David Abel

0. Instructions

1. Basics

2. Functions

3. Dictionaries

4. OOP

► 5. Modules

6. Debugging

7. Tips & Tricks

Python Tutorial

This tutorial is intended as an introduction to Python for folks with some prior programming experience.

Section 5: Modules (no file)

There are loads of useful modules in python. Here we will address a few of the modules I use regularly that I think it's good to know about.

One of the most helpful commands in python is __doc__, which allows you to access the doc string for any object, function, class, module, or method that has a doc string (a detailed comment explaining what the object/class/method is/does). This is especially helpful when dealing with large code bases or modules that you are unfamiliar with.

For instance:


        >>> import math 

        >>> print math.__doc__ 

        This module is always available.  It provides access to the mathematical functions defined by the C standard. 

        >>> print math.pow.__doc__ 

        pow(x, y) 

        Return x**y (x to the power of y).

Another way of getting useful information is the help() command. Calling help on a module, class, or function, brings up the man page for that object.

Here are some of my most used modules:

math: The math module contains most the mathematical operations for python. Some highlights include:
random: The random module is used for all operations related to random number generation. It has functionality to randomly shuffle lists, generate random lists, and generate random values of a variety of data types.
numpy: The numpy module is one of the real work horses of scientific programming in python. It is equipped with an arbitrary dimensional array, as well as a variety of linear algebra operations.
pickle: For serializing data.
pygame: For game development (and some nice GUI functionality)
matplotlib: Basic plots
PIL: Python Imaging Library. Basic functionality for opening and manipulating images. For more advanced techniques, check out SimpleCV and OpenCV.
SciPy: Lots of nice stuff for handling scientific computing
ArgParser: For handling command line arguments
os: For basic operating system functionality (opening files, checking if directories are empty, etc.).

numpy

Lets talk about numpy for a bit, since it's one of the most used modules for scientific computing with python. The arrays provided by numpy are extremely powerful, yet relatively easy to create and manipulate:


        >>> import numpy as np 

        >>> arr = np.arange(6) 

        >>> print arr 

        [0 1 2 3 4 5] 

        >>> arr = arr.reshape(2,3) 

        >>> print arr 

           [[0 1 2] 

            [3 4 5]] 

        >>> print arr.ndim 

        2

You can also quickly make matrices of common values quickly:


        >>> print zeros((2,3)) 

        [[ 0.  0.  0.] 

         [ 0.  0.  0.]] 

        >>> print ones((2,2,3)) 

        [[[ 1.  1.  1.] 

          [ 1.  1.  1.]] 

         [[ 1.  1.  1.] 

          [ 1.  1.  1.]]]

The arange() function is similar to python's range() function, but it can be used to create numpy arrays:


        >>> print arange(0,50,5).reshape(2,5) 

        [[ 0  5 10 15 20] 

         [25 30 35 40 45]]

The randn() function in the numpy.random module creates an array of the specified dimenisions with random floats between 0 and 1:


        >>> from numpy.random import * 

        >>> print randn(3,4) 

        [[-0.3157525   1.04280707  1.01558404 -0.53489289] 

         [-0.51217785  0.32228728 -0.74847843 -0.70625552] 

         [ 0.00748319 -0.71369062  0.98813116 -0.08820002]]

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