Almost all numpy functions operate on whole arrays, and/or can be told to operate on a particular axis (row or column).
As long as you can define your function in terms of numpy functions acting on numpy arrays or array slices, your function will automatically operate on whole arrays, rows or columns.
It may be more helpful to ask about how to implement a particular function to get more concrete advice.
Numpy provides np.vectorize and np.frompyfunc to turn Python functions which operate on numbers into functions that operate on numpy arrays.
For example,
def myfunc(a,b):
if (a>b): return a
else: return b
vecfunc = np.vectorize(myfunc)
result=vecfunc([[1,2,3],[5,6,9]],[7,4,5])
print(result)
# [[7 4 5]
# [7 6 9]]
(The elements of the first array get replaced by the corresponding element of the second array when the second is bigger.)
But don’t get too excited; np.vectorize and np.frompyfunc are just syntactic sugar. They don’t actually make your code any faster. If your underlying Python function is operating on one value at a time, then np.vectorize will feed it one item at a time, and the whole
operation is going to be pretty slow (compared to using a numpy function which calls some underlying C or Fortran implementation).
To count how many elements of column x are smaller than a number y, you could use an expression such as:
(array['x']<y).sum()
For example:
import numpy as np
array=np.arange(6).view([('x',np.int),('y',np.int)])
print(array)
# [(0, 1) (2, 3) (4, 5)]
print(array['x'])
# [0 2 4]
print(array['x']<3)
# [ True True False]
print((array['x']<3).sum())
# 2