This lesson is being piloted (Beta version)

Running and Quitting


Teaching: 15 min
Exercises: 0 min
  • How can I run Python code?

  • Launch the JupyterLab server.

  • Create a new Python script.

  • Create a Jupyter notebook.

  • Shutdown the JupyterLab server.

  • Understand the difference between a Python script and a Jupyter notebook.

  • Create Markdown cells in a notebook.

  • Create and run Python cells in a notebook.

Getting Started with JupyterLab

While many software developers will often use an integrated development environment (IDE) or a text editor to create and edit their Python programs we will be using JupyterLab during this lesson.

JupyterLab is an application with a web-based user interface from Project Jupyter that enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, or even custom components in a flexible, integrated, and extensible manner. JupyterLab requires a reasonably up-to-date browser (ideally a current version of Chrome, Safari, or Firefox); Internet Explorer versions 9 and below are not supported.

JupyterLab is included as part of the the Anaconda Python distribution. If you have not already installed the Anaconda Python distribution, see the setup instructions.

Even though JupyterLab is a web-based application, it runs locally on your machine and does not require an internet connection.

JupyterLab? What about Jupyter notebooks?

JupyterLab is the next stage in the evolution of the Jupyter Notebook. If you have prior experience working with Jupyter notebooks, then you will have a a good idea of what to expect from JupyterLab.

Experienced users of Jupyter notebooks interested in a more detailed discussion of the similarities and differences between the JupyterLab and Jupyter notebook user interfaces can find more information in the JupyterLab user interface documentation.

Starting JupyterLab

For this workshop, we call JupyterLab using the virtual environment we installed.

cd ~/Desktop/workshoppython
pipenv run jupyter lab

The JupyterLab Interface

JupyterLab has many features found in traditional integrated development environments (IDEs) but is focused on providing flexible building blocks for interactive, exploratory computing.

The JupyterLab Interface consists of the Menu Bar, a collapsable Left Side Bar, and the Main Work Area which contains tabs of documents and activities.

The Menu Bar at the top of JupyterLab has the top-level menus that expose various actions available in JupyterLab along with their keyboard shortcuts (where applicable). The following menus are included by default.

A screenshot of the default Menu Bar is provided below.

JupyterLab Menu Bar

The left sidebar contains a number of commonly-used tabs, such as a file browser (showing the contents of the directory in which the JupyterLab server was launched!), a list of running kernels and terminals, the command palette, and a list of open tabs in the main work area. A screenshot of the default Left Side Bar is provided below.

JupyterLab Left Side Bar

The left sidebar can be collapsed or expanded by selecting “Show Left Sidebar” in the View menu or by clicking on the active sidebar tab.

Main Work Area

The main work area in JupyterLab enables you to arrange documents (notebooks, text files, etc.) and other activities (terminals, code consoles, etc.) into panels of tabs that can be resized or subdivided. A screenshot of the default Menu Bar is provided below.

JupyterLab Main Work Area

Drag a tab to the center of a tab panel to move the tab to the panel. Subdivide a tab panel by dragging a tab to the left, right, top, or bottom of the panel. The work area has a single current activity. The tab for the current activity is marked with a colored top border (blue by default).

Creating a Jupyter Notebook

To open a new notebook click the Python 3 icon under the Notebook header in the Launcher tab in the main work area. You can also create a new notebook by selecting New -> Notebook from the File menu in the Menu Bar.

Additional notes on Jupyter notebooks.

Below is a screenshot of a Jupyter notebook running inside JupyterLab. If you are interested in more details, then see the official notebook documentation.

Example Jupyter Notebook

How It’s Stored

  • The notebook file is stored in a format called JSON.
  • Just like a webpage, what’s saved looks different from what you see in your browser.
  • But this format allows Jupyter to mix source code, text, and images, all in one file.

Arranging Documents into Panels of Tabs

In the JupyterLab Main Work Area you can arrange documents into panels of tabs. Here is an example from the official documentation:

Multi-panel JupyterLab

First, create a text file, Python console, and terminal window and arrange then into three panels in the main work area. Next, create a notebook, terminal window, and text file and arrange then into three panels in the main work area. Finally, create your own combination of panels and tabs. What combination of panels and tabs do you think will be most useful for your workflow?


After creating the necessary tabs, you can drag one of the tabs to the center of a panel to move the tab to the panel; next you can subdivide a tab panel by dragging a tab to the left, right, top, or bottom of the panel.

Use the Jupyter Notebook for editing and running Python.

Code vs. Text

Jupyter mixes code and text in different types of blocks, called cells. We often use the term “code” to mean “the source code of software written in a language such as Python”. A “code cell” in a Notebook is a cell that contains software; a “text cell” is one that contains ordinary prose written for human beings.

The Notebook has Command and Edit modes.

Command Vs. Edit

In the Jupyter notebook page are you currently in Command or Edit mode?
Switch between the modes. Use the shortcuts to generate a new cell. Use the shortcuts to delete a cell. Use the shortcuts to undo the last cell operation you performed.


Command mode has a grey border and Edit mode has a green border. Use Esc and Return to switch between modes. You need to be in Command mode (Press Esc if your cell is blue). Type B or A. You need to be in Command mode (Press Esc if your cell is blue). Type X. You need to be in Command mode (Press Esc if your cell is blue). Type Z.

Use the keyboard and mouse to select and edit cells.

The Notebook will turn Markdown into pretty-printed documentation.

The Notebook will evaluate code and display results

Running Python Cells

What is displayed when a Python cell in a notebook that contains several lines of code is executed? For example, what happens when this cell is executed?

import os

Solution (for Mac)

Jupyter returns the output of only the last line.


Running More Python Cells

Why do you think that nothing is displayed when this cell is executed?

current_dir = os.getcwd()


Jupyter prints the output of the last line. In this case the last line assigned a value to a variable and did not have output.

Python Syntax: =

In order to parse our input, Python has rules about how it should be written. We can use these same rules when reading code to understand what is happening. As different syntaxes appear, we will point them out in these callout boxes.

In Python, the equals sign = works a little differently than in a math class. = assigns a value to variable, like this:

variable = value

A variable is an object that can hold data. We will experiment with this in the next lesson. For now, we can update the value of the variable

current_dir = 'Downloads'

One of the hardest parts of programming is picking good names for your variables. You tend to read code more often than writing, so it’s very useful to use descriptive variable names.

Running Even More Python Cells

How would you display the data stored as current_dir?



Running the Last Python Cell for this Lesson

What information is stored as current_dir when this cell is executed?

current_dir = os.getcwd()



After changing directories, os.getcwd() returns a different value than before. This value is than stored in the variable cwd.

Change an Existing Cell from Code to Markdown

What happens if you write some Python in a code cell and then you switch it to a Markdown cell? For example, put the following in a code cell:


And then run it with Shift+Return to be sure that it works as a code cell. Now go back to the cell and use Esc then M to switch the cell to Markdown and “run” it with Shift+Return. What happened and how might this be useful?


The Python code gets treated like Markdown text. The lines appear as if they are part of one contiguous paragraph. This could be useful to temporarily turn on and off cells in notebooks that get used for multiple purposes.


Closing JupyterLab

Closing JupyerLab

Practice closing and restarting the JupyterLab server.

Key Points

  • Python scripts are plain text files.

  • Use the Jupyter Notebook for editing and running Python.

  • The Notebook has Command and Edit modes.

  • Use the keyboard and mouse to select and edit cells.

  • The Notebook will turn Markdown into pretty-printed documentation.