02 Introduction to Python

The Python programming language is used widely in the sciences, the computational physics, biology, and economics/quantitative finance communities, and in big companies such as Google and Facebook.

For this class we are using Python 3 (e.g. Python 3.4 or Python 3.5), which is the current standard. A lot of older code is still only available for Python 2.7 (and there are a number of sometimes subtle incompatibilities between 2 and 3) but once you know Python 3 you will have no problems dealing with Python 2 code.

1. Resources
2. Starting Python
3. Tutorial

Resources

• Official Beginner's Guide to Python
• Official Python 3 Tutorial
• Python Scripting for Computational Science, Hans Petter Langtangen. Texts in Computational Science and Engineering, Volume 3, 2008. Springer. DOI: 10.1007/978-3-540-73916-6 (free access to the PDF through the ASU Library — requires ASU login)

Keep the Python documentation close by and have a look at Python questions on StackOverflow.

Starting Python

Starting the Python Interpreter

Python is an interpreted language and the Python interpreter is called … python. In the shell, type

python

and you should see something similar

Python 3.5.1 (default, Dec  6 2015, 22:55:58)
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

The symbol >>> is the standard input prompt. Type commands and submit them to the interpreter with Enter.

To exit, type

exit()

Running a Python script

Create a simple Python program (or "script"), e.g. using nano, and save it as gutentag.py.

import os

homedir = os.environ['HOME']
print("Guten Tag, Dein Heimatverzeichnis ist ", homedir)

Let Python execute the command:

python gutentag.py

Interactive Python with ipython

The ipython interpreter is like Python but with lots of improvements such as TAB-completion, help with command? (one question mark directly following a command) and source code with command?? (two question marks), commandline history, and many additional shell-like commands (so-called "magic" commands such as %cd, %ls, %pwd, %run, %time and %timeit — see %magic for help).

To exit, give the exit() command or ^D (Control + D).

Just use ipython instead of python for interactive work.

Interactive Python with the Jupyter notebook

Start the Jupyter notebook interface (formerly called ipython notebook) with

jupyter notebook

and open http://localhost:8888 with a modern browser. (Note: ipython notebook will also work but the name was recently changed.)

We will often use the notebook interface to develop and demonstrate code. It is perfect for prototyping and quick analysis tasks as well as plotting.

To open a new notebook: Go to the New menu and choose under Notebooks: Python 3. This will open a new browser window with an empty notebook. Press ESC and then H for help.

A notebook has two modes, command mode and edit mode.

• ESC enables command mode with the following useful commands:
  • H for help
  • cursor keys: move between cells
  • B for new cell below
  • A for new cell above
  • X to cut cell
  • M to turn a cell into a text cell for notes
  • 1 – 6 to make a text heading at level 1, 2, … 6
• Enter enables edit mode (type in a cell)
• Cells can be executed by the Python "kernel" in either mode with
  • Control + Enter: execute a cell
  • Shift + Enter: execute cell and move one cell down
  • Alt + Enter: execute cell and create a new one
• Give the notebook a name by editing "Untitled" in the top bar.
• Save with S in command mode (or use the mouse)
• You can move between cells to edit code and rerun. As long as you don't quit or restart the Python kernel, you have information from all cells available in the whole notebook. If you open it new, you have to evaluate all cells again.

Tutorial

The tutorial will be live-coded in a Jupyter notebook. Open a new notebook and follow my lead. Type and run commands. Ask questions (use red stickies when stuck).

After the class, this link to the tutorial notebook will be enabled. The notebook is stored in the git repository ASU-CompMethodsPhysics-PHY494/PHY494-resources-2016: git pull to update¹ and find PHY494-resources-2016/02_python/02-intro-python.ipynb.

Next steps

Go to the next lesson: 03 Introduction to NumPy, where we will learn how to efficiently work with the scientist's favorite data structure: the N-dimensional array and how to plot data.

Footnotes