delete 'nan' or reduce length of numpy array if array contains nan after converting to numpy from pandas [duplicate]

Question

I want to delete nan from a numpy array. Lets say my numpy array contains:

np_array = ["123","pqr","123",nan,"avb", nan]

Expected output:

["123","pqr","123","avb"]

If we do it in pandas using pandas.dropna() it deletes the whole row which I don't want to do. I just want to delete the value and reduce the array size.

Is there any possible way to do so?

Answer 1

You can't use np.isnan because the NaNs are strings in your array but you can use boolean indexing by comparing with the string: "nan":

>>> import numpy as np
>>> np_array = np.array(["123","pqr","123",np.nan,"avb", np.nan])
>>> np_array[np_array != 'nan']
array(['1234', 'pqr', '123', 'avb'], 
      dtype='<U4')

Answer 2

isnan() should do the trick. Working minimal example on how to do it:

>>> import numpy as np
>>> np_array = np.array([1,2,3,np.nan,4])
>>> np_array
array([  1.,   2.,   3.,  nan,   4.])
>>> np_array = np_array[~np.isnan(np_array)]
>>> np_array
array([ 1.,  2.,  3.,  4.])