Running and Quitting

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• How can I run Python programs?

Objectives

• Launch the JupyterLab server.

• Create a Jupyter notebook.

• Shutdown the JupyterLab server.

• Create and run Python cells in a notebook.

Getting the Data

The data we will be using is taken from the gapminder dataset. To obtain it, download and unzip the file python-novice-gapminder-data.zip. In order to follow the presented material, you should put the resulting data directory/folder in your home folder, and you should launch the JupyterLab server in the same directory that you put the data directory (see Starting JupyterLab).

Why JupyterLab

There are many ways to write and run Python code, such as using a text editor to create and edit a file with Python code in it then running the file at the commandline (show file containing Python code). Another way is to run Python in the terminal (show launching interpreter). While these are common approach, we are going to use the Jupyter Notebook via JupyterLab for the remainder of this workshop.

This has several advantages:

• You can easily type, edit, and copy and paste blocks of code.
• Tab complete allows you to easily access the names of things you are using and learn more about them.
• It allows you to annotate your code with links, different sized text, bullets, etc. to make it more accessible to you and your collaborators.
• It allows you to display figures next to the code that produces them to tell a complete story of the analysis.

Each notebook contains one or more cells that contain code, text, or images.

Getting Started with JupyterLab

JupyterLab is an application with a user interface you use in your browser. It enables us to work with Jupyter notebooks, text files, and terminals in an integrated manner.

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

Starting JupyterLab

You can start the JupyterLab server through the command line or through an application called Anaconda Navigator. Anaconda Navigator is included as part of the Anaconda Python distribution.

macOS - Command Line

To start the JupyterLab server you will need to access the command line through the Terminal. There are two ways to open Terminal on Mac.

1. In your Applications folder, open Utilities and double-click on Terminal
2. Press Command + spacebar to launch Spotlight. Type Terminal and then double-click the search result or hit Enter

After you have launched Terminal, type the command to launch the JupyterLab server.

$ jupyter lab

Windows Users - Command Line

To start the JupyterLab server you will need to access the Anaconda Prompt.

Press Windows Logo Key and search for Anaconda Prompt, click the result or press enter.

After you have launched the Anaconda Prompt, type the command:

$ jupyter lab

Anaconda Navigator

To start a JupyterLab server from Anaconda Navigator you must first start Anaconda Navigator (click for detailed instructions on macOS, Windows, and Linux). You can search for Anaconda Navigator via Spotlight on macOS (Command + spacebar), the Windows search function (Windows Logo Key) or opening a terminal shell and executing the anaconda-navigator executable from the command line.

After you have launched Anaconda Navigator, click the Launch button under JupyterLab. You may need to scroll down to find it.

Here is a screenshot of an Anaconda Navigator page similar to the one that should open on either macOS or Windows.

And here is a screenshot of a JupyterLab landing page that should be similar to the one that opens in your default web browser after starting the JupyterLab server on either macOS or Windows.

The JupyterLab Interface

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.

Menu Bar

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.

• File: Actions related to files and directories such as New, Open, Close, Save, etc. The File menu also includes the Shut Down action used to shutdown the JupyterLab server.
• Edit: Actions related to editing documents and other activities such as Undo, Cut, Copy, Paste, etc.
• View: Actions that alter the appearance of JupyterLab.
• Run: Actions for running code in different activities such as notebooks and code consoles (discussed below).
• Kernel: Actions for managing kernels. Kernels in Jupyter will be explained in more detail below.
• Tabs: A list of the open documents and activities in the main work area.
• Settings: Common JupyterLab settings can be configured using this menu. There is also an Advanced Settings Editor option in the dropdown menu that provides more fine-grained control of JupyterLab settings and configuration options.
• Help: A list of JupyterLab and kernel help links.

Kernels

The JupyterLab docs define kernels as “separate processes started by the server that run your code in different programming languages and environments.” When we open a Jupyter Notebook, that starts a kernel - a process - that is going to run the code. In this lesson, we’ll be using the Jupyter ipython kernel which lets us run Python 3 code interactively.

Using other Jupyter kernels for other programming languages would let us write and execute code in other programming languages in the same JupyterLab interface, like R, Java, Julia, Ruby, JavaScript, Fortran, etc.

A screenshot of the default Menu Bar is provided below.

Left Sidebar

The left sidebar contains a number of commonly used tabs, such as a file browser (showing the contents of the directory where the JupyterLab server was launched). If you don’t see the data/ folder that you downloaded for the workshop, move it there now. You can hover your mouse over the folder path icon to see where to put the data/ folder.

The sidebar also includes 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.

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 Main Work Area is provided below.

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.

• Notebook files have the extension .ipynb to distinguish them from plain-text Python programs.
• Notebooks can be exported as Python scripts that can be run from the command line.

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

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.

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.

• If you press Esc and Return alternately, the outer border of your code cell will change from gray to blue.
• These are the Command (gray) and Edit (blue) modes of your notebook.
• Command mode allows you to edit notebook-level features, and Edit mode changes the content of cells.
• When in Command mode (esc/gray),
  • The b key will make a new cell below the currently selected cell.
  • The a key will make one above.
  • The x key will delete the current cell.
  • The z key will undo your last cell operation (which could be a deletion, creation, etc).
• All actions can be done using the menus, but there are lots of keyboard shortcuts to speed things up.

More Math

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

7 * 3
2 + 1

Solution

Python returns the output of the last calculation.

3

Closing JupyterLab

• From the Menu Bar select the “File” menu and then choose “Shut Down” at the bottom of the dropdown menu. You will be prompted to confirm that you wish to shutdown the JupyterLab server (don’t forget to save your work!). Click “Shut Down” to shutdown the JupyterLab server.
• To restart the JupyterLab server you will need to re-run the following command from a shell.

$ jupyter lab

Closing JupyterLab

Practice closing and restarting the JupyterLab server.

Key Points

• 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.