Launching JupyterLab
Now that JupyterLab has been installed, it's time to launch it for the first time.
Open Terminal and navigate back to the project directory you created in Part 1.
cd ~/jupyterIf you're using a virtual environment, activate it before starting JupyterLab.
source .venv/bin/activateOnce the virtual environment has been activated, launch JupyterLab.
jupyter labAfter a few seconds, Terminal will display several startup messages before automatically opening your default web browser.
If the browser doesn't open automatically, you'll see a URL similar to the following.
http://localhost:8888/lab?token=...Copy the URL into your browser if necessary.
JupyterLab runs entirely on your local machine. Although it uses a web browser for its interface, no internet connection is required after installation. A local server is started, and your browser communicates with that server to execute Python code and display results.
This is one reason I like Jupyter for early experimentation: it feels like a browser app, but the code is still running locally on my machine.
Understanding the JupyterLab Interface
If this is your first time using JupyterLab, the interface may look different from a traditional Integrated Development Environment (IDE). Rather than focusing on a single project window, JupyterLab provides a flexible workspace where notebooks, terminals, text files, and other resources can coexist.
+----------------------------------------------+
| Menu Bar |
+----------------------------------------------+
| File Browser | |
| | Main Workspace |
| | |
| | |
| | |
+--------------+-------------------------------+
| Running | Terminals | Command Palette |
+----------------------------------------------+The left-hand sidebar provides quick access to your project files, running notebooks, terminals, and command palette. The center workspace is where notebooks and other documents are opened.
Unlike many editors, JupyterLab allows multiple notebooks, terminals, Markdown documents, and Python scripts to remain open simultaneously in separate tabs, making it much easier to switch between different tasks.
Creating Your First Notebook
Let's create our first notebook.
Click the Python 3 notebook icon in the Launcher.
Alternatively, you can select:
File
|
+-- New
|
+-- NotebookA new notebook opens with a single empty code cell.
You'll notice that notebooks use the .ipynb file extension.
This format stores not only your Python code, but also outputs, charts, images, and documentation in a single file.
Understanding Notebook Cells
Everything inside a notebook is organized into cells.
The two most common cell types are:
- Code Cells
- Markdown Cells
A Code Cell executes Python code.
A Markdown Cell allows you to write documentation, notes, headings, tables, and explanations alongside your code.
This combination is one of the primary reasons Jupyter has become so popular among engineers, researchers, and educators.
Notebook
|
+-- Markdown Cell
|
+-- Code Cell
|
+-- Code Cell
|
+-- Markdown Cell
|
+-- Code CellRather than maintaining separate documentation, everything stays together inside the notebook.
Running Your First Python Program
Inside the first code cell, enter the following Python statement.
print("Hello, Desk2Mob!")Execute the cell by pressing:
- Shift + Enter
or by clicking the Run button in the toolbar.
You should see the following output.
Hello, Desk2Mob!Congratulations. You've successfully executed your first Python program inside JupyterLab.
Working with Variables
One advantage of notebooks is that variables remain available after a cell has been executed.
x = 25
y = 75
x + yExecute the cell.
The notebook returns:
100You can now reference x and y in later cells without redefining them.
This interactive execution model makes experimentation significantly faster than repeatedly running an entire Python application.
Using Markdown Cells
Not every cell needs to contain Python code.
Markdown cells allow you to document your work, explain algorithms, describe architecture decisions, and organize notebooks into logical sections.
For example:
# Sales Forecast
This notebook predicts quarterly revenue
using historical sales data.When executed as Markdown, the text becomes formatted documentation rather than executable code.
Good notebooks combine executable code with clear explanations, making them much easier to understand months later.
In practice, these short notes become valuable when you return to a notebook weeks later and need to remember why a certain approach was tested.
Saving Your Notebook
Like any editor, your work should be saved regularly.
Select:
File
|
+-- Save Notebookor simply press:
Command + SChoose a meaningful filename such as:
getting_started.ipynbJupyterLab stores both your code and its output inside the notebook, making it easy to reopen your work exactly as you left it.
Installing Additional Python Packages
As your projects grow, you'll need additional Python libraries.
Return to Terminal while your virtual environment is still activated.
python -m pip install pandas numpy matplotlib requests openpyxlThese libraries provide a solid foundation for data analysis, visualization, HTTP requests, and Excel processing.
Later in this series we'll install additional packages for AWS, Azure, Google Cloud, Retrieval-Augmented Generation (RAG), and Agentic AI.
Stopping JupyterLab
When you've finished working, return to the Terminal window where JupyterLab is running.
Press:
CTRL + CYou'll be prompted to confirm shutdown.
Shutdown this Jupyter server (y/[n])?Enter:
yThis stops the local server and closes all active notebook sessions.
What's Next?
Congratulations! You now have a fully functional Python development environment running on macOS with JupyterLab and Python 3.14.
In the next article, we'll begin writing real Python programs while exploring one of the most widely used cloud development libraries: AWS Boto3. We'll learn how to install Boto3, configure AWS credentials, and use JupyterLab to interact with AWS services directly from Python notebooks.
JupyterLab series:
