Python has a vast number of libraries, covering a wide range of domains, such as data science, machine learning, web development, game development, scientific computing, and more. Here are some commonly used libraries in Python:

1. NumPy - A library for scientific computing with Python. It provides a multidimensional array object, mathematical functions, and tools for working with arrays.

2. Pandas - A library for data manipulation and analysis. It provides tools for working with tabular data, such as data frames, and offers functionality for data cleaning, transformation, and exploration.

3. Matplotlib - A library for creating static, animated, and interactive visualizations in Python. It offers a wide range of plots, charts, and graphs, and provides fine-grained control over the appearance and layout of the output.

4. Scikit-learn - A library for machine learning in Python. It provides tools for classification, regression, clustering, and dimensionality reduction, and offers a range of algorithms for each type of problem.

5. TensorFlow - An open-source machine learning framework developed by Google. It provides a set of tools for building and training deep neural networks, and has been widely adopted in industry and academia.

6. Django - A high-level Python web framework that follows the model-template-view architectural pattern. It provides a robust set of tools for building web applications, including a powerful ORM, a templating system, and a built-in admin interface.

7. Flask - A lightweight web framework that provides the bare essentials for building web applications. It is highly customizable and flexible, and is often used for building APIs or small to medium-sized web applications.

8. Requests - A library for making HTTP requests in Python. It provides a simple and intuitive API for sending HTTP/1.1 requests, and offers functionality for handling cookies, authentication, and more.Tasks

9. Create a Dictionary in Python and write it to a json File.

10.  import json
    
     # Create a dictionary
     my_dict = {"name": "Shubham", 
     "Course": "Devops", 
     "city": "New York"}
    
     # Write dictionary to a JSON file
     with open("my_dict.json", "w") as f:
         json.dump(my_dict, f)
    

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