There are two ways of launching a Jupyter notebook through Anaconda. The first method is to open Jupyter using the Anaconda Navigator application available in the Anaconda folder of the Windows Start menu. Click on the Launch button and your default internet browser will then launch at the default address, http://localhost:8888, and will start in a default folder path.

The second method is to launch Jupyter via the Anaconda prompt. To launch the Anaconda prompt, simply click on the Anaconda Prompt menu item, also in the Windows Start menu, and you should see a pop-up window similar to the following screenshot:

Figure 1.4: Anaconda prompt

Once in the Anaconda prompt, change to the desired directory using the cd (change directory) command. For example, to change into the Desktop directory for the Packt user, do the following:

C:\Users\Packt> cd C:\Users\Packt\Desktop

Once in the desired directory, launch a Jupyter notebook using the following command:

C:\Users\Packt> jupyter notebook

The notebook will launch with the working directory from the one you specified earlier. This then allows you to navigate and save your notebooks in the directory of your choice as opposed to the default, which can vary between systems, but is typically your home or My Computer directory. Irrespective of the method of launching Jupyter, a window similar to the following will open in your default browser. If there are existing files in the directory, you should also see them here:

Figure 1.5: Jupyter notebook launch window

Start by creating a new Jupyter notebook by clicking on the New button and selecting Python 3. Jupyter allows you to run different versions of Python and other languages, such as R and Julia, all in the same interface. We can also create new folders or text files here too. But for now, we will start with a Python 3 notebook:

Figure 1.6: Creating a new notebook

This will launch a new Jupyter notebook in a new browser window. We will first spend some time looking over the various tools that are available in the notebook:

Figure 1.7: The new notebook

There are three main sections in each Jupyter notebook, as shown in the following screenshot: the title bar (1), the toolbar (2), and the body of the document (3). Let's look at each of these components in order:

Figure 1.8: Components of the notebook

The title bar simply displays the name of the current Jupyter notebook and allows the notebook to be renamed. Click on the Untitled text and a popup will appear allowing you to rename the notebook. Enter Hello World and click Rename:

Figure 1.9: Renaming the notebook

For the most part, the toolbar contains all the normal functionality that you would expect. You can open, save, and make copies of—or create new—Jupyter notebooks in the File menu. You can search replace, copy, and cut content in the Edit menu and adjust the view of the document in the View menu. As we discuss the body of the document, we will also describe some of the other functionalities in more detail, such as the ones included in the Insert, Cell, and Kernel menus. One aspect of the toolbar that requires further examination is the far right-hand side, the outline of the circle on the right of Python 3.

Hover your mouse over the circle and you will see the Kernel Idle popup. This circle is an indicator to signify whether the Python kernel is currently processing; when processing, this circle indicator will be filled in. If you ever suspect that something is running or is not running, you can easily refer to this icon for more information. When the Python kernel is not running, you will see this:

Figure 1.10: Kernel idle

When the Python kernel is running, you will see this:

Figure 1.11: Kernel busy

This brings us to the body of the document, where the actual content of the notebook will be entered. Jupyter notebooks differ from standard Python scripts or modules, in that they are divided into separate executable cells. While Python scripts or modules will run the entirety of the script when executed, Jupyter notebooks can run all of the cells sequentially, or can also run them separately and in a different order if manually executed.

Double-click on the first cell and enter the following:

>>> print('Hello World!')

Click on Run (or use the Ctrl + Enter keyboard shortcut):

Figure 1.12: Running a cell

Launch a new Jupyter notebook and then, in three separate cells, enter the code shown in the following screenshot:

Figure 1.13: Entering code into multiple cells

Click Restart & Run All.

Notice that there are three executable cells, and the order of execution is shown in rectangular brackets; for example, In [1], In [2], and In [3]. Also note how the hello_world variable is declared (and thus executed) in the second cell and remains in memory, and thus is printed in the third cell. As we mentioned before, you can also run the cells out of order.

Click on the second cell, containing the declaration of hello_world, change the value to add a few more exclamation points, and run the cell again:

Figure 1.14: Changing the content of the second cell

Notice that the second cell is now the most recently executed cell (In [4]), and that the print statement after it has not been updated. To update the print statement, you would then need to execute the cell below it. Warning: be careful about your order of execution. If you are not careful, you can easily override values or declare variables in cells below their first use, as in notebooks, you no longer need to run the entire script at once. As such, it is good practice to regularly click Kernel | Restart & Run All. This will clear all variables from memory and run all cells from top to bottom in order. There is also the option to run all cells below or above a particular cell in the Cell menu:

Figure 1.15: Restarting the kernel

You can also move cells around using either the up/down arrows on the left of Run or through the Edit toolbar. Move the cell that prints the hello_world variable to above its declaration:

Figure 1.16: Moving cells

Click on Restart & Run All cells:

Figure 1.17: Variable not defined error

Notice the error reporting that the variable is not defined. This is because it is being used before its declaration. Also, notice that the cell after the error has not been executed as shown by the empty In [ ].

Jupyter notebooks can execute commands directly within the Anaconda prompt by including an exclamation point prefix (!). Enter the code shown in the following screenshot and run the cell:

Figure 1.18: Running Anaconda commands

One of the best features of Jupyter notebooks is the ability to create live reports that contain executable code. Not only does this save time in preventing separate creation of reports and code, but it can also assist in communicating the exact nature of the analysis being completed. Through the use of Markdown and HTML, we can embed headings, sections, images, or even JavaScript for dynamic content.

To use Markdown in our notebook, we first need to change the cell type. First, click on the cell you want to change to Markdown, then click on the Code drop-down menu and select Markdown:

Figure 1.19: Running Anaconda commands

Notice that In [ ] has disappeared and the color of the box lining the cell is no longer blue.

You can now enter valid Markdown syntax and HTML by double-clicking in the cell and then clicking Run to render the markdown. Enter the syntax shown in the following screenshot and run the cell to see the output:

Figure 1.20: Markdown syntax

The output will be as follows:

Figure 1.21: Markdown output