CLN: _try_coerce_result, redundant dtypes.missing method

pandas/_libs/missing.pyx

@@ -12,8 +12,7 @@ cimport pandas._libs.util as util
 
 from pandas._libs.tslibs.np_datetime cimport (
     get_timedelta64_value, get_datetime64_value)
-from pandas._libs.tslibs.nattype cimport checknull_with_nat
-from pandas._libs.tslibs.nattype import NaT
+from pandas._libs.tslibs.nattype cimport checknull_with_nat, c_NaT
 
 cdef float64_t INF = <float64_t>np.inf
 cdef float64_t NEGINF = -INF
@@ -27,7 +26,7 @@ cdef inline bint _check_all_nulls(object val):
 
     if isinstance(val, (float, complex)):
         res = val != val
-    elif val is NaT:
+    elif val is c_NaT:
         res = 1
     elif val is None:
         res = 1
@@ -67,7 +66,7 @@ cpdef bint checknull(object val):
         return val != val  # and val != INF and val != NEGINF
     elif util.is_datetime64_object(val):
         return get_datetime64_value(val) == NPY_NAT
-    elif val is NaT:
+    elif val is c_NaT:
         return True
     elif util.is_timedelta64_object(val):
         return get_timedelta64_value(val) == NPY_NAT
@@ -106,7 +105,7 @@ cpdef bint checknull_old(object val):
         return val != val or val == INF or val == NEGINF
     elif util.is_datetime64_object(val):
         return get_datetime64_value(val) == NPY_NAT
-    elif val is NaT:
+    elif val is c_NaT:
         return True
     elif util.is_timedelta64_object(val):
         return get_timedelta64_value(val) == NPY_NAT
@@ -190,7 +189,7 @@ def isnaobj_old(ndarray arr):
     result = np.zeros(n, dtype=np.uint8)
     for i in range(n):
         val = arr[i]
-        result[i] = val is NaT or _check_none_nan_inf_neginf(val)
+        result[i] = val is c_NaT or _check_none_nan_inf_neginf(val)
     return result.view(np.bool_)
 
 

pandas/_libs/tslibs/__init__.py

@@ -2,7 +2,7 @@
 # flake8: noqa
 
 from .conversion import normalize_date, localize_pydatetime, tz_convert_single
-from .nattype import NaT, iNaT
+from .nattype import NaT, iNaT, is_null_datetimelike
 from .np_datetime import OutOfBoundsDatetime
 from .period import Period, IncompatibleFrequency
 from .timestamps import Timestamp

pandas/_libs/tslibs/nattype.pxd

@@ -17,4 +17,4 @@ cdef _NaT c_NaT
 
 
 cdef bint checknull_with_nat(object val)
-cdef bint is_null_datetimelike(object val)
+cpdef bint is_null_datetimelike(object val)

pandas/_libs/tslibs/nattype.pyx

@@ -686,7 +686,7 @@ cdef inline bint checknull_with_nat(object val):
     return val is None or util.is_nan(val) or val is c_NaT
 
 
-cdef inline bint is_null_datetimelike(object val):
+cpdef bint is_null_datetimelike(object val):
     """
     Determine if we have a null for a timedelta/datetime (or integer versions)
 

pandas/core/dtypes/missing.py

@@ -10,7 +10,7 @@
     _NS_DTYPE, _TD_DTYPE, ensure_object, is_bool_dtype, is_complex_dtype,
     is_datetime64_dtype, is_datetime64tz_dtype, is_datetimelike,
     is_datetimelike_v_numeric, is_dtype_equal, is_extension_array_dtype,
-    is_float_dtype, is_integer, is_integer_dtype, is_object_dtype,
+    is_float_dtype, is_integer_dtype, is_object_dtype,
     is_period_dtype, is_scalar, is_string_dtype, is_string_like_dtype,
     is_timedelta64_dtype, needs_i8_conversion, pandas_dtype)
 from .generic import (
@@ -339,22 +339,6 @@ def notna(obj):
 notnull = notna
 
 
-def is_null_datelike_scalar(other):
-    """ test whether the object is a null datelike, e.g. Nat
-    but guard against passing a non-scalar """
-    if other is NaT or other is None:
-        return True
-    elif is_scalar(other):
-
-        # a timedelta
-        if hasattr(other, 'dtype'):
-            return other.view('i8') == iNaT
-        elif is_integer(other) and other == iNaT:
-            return True
-        return isna(other)
-    return False
-
-
 def _isna_compat(arr, fill_value=np.nan):
     """
     Parameters

pandas/core/internals/blocks.py

@@ -8,7 +8,7 @@
 import numpy as np
 
 from pandas._libs import internals as libinternals, lib, tslib, tslibs
-from pandas._libs.tslibs import Timedelta, conversion
+from pandas._libs.tslibs import Timedelta, conversion, is_null_datetimelike
 import pandas.compat as compat
 from pandas.compat import range, zip
 from pandas.util._validators import validate_bool_kwarg
@@ -31,7 +31,7 @@
     ABCDataFrame, ABCDatetimeIndex, ABCExtensionArray, ABCIndexClass,
     ABCSeries)
 from pandas.core.dtypes.missing import (
-    _isna_compat, array_equivalent, is_null_datelike_scalar, isna, notna)
+    _isna_compat, array_equivalent, isna, notna)
 
 import pandas.core.algorithms as algos
 from pandas.core.arrays import (
@@ -2077,10 +2077,6 @@ def get_values(self, dtype=None):
             return values
         return self.values
 
-    @property
-    def asi8(self):
-        return self.values.view('i8')
-
 
 class DatetimeBlock(DatetimeLikeBlockMixin, Block):
     __slots__ = ()
@@ -2162,7 +2158,7 @@ def _try_coerce_args(self, values, other):
 
         if isinstance(other, bool):
             raise TypeError
-        elif is_null_datelike_scalar(other):
+        elif is_null_datetimelike(other):
             other = tslibs.iNaT
         elif isinstance(other, (datetime, np.datetime64, date)):
             other = self._box_func(other)
@@ -2175,18 +2171,16 @@ def _try_coerce_args(self, values, other):
         else:
             # coercion issues
             # let higher levels handle
-            raise TypeError
+            raise TypeError(other)
 
         return values, other
 
     def _try_coerce_result(self, result):
         """ reverse of try_coerce_args """
         if isinstance(result, np.ndarray):
-            if result.dtype.kind in ['i', 'f', 'O']:
-                try:
-                    result = result.astype('M8[ns]')
-                except ValueError:
-                    pass
+            if result.dtype.kind in ['i', 'f']:
+                result = result.astype('M8[ns]')
+
         elif isinstance(result, (np.integer, np.float, np.datetime64)):
             result = self._box_func(result)
         return result
@@ -2250,6 +2244,8 @@ def __init__(self, values, placement, ndim=2, dtype=None):
         # and just use DatetimeBlock's.
         if dtype is not None:
             values = self._maybe_coerce_values(values, dtype=dtype)
+            # TODO: this gets hit in msgpack tests, but in all cases the values
+            #  are already DatetimeArrays with the appropriate dtype
         super(DatetimeTZBlock, self).__init__(values, placement=placement,
                                               ndim=ndim)
 
@@ -2364,8 +2360,7 @@ def _try_coerce_args(self, values, other):
             # add the tz back
             other = self._holder(other, dtype=self.dtype)
 
-        elif (is_null_datelike_scalar(other) or
-              (lib.is_scalar(other) and isna(other))):
+        elif is_null_datetimelike(other):
             other = tslibs.iNaT
         elif isinstance(other, self._holder):
             if other.tz != self.values.tz:
@@ -2380,17 +2375,19 @@ def _try_coerce_args(self, values, other):
                 raise ValueError("incompatible or non tz-aware value")
             other = other.value
         else:
-            raise TypeError
+            raise TypeError(other)
 
         return values, other
 
     def _try_coerce_result(self, result):
         """ reverse of try_coerce_args """
         if isinstance(result, np.ndarray):
-            if result.dtype.kind in ['i', 'f', 'O']:
+            if result.dtype.kind in ['i', 'f']:
                 result = result.astype('M8[ns]')
+
         elif isinstance(result, (np.integer, np.float, np.datetime64)):
             result = self._box_func(result)
+
         if isinstance(result, np.ndarray):
             # allow passing of > 1dim if its trivial
 
@@ -2531,32 +2528,30 @@ def _try_coerce_args(self, values, other):
 
         if isinstance(other, bool):
             raise TypeError
-        elif is_null_datelike_scalar(other):
+        elif is_null_datetimelike(other):
             other = tslibs.iNaT
-        elif isinstance(other, Timedelta):
-            other = other.value
-        elif isinstance(other, timedelta):
-            other = Timedelta(other).value
-        elif isinstance(other, np.timedelta64):
+        elif isinstance(other, (timedelta, np.timedelta64)):
             other = Timedelta(other).value
         elif hasattr(other, 'dtype') and is_timedelta64_dtype(other):
             other = other.astype('i8', copy=False).view('i8')
         else:
             # coercion issues
             # let higher levels handle
-            raise TypeError
+            raise TypeError(other)
 
         return values, other
 
     def _try_coerce_result(self, result):
         """ reverse of try_coerce_args / try_operate """
         if isinstance(result, np.ndarray):
             mask = isna(result)
-            if result.dtype.kind in ['i', 'f', 'O']:
+            if result.dtype.kind in ['i', 'f']:
                 result = result.astype('m8[ns]')
             result[mask] = tslibs.iNaT
+
         elif isinstance(result, (np.integer, np.float)):
             result = self._box_func(result)
+
         return result
 
     def should_store(self, value):

pandas/core/sparse/series.py

@@ -16,9 +16,9 @@
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution
 
-from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.common import is_integer, is_scalar
 from pandas.core.dtypes.generic import ABCSeries, ABCSparseSeries
-from pandas.core.dtypes.missing import is_integer, isna, notna
+from pandas.core.dtypes.missing import isna, notna
 
 from pandas.core import generic
 from pandas.core.arrays import SparseArray

pandas/tests/tslibs/test_api.py

@@ -23,6 +23,7 @@ def test_namespace():
 
     api = ['NaT',
            'iNaT',
+           'is_null_datetimelike',
            'OutOfBoundsDatetime',
            'Period',
            'IncompatibleFrequency',