Python is a useful programming language. It is very handy, and has a number of interesting features. Some highlights are:

• No variable type declaration
• Good libraries: math, string, random, etc
• Good interpreter: you can write codes, run and debug it at the same time, using the python interpreter. I particularly enjoy it, because if I'm writing in C, the program will not run until all the codes seem to be correct to the compiler; but in Python, the interpreter will run and stop at the line you make errors, and usually give you a clear reason of why it stops running.
• The program is free for both Unix and Windows, you can download it at www.python.org

For the research I've been working on, I'm writing these codes in Python:

• K-Means for 1 dimentional data: kmeans1.py: the program reads in data from the user, how many clusters the user wants, and outputs the clusters, with their members. For debugging purposes, all the steps are printed out in the output as well, so that anyone who uses the codes can check for every step
• K-Means for multi-dimentional data kmeans2.py: this program is similar to the first one; but it reads in a data file, which is a matrix of multi-dimentional data. It will ask you for the path of the program and runs after that

What is the algorithm backing these programs? Please visit my Clustering Algorithms website for information.

If you have any suggestion, please email me at tle@email.smith.edu.