Python

[erent8]

Which framework should be learned after python?

[NitkarshChourasia]

Django, Flask, Unittest, Request, etc... But this is not a thing for query. This is not google or chatGPT, please remove this.

Issue opener please remove this.

@geekcomputers Please remove this useless, issue, repository owner.

[Yggdrasill501]

I would start with standard library if didn't go trough it yet, than i recommend some libraries like numpy, scipy, xarray for more scientific stuff, if you want do something with pictures are amazing pillow, cv2, for web socket, django, flask, and i would even recommend py-game, i now python is not the best for games but good for fun projects

[NitkarshChourasia]

I would start with standard library if didn't go trough it yet, than i recommend some libraries like numpy, scipy, xarray for more scientific stuff, if you want do something with pictures are amazing pillow, cv2, for web socket, django, flask, and i would even recommend py-game, i now python is not the best for games but good for fun projects

Yes.

[DonMiller9294]

It really all depends what career path are you trying to take? If you are looking to learn web development learn django and flask.

[NitkarshChourasia]

Yes! What other career path be taken by coding in python?

[DonMiller9294]

You can go with machine learning.

here is a list of different libraries and frameworks that go with machine learning.

NumPy:

NumPy is a fundamental library for numerical computing in Python. It provides support for working with arrays and matrices, which are crucial for data manipulation in machine learning. pandas:

pandas is a powerful data analysis library that offers data structures like DataFrames and Series. It's excellent for data preprocessing, cleaning, and transformation tasks. scikit-learn:

scikit-learn is a popular machine learning library that provides a wide range of tools for classification, regression, clustering, dimensionality reduction, and more. It's known for its user-friendly and consistent API. TensorFlow:

TensorFlow is an open-source machine learning framework developed by Google. It's widely used for deep learning tasks, particularly for building and training neural networks. PyTorch:

PyTorch is another deep learning framework that's gained popularity in recent years. It's known for its dynamic computation graph and is often favored by researchers and practitioners in deep learning. Keras:

Keras is a high-level neural networks API that runs on top of other deep learning frameworks like TensorFlow and Theano. It simplifies the process of building and training neural networks. Matplotlib and Seaborn:

These libraries are essential for data visualization. Matplotlib provides a versatile and customizable plotting system, while Seaborn offers higher-level, aesthetically pleasing statistical visualizations. Jupyter Notebooks:

While not a library or framework, Jupyter Notebooks are a vital tool for interactive data analysis and machine learning experimentation. They allow you to create and share documents that contain live code, equations, visualizations, and narrative text. XGBoost:

XGBoost is a popular gradient boosting library known for its efficiency and performance in structured data problems. It's especially useful for tabular data and winning Kaggle competitions. LightGBM:

LightGBM is another gradient boosting library that's highly efficient and optimized for large datasets. It's known for its speed and low memory usage. CatBoost:

CatBoost is a gradient boosting library developed by Yandex. It's designed to handle categorical features effectively and often works well "out of the box" with minimal hyperparameter tuning. spaCy or NLTK (Natural Language Toolkit):

If you're working on natural language processing (NLP) tasks, you should consider learning spaCy or NLTK. They provide tools and resources for working with text data. Dask:

Dask is a parallel computing library that helps you scale your machine learning workflows to larger datasets and distributed computing environments. Flask or Django:

If you plan to deploy machine learning models as web applications, learning a web framework like Flask or Django is essential. These frameworks can help you create APIs for serving your models. FastAPI:

FastAPI is a modern web framework for building APIs with Python. It's known for its high performance and ease of use, making it a great choice for deploying machine learning models. Keep in mind that the specific libraries and frameworks you need to learn may vary depending on your machine learning specialization and project requirements. Start with the basics (NumPy, pandas, scikit-learn) and gradually explore additional libraries based on your interests and needs.

[NitkarshChourasia]

Wonderful @DonMiller9294

Thanks for your effort to explain us.

[Engr-Newton]

It depends on which area you are specializing at. E.g. Automation, Machine learning, Web or App develoment, etc.

[Muhammad-Abdullah3]

It all depends on which area are you specifying but some libraries given below are must

Numpy

JSON

Math

OS

[baravind031]

Which framework should be learned after python?

if you want to choose data science and (ML) Machine Learning:

NumPy and pandas: These libraries are essential for data manipulation and analysis. scikit-learn: Ideal for machine learning and predictive modeling. TensorFlow or PyTorch: For deep learning and neural network projects.

Web Development:

Django or Flask: Popular Python web frameworks for building web applications. And FastAPI is a modern web framework for building APIs with Python

[NitkarshChourasia]

Why do we develop APIs? What is the use of API, if I make a custom one, using any of the recommended python library to make an API ? How and where can I implement it?

Like suppose If I make one and deploy it on vercel if that is the right place to do so....then can I show it on my GitHub's README.md file of my profile?

[baravind031]

APIs enable different software systems to communicate and interact with each other. They act as intermediaries that allow applications to understand and use the functionality provided by other applications or services.

Choose a python library or framework like flask ,django Define the endpoints and choose the data format whether your API will use JSON, XML or any other... Write the code for your API using the chosen framework. Define routes, request handlers, and data processing logic. Implement any necessary authentication and authorization mechanisms if your API requires them Test your API endpoints, Verify that they behave as expected Create documentation for your API. Include information about the endpoints, request and response formats, and example usage. Choose a hosting platform to deploy your API, platform like Heroku and there are other services If you want to showcase your API on your GitHub add README.md file, include information about how to use it

[NitkarshChourasia]

APIs enable different software systems to communicate and interact with each other. They act as intermediaries that allow applications to understand and use the functionality provided by other applications or services.

Choose a python library or framework like flask ,django Define the endpoints and choose the data format whether your API will use JSON, XML or any other... Write the code for your API using the chosen framework. Define routes, request handlers, and data processing logic. Implement any necessary authentication and authorization mechanisms if your API requires them Test your API endpoints, Verify that they behave as expected Create documentation for your API. Include information about the endpoints, request and response formats, and example usage. Choose a hosting platform to deploy your API, platform like Heroku and there are other services If you want to showcase your API on your GitHub add README.md file, include information about how to use it

Well explained.

[Fayizahamed95]

Im a bigginer how to I improve my coding skills

[Muhammad-Abdullah3]

By doing coding and practice as much as you can

[NitkarshChourasia]

Yes, indeed.

[AzizbekBarotboyev]

where were we

[AzizbekBarotboyev]

what wil be choise to teach yourself: 1 ChatGPT 2 You tube 3 GitHub 4 Expensive lesson in other web sites

[Fayizahamed95]

Can I use the build in functions of python language for crack the technical round in interview

[jamil-bot]

I would start with standard library if didn't go trough it yet, than i recommend some libraries like numpy, scipy, xarray for more scientific stuff, if you want do something with pictures are amazing pillow, cv2, for web socket, django, flask, and i would even recommend py-game, i now python is not the best for games but good for fun projects

standard library? like what?