To create a new array, it seems numpy.zeros is the way to go

import numpy as np
a = np.zeros(shape=(x, y))

You can also set a datatype to allocate it sensibly

>>> np.zeros(shape=(5,2), dtype=np.uint8)
array([[0, 0],
       [0, 0],
       [0, 0],
       [0, 0],
       [0, 0]], dtype=uint8)
>>> np.zeros(shape=(5,2), dtype="datetime64[ns]")
array([['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000'],
       ['1970-01-01T00:00:00.000000000', '1970-01-01T00:00:00.000000000']],
      dtype='datetime64[ns]')

See also

• How do I create an empty array/matrix in NumPy?
• np.full(size, 0) vs. np.zeros(size) vs. np.empty()

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.