Ensure TDA.__init__ validates freq

pandas/core/arrays/timedeltas.py

@@ -15,8 +15,8 @@
 from pandas.util._decorators import Appender
 
 from pandas.core.dtypes.common import (
-    _NS_DTYPE, _TD_DTYPE, ensure_int64, is_datetime64_dtype, is_float_dtype,
-    is_integer_dtype, is_list_like, is_object_dtype, is_scalar,
+    _NS_DTYPE, _TD_DTYPE, ensure_int64, is_datetime64_dtype, is_dtype_equal,
+    is_float_dtype, is_integer_dtype, is_list_like, is_object_dtype, is_scalar,
     is_string_dtype, is_timedelta64_dtype, is_timedelta64_ns_dtype,
     pandas_dtype)
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
@@ -134,55 +134,38 @@ def dtype(self):
     _attributes = ["freq"]
 
     def __init__(self, values, dtype=_TD_DTYPE, freq=None, copy=False):
-        if isinstance(values, (ABCSeries, ABCIndexClass)):
-            values = values._values
-
-        if isinstance(values, type(self)):
-            values, freq, freq_infer = extract_values_freq(values, freq)
-
-        if not isinstance(values, np.ndarray):
-            msg = (
+        if not hasattr(values, "dtype"):
+            raise ValueError(
                 "Unexpected type '{}'. 'values' must be a TimedeltaArray "
                 "ndarray, or Series or Index containing one of those."
-            )
-            raise ValueError(msg.format(type(values).__name__))
-
-        if values.dtype == 'i8':
-            # for compat with datetime/timedelta/period shared methods,
-            #  we can sometimes get here with int64 values.  These represent
-            #  nanosecond UTC (or tz-naive) unix timestamps
-            values = values.view(_TD_DTYPE)
-
-        if values.dtype != _TD_DTYPE:
-            raise TypeError(_BAD_DTYPE.format(dtype=values.dtype))
-
-        try:
-            dtype_mismatch = dtype != _TD_DTYPE
-        except TypeError:
-            raise TypeError(_BAD_DTYPE.format(dtype=dtype))
-        else:
-            if dtype_mismatch:
-                raise TypeError(_BAD_DTYPE.format(dtype=dtype))
-
+                .format(type(values).__name__))
         if freq == "infer":
-            msg = (
+            raise ValueError(
                 "Frequency inference not allowed in TimedeltaArray.__init__. "
-                "Use 'pd.array()' instead."
-            )
-            raise ValueError(msg)
+                "Use 'pd.array()' instead.")
 
-        if copy:
-            values = values.copy()
-        if freq:
-            freq = to_offset(freq)
+        if dtype is not None and not is_dtype_equal(dtype, _TD_DTYPE):
+            raise TypeError("dtype {dtype} cannot be converted to "
+                            "timedelta64[ns]".format(dtype=dtype))
+
+        if values.dtype == 'i8':
+            values = values.view('timedelta64[ns]')
 
-        self._data = values
-        self._dtype = dtype
-        self._freq = freq
+        result = type(self)._from_sequence(values, dtype=dtype,
+                                           copy=copy, freq=freq)
+        self._data = result._data
+        self._freq = result._freq
+        self._dtype = result._dtype
 
     @classmethod
     def _simple_new(cls, values, freq=None, dtype=_TD_DTYPE):
-        return cls(values, dtype=dtype, freq=freq)
+        assert isinstance(values, np.ndarray), type(values)
+
+        result = object.__new__(cls)
+        result._data = values.view(_TD_DTYPE)
+        result._freq = to_offset(freq)
+        result._dtype = _TD_DTYPE
+        return result
 
     @classmethod
     def _from_sequence(cls, data, dtype=_TD_DTYPE, copy=False,
@@ -998,18 +981,3 @@ def _generate_regular_range(start, end, periods, offset):
 
     data = np.arange(b, e, stride, dtype=np.int64)
     return data
-
-
-def extract_values_freq(arr, freq):
-    # type: (TimedeltaArray, Offset) -> Tuple[ndarray, Offset, bool]
-    freq_infer = False
-    if freq is None:
-        freq = arr.freq
-    elif freq and arr.freq:
-        freq = to_offset(freq)
-        freq, freq_infer = dtl.validate_inferred_freq(
-            freq, arr.freq,
-            freq_infer=False
-        )
-    values = arr._data
-    return values, freq, freq_infer

pandas/core/indexes/timedeltas.py

@@ -233,12 +233,14 @@ def _simple_new(cls, values, name=None, freq=None, dtype=_TD_DTYPE):
         if not isinstance(values, TimedeltaArray):
             values = TimedeltaArray._simple_new(values, dtype=dtype,
                                                 freq=freq)
+        else:
+            if freq is None:
+                freq = values.freq
         assert isinstance(values, TimedeltaArray), type(values)
         assert dtype == _TD_DTYPE, dtype
         assert values.dtype == 'm8[ns]', values.dtype
 
-        freq = to_offset(freq)
-        tdarr = TimedeltaArray._simple_new(values, freq=freq)
+        tdarr = TimedeltaArray._simple_new(values._data, freq=freq)
         result = object.__new__(cls)
         result._data = tdarr
         result.name = name

pandas/tests/arrays/test_timedeltas.py

@@ -9,6 +9,15 @@
 
 
 class TestTimedeltaArrayConstructor(object):
+    def test_freq_validation(self):
+        # ensure that the public constructor cannot create an invalid instance
+        arr = np.array([0, 0, 1], dtype=np.int64) * 3600 * 10**9
+
+        msg = ("Inferred frequency None from passed values does not "
+               "conform to passed frequency D")
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray(arr.view('timedelta64[ns]'), freq="D")
+
     def test_non_array_raises(self):
         with pytest.raises(ValueError, match='list'):
             TimedeltaArray([1, 2, 3])
@@ -34,7 +43,7 @@ def test_incorrect_dtype_raises(self):
     def test_copy(self):
         data = np.array([1, 2, 3], dtype='m8[ns]')
         arr = TimedeltaArray(data, copy=False)
-        assert arr._data is data
+        assert arr._data.base is data
 
         arr = TimedeltaArray(data, copy=True)
         assert arr._data is not data