Ideally, you would be able to implement a vectorized version of your function and use that to do boolean indexing. For the vast majority of problems this is the right solution. Numpy provides quite a few functions that can act over various axes as well as all the basic operations and comparisons, so most useful conditions should be vectorizable.
import numpy as np
x = np.random.randn(20, 3)
x_new = x[np.sum(x, axis=1) > .5]
If you are absolutely sure that you can’t do the above, I would suggest using a list comprehension (or np.apply_along_axis) to create an array of bools to index with.
def myfunc(row):
return sum(row) > .5
bool_arr = np.array([myfunc(row) for row in x])
x_new = x[bool_arr]
This will get the job done in a relatively clean way, but will be significantly slower than a vectorized version. An example:
x = np.random.randn(5000, 200)
%timeit x[np.sum(x, axis=1) > .5]
# 100 loops, best of 3: 5.71 ms per loop
%timeit x[np.array([myfunc(row) for row in x])]
# 1 loops, best of 3: 217 ms per loop