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?
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')
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.])
dtype of your array. isnan raises that error for object arrays. If there's no reason to have an object array, you can do arr.dtype=np.float64 or whatever you want and then isnan will work. If you do need objects, use MSeifert's answer above.Try this
np_clean = [x for x in np_array if str(x) != 'nan']
It will remove nan from your list
This works for numerical arrays.
filter(lambda x: np.isfinite(x), np.array([1,2,3,np.nan]))
>>>[1.0, 2.0, 3.0]
