REF: share DTA/TDA.__init__ (#47583)

Author: jbrockmendel

pandas/core/arrays/datetimelike.py

@@ -25,6 +25,7 @@
     algos,
     lib,
 )
+from pandas._libs.arrays import NDArrayBacked
 from pandas._libs.tslibs import (
     BaseOffset,
     IncompatibleFrequency,
@@ -1906,6 +1907,81 @@ class TimelikeOps(DatetimeLikeArrayMixin):
     Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.
     """
 
+    _default_dtype: np.dtype
+
+    def __init__(self, values, dtype=None, freq=lib.no_default, copy: bool = False):
+        values = extract_array(values, extract_numpy=True)
+        if isinstance(values, IntegerArray):
+            values = values.to_numpy("int64", na_value=iNaT)
+
+        inferred_freq = getattr(values, "_freq", None)
+        explicit_none = freq is None
+        freq = freq if freq is not lib.no_default else None
+
+        if isinstance(values, type(self)):
+            if explicit_none:
+                # don't inherit from values
+                pass
+            elif freq is None:
+                freq = values.freq
+            elif freq and values.freq:
+                freq = to_offset(freq)
+                freq, _ = validate_inferred_freq(freq, values.freq, False)
+
+            if dtype is not None:
+                dtype = pandas_dtype(dtype)
+                if not is_dtype_equal(dtype, values.dtype):
+                    # TODO: we only have tests for this for DTA, not TDA (2022-07-01)
+                    raise TypeError(
+                        f"dtype={dtype} does not match data dtype {values.dtype}"
+                    )
+
+            dtype = values.dtype
+            values = values._ndarray
+
+        elif dtype is None:
+            dtype = self._default_dtype
+
+        if not isinstance(values, np.ndarray):
+            raise ValueError(
+                f"Unexpected type '{type(values).__name__}'. 'values' must be a "
+                f"{type(self).__name__}, ndarray, or Series or Index "
+                "containing one of those."
+            )
+        if values.ndim not in [1, 2]:
+            raise ValueError("Only 1-dimensional input arrays are supported.")
+
+        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(self._default_dtype)
+
+        dtype = self._validate_dtype(values, dtype)
+
+        if freq == "infer":
+            raise ValueError(
+                f"Frequency inference not allowed in {type(self).__name__}.__init__. "
+                "Use 'pd.array()' instead."
+            )
+
+        if copy:
+            values = values.copy()
+        if freq:
+            freq = to_offset(freq)
+
+        NDArrayBacked.__init__(self, values=values, dtype=dtype)
+        self._freq = freq
+
+        if inferred_freq is None and freq is not None:
+            type(self)._validate_frequency(self, freq)
+
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        raise AbstractMethodError(cls)
+
+    # --------------------------------------------------------------
+
     @cache_readonly
     def _reso(self) -> int:
         return get_unit_from_dtype(self._ndarray.dtype)
@@ -1917,6 +1993,8 @@ def _unit(self) -> str:
         # "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]"
         return dtype_to_unit(self.dtype)  # type: ignore[arg-type]
 
+    # --------------------------------------------------------------
+
     def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
         if (
             ufunc in [np.isnan, np.isinf, np.isfinite]

pandas/core/arrays/datetimes.py

@@ -19,7 +19,6 @@
     lib,
     tslib,
 )
-from pandas._libs.arrays import NDArrayBacked
 from pandas._libs.tslibs import (
     BaseOffset,
     NaT,
@@ -30,7 +29,6 @@
     fields,
     get_resolution,
     get_unit_from_dtype,
-    iNaT,
     ints_to_pydatetime,
     is_date_array_normalized,
     is_unitless,
@@ -72,9 +70,7 @@
 
 from pandas.core.arrays import datetimelike as dtl
 from pandas.core.arrays._ranges import generate_regular_range
-from pandas.core.arrays.integer import IntegerArray
 import pandas.core.common as com
-from pandas.core.construction import extract_array
 
 from pandas.tseries.frequencies import get_period_alias
 from pandas.tseries.offsets import (
@@ -254,75 +250,14 @@ def _scalar_type(self) -> type[Timestamp]:
 
     _dtype: np.dtype | DatetimeTZDtype
     _freq = None
+    _default_dtype = DT64NS_DTYPE  # used in TimeLikeOps.__init__
 
-    def __init__(
-        self, values, dtype=None, freq=lib.no_default, copy: bool = False
-    ) -> None:
-        values = extract_array(values, extract_numpy=True)
-        if isinstance(values, IntegerArray):
-            values = values.to_numpy("int64", na_value=iNaT)
-
-        inferred_freq = getattr(values, "_freq", None)
-        explicit_none = freq is None
-        freq = freq if freq is not lib.no_default else None
-
-        if isinstance(values, type(self)):
-            if explicit_none:
-                # don't inherit from values
-                pass
-            elif freq is None:
-                freq = values.freq
-            elif freq and values.freq:
-                freq = to_offset(freq)
-                freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)
-
-            if dtype is not None:
-                dtype = pandas_dtype(dtype)
-                if not is_dtype_equal(dtype, values.dtype):
-                    raise TypeError(
-                        f"dtype={dtype} does not match data dtype {values.dtype}"
-                    )
-
-            dtype = values.dtype
-            values = values._ndarray
-
-        elif dtype is None:
-            dtype = DT64NS_DTYPE
-
-        if not isinstance(values, np.ndarray):
-            raise ValueError(
-                f"Unexpected type '{type(values).__name__}'. 'values' must be a "
-                f"{type(self).__name__}, ndarray, or Series or Index "
-                "containing one of those."
-            )
-        if values.ndim not in [1, 2]:
-            raise ValueError("Only 1-dimensional input arrays are supported.")
-
-        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(DT64NS_DTYPE)
-
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        # used in TimeLikeOps.__init__
         _validate_dt64_dtype(values.dtype)
         dtype = _validate_dt64_dtype(dtype)
-
-        if freq == "infer":
-            raise ValueError(
-                f"Frequency inference not allowed in {type(self).__name__}.__init__. "
-                "Use 'pd.array()' instead."
-            )
-
-        if copy:
-            values = values.copy()
-        if freq:
-            freq = to_offset(freq)
-
-        NDArrayBacked.__init__(self, values=values, dtype=dtype)
-        self._freq = freq
-
-        if inferred_freq is None and freq is not None:
-            type(self)._validate_frequency(self, freq)
+        return dtype
 
     # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
     @classmethod

pandas/core/arrays/timedeltas.py

@@ -12,7 +12,6 @@
     lib,
     tslibs,
 )
-from pandas._libs.arrays import NDArrayBacked
 from pandas._libs.tslibs import (
     BaseOffset,
     NaT,
@@ -54,13 +53,9 @@
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import nanops
-from pandas.core.arrays import (
-    IntegerArray,
-    datetimelike as dtl,
-)
+from pandas.core.arrays import datetimelike as dtl
 from pandas.core.arrays._ranges import generate_regular_range
 import pandas.core.common as com
-from pandas.core.construction import extract_array
 from pandas.core.ops.common import unpack_zerodim_and_defer
 
 if TYPE_CHECKING:
@@ -171,64 +166,14 @@ def dtype(self) -> np.dtype:  # type: ignore[override]
     # Constructors
 
     _freq = None
+    _default_dtype = TD64NS_DTYPE  # used in TimeLikeOps.__init__
 
-    def __init__(
-        self, values, dtype=TD64NS_DTYPE, freq=lib.no_default, copy: bool = False
-    ) -> None:
-        values = extract_array(values, extract_numpy=True)
-        if isinstance(values, IntegerArray):
-            values = values.to_numpy("int64", na_value=tslibs.iNaT)
-
-        inferred_freq = getattr(values, "_freq", None)
-        explicit_none = freq is None
-        freq = freq if freq is not lib.no_default else None
-
-        if isinstance(values, type(self)):
-            if explicit_none:
-                # don't inherit from values
-                pass
-            elif freq is None:
-                freq = values.freq
-            elif freq and values.freq:
-                freq = to_offset(freq)
-                freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)
-
-            values = values._ndarray
-
-        if not isinstance(values, np.ndarray):
-            raise ValueError(
-                f"Unexpected type '{type(values).__name__}'. 'values' must be a "
-                f"{type(self).__name__}, ndarray, or Series or Index "
-                "containing one of those."
-            )
-        if values.ndim not in [1, 2]:
-            raise ValueError("Only 1-dimensional input arrays are supported.")
-
-        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(TD64NS_DTYPE)
-
+    @classmethod
+    def _validate_dtype(cls, values, dtype):
+        # used in TimeLikeOps.__init__
         _validate_td64_dtype(values.dtype)
         dtype = _validate_td64_dtype(dtype)
-
-        if freq == "infer":
-            raise ValueError(
-                f"Frequency inference not allowed in {type(self).__name__}.__init__. "
-                "Use 'pd.array()' instead."
-            )
-
-        if copy:
-            values = values.copy()
-        if freq:
-            freq = to_offset(freq)
-
-        NDArrayBacked.__init__(self, values=values, dtype=dtype)
-        self._freq = freq
-
-        if inferred_freq is None and freq is not None:
-            type(self)._validate_frequency(self, freq)
+        return dtype
 
     # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
     @classmethod