COMPAT: numpy compat with NaT != NaT, #12049

Author: jreback

doc/source/whatsnew/v0.18.0.txt

@@ -445,7 +445,7 @@ Bug Fixes
 - Accept unicode in ``Timedelta`` constructor (:issue:`11995`)
 - Bug in value label reading for ``StataReader`` when reading incrementally (:issue:`12014`)
 - Bug in vectorized ``DateOffset`` when ``n`` parameter is ``0`` (:issue:`11370`)
-
+- Compat for numpy 1.11 w.r.t. ``NaT`` comparison changes (:issue:`12049`)
 
 
 

pandas/core/common.py

@@ -379,12 +379,13 @@ def array_equivalent(left, right, strict_nan=False):
     """
 
     left, right = np.asarray(left), np.asarray(right)
+
+    # shape compat
     if left.shape != right.shape:
         return False
 
     # Object arrays can contain None, NaN and NaT.
-    if (issubclass(left.dtype.type, np.object_) or
-            issubclass(right.dtype.type, np.object_)):
+    if is_object_dtype(left) or is_object_dtype(right):
 
         if not strict_nan:
             # pd.isnull considers NaN and None to be equivalent.
@@ -405,13 +406,21 @@ def array_equivalent(left, right, strict_nan=False):
         return True
 
     # NaNs can occur in float and complex arrays.
-    if issubclass(left.dtype.type, (np.floating, np.complexfloating)):
+    if is_float_dtype(left) or is_complex_dtype(left):
         return ((left == right) | (np.isnan(left) & np.isnan(right))).all()
 
     # numpy will will not allow this type of datetimelike vs integer comparison
     elif is_datetimelike_v_numeric(left, right):
         return False
 
+    # M8/m8
+    elif needs_i8_conversion(left) and needs_i8_conversion(right):
+        if not is_dtype_equal(left.dtype, right.dtype):
+            return False
+
+        left = left.view('i8')
+        right = right.view('i8')
+
     # NaNs cannot occur otherwise.
     return np.array_equal(left, right)
 

pandas/tests/test_common.py

@@ -8,7 +8,9 @@
 import numpy as np
 import pandas as pd
 from pandas.tslib import iNaT, NaT
-from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp, Float64Index
+from pandas import (Series, DataFrame, date_range,
+                    DatetimeIndex, TimedeltaIndex,
+                    Timestamp, Float64Index)
 from pandas import compat
 from pandas.compat import range, long, lrange, lmap, u
 from pandas.core.common import notnull, isnull, array_equivalent
@@ -322,20 +324,40 @@ def test_array_equivalent():
                             np.array([np.nan, 1, np.nan]))
     assert array_equivalent(np.array([np.nan, None], dtype='object'),
                             np.array([np.nan, None], dtype='object'))
-    assert array_equivalent(np.array([np.nan, 1+1j], dtype='complex'),
-                            np.array([np.nan, 1+1j], dtype='complex'))
-    assert not array_equivalent(np.array([np.nan, 1+1j], dtype='complex'),
-                                np.array([np.nan, 1+2j], dtype='complex'))
+    assert array_equivalent(np.array([np.nan, 1 + 1j], dtype='complex'),
+                            np.array([np.nan, 1 + 1j], dtype='complex'))
+    assert not array_equivalent(np.array([np.nan, 1 + 1j], dtype='complex'),
+                                np.array([np.nan, 1 + 2j], dtype='complex'))
     assert not array_equivalent(np.array([np.nan, 1, np.nan]),
                                 np.array([np.nan, 2, np.nan]))
-    assert not array_equivalent(np.array(['a', 'b', 'c', 'd']), np.array(['e', 'e']))
-    assert array_equivalent(Float64Index([0, np.nan]), Float64Index([0, np.nan]))
-    assert not array_equivalent(Float64Index([0, np.nan]), Float64Index([1, np.nan]))
-    assert array_equivalent(DatetimeIndex([0, np.nan]), DatetimeIndex([0, np.nan]))
-    assert not array_equivalent(DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan]))
+    assert not array_equivalent(np.array(['a', 'b', 'c', 'd']),
+                                np.array(['e', 'e']))
+    assert array_equivalent(Float64Index([0, np.nan]),
+                            Float64Index([0, np.nan]))
+    assert not array_equivalent(Float64Index([0, np.nan]),
+                                Float64Index([1, np.nan]))
+    assert array_equivalent(DatetimeIndex([0, np.nan]),
+                            DatetimeIndex([0, np.nan]))
+    assert not array_equivalent(DatetimeIndex([0, np.nan]),
+                                DatetimeIndex([1, np.nan]))
+    assert array_equivalent(TimedeltaIndex([0, np.nan]),
+                            TimedeltaIndex([0, np.nan]))
+    assert not array_equivalent(TimedeltaIndex([0, np.nan]),
+                                TimedeltaIndex([1, np.nan]))
+    assert array_equivalent(DatetimeIndex([0, np.nan], tz='US/Eastern'),
+                            DatetimeIndex([0, np.nan], tz='US/Eastern'))
+    assert not array_equivalent(DatetimeIndex([0, np.nan], tz='US/Eastern'),
+                                DatetimeIndex([1, np.nan], tz='US/Eastern'))
+    assert not array_equivalent(DatetimeIndex([0, np.nan]),
+                                DatetimeIndex([0, np.nan], tz='US/Eastern'))
+    assert not array_equivalent(DatetimeIndex([0, np.nan], tz='CET'),
+                                DatetimeIndex([0, np.nan], tz='US/Eastern'))
+    assert not array_equivalent(DatetimeIndex([0, np.nan]),
+                                TimedeltaIndex([0, np.nan]))
+
 
 def test_datetimeindex_from_empty_datetime64_array():
-    for unit in [ 'ms', 'us', 'ns' ]:
+    for unit in ['ms', 'us', 'ns']:
         idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit))
         assert(len(idx) == 0)
 

pandas/tseries/tests/test_tslib.py

@@ -399,8 +399,10 @@ def test_asm8(self):
             1000,
         ]
         for n in ns:
-            self.assertEqual(Timestamp(n).asm8, np.datetime64(n, 'ns'), n)
-        self.assertEqual(Timestamp('nat').asm8, np.datetime64('nat', 'ns'))
+            self.assertEqual(Timestamp(n).asm8.view('i8'),
+                             np.datetime64(n, 'ns').view('i8'), n)
+        self.assertEqual(Timestamp('nat').asm8.view('i8'),
+                         np.datetime64('nat', 'ns').view('i8'))
 
     def test_fields(self):
 
@@ -752,13 +754,11 @@ def test_coercing_dates_outside_of_datetime64_ns_bounds(self):
                 np.array([invalid_date], dtype='object'),
                 errors='raise',
             )
-            self.assertTrue(
-                np.array_equal(
-                    tslib.array_to_datetime(
-                        np.array([invalid_date], dtype='object'), errors='coerce',
-                    ),
-                    np.array([tslib.iNaT], dtype='M8[ns]')
-                )
+            self.assert_numpy_array_equal(
+                tslib.array_to_datetime(
+                    np.array([invalid_date], dtype='object'),
+                    errors='coerce'),
+                np.array([tslib.iNaT], dtype='M8[ns]')
             )
 
         arr = np.array(['1/1/1000', '1/1/2000'], dtype=object)