What is Jupyter Notebook and why use it for Python Programming?
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It’s a powerful tool that makes it easy to combine code, output, and explanations in a single document, making it ideal for data science, scientific computing, and machine learning.
For beginners, Jupyter Notebook provides a friendly and intuitive interface that makes it easy to learn and experiment with Python. It also allows you to execute code cells one by one, which is useful for debugging and testing. Furthermore, Jupyter Notebook lets you save your work in a variety of formats, so you can share your notebooks with others, either as a standalone document or as part of a larger project.
In this blog post, we will show you how to use Jupyter Notebook for Python, from installation to creating and running your first notebook. Whether you’re new to Python or an experienced user, we hope this guide will help you get started with Jupyter Notebook and discover its many possibilities.
How to Install Jupyter Notebook
To use Jupyter Notebook, you need to have Python installed on your computer. If you don’t already have it, you can download and install the latest version from [python.org](https://www.python.org/downloads/).
Once you have Python, you can install Jupyter Notebook using conda or pip, the package managers for the Anaconda and Python distributions, respectively. Here are the steps for each method:
Installation with conda
- Open a terminal or command prompt.
- Type `conda install -c conda-forge jupyterlab` and press Enter.
This will install Jupyter Notebook and all its dependencies.
Installation with pip
- Open a terminal or command prompt.
- Type `python -m pip install jupyterlab` and press Enter.
This will install Jupyter Notebook and all its dependencies.
After the installation is complete, you can verify that Jupyter Notebook is installed by typing `jupyter-notebook –version` in the terminal or command prompt. This should print the version number of Jupyter Notebook, such as `6.1.5`.
That’s it! You’re now ready to launch Jupyter Notebook and start creating your first notebook.
Launching Jupyter Notebook: How to open the application
Once you have installed Jupyter Notebook, you can launch it by typing `jupyter-notebook` in the terminal or command prompt. This will open a new tab in your web browser, showing the Jupyter Notebook dashboard.
The dashboard is a web-based user interface that allows you to manage your Jupyter Notebook files, including notebooks, folders, and scripts. It has three main areas:
- The **file browser** on the left side shows the list of notebooks, folders, and scripts in your current directory. You can navigate through the directories, create new folders, upload files, and so on.
- The **list of running notebooks** on the right side shows the notebooks that are currently running on your local machine. You can stop a running notebook, rename it, or shut down the kernel.
- The **toolbar** at the top provides various actions that you can perform on the selected file or folder, such as creating a new notebook, uploading a file, or opening a terminal.
To create a new notebook, click on the “New” button in the toolbar, and select “Python [default]” from the dropdown menu. This will open a new notebook in a new tab, with a default kernel named “Python [default]”.
You can also open an existing notebook by clicking on its name in the file browser, or by dragging and dropping the notebook file into the dashboard.
That’s it! You’re now ready to start working on your notebook. In the next section, we will show you how to create and run your first code cells.
Creating a new notebook: How to create and edit cells
Once you have opened a new notebook, you can start creating and editing cells. A cell is a container for text or code that you can execute or render in the notebook. There are two main types of cells in Jupyter Notebook:
- **Code cells** contain Python code that you can run in the kernel. To create a code cell, click on the “Code” button in the toolbar, or use the shortcut `Shift + Enter`.
- **Markdown cells** contain text written in the Markdown markup language, which you can render as formatted text in the notebook. To create a markdown cell, click on the “Markdown” button in the toolbar, or use the shortcut `Ctrl + M`.
You can edit a cell by double-clicking on it, or by selecting it and pressing `Enter`. This will put the cell in edit mode, where you can type or modify the contents of the cell. To exit edit mode, press `Shift + Enter`, or click on the “Run” button in the toolbar. This will execute the code in a code cell, or render the text in a markdown cell.
You can also move, copy, and paste cells using the toolbar, the context menu, or the keyboard shortcuts. For example, to move a cell up or down, you can click on the “Move Cell Up” or “Move Cell Down” buttons in the toolbar, or use the shortcuts `Ctrl + Shift + Up Arrow` or `Ctrl + Shift + Down Arrow`.
That’s it! You now know how to create and edit cells in a Jupyter Notebook. In the next section, we will show you how to run code in a notebook.
Running code in a notebook
To run code in a Jupyter Notebook, you need to use a kernel, which is a computational engine that executes the code in your notebook. By default, a new notebook uses a kernel named “Python [default]”, which is a Python 3 kernel provided by the IPython project.
To run code in a code cell, you can either click on the “Run” button in the toolbar, or use the keyboard shortcut `Shift + Enter`. This will send the code in the cell to the kernel, which will execute it and return the results. For example, if you type and run the following code:
print("Hello, world!")
You should see the output `Hello, world!` displayed below the code cell.
If you run into an error, or if you want to interrupt the execution of the code, you can use the “Interrupt” or “Restart” buttons in the toolbar, or the keyboard shortcuts `I, I` or `0, 0`, respectively. This will stop the kernel, or restart it and clear all the previous outputs.
You can also get help on a specific function or object by using the `?` operator, or the `help()` function. For example, if you type and run the following code:
?print
help(print)
You should see a pop-up window with the documentation for the `print` function, including its signature, arguments, and examples.
That’s it! You now know how to run code in a Jupyter Notebook using the kernel and execute cells. In the next section, we will show you how to save and share notebooks.
Saving and sharing notebooks: How to save and share your work
Jupyter Notebook allows you to save your notebook in a variety of formats, so you can share your work with others, or use it in other applications. To save a notebook, you can either click on the “Save” button in the toolbar, or use the keyboard shortcut `Ctrl + S`. This will save the notebook as a `.ipynb` file in the current directory.
You can also save the notebook in other formats, such as a `.py` file (plain Python script), a `.html` file (HTML page), or a `.pdf` file (PDF document). To do this, you can click on the “File” menu, and select “Export Notebook As…”. Then, you can choose the desired format from the dropdown menu, and click on the “Export” button.
If you want to share your notebook with others, you can use a cloud service like Google Colab or Azure Notebooks. These services allow you to upload and share your notebooks, as well as collaborate with others in real-time. To use them, you need to create an account and upload your notebook to their servers. Then, you can share the link to your notebook with others, who can view, edit, and run the notebook in their own browsers.
That’s it! You now know how to save and share your notebooks with others. In the next section, we will summarize the main points of the post and encourage you to experiment with Jupyter Notebook on your own.
What you learned and what's next
- In this blog post, we showed you how to use Jupyter Notebook for Python, from installation to creating and running your first notebook. We explained the different components of the Jupyter Notebook interface, and demonstrated how to create and edit cells, run code, and save and share notebooks.
We hope this guide has been useful for you, and that it has helped you learn more about Jupyter Notebook and its capabilities. If you want to continue learning and experimenting with Jupyter Notebook, here are some suggestions for what to do next:
- Explore the Jupyter Notebook documentation, which provides detailed information about the features and functionality of Jupyter Notebook, as well as many examples and tutorials. You can find the documentation at: https://jupyter.org/documentation.
- Try the Jupyter Notebook examples, which are a collection of notebooks that showcase the capabilities of Jupyter Notebook for various tasks, such as data visualization, machine learning, and scientific computing.
- Join the Jupyter community, which is a group of users, contributors, and developers who share their experiences, ideas, and resources related to Jupyter Notebook. You can join the community by participating in forums, mailing lists, and chat rooms, or by attending conferences and workshops. You can find more information at: https://jupyter.org/community.
We hope you will take advantage of these resources and continue your journey with Jupyter Notebook. Happy coding!
