python - Find missing values in NumPy array of dtype obj -


i'm being driven crazy numpy array of dtype obj missing value (in example below, penultimate value).

>> array([0, 3, 'braund, mr. owen harris', 'male', 22.0, 1, 0, 'a/5 21171',        7.25, nan, 's'], dtype=object)

i want find missing value programatically function returns boolean vector true values in elements correspond missing values in array (as per example below).

>> some_function(a) array([false, false, false, false, false, false, false, false, false, true, false],       dtype=bool)

i tried isnan no avail.

>> isnan(a) traceback (most recent call last):   file "<stdin>", line 1, in <module> typeerror: ufunc 'isnan' not supported input types, , inputs not safely coerced supported types according casting rule ''safe''

i attempted performing operation explicitly on every element of array apply_along_axis, same error returned.

>> apply_along_axis(isnan, 0, a) traceback (most recent call last):   file "<stdin>", line 1, in <module> typeerror: ufunc 'isnan' not supported input types, , inputs not safely coerced supported types according casting rule ''safe''

can explain me (1) i'm doing wrong , (2) can solve problem? error, gather has 1 of elements not being in appropriate type. easiest way around issue?

another workaround is:

in [148]: [item != item item in a] out[148]: [false, false, false, false, false, false, false, false, false, true, false]

since nans not equal themselves. note, however, possible define custom objects which, nan, not equal themselves:

class foo(object):     def __cmp__(self, obj):         return -1 foo = foo() assert foo != foo

so using item != item not mean item nan.

note idea avoid numpy arrays of dtype object if possible.

  • they not particularly quick -- operations on contents devolve python calls on underlying python objects. normal python list has better performance.
  • unlike numeric arrays can more space efficient python list of numbers, object arrays not particularly space efficient since every item reference python object.
  • they not particular convenient since many numpy operations not work on arrays of dtype object. isnan 1 such example.

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