Jupyter Notebook is a widely used and accessible open-source application that allows to create documents containing code, equations, visualizations, and narrative text. It’s widely used for data cleaning and transformation, numerical simulation, statistical modelling, data visualization, machine learning, and more. Ideal for students, data scientists, and researchers, Jupyter Notebook supports over 40 programming languages like Python, R, Julia, and Scala. In this guide, you will learn how to use the Jupyter Notebook for Python.
Searching for Jupyter Notebook on Your Desktop
Once Jupyter Notebook is installed on your system, you can easily find it by searching “Jupyter Notebook†in your desktop’s search bar. This action will typically show the application as a search result, allowing you to open it directly.
Opening a New Notebook
After launching Jupyter Notebook, you’ll be directed to your browser to navigate the application interface. To open a new notebook, click the “New†button at the top right corner and select the type of notebook you wish to create (e.g., Python 3).
Renaming the Notebook
To rename your notebook, click on the title at the top of the page. You can rename your notebook in the pop-up. This feature helps organize and identify your projects easily.
Writing Code and Running It
It allows you to write and implement code in an interactive environment. Enter your code in a cell, and press ‘Shift + Enter’ to run it. The output will be displayed directly below the cell.
Kernel-Related Activities
The kernel is the computational engine that executes the code contained in your notebook. You can manage the kernel by restarting, interrupting, or reconnecting to it, which is essential for troubleshooting and managing the notebook’s execution state.
Opening Terminal
Jupyter Notebook also offers a terminal interface within its environment. To open a terminal, go back to the dashboard and click on “New†> “Terminalâ€. This feature is crucial for executing shell commands directly from the Jupyter environment.
Installing Libraries
You can install any required libraries directly within a notebook or the terminal using pip or conda. For instance, to install a library in a notebook cell, type ‘!pip install library_name’ and run the cell.
Uploading an Existing Notebook
To run or edit an existing notebook, you can upload it to the Jupyter dashboard. Click on the “Upload†button at the top right corner of the dashboard, select the notebook file from your computer, and click “Open†to upload it.
Key Features of Jupyter Notebook
Interactive Data Science and Scientific Computing: Supports interactive exploration across all programming languages.
Rich Media Integration: Mixes executable code, rich text, mathematics, and plots in a single document for comprehensive documentation.
Real-Time Code Execution: Provides immediate feedback for iterative analysis, which is crucial for exploratory programming.
Collaboration and Sharing: Facilitates easy sharing of notebooks for collaborative work, enhancing productivity and knowledge exchange.
Extensibility: Supports numerous extensions that add useful functionalities, from advanced editing features to version control.
Web-Based Interface: Ensures accessibility from any device with a web browser, allowing flexibility in working environments.
Conclusion
Jupyter Notebook is a famous and powerful tool for interactive computing. Its ease of use and versatility in handling data analysis, visualization, and computational science projects make it an invaluable resource for students, professionals, and tech enthusiasts. With this guide and the accompanying screenshots, you should be well on your way to mastering Jupyter Notebook and leveraging its full potential in your projects.
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