Navigate to the companion material directory in the terminal.

Start a new local Notebook server here by typing the following into the terminal:

jupyter notebook

A new window or tab of your default browser will open the Notebook Dashboard to the working directory. Here, you will see a list of folders and files contained therein.

Click on a folder to navigate to that particular path and open a file by clicking on it. Although its main use is editing IPYNB Notebook files, Jupyter functions as a standard text editor as well.

Reopen the terminal window used to launch the app. We can see the NotebookApp being run on a local server. In particular, you should see a line like this:

[I 20:03:01.045 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/ ?token=e915bb06866f19ce462d959a9193a94c7c088e81765f9d8a

Going to that HTTP address will load the app in your browser window, as was done automatically when starting the app. Closing the window does not stop the app; this should be done from the terminal by typing Ctrl + C.

Close the app by typing Ctrl + C in the terminal. You may also have to confirm by entering y. Close the web browser window as well.

When loading the NotebookApp, there are various options available to you. In the terminal, see the list of available options by running the following:

jupyter notebook –-help

One such option is to specify a specific port. Open a NotebookApp at local port 9000 by running the following:

jupyter notebook --port 9000

The primary way to create a new Jupyter Notebook is from the Jupyter Dashboard. Click New in the upper-right corner and select a kernel from the drop-down menu (that is, select something in the Notebooks section):

Kernels provide programming language support for the Notebook. If you have installed Python with Anaconda, that version should be the default kernel. Conda virtual environments will also be available here.

With the newly created blank Notebook, click in the top cell and type print('hello world'), or any other code snippet that writes to the screen. Execute it by clicking in the cell and pressing Shift + Enter, or by selecting Run Cell in the Cell menu.

Any stdout or stderr output from the code will be displayed beneath as the cell runs. Furthermore, the string representation of the object written in the final line will be displayed as well. This is very handy, especially for displaying tables, but sometimes we don't want the final object to be displayed. In such cases, a semicolon (;) can be added to the end of the line to suppress the display.

New cells expect and run code input by default; however, they can be changed to render Markdown instead.

Click into an empty cell and change it to accept Markdown-formatted text. This can be done from the drop-down menu icon in the toolbar or by selecting Markdown from the Cell menu. Write some text in here (any text will do), making sure to utilize Markdown formatting symbols such as #.

Focus on the toolbar at the top of the Notebook:

There is a Play icon in the toolbar, which can be used to run cells. As we'll see later, however, it's handier to use the keyboard shortcut Shift + Enter to run cells. Right next to this is a Stop icon, which can be used to stop cells from running. This is useful, for example, if a cell is taking too long to run:

New cells can be manually added from the Insert menu:

Cells can be copied, pasted, and deleted using icons or by selecting options from the Edit menu:

Cells can also be moved up and down this way:

There are useful options under the Cell menu to run a group of cells or the entire Notebook:

Experiment with the toolbar options to move cells up and down, insert new cells, and delete cells.

An important thing to understand about these Notebooks is the shared memory between cells. It's quite simple: every cell existing on the sheet has access to the global set of variables. So, for example, a function defined in one cell could be called from any other, and the same applies to variables. As one would expect, anything within the scope of a function will not be a global variable and can only be accessed from within that specific function.

Open the Kernel menu to see the selections. The Kernel menu is useful for stopping script executions and restarting the Notebook if the kernel dies. Kernels can also be swapped here at any time, but it is unadvisable to use multiple kernels for a single Notebook due to reproducibility concerns.

Open the File menu to see the selections. The File menu contains options for downloading the Notebook in various formats. In particular, it's recommended to save an HTML version of your Notebook, where the content is rendered statically and can be opened and viewed "as you would expect" in web browsers.

The Notebook name will be displayed in the upper-left corner. New Notebooks will automatically be named Untitled.

Change the name of your IPYNB Notebook file by clicking on the current name in the upper-left corner and typing the new name. Then, save the file.

Close the current tab in your web browser (exiting the Notebook) and go to the Jupyter Dashboard tab, which should still be open. (If it's not open, then reload it by copy and pasting the HTTP link from the terminal.)

Since we didn't shut down the Notebook, we just saved and exited, it will have a green book symbol next to its name in the Files section of the Jupyter Dashboard and will be listed as Running on the right side next to the last modified date. Notebooks can be shut down from here.

Quit the Notebook you have been working on by selecting it (checkbox to the left of the name) and clicking the orange Shutdown button:

From the Jupyter Dashboard, navigate to the lesson-1 directory and open the lesson-1-workbook.ipynb file by selecting it. The standard file extension for Jupyter Notebooks is .ipynb, which was introduced back when they were called IPython Notebooks.

Scroll down to Subtopic C: Jupyter Features in the Jupyter Notebook. We start by reviewing the basic keyboard shortcuts. These are especially helpful to avoid having to use the mouse so often, which will greatly speed up the workflow. Here are the most useful keyboard shortcuts. Learning to use these will greatly improve your experience with Jupyter Notebooks as well as your own efficiency:

Moving on from shortcuts, the help option is useful for beginners and experienced coders alike. It can help provide guidance at each uncertain step.

Users can get help by adding a question mark to the end of any object and running the cell. Jupyter finds the docstring for that object and returns it in a pop-out window at the bottom of the app.

Run the Getting Help section cells and check out how Jupyter displays the docstrings at the bottom of the Notebook. Add a cell in this section and get help on the object of your choice:

Tab completion can be used to do the following:

This can be especially useful when you need to know the available input arguments for a module, when exploring a new library, to discover new modules, or simply to speed up workflow. They will save time writing out variable names or functions and reduce bugs from typos. The tab completion works so well that you may have difficulty coding Python in other editors after today!

Click into an empty code cell in the Tab Completion section and try using tab completion in the ways suggested immediately above. For example, the first suggestion can be done by typing import (including the space after) and then pressing the Tab key:

Last but not least of the basic Jupyter Notebook features are magic commands. These consist of one or two percent signs followed by the command. Magics starting with %% will apply to the entire cell, and magics starting with % will only apply to that line. This will make sense when seen in an example.

Scroll to the Jupyter Magic Functions section and run the cells containing %lsmagic and %matplotlib inline:

%lsmagic lists the available options. We will discuss and show examples of some of the most useful ones. The most common magic command you will probably see is %matplotlib inline, which allows matplotlib figures to be displayed in the Notebook without having to explicitly use plt.show().

The timing functions are very handy and come in two varieties: a standard timer (%time or %%time) and a timer that measures the average runtime of many iterations (%timeit and %%timeit).

Run the cells in the Timers section. Note the difference between using one and two percent signs.

Even by using a Python kernel (as you are currently doing), other languages can be invoked using magic commands. The built-in options include JavaScript, R, Pearl, Ruby, and Bash. Bash is particularly useful, as you can use Unix commands to find out where you are currently (pwd), what's in the directory (ls), make new folders (mkdir), and write file contents (cat / head / tail).

Run the first cell in the Using bash in the notebook section. This cell writes some text to a file in the working directory, prints the directory contents, prints an empty line, and then writes back the contents of the newly created file before removing it:

Run the following cells containing only ls and pwd. Note how we did not have to explicitly use the Bash magic command for these to work.

There are plenty of external magic commands that can be installed. A popular one is ipython-sql, which allows for SQL code to be executed in cells.

If you've not already done so, install ipython-sql now. Open a new terminal window and execute the following code:

pip install ipython-sql

Run the %load_ext sql cell to load the external command into the Notebook:

This allows for connections to remote databases so that queries can be executed (and thereby documented) right inside the Notebook.

Run the cell containing the SQL sample query:

Here, we first connect to the local sqlite source; however, this line could instead point to a specific database on a local or remote server. Then, we execute a simple SELECT to show how the cell has been converted to run SQL code instead of Python.

Moving on to other useful magic functions, we'll briefly discuss one that helps with documentation. The command is %version_information, but it does not come as standard with Jupyter. Like the SQL one we just saw, it can be installed from the command line with pip.

If not already done, install the version documentation tool now from the terminal using pip. Open up a new window and run the following code:

pip install version_information

Once installed, it can then be imported into any Notebook using %load_ext version_information. Finally, once loaded, it can be used to display the versions of each piece of software in the Notebook.

Run the cell that loads and calls the version_information command:

Open up the lesson 1 Jupyter Notebook and scroll to the Subtopic D: Python Libraries section.

Just like for regular Python scripts, libraries can be imported into the Notebook at any time. It's best practice to put the majority of the packages you use at the top of the file. Sometimes it makes sense to load things midway through the Notebook and that is completely OK.

Run the cells to import the external libraries and set the plotting options:

For a nice Notebook setup, it's often useful to set various options along with the imports at the top. For example, the following can be run to change the figure appearance to something more aesthetically pleasing than the matplotlib and Seaborn defaults:

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# See here for more options: https://matplotlib.org/users/customizing.html
%config InlineBackend.figure_format='retina'
sns.set() # Revert to matplotlib defaults
plt.rcParams['figure.figsize'] = (9, 6)
plt.rcParams['axes.labelpad'] = 10
sns.set_style("darkgrid")