Chapter 2. The Notebook Interface

The IPython notebook has an extensive user interface that makes it appropriate for the creation of richly formatted documents. In this chapter, we will thoroughly explore the notebook's capabilities. We will also consider the pitfalls and best practices of using the notebook.

In this chapter, the following topics will be covered:

• Notebook editing and navigation, which includes cell types; adding, deleting, and moving cells; loading and saving notebooks; and keyboard shortcuts

• IPython magics

• Interacting with the operating system

• Running scripts, loading data, and saving data

• Embedding images, video, and other media with IPython's rich display system

Editing and navigating a notebook

When we open a notebook (by either clicking on its name in the dashboard or creating a new notebook), we see the following in the browser window:

In the preceding screenshot, from the top to the bottom, we see the following components:

• The Title bar (area marked 1) that contains the name of the notebook (in the preceding example, we can see Chapter 2) and information about the notebook version

• The Menu bar (area marked 2) looks like a regular application menu

• The Toolbar (area marked 3) is used for quick access to the most frequently used functionality

• In the area marked 4, an empty computation cell is shown

Starting with IPython Version 2.0, the notebook has two modes of operation:

• Edit: In this mode, a single cell comes into focus and we can enter text, execute code, and perform tasks related to that single cell. The Edit mode is activated by clicking on a cell or pressing the Enter key.

• Command: In this mode, we perform tasks related to the whole notebook...

IPython magics

Magics are special instructions to the IPython interpreter that perform specialized actions. There are two types of magics:

• Line-oriented: This type of magics start with a single percent (%) sign

• Cell-oriented: This type of magics start with double percent (%%) signs

We are already familiar with one of the magic command, that is, %pylab inline. This particular magic does two of the following things: it imports NumPy and matplotlib, and sets up the notebook for inline plots. To see one of the other options, change the cell to %pylab.

Run this cell and then run the cell that produces the plot again. Instead of drawing the graph inline, IPython will now open a new window with the plot as shown in the following screenshot:

This window is interactive and you can resize the graph, move it, and save it to a file from here.

Another useful magic is %timeit, which records the time it takes to run a line of Python code. Run the following code in a new cell in the notebook:

%timeit return_on_investment...

Interacting with the operating system

Any code with some degree of complexity will interact with the computer's operating system when files must be opened and closed, scripts must be run, or online data must be accessed. Plain Python already has a lot of tools to access these facilities, and IPython and the notebook add another level of functionality and convenience.

Saving the notebook

The notebook is autosaved in periodic intervals. The default interval is 2 minutes, but this can be changed in the configuration files or using the %autosave magic. For example, to change the autosave interval to 5 minutes, run the following command:

%autosave 300

Notice that the time is entered in seconds. To disable the autosave feature, run the following command:

%autosave 0

We can also save the notebook using the File menu or by clicking on the disk icon on the toolbar. This creates a checkpoint. Checkpoints are stored in a hidden folder and can be restored from the File menu. Notice that only the latest...

Running scripts, loading data, and saving data

When working with projects of some complexity, it is common to have the need to run scripts written by others. It is also always necessary to load data and save results. In this section, we will describe the facilities that IPython provides for these tasks.

Running Python scripts

The following Python script generates a plot of a solution of the Lorenz equations, a famous example in the theory of chaos. If you are typing the code, do not type it in a cell in the notebook. Instead, use a text editor and save the file with the name lorenz.py in the same directory that contains the notebook file. The code is as follows:

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from mpl_toolkits.mplot3d import Axes3D

def make_lorenz(sigma, r, b):
    def func(statevec, t):
        x, y, z = statevec
        return [ sigma * (y - x),
                 r * x - y - x * z,
                 x * y - b * z ]
    return func
   ...

The rich display system

In an exciting development, recent versions of IPython include the capability to display images, video, sound, and other media directly in the notebook. The classes that support the display system are in the IPython.display module. In this section, we will discuss some of the supported formats.

Images and YouTube videos

Images can be loaded either from the local filesystem or from the web. To display the image contained in the character.png file, for example, run the following command in a cell:

from IPython.display import Image
Image('character.png')

It is also possible to store the image in a variable to be displayed at a later time:

img = Image('character.png')

The character.png file can be downloaded from the web page of this book.

To display the image, we can use either img or display(img). The following image is displayed:

To load an image from the Web, simply give its URL as an argument:

Image('http://www.imagesource.com/Doc/IS0/Media/TR5/7/7/f/4/IS09A9H4K.jpg...

Summary

In this chapter, we went through a comprehensive tour of the IPython Notebook Interface. We covered features that are used daily when working with the notebook, such as navigation, magics, interacting with the operating system, running scripts, and loading and saving data. We finished with a discussion of how to display richly formatted data in the notebook.

In the next chapter, you will learn how to use the matplotlib library to produce presentation-quality scientific graphs and data displays, with an emphasis on interactive graphs.