BUG/TST: Fixes isnull behavior on NaT in array. Closes #5443

common._isnull_ndarraylike(...) uses lib.isnullobj to check
nulls/NaN/NaT in ndarray, which in turn relies on util._checknull.
_checknull did not know about NaT, but now lib.isnullobj does, while
still maintaining performance by doing arr[i] only once.

Added a test case test_isnull_nat() to test_common.py and
check for NaT in lib.isnullobj. pd.isnull(np.array([pd.NaT]))
now yields the correct results ([True]).

Author: commonlisp

pandas/lib.pyx

@@ -170,7 +170,6 @@ cdef inline int64_t get_timedelta64_value(val):
 cdef double INF = <double> np.inf
 cdef double NEGINF = -INF
 
-
 cpdef checknull(object val):
     if util.is_float_object(val) or util.is_complex_object(val):
         return val != val # and val != INF and val != NEGINF
@@ -183,7 +182,7 @@ cpdef checknull(object val):
     elif is_array(val):
         return False
     else:
-        return util._checknull(val)
+        return _checknull(val)
 
 cpdef checknull_old(object val):
     if util.is_float_object(val) or util.is_complex_object(val):
@@ -213,7 +212,8 @@ def isnullobj(ndarray[object] arr):
     n = len(arr)
     result = np.zeros(n, dtype=np.uint8)
     for i from 0 <= i < n:
-        result[i] = util._checknull(arr[i])
+        arobj = arr[i]
+        result[i] = arobj is NaT or _checknull(arobj)
     return result.view(np.bool_)
 
 @cython.wraparound(False)

pandas/src/util.pxd

@@ -67,7 +67,7 @@ cdef inline is_array(object o):
 
 cdef inline bint _checknull(object val):
     try:
-        return val is None or (cpython.PyFloat_Check(val) and val != val)
+        return val is None or (cpython.PyFloat_Check(val) and val != val) 
     except ValueError:
         return False
 

pandas/tests/test_common.py

@@ -5,7 +5,7 @@
 import nose
 from nose.tools import assert_equal
 import numpy as np
-from pandas.tslib import iNaT
+from pandas.tslib import iNaT, NaT
 
 from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp
 from pandas import compat
@@ -114,6 +114,14 @@ def test_isnull_lists():
     result = isnull([u('foo'), u('bar')])
     assert(not result.any())
 
+def test_isnull_nat():
+    result = isnull([NaT])
+    exp = np.array([True])
+    assert(np.array_equal(result, exp))
+
+    result = isnull(np.array([NaT], dtype=object))
+    exp = np.array([True])
+    assert(np.array_equal(result, exp))
 
 def test_isnull_datetime():
     assert (not isnull(datetime.now()))