My numpy arrays use
np.nan to designate missing values. As I iterate over the data set, I need to detect such missing values and handle them in special ways.
Naively I used
numpy.isnan(val), which works well unless
val isn’t among the subset of types supported by
numpy.isnan(). For example, missing data can occur in string fields, in which case I get:
>>> np.isnan('some_string') Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: Not implemented for this type
Other than writing an expensive wrapper that catches the exception and returns
False, is there a way to handle this elegantly and efficiently?