Overview

Teaching: 40 min
Exercises: 20 min

Questions

• How can I run Python programs?

Objectives

• Launch the Jupyter Notebook, create new notebooks, and exit the Notebook.

• Create Markdown cells in a notebook.

• Create and run Python cells in a notebook.

• Get help in Jupyter

Python programs are plain text files.

• They have the “.py” extension to let everyone (including the operating system) know it is a Python program.
  • This is convention, not a requirement.
• It’s common to write them using a text editor but we are going to use the Jupyter Notebook.
• The bit of extra setup is well worth it because the Notebook provides code completion and other helpful features.
• Notebook files have the extension “.ipynb” to distinguish them from plain-text Python programs.

Use the Jupyter Notebook for editing and running Python.

• The Anaconda package manager is an automated way to install the Jupyter notebook.
• It also installs all the extra libraries it needs to run.

Opening a Jupyter Notebook

• Once you have installed Anaconda, search for Jupyter Notebook in the Windows search and click to start it.
• Alternately you can open a shell and type:

  $ jupyter notebook
  

• Once you have installed Anaconda, search for Jupyter Notebook in the Windows search and click to start it.
• This will start a Jupyter Notebook server and open your default web browser.
• The server runs locally on your machine only and does not use an internet connection.
• The server sends messages to your browser.
• The server does the work and the web browser renders the notebook.
• 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.
• The notebook is stored as JSON but can be saved as a .py file if you would like to run it from the bash shell or a python interpreter.
• Just like a webpage, the saved notebook looks different to what you see when it gets rendered by your browser.

The Notebook has Control and Edit modes.

• Open a new notebook from the dropdown menu in the top right corner of the file browser page.
• Each notebook contains one or more cells of various types.

Code vs. Text

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.

• If you press Esc and Return alternately, the outer border of your code cell will change from grey to green.
  • The difference in colour is subtle.
• These are the control (grey) and edit (green) modes of your notebook.
• If you use the Esc and Return keys to make the surround grey and then press the H key, a list of all the shortcut keys will appear.
• When in control mode (esc/grey),
  • 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.
• There are lots of shortcuts you can try out and most actions can be done with the menus at the top of the page if you forget the shortcuts.
• If you remember the Esc and H shortcut, you will be able to find out all the rest.

Use the keyboard and mouse to select and edit cells.

• Pressing the Return key turns the surround green to signify edit mode and you can type into the cell.
• Because we want to be able to write many lines of code in a single cell, pressing the Return key when the border is green moves the cursor to the next line in the cell just like in a text editor.
• We need some other way to tell the Notebook we want to run what’s in the cell.
• Pressing the Return key and the Shift key together will execute the contents of the cell.
• Notice that the Return and Shift keys on the right of the keyboard are right next to each other.

Pause for a play.

The Notebook will turn Markdown into pretty-printed documentation.

• Notebooks can also render Markdown.
  • A simple plain-text format for writing lists, links, and other things that might go into a web page.
  • Equivalently, a subset of HTML that looks like what you’d send in an old-fashioned email.
• Turn the current cell into a Markdown cell by entering the control mode (esc/grey) and press the M key.
• In [ ]: will disappear to show it is no longer a code cell and you will be able to write in Markdown.
• Turn the current cell into a Code cell by entering the control mode (esc/grey) and press the Y key.

Markdown does most of what HTML does.

*   Use asterisks
*   to create
*   bullet lists.

1.  Use numbers
1.  to create
1.  numbered lists.

# A Level-1 Heading

## A Level-2 Heading (etc.)

Line breaks
don't matter.

But blank lines
create new paragraphs.

[Create links](http://software-carpentry.org) with `[...](...)`.
Or use [named links][data_carpentry].

[links]: http://datacarpentry.org

Getting HELP!!

Get help inside Python: the help() function

Knowing how to find and navigate the online documentation is vital, but there are times when looking something up inside an interactive Python session is also useful. The built-in help() function looks up the documentation for Python objects. For example:

>>> help(print)
Help on built-in function print in module builtins:

print(...)
    print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)

    Prints the values to a stream, or to sys.stdout by default.
    Optional keyword arguments:
    file:  a file-like object (stream); defaults to the current sys.stdout.
    sep:   string inserted between values, default a space.
    end:   string appended after the last value, default a newline.
    flush: whether to forcibly flush the stream.

Get help easily in a Jupyter Notebook

In a Jupyter Notebook, there is another option for the help() function. Instead of calling help(), simply prepend a Python object with ? to display HTML formatted help in a separate panel. If you are using or have access to a Jupyter Notebook, compare the output from the following two statements:

help(print)

?print

The Jupyter Notebook has another way to get help.

• Place the cursor inside the parenthesis of the function, hold down Shift, and press Tab.

Creating Lists in Markdown

Create a nested list in a Markdown cell in a notebook that looks like this:

1. Get funding.
2. Do work.
   • Design experiment.
   • Collect data.
   • Analyse.
3. Write up.
4. Publish.

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

Change an Existing Cell from Code to Markdown

What happens if you write some Python in a code cell and then you switch it to a Markdown cell? For example, put the following in a code cell:

x = 6 * 7 + 12
print(x)

And then run it with shift+return to be sure that it works as a code cell. Now go back to the cell and use escape+M to switch the cell to Markdown and “run” it with shift+return. What happened and how might this be useful?

Mathematics

Standard Markdown (such as we’re using for these notes) won’t render equations, but the Notebook will. Create a new Markdown cell and enter the following:

$\Sigma_{i=1}^{N} 2^{-i} \approx 1$

(It’s probably easier to copy and paste.) What does it display? What do you think the underscore _, circumflex ^, and dollar sign $ do?

Key Points

• Python programs are plain text files.

• Use the Jupyter Notebook for editing and running Python.

• The Notebook has Control and Edit modes.

• Use the keyboard and mouse to select and edit cells.

• The Notebook will turn Markdown into pretty-printed documentation.

• Markdown does most of what HTML does.

• In Jupyter Notebooks, the ? command will display the same result as help() in a separate panel, with formatted text.

• The official Python documentation is a good reference to the core Python language.