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

I have an array of floats (some normal numbers, some nans) that is coming out of an apply on a pandas dataframe.

For some reason, numpy.isnan is failing on this array, however as shown below, each element is a float, numpy.isnan runs correctly on each element, the type of the variable is definitely a numpy array.

What's going on?!

set([type(x) for x in tester])
Out[59]: {float}

tester
Out[60]: 
array([-0.7000000000000001, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan], dtype=object)

set([type(x) for x in tester])
Out[61]: {float}

np.isnan(tester)
Traceback (most recent call last):

File "<ipython-input-62-e3638605b43c>", line 1, in <module>
np.isnan(tester)

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''

set([np.isnan(x) for x in tester])
Out[65]: {False, True}

type(tester)
Out[66]: numpy.ndarray

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 pd
pd.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

Make sure you import csv file using Pandas

import pandas as pd

condition = pd.isnull(data[i][j])