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.

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.