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How to Set Up a Jupyter Project on Your Local System

Locally Set Up Jupyter Project

If you are interested in setting up a Jupyter project on your local system (Mac, Windows or Linux), this article can serve as your blueprint. This method involves using Python’s built-in virtual environment manager. Jupyter, an interactive notebook, allows for the execution of all sorts of interesting tasks. However, this article focuses primarily on its installation and setup 🏗️. Crucially, it’s worth noting that there are other methods and platforms for running notebooks, such as Google Colab and Deepnote 📓.


Download Python

Visit and click on the latest Python version. Follow the installation steps. Remember, for Windows users, it’s crucial to add Python to your path during installation.


Open Command Line Interface

Once you have Python successfully installed, open your Command Line Interface (CLI). If you’re on Windows, it’s called PowerShell; for Mac or Linux users, it’s the Terminal 🖥️. It should be noted that you can also use any IDE that allows you to write inside of the Terminal or Command Line.


Set Up the Virtual Environment

Upon Python installation completing, you can create your virtual environment (often referred to as ‘venv’). This is done to isolate your code from other projects. By running the ‘python -m pip freeze’ command, you can see all the Python packages installed in your system. If Jupyter is not found, this is not an issue because we are about to install it in our isolated environment.


Activate and Install Jupyter

Next, we activate our virtual environment and install Jupyter. Jupyter installs various packages allowing it to run as a server 🖧. You can start the Jupyter notebook server by typing ‘jupyter notebook’ in your Terminal/CLI which generates links you can access your local browser.


Create a New Notebook and Test

Now that we have Jupyter installed and running, we can create a new notebook to test it. In the notebook, we can write some Python code, like ‘print(“Hello, World!”)’, and then run it by pressing the ‘run’ button or by pressing ‘Shift + Enter’. One of the benefits of a notebook is the ability to make rapid code iterations, especially useful in the realm of data. So it’s worth noting that Jupyter is not only an environment for writing Python code but enables different coding styles.

Conclusion: 🏁

In conclusion, setting up a Jupyter project on your local system is relatively straightforward 🚀. While there are other platforms out there such as Google Colab and Deepnote, having the ability to run Jupyter locally offers flexibility and control over your development environment, especially when it comes to isolating your project using Python virtual environments. Enjoy exploring the vast capabilities and features that Jupyter notebooks offer. Stay persistent, and happy coding! 💻


Jupyter Notebook can be installed using pip. Run the following command in your terminal: ‘pip install notebook’.

Yes, Jupyter Notebook is developed in Python and for its functioning, Python is required.

Python 3.3 or later would work best with Jupyter Notebook.

After installation, you can run Jupyter Notebook by typing ‘jupyter notebook’ in your terminal.

Yes, even though Python 3 is recommended, you can use it for Python 2 by running ‘jupyter notebook –py=2’.

After starting Jupyter Notebook, you can create project by clicking on ‘New’ button in the dashboard.

You can use ‘Ctrl + s’ shortcut or click on ‘File’ > ‘Save’ to save your project.

Yes, to execute the code in a cell, you can click on ‘Run’ or use the shortcut ‘Shift + Enter’.

Click on the current project name at the top of the Notebook to rename it.

Kernel is a computation engine that executes the code contained in a Jupyter Notebook document.

To change kernel, go to ‘Kernel’ > ‘Change kernel’ > select your desired kernel.

To stop a running kernel, you can click ‘Kernel’ > ‘Interrupt’.

No, each notebook is associated with a single kernel.

You can use ‘jupyter kernelspec’ to manage the kernels in your Jupyter Notebook.

Yes, Jupyter Notebook supports display of all sorts of media including images, videos and even interactive widgets.

To export your Jupyter Notebook, go to ‘File’ > ‘Download as’ > select the format you want to export it in.

A .ipynb file is a text file that describes the contents of your Jupyter Notebook, including all of the elements that are present in the interactive version of your notebook.

Yes, .ipynb files can be converted to .py files using the ‘nbconvert’ tool.

You can share the .ipynb file or you can convert it to HTML or PDF file and then share it.

A cell is a container for text to be displayed in the notebook or code to be executed by the notebook kernel.

Yes, you can add comments to your code in Jupyter Notebook. This can be done by using the ‘#’ symbol before your comment.

To add a Markdown cell, you can go to ‘Cell’ > ‘Cell Type’ > ‘Markdown’ or use the ‘M’ shortcut.

Yes, LaTex can be used in Jupyter Notebook for typesetting formulas.

Magic commands in Jupyter Notebook are special methods that provide a quick way to solve common problems.

Yes, you can visualise data in Jupyter Notebook using libraries like matplotlib and seaborn.

To add line numbers, go to ‘View’ > ‘Toggle Line Numbers’

Notebook extensions are plug-ins that you can easily add to your Jupyter notebooks.

To install notebook extensions, use the following command ‘pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install’.

You can use magic command ‘%run -d’ to debug your code in Jupyter Notebook.

Yes, shell commands can be run inside a Jupyter Notebook by placing an exclamation point ‘!’ before the command.