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2. Explain the process of starting Jupyter Notebook, and what role does it play in Python programming?
Ans. To start Jupyter Notebook, one can use the command jupyter notebook in the command line. It opens
a web-based interface where users can integrate code, text, and graphics within a document. It is a
powerful tool widely used by data scientists for scientific computing.
3. How do you create a new virtual environment in Anaconda, and why is it essential for Python development?
Ans. To create a new virtual environment in Anaconda, use the command conda create -n env python=3.11.
This creates an isolated environment for different projects, ensuring that dependencies are separate.
Virtual environments are crucial for maintaining project-specific dependencies and avoiding conflicts.
4. Discuss the significance of the ‘File’ menu in Jupyter Notebook, and what operations can be performed
through it?

Ans. The ‘File’ menu in Jupyter Notebook is vital for basic operations such as opening new documents, saving
notebooks, and performing file-related tasks. It provides essential functionalities for managing and
organizing your Jupyter Notebook projects.

5. Explain the purpose and functionality of the print() function in Python. How does it contribute to code
readability?
Ans. The print() function in Python is used to display messages or values on the screen. It enhances code
readability by allowing developers to showcase outputs and results. It is a fundamental function for
communicating information from the code to the user.
6. Explain the steps involved in creating and activating a virtual environment in Python using Anaconda.
Ans. To create a virtual environment, use the command conda create -n env python=3.11. After creating,
activate it with conda activate env. This process isolates Python environments for different projects,
ensuring a clean and separate development space.

G. Application-based questions.
1. Explain a real-world scenario where creating a virtual environment in Python would be beneficial.

Ans. Imagine you are working on two different projects, one involving data analysis and the other web
development. By creating separate virtual environments for each project, you can manage dependencies
independently. This ensures that the libraries and packages required for data analysis won’t interfere
with those needed for web development, providing a clean and organized development environment.

2. How can Jupyter Notebook be useful in a data science project?
Ans. In a data science project, Jupyter Notebook is highly valuable for its interactive and visual capabilities.
Data scientists can seamlessly integrate code, text, and visualizations within a document. This allows for
step-by-step data analysis, making it easier to communicate findings and insights. The ability to export
notebooks to various formats also facilitates sharing and collaboration.
3. Describe a scenario where using the input() function in Python would be practical.
Ans. Consider a situation where you are building a simple calculator program. Using the input() function, you
can prompt the user to enter numeric values for calculations. This makes the program interactive, as it
waits for user input before proceeding. For instance, the program may ask the user to input two numbers
and then perform addition, subtraction, multiplication, or division based on the user’s choice.

H. Assertion and reason based questions.
1. Assertion: Anaconda Navigator is a desktop GUI for managing environments and packages.

Reason: It provides a user-friendly interface to launch applications, manage conda packages, and handle

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