1.10 Test-Drives: Using IPython and Jupyter Notebooks

• Test-drive the IPython interpreter in two modes:
  • interactive mode—enter small bits of code called snippets and immediately see their results
  • script mode—execute code loaded from a file that has the .py extension (short for Python)
    • Called scripts or programs
• Use browser-based Jupyter Notebook for writing and executing Python code

1.10.1 Using IPython Interactive Mode as a Calculator

• Use IPython interactive mode to evaluate simple arithmetic expressions

Entering IPython in Interactive Mode

• Open a command-line window on your system
  • On macOS, open a Terminal from the Applications folder’s Utilities subfolder
  • On Windows, open the Anaconda Command Prompt from the start menu
  • On Linux, open your system’s Terminal or shell (this varies by Linux distribution)
• Type ipython, then press Enter (or Return)

Entering IPython in Interactive Mode (cont.)

• You’ll see text like:

Python 3.7.0 | packaged by conda-forge | (default, Jan 20 2019, 17:24:52) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.5.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]:

• "In [1]:" is a prompt, indicating that IPython is waiting for your input
• Can type ? for help or begin entering snippets, as you’ll do momentarily

Evaluating Expressions

• Can evaluate expressions:

45 + 72

117

• After you type 45 + 72 and press Enter, IPython
  • reads the snippet
  • evaluates it
  • prints its result in Out[1]
  • Displays the In [2] prompt to show that it’s waiting for you to enter your second snippet
• For each new snippet, IPython adds 1 to the number in the square brackets

Evaluating Expressions (cont.)

• Evaluate a more complex expression:

5 * (12.7 - 4) / 2

21.75

• Asterisk (*) for multiplication and forward slash (/) for division
• Parentheses force the evaluation order
• IPython displays result in Out[2]
• Whole numbers, like 5, 4 and 2, are called integers
• Numbers with decimal points, like 12.7, 43.5 and 21.75, are called floating-point numbers

Exiting Interactive Mode

• To leave interactive mode:
  • Type exit and press Enter to exit immediately
  • Type Ctrl + d (or control + d) then confirm
  • Type Ctrl + d (or control + d) twice

1.10.2 Executing a Python Program Using the IPython Interpreter

• Execute a script named RollDieDynamic.py that you’ll write in Chapter 6
• .py extension indicates the file contains Python source code
• RollDieDynamic.py simulates rolling a six-sided die, presenting a colorful animated visualization that dynamically graphs the frequencies of each die face

Changing to This Chapter’s Examples Folder

• In the Before You Begin section you extracted the examples folder to your user account’s Documents folder
• Each chapter has a folder containing that chapter’s source code
  • The folder is named ch##, where ## is a two-digit chapter number from 01 to 17
• Open your system’s command-line window
• Use the cd (“change directory”) command to change to the ch01 folder:
  • On macOS/Linux, type cd ~/Documents/examples/ch01, then press Enter
  • On Windows, type cd C:\Users\YourAccount\Documents\examples\ch01, then press Enter

Executing the Script

ipython RollDieDynamic.py 6000 1

• The script displays a window, showing the visualization
• The numbers 6000 and 1 tell this script the number of times to roll dice and how many dice to roll each time
  • Update the chart 6000 times for 1 die at a time

Executing the Script (cont.)

• For a six-sided die, the values 1 through 6 should each occur with “equal likelihood”—1/6th or about 16.667%
• For 6000 rolls, about 1000 of each face
• random so there could be some faces with fewer than 1000, some with 1000 and some with more than 1000
• Experiment with the script by changing the value 1 to 100, 1000 and 10000
• As the number of die rolls gets larger, the frequencies zero in on 16.667%
  • “Law of Large Numbers”

Creating Scripts

• Typically, create in an editor that enables you to type text
• Integrated development environments (IDEs) provide tools that support the entire software-development process
  • editors, debuggers for locating logic errors that cause programs to execute incorrectly and more
• Popular Python IDEs include Spyder, PyCharm and Visual Studio Code and many more

Problems That May Occur at Execution Time

• Programs often do not work on the first try
  • An executing program might try to divide by zero (illegal in Python)
  • Would cause the program to display an error message
  • You’d return to the editor, make corrections and re-execute the script to determine whether the corrections fixed the problem(s)
• Errors such as division by zero occur as a program runs, so they’re called runtime errors or execution-time errors
  • Fatal runtime errors cause programs to terminate immediately without having successfully performed their jobs
  • Non-fatal runtime errors allow programs to run to completion, often producing incorrect results

1.10.3 Writing and Executing Code in a Jupyter Notebook

• The Anaconda Python Distribution comes with the Jupyter Notebook
  • Interactive, browser-based environment
  • You can write and execute code and intermix the code with text, images and video
• Widely used in data-science and broader scientific communities
• Preferred means of doing Python-based data analytics studies and reproducibly communicating their results
• Supports a growing number of programming languages

1.10.3 Writing and Executing Code in a Jupyter Notebook (cont.)

• The JupyterLab interface enables you to manage your notebook files and other files that your notebooks use (like images and videos)
  • Makes it convenient to write code, execute it, see the results, modify the code and execute it again
• Coding in a Jupyter Notebook is similar to working with IPython—in fact, Jupyter Notebooks use IPython by default

Opening JupyterLab in Your Browser

• To open JupyterLab, change to the ch01 examples folder in your Terminal, shell or Anaconda Command Prompt, the execute

  jupyter lab

• Launches the Jupyter Notebook server on your computer
• Opens JupyterLab in your default web browser, showing the ch01 folder’s contents in the File Browser tab

  

Opening JupyterLab in Your Browser (cont.)

• The Jupyter Notebook server enables you to load and run Jupyter Notebooks in your web browser
• From the Files tab, can double-click files to open them in the right side of the window
• Each file you open appears as a separate tab
• If you accidentally close your browser, you can reopen JupyterLab by entering the following address in your web browser

  http://localhost:8888/lab

Creating a New Jupyter Notebook

• In the Launcher tab under Notebook, click the Python 3 button to create a new Jupyter Notebook named Untitled.ipynb
• The file extension .ipynb is short for IPython Notebook—the original name of the Jupyter Notebook

Renaming the Notebook

• Rename Untitled.ipynb as TestDrive.ipynb:
  1. Right-click the Untitled.ipynb tab and select Rename Notebook….
  2. Change the name to TestDrive.ipynb and click RENAME.

Evaluating an Expression

• Unit of work in a notebook is a cell in which you can enter code snippets
• By default, a new notebook contains one cell, but you can add more
• To the cell’s left, the notation [ ]: is where the Jupyter Notebook will display the cell’s snippet number after you execute the cell
• Click in the cell, then type the expression

  45 + 72
  

• To execute the current cell’s code, type Ctrl + Enter (or control + Enter)
• JupyterLab executes the code in IPython, then displays the results below the cell

Adding and Executing Another Cell

• Evaluate a more complex expression
• Click the + button in the toolbar above the notebook’s first cell—this adds a new cell below the current one

• Click in the new cell, then type the expression

  5 * (12.7 - 4) / 2
  

• Execute the cell by typing Ctrl + Enter (or control + Enter)

Saving the Notebook

• If your notebook has unsaved changes, the Xin the notebook’s tab will change to

  

• To save the notebook, select the File menu in JupyterLab (not at the top of your browser’s window), then select Save Notebook

Notebooks Provided with Each Chapter’s Examples

• For your convenience, each chapter’s examples also are provided as ready-to-execute notebooks without their outputs
• Enables you to work through them snippet-by-snippet and see the outputs appear as you execute each snippet
• So that we can show you how to load an existing notebook and execute its cells, let’s reset the TestDrive.ipynb notebook to remove its output and snippet numbers
  • This will return it to a state like the notebooks we provide for the subsequent chapters’ examples
• From the Kernel menu select Restart Kernel and Clear All Outputs…, then click the RESTART button
  • Also is helpful whenever you wish to re-execute a notebook’s snippets
• The notebook should now appear as follows

• From the File menu, select Save Notebook, then click the TestDrive.ipynb tab’s X button to close the notebook

Opening and Executing an Existing Notebook

• When you launch JupyterLab from a given chapter’s examples folder, you’ll be able to open notebooks from that folder or any of its subfolders
• Once you locate a specific notebook, double-click it to open it
• Open the TestDrive.ipynb notebook again now
• Once a notebook is open, you can execute each cell individually, as you did earlier in this section, or you can execute the entire notebook at once
  • To do so, from the Run menu select Run All Cells

Closing JupyterLab

• When you’re done with JupyterLab, you can close its browser tab, then in the Terminal, shell or Anaconda Command Prompt from which you launched JupyterLab, type Ctrl + c (or control + c) twice

JupyterLab Tips

• While working in JupyterLab, you might find these tips helpful:
  • If you need to enter and execute many snippets, you can execute the current cell and add a new one below it by typing Shift + Enter, rather than Ctrl + Enter (or control + Enter).
  • As you get into the later chapters, some of the snippets you’ll enter in Jupyter Notebooks will contain many lines of code. To display line numbers within each cell, select Show line numbers from JupyterLab’s View menu.

More Information on Working with JupyterLab

• JupyterLab has many more features that you’ll find helpful
• Read the Jupyter team’s introduction to JupyterLab at:

  https://jupyterlab.readthedocs.io/en/stable/index.html

• For a quick overview, click Overview under GETTING STARTED
• Under USER GUIDE read the introductions to The JupyterLab Interface, Working with Files, Text Editor and Notebooks for many additional features

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©1992–2020 by Pearson Education, Inc. All Rights Reserved. This content is based on Chapter 1 of the book Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud.

DISCLAIMER: The authors and publisher of this book have used their best efforts in preparing the book. These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The authors and publisher make no warranty of any kind, expressed or implied, with regard to these programs or to the documentation contained in these books. The authors and publisher shall not be liable in any event for incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of these programs.