Detect if a NumPy array contains at least one non-numeric value?

Question

I need to write a function which will detect if the input contains at least one value which is non-numeric. If a non-numeric value is found I will raise an error (because the ca

Answer 1

Pfft! Microseconds! Never solve a problem in microseconds that can be solved in nanoseconds.

Note that the accepted answer:

• iterates over the whole data, regardless of whether a nan is found
• creates a temporary array of size N, which is redundant.

A better solution is to return True immediately when NAN is found:

import numba
import numpy as np

NAN = float("nan")

@numba.njit(nogil=True)
def _any_nans(a):
    for x in a:
        if np.isnan(x): return True
    return False

@numba.jit
def any_nans(a):
    if not a.dtype.kind=='f': return False
    return _any_nans(a.flat)

array1M = np.random.rand(1000000)
assert any_nans(array1M)==False
%timeit any_nans(array1M)  # 573us

array1M[0] = NAN
assert any_nans(array1M)==True
%timeit any_nans(array1M)  # 774ns  (!nanoseconds)

and works for n-dimensions:

array1M_nd = array1M.reshape((len(array1M)/2, 2))
assert any_nans(array1M_nd)==True
%timeit any_nans(array1M_nd)  # 774ns

Compare this to the numpy native solution:

def any_nans(a):
    if not a.dtype.kind=='f': return False
    return np.isnan(a).any()

array1M = np.random.rand(1000000)
assert any_nans(array1M)==False
%timeit any_nans(array1M)  # 456us

array1M[0] = NAN
assert any_nans(array1M)==True
%timeit any_nans(array1M)  # 470us

%timeit np.isnan(array1M).any()  # 532us

The early-exit method is 3 orders or magnitude speedup (in some cases). Not too shabby for a simple annotation.