np.isnan for array in Python

Question

I have an array which I want to change into an array of True and False values to I can remove nan values.

When trying this using np.isnan as follows it's fine:

import numpy as np

a = np.array([1.,2.,np.nan])

a
Out[4]: array([ 1.,  2., nan])

np.isnan(a)
Out[5]: array([False, False,  True])

But when I try to do the same on my array, it doesn't work:

a
Out[9]: 
array([73788400000.0, 80017300000.0, 83680400000.0, 84939700000.0,
       83877800000.0, 83911700000.0, 85368100000.0, 83808200000.0,
       85936400000.0, 85177800000.0, 82705400000.0, 82119100000.0,
       73935400.0, 64018400.0, 42796500.0, 43130000.0, 42637600.0,
       167911000.0, nan], dtype=object)
np.isnan(a)

Traceback (most recent call last):

  File "<ipython-input-10-f4b5b5e7f347>", line 1, in <module>
    np.isnan(a)

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''

I suspect, given the error, this has something to do with the object type but I'm not sure how exactly.

Please note, when trying math.isnan, it only appears to take single values:

math.isnan(a)

Traceback (most recent call last):

  File "<ipython-input-11-6d4d8c26d370>", line 1, in <module>
    math.isnan(a)

TypeError: only size-1 arrays can be converted to Python scalars

Any help would be greatly appreciated!

Could you make use of pandas, and use isnull()? It checks for "NaN in numeric arrays, None/NaN in object arrays" — user3483203
– user3483203, Commented Mar 10, 2018 at 0:34
That worked but the solution below requires less switching between pandas and numpy arrays but thanks! — user8188120
– user8188120, Commented Mar 10, 2018 at 0:38

jpp · Accepted Answer · 2018-03-10 00:36:15Z

2

Convert your array to float and this will work:

import numpy as np

a = np.array([73788400000.0, 80017300000.0, 83680400000.0, 84939700000.0,
              83877800000.0, 83911700000.0, 85368100000.0, 83808200000.0,
              85936400000.0, 85177800000.0, 82705400000.0, 82119100000.0,
              73935400.0, 64018400.0, 42796500.0, 43130000.0, 42637600.0,
              167911000.0, np.nan], dtype=object)

res = np.isnan(a.astype(float))

# [False False False False False False False False False False False False
#  False False False False False False  True]

answered Mar 10, 2018 at 0:36

jpp

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