through the use of asynchronous functions and coroutines. Unlike synchronous code, which executes each task sequentially, asynchronous code can run multiple tasks “in parallel” and organize their execution using iterations and callback calls.

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PYTHON INTERVIEW QUESTIONS

1 what is python ?
2 why python ?
3 what are advantage of python ?
4 what is pep 8 ?
5. What do you mean by literal ?
6 explain python function ?
7 what is use of break statement ?
8 what is tuple ?
9 python libraries /module ?
10. What is an in operator in python ?
11 why python interpreted ?
12 how is memory managed in python ?
13 python decorator ?
14 global variable / local variable ?
15 what is iterators in python ?
16 what is slicing in python ?
17 what is a dictionary in python ?
18 what is pass in python ?
19 what isinit ?
20 what is self in python ?

Most important for Technical round interview.

11. Python + BeautifulSoup = Web Scraping
12. Python + Scrapy = Web Scraping and Crawling
13. Python + PySpark = Big Data Processing
14. Python + OpenCV = Computer Vision
15. Python + PyTorch = Deep Learning
16. Python + FastAPI = Web Development (high-performance APIs)
17. Python + SQLAlchemy = Database Management
18. Python + Jupyter Notebook = Interactive Computing and Data Analysis
19. Python + Celery = Distributed Task Queue
20. Python + Pygame = Game Development

#python

21. Python + Ansible = IT Automation and Configuration Management
22. Python + Fabric = Automation and Deployment
23. Python + NLTK = NLP
24. Python + spaCy = Industrial-Strength Natural Language Processing
25. Python + Bokeh = Interactive Web Visualization
26. Python + Dash = Web-Based Data Visualization
27. Python + Scikit-learn = ML
28. Python + NetworkX = Network Analysis and Graph Theory
29. Python + Twisted = Network Programming
30. Python + PyQt = GUI Application Development

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⌨️ Useful Python Modules

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Python for FinTech Roadmap

Stage 1 – Learn Python (Syntax, OOP, Libraries)
Stage 2 – Understand FinTech Concepts (Finance, Payments, Regulations)
Stage 3 – Data Handling (Pandas, NumPy, CSV, APIs)
Stage 4 – Financial Modeling (Risk Analysis, Pricing Models)
Stage 5 – Automation (Web Scraping, Report Generation)
Stage 6 – Security (Encryption, Compliance)
Stage 7 – Build Financial Tools (Payment Gateways, Trading Bots)
Stage 8 – Deploy FinTech Apps (Cloud, SaaS)

🏆 – Python FinTech Developer

#python

Python for Cloud Computing Roadmap

Stage 1 – Learn Python (Syntax, OOP)
Stage 2 – Understand Cloud Concepts (IaaS, PaaS, SaaS)
Stage 3 – Work with AWS/Azure SDKs (Boto3, Azure SDK)
Stage 4 – Cloud Storage (S3, Blob Storage)
Stage 5 – Deploy with Docker and Kubernetes
Stage 6 – Implement Serverless (Lambda, Functions)
Stage 7 – Automate Infrastructure (Terraform, CloudFormation)
Stage 8 – Monitor and Scale Cloud Apps

🏆 – Python Cloud Developer

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DICTIONARY FOR PYTHON DEVELOPER

Python Code to remove Image Background
—————————————————————-
from rembg import remove
from PIL import Image

image_path = 'Image Name' ## ---> Change to Image name

output_image = 'ImageNew' ## ---> Change to new name your image

input = Image.open(image_path)

output = remove(input)

output.save(output_image)

Functions are fundamental in 𝗣𝘆𝘁𝗵𝗼𝗻, reusable blocks of code that streamline our work. Whether you're a beginner or an experienced coder, understanding Normal Functions vs. Lambda Functions can level up your coding efficiency.

Let's break it down:

🔹 𝗡𝗼𝗿𝗺𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻
With a name, body, and return statement, this function is ideal for tasks that require multiple lines of code or complex logic.

🔹 𝗟𝗮𝗺𝗯𝗱𝗮 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻
Need a quick, single-use function? Lambda is your friend! It’s anonymous and perfect for concise operations.

While Normal Functions are great for more extensive operations, Lambda Functions are excellent for small, single-use operations where you need simplicity.

Choose the function type that best fits your task’s complexity!

Python for Scientific Computing Roadmap

Stage 1 – Learn Python (Syntax, OOP)
Stage 2 – Use NumPy and SciPy for Mathematical Computing
Stage 3 – Work with Matplotlib for Data Visualization
Stage 4 – Statistical Analysis (Pandas, Statsmodels)
Stage 5 – Use SymPy for Symbolic Mathematics
Stage 6 – Simulate Models (ODE, PDE)
Stage 7 – Learn Jupyter for Scientific Workflows
Stage 8 – Perform Optimization and Numerical Solvers

🏆 – Python Scientific Developer

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