Merge remote-tracking branch 'upstream/master' into disown-tz-only-rebased

Author: TomAugspurger

.travis.yml

@@ -23,7 +23,7 @@ env:
 
 git:
     # for cloning
-    depth: 1500
+    depth: 2000
 
 matrix:
     fast_finish: true

pandas/core/arrays/datetimes.py

@@ -1632,6 +1632,8 @@ def sequence_to_dt64ns(data, dtype=None, copy=False,
 
     inferred_freq = None
 
+    dtype = _validate_dt64_dtype(dtype)
+
     if not hasattr(data, "dtype"):
         # e.g. list, tuple
         if np.ndim(data) == 0:
@@ -1836,7 +1838,7 @@ def maybe_convert_dtype(data, copy):
         data = data.view(_NS_DTYPE)
 
     elif is_period_dtype(data):
-        # Note: without explicitly raising here, PeriondIndex
+        # Note: without explicitly raising here, PeriodIndex
         #  test_setops.test_join_does_not_recur fails
         raise TypeError("Passing PeriodDtype data is invalid.  "
                         "Use `data.to_timestamp()` instead")
@@ -1889,6 +1891,38 @@ def maybe_infer_tz(tz, inferred_tz):
     return tz
 
 
+def _validate_dt64_dtype(dtype):
+    """
+    Check that a dtype, if passed, represents either a numpy datetime64[ns]
+    dtype or a pandas DatetimeTZDtype.
+
+    Parameters
+    ----------
+    dtype : object
+
+    Returns
+    -------
+    dtype : None, numpy.dtype, or DatetimeTZDtype
+
+    Raises
+    ------
+    ValueError : invalid dtype
+
+    Notes
+    -----
+    Unlike validate_tz_from_dtype, this does _not_ allow non-existent
+    tz errors to go through
+    """
+    if dtype is not None:
+        dtype = pandas_dtype(dtype)
+        if ((isinstance(dtype, np.dtype) and dtype != _NS_DTYPE)
+                or not isinstance(dtype, (np.dtype, DatetimeTZDtype))):
+            raise ValueError("Unexpected value for 'dtype': '{dtype}'. "
+                             "Must be 'datetime64[ns]' or DatetimeTZDtype'."
+                             .format(dtype=dtype))
+    return dtype
+
+
 def validate_tz_from_dtype(dtype, tz):
     """
     If the given dtype is a DatetimeTZDtype, extract the implied

pandas/core/reshape/tile.py

@@ -390,10 +390,10 @@ def _coerce_to_type(x):
         dtype = x.dtype
     elif is_datetime64_dtype(x):
         x = to_datetime(x)
-        dtype = np.datetime64
+        dtype = np.dtype('datetime64[ns]')
     elif is_timedelta64_dtype(x):
         x = to_timedelta(x)
-        dtype = np.timedelta64
+        dtype = np.dtype('timedelta64[ns]')
 
     if dtype is not None:
         # GH 19768: force NaT to NaN during integer conversion

pandas/core/series.py

@@ -130,6 +130,7 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
     dtype : str, numpy.dtype, or ExtensionDtype, optional
         dtype for the output Series. If not specified, this will be
         inferred from `data`.
+        See the :ref:`user guide <basics.dtypes>` for more usages.
     copy : bool, default False
         Copy input data.
     """

pandas/tests/arrays/test_datetimelike.py

@@ -64,7 +64,7 @@ class SharedTests(object):
     def test_compare_len1_raises(self):
         # make sure we raise when comparing with different lengths, specific
         #  to the case where one has length-1, which numpy would broadcast
-        data = np.arange(10, dtype='i8')
+        data = np.arange(10, dtype='i8') * 24 * 3600 * 10**9
 
         idx = self.index_cls._simple_new(data, freq='D')
         arr = self.array_cls(idx)
@@ -77,7 +77,7 @@ def test_compare_len1_raises(self):
             idx <= idx[[0]]
 
     def test_take(self):
-        data = np.arange(100, dtype='i8')
+        data = np.arange(100, dtype='i8') * 24 * 3600 * 10**9
         np.random.shuffle(data)
 
         idx = self.index_cls._simple_new(data, freq='D')
@@ -96,7 +96,7 @@ def test_take(self):
         tm.assert_index_equal(self.index_cls(result), expected)
 
     def test_take_fill(self):
-        data = np.arange(10, dtype='i8')
+        data = np.arange(10, dtype='i8') * 24 * 3600 * 10**9
 
         idx = self.index_cls._simple_new(data, freq='D')
         arr = self.array_cls(idx)
@@ -121,7 +121,7 @@ def test_take_fill(self):
                      fill_value=pd.Timestamp.now().time)
 
     def test_concat_same_type(self):
-        data = np.arange(10, dtype='i8')
+        data = np.arange(10, dtype='i8') * 24 * 3600 * 10**9
 
         idx = self.index_cls._simple_new(data, freq='D').insert(0, pd.NaT)
         arr = self.array_cls(idx)

pandas/tests/arrays/test_datetimes.py

@@ -17,11 +17,11 @@
 
 class TestDatetimeArrayConstructor(object):
     def test_mismatched_timezone_raises(self):
-        a = DatetimeArray(np.array(['2000-01-01T06:00:00'], dtype='M8[ns]'),
-                          dtype=DatetimeTZDtype(tz='US/Central'))
+        arr = DatetimeArray(np.array(['2000-01-01T06:00:00'], dtype='M8[ns]'),
+                            dtype=DatetimeTZDtype(tz='US/Central'))
         dtype = DatetimeTZDtype(tz='US/Eastern')
-        with pytest.raises(TypeError, match='do not match'):
-            DatetimeArray(a, dtype=dtype)
+        with pytest.raises(TypeError, match='data is already tz-aware'):
+            DatetimeArray(arr, dtype=dtype)
 
     def test_non_array_raises(self):
         with pytest.raises(ValueError, match='list'):

pandas/tests/arrays/test_timedeltas.py

@@ -19,17 +19,16 @@ def test_other_type_raises(self):
             TimedeltaArray(np.array([1, 2, 3], dtype='bool'))
 
     def test_incorrect_dtype_raises(self):
-        with pytest.raises(TypeError, match="dtype category"):
+        # TODO: why TypeError for 'category' but ValueError for i8?
+        with pytest.raises(TypeError,
+                           match='data type "category" not understood'):
             TimedeltaArray(np.array([1, 2, 3], dtype='i8'), dtype='category')
 
-        with pytest.raises(TypeError, match="dtype int"):
+        with pytest.raises(ValueError,
+                           match=r"Only timedelta64\[ns\] dtype is valid"):
             TimedeltaArray(np.array([1, 2, 3], dtype='i8'),
                            dtype=np.dtype(int))
 
-    def test_freq_infer_raises(self):
-        with pytest.raises(ValueError, match='Frequency inference'):
-            TimedeltaArray(np.array([1, 2, 3], dtype='i8'), freq="infer")
-
     def test_copy(self):
         data = np.array([1, 2, 3], dtype='m8[ns]')
         arr = TimedeltaArray(data, copy=False)

pandas/tests/dtypes/test_common.py

@@ -340,8 +340,8 @@ def test_is_datetime64_any_dtype():
     assert com.is_datetime64_any_dtype(np.datetime64)
     assert com.is_datetime64_any_dtype(np.array([], dtype=np.datetime64))
     assert com.is_datetime64_any_dtype(DatetimeTZDtype("ns", "US/Eastern"))
-    assert com.is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3],
-                                                        dtype=np.datetime64))
+    assert com.is_datetime64_any_dtype(
+        pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]"))
 
 
 def test_is_datetime64_ns_dtype():
@@ -356,8 +356,8 @@ def test_is_datetime64_ns_dtype():
     assert not com.is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]"))
 
     assert com.is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern"))
-    assert com.is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3],
-                                                       dtype=np.datetime64))
+    assert com.is_datetime64_ns_dtype(
+        pd.DatetimeIndex([1, 2, 3], dtype=np.dtype('datetime64[ns]')))
 
 
 def test_is_timedelta64_ns_dtype():