There are several ways to delete rows from NumPy array.

The easiest one is to use basic indexing as with standard Python lists:

>>> import numpy as np
>>> x = np.arange(35).reshape(7, 5)
>>> x
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34]])
>>> result = x[5:]
>>> result
array([[25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34]])

You can select not only rows but columns as well:

>>> x[:2, 1:4]
array([[1, 2, 3],
       [6, 7, 8]])

Another way is to use "fancy indexing" (indexing arrays using arrays):

>>> x[[0, 2, 6]]
array([[ 0,  1,  2,  3,  4],
       [10, 11, 12, 13, 14],
       [30, 31, 32, 33, 34]])

You can achieve the same using np.take:

>>> np.take(x, [0, 2, 6], axis=0)
array([[ 0,  1,  2,  3,  4],
       [10, 11, 12, 13, 14],
       [30, 31, 32, 33, 34]])

Yet another option is to use np.delete as in the question. For selecting the rows/columns for deletion it can accept slice objects, int, or array of ints:

>>> np.delete(x, slice(0, 5), axis=0)
array([[25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34]])
>>> np.delete(x, [0, 2, 3], axis=0)
array([[ 5,  6,  7,  8,  9],
       [20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34]])

But all this time that I've been using NumPy I never needed this np.delete, as in this case it's much more convenient to use boolean indexing.

As an example, if I would want to remove/select those rows that start with a value greater than 12, I would do:

>>> mask_array = x[:, 0] < 12  # comparing values of the first column
>>> mask_array
array([ True,  True,  True, False, False, False, False])
>>> x[mask_array]
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])
>>> x[~mask_array]  # ~ is an element-wise inversion
array([[15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34]])

For more information refer to the documentation on indexing: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html

The simplest way to delete rows and columns from arrays is the numpy.delete method.

Suppose I have the following array x:

x = array([[1,2,3],
        [4,5,6],
        [7,8,9]])

To delete the first row, do this:

x = numpy.delete(x, (0), axis=0)

To delete the third column, do this:

x = numpy.delete(x,(2), axis=1)

So you could find the indices of the rows which have a 0 in them, put them in a list or a tuple and pass this as the second argument of the function.

The condition

sb['PercentCnt'] >= 50

is the condition for keeping things on this column, and the condition

sb['ModelType'] >= 2

is the same for the other column.

You can combine these with np.logical_and:

keep = np.logical_and(sb['PercentCnt'] >= 50, sb['ModelType'] >= 2)

Finally, just keep the rows you wish to keep:

sb[keep]

Instead of deleting the columns and rows you don't want, it is easier to select the ones you do want. Also note that it is standard to start counting rows and columns from zeros. To get your first example, you thus want to select all elements in rows 0 and 3 and in rows 0 and 3. This requires advanced indexing, for which you can use the ix_ utility function:

In [25]: np.count_nonzero(a[np.ix_([0,3], [0,3])])  
Out[25]: 0

For your second example, you want to select rows 0 and 1 and columns 0 and 1, which can be done using basic slicing:

In [26]: np.count_nonzero(a[:2,:2])
Out[26]: 3