Found out the answer myself: This code does what I want, and shows that I can put a python array ("a") and have it turn into a numpy array. For my code that draws it to a window, it drew it upside down, which is why I added the last line of code.

# generate grid
    a = [ ]
    allZeroes = []
    allOnes = []

    for i in range(0,800):
        allZeroes.append(0)
        allOnes.append(1)

    # append 400 rows of 800 zeroes per row.
    for i in range(0, 400):
        a.append(allZeroes)

    # append 400 rows of 800 ones per row.
    for i in range(0,400):
        a.append(allOnes)


#So this is a 2D 800 x 800 array of zeros on the top half, ones on the bottom half.
array = numpy.array(a)

# Need to flip the array so my other code that draws 
# this array will draw it right-side up
array = numpy.flipud(array)

A pattern that often came up in Python was

bar = []
for item in some_iterable:
    bar.append(SOME EXPRESSION)

which helped motivate the introduction of list comprehensions, which convert that snippet to

bar = [SOME_EXPRESSION for item in some_iterable]

which is shorter and sometimes clearer. Usually, you get in the habit of recognizing these and often replacing loops with comprehensions.

Your code follows this pattern twice

twod_list = []                                       \                      
for i in range (0, 10):                               \
    new = []                  \ can be replaced        } this too
    for j in range (0, 10):    } with a list          /
        new.append(foo)       / comprehension        /
    twod_list.append(new)                           /

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()

A pattern that often came up in Python was

bar = []
for item in some_iterable:
    bar.append(SOME EXPRESSION)

which helped motivate the introduction of list comprehensions, which convert that snippet to

bar = [SOME_EXPRESSION for item in some_iterable]

which is shorter and sometimes clearer. Usually, you get in the habit of recognizing these and often replacing loops with comprehensions.

Your code follows this pattern twice

twod_list = []                                       \                      
for i in range (0, 10):                               \
    new = []                  \ can be replaced        } this too
    for j in range (0, 10):    } with a list          /
        new.append(foo)       / comprehension        /
    twod_list.append(new)                           /