If you really want a matrix, you might be better off using numpy. Matrix operations in numpy most often use an array type with two dimensions. There are many ways to create a new array; one of the most useful is the zeros function, which takes a shape parameter and returns an array of the given shape, with the values initialized to zero:

>>> import numpy
>>> numpy.zeros((5, 5))
array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

Here are some other ways to create 2-d arrays and matrices (with output removed for compactness):

numpy.arange(25).reshape((5, 5))         # create a 1-d range and reshape
numpy.array(range(25)).reshape((5, 5))   # pass a Python range and reshape
numpy.array([5] * 25).reshape((5, 5))    # pass a Python list and reshape
numpy.empty((5, 5))                      # allocate, but don't initialize
numpy.ones((5, 5))                       # initialize with ones

numpy provides a matrix type as well, but it is no longer recommended for any use, and may be removed from numpy in the future.

Depending what you're doing, you may not really have a 2-D array.

80% of the time you have simple list of "row-like objects", which might be proper sequences.

myArray = [ ('pi',3.14159,'r',2), ('e',2.71828,'theta',.5) ]

myArray[0][1] == 3.14159
myArray[1][1] == 2.71828

More often, they're instances of a class or a dictionary or a set or something more interesting that you didn't have in your previous languages.

myArray = [ {'pi':3.1415925,'r':2}, {'e':2.71828,'theta':.5} ]

20% of the time you have a dictionary, keyed by a pair

myArray = { (2009,'aug'):(some,tuple,of,values), (2009,'sep'):(some,other,tuple) }

Rarely, will you actually need a matrix.

You have a large, large number of collection classes in Python. Odds are good that you have something more interesting than a matrix.