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?

pandas.isnull() (also pd.isna(), in newer versions) checks for missing values in both numeric and string/object arrays. From the documentation, it checks for:

NaN in numeric arrays, None/NaN in object arrays

Quick example:

import pandas as pd
import numpy as np
s = pd.Series(['apple', np.nan, 'banana'])
pd.isnull(s)
Out[9]: 
0    False
1     True
2    False
dtype: bool

The idea of using numpy.nan to represent missing values is something that pandas introduced, which is why pandas has the tools to deal with it.

Datetimes too (if you use pd.NaT you won’t need to specify the dtype)

In [24]: s = Series([Timestamp('20130101'),np.nan,Timestamp('20130102 9:30')],dtype='M8[ns]')

In [25]: s
Out[25]: 
0   2013-01-01 00:00:00
1                   NaT
2   2013-01-02 09:30:00
dtype: datetime64[ns]``

In [26]: pd.isnull(s)
Out[26]: 
0    False
1     True
2    False
dtype: bool

Is your type really arbitrary? If you know it is just going to be a int float or string you could just do

 if val.dtype == float and np.isnan(val):

assuming it is wrapped in numpy , it will always have a dtype and only float and complex can be NaN