. Numpy is about the same speed as Matlab (both 50X faster than just Python), and about a 10X speedup is gained by going to some form of C++ (good examples in the document)
Another great option is the scientific toolboxes in Python.
See top link for great Python tutorial
is the best to start with. Includes most packages you need
DIVISION IN PYTHON TAKES FLOOR
In Python 3.0, integer division will return a float, e.g., 1/3 will be 0.3333… At Scipy 2006, Guido explicitly stated in his keynote talk that the design choice he made in Python (i.e., that n/m is floor(n/m)) was a mistake.
In Sage (http://sagemath.org), which is built on Python, we do some very minimal preparsing of input, so that 1/3 is the exact rational number 1/3 (instead of Python's stupid 1/3 == 0). We also replace, e.g., 2^3 by 23. Sage is does a lot of exact symbolic and high precision arithmetic, so 1/3 staying the rational 1/3 makes sense as the default (though one can easily change this).
Print out stack (useful for try / except stuff):
# By Peter Norvig
ranks = ['--23456789TJQKA'.index(r) for r,c in hand] #outputs index of char...nice
list = 
for i in range(10):
Fast File Importing
#Gather all the elements returning from the generator
big_list = list(the_generator)
I use matlab for offline analysis and python for on the fly processing <and GUIs, and end products>.
Matlab is more complete but heavier.
Python is open source and faster.
I use both, for different purposes.