Numpy isnan() fails on an array of floats (from pandas dataframe apply)

np.isnan can be applied to NumPy arrays of native dtype (such as np.float64):

In [99]: np.isnan(np.array([np.nan, 0], dtype=np.float64))Out[99]: array([ True, False], dtype=bool)

but raises TypeError when applied to object arrays:

In [96]: np.isnan(np.array([np.nan, 0], dtype=object))TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Since you have Pandas, you could use pd.isnull instead -- it can accept NumPy arrays of object or native dtypes:

In [97]: pd.isnull(np.array([np.nan, 0], dtype=float))Out[97]: array([ True, False], dtype=bool)In [98]: pd.isnull(np.array([np.nan, 0], dtype=object))Out[98]: array([ True, False], dtype=bool)

Note that None is also considered a null value in object arrays.

A great substitute for np.isnan() and pd.isnull() is

for i in range(0,a.shape[0]):    if(a[i]!=a[i]):       //do something here       //a[i] is nan

since only nan is not equal to itself.

On top of @unutbu answer, you could coerce pandas numpy object array to native (float64) type, something along the line

import pandas as pdpd.to_numeric(df['tester'], errors='coerce')

Specify errors='coerce' to force strings that can't be parsed to a numeric value to become NaN. Column type would be dtype: float64, and then isnan check should work