REF: share some constructor code

pandas/core/arrays/datetimelike.py

@@ -94,6 +94,10 @@
     DatetimeTZDtype,
     ExtensionDtype,
 )
+from pandas.core.dtypes.generic import (
+    ABCCategorical,
+    ABCMultiIndex,
+)
 from pandas.core.dtypes.missing import (
     is_valid_na_for_dtype,
     isna,
@@ -114,6 +118,8 @@
     NDArrayBackedExtensionArray,
     ravel_compat,
 )
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.integer import IntegerArray
 import pandas.core.common as com
 from pandas.core.construction import (
     array as pd_array,
@@ -2024,6 +2030,36 @@ def factorize(  # type:ignore[override]
 # Shared Constructor Helpers
 
 
+def ensure_arraylike_for_datetimelike(data, copy: bool, cls_name: str):
+    if not hasattr(data, "dtype"):
+        # e.g. list, tuple
+        if np.ndim(data) == 0:
+            # i.e. generator
+            data = list(data)
+        data = np.asarray(data)
+        copy = False
+    elif isinstance(data, ABCMultiIndex):
+        raise TypeError(f"Cannot create a {cls_name} from a MultiIndex.")
+    else:
+        data = extract_array(data, extract_numpy=True)
+
+    if isinstance(data, IntegerArray):
+        data = data.to_numpy("int64", na_value=iNaT)
+        copy = False
+    elif not isinstance(data, (np.ndarray, ExtensionArray)):
+        # GH#24539 e.g. xarray, dask object
+        data = np.asarray(data)
+
+    elif isinstance(data, ABCCategorical):
+        # GH#18664 preserve tz in going DTI->Categorical->DTI
+        # TODO: cases where we need to do another pass through maybe_convert_dtype,
+        #  e.g. the categories are timedelta64s
+        data = data.categories.take(data.codes, fill_value=NaT)._values
+        copy = False
+
+    return data, copy
+
+
 def validate_periods(periods):
     """
     If a `periods` argument is passed to the Datetime/Timedelta Array/Index

pandas/core/arrays/datetimes.py

@@ -53,7 +53,6 @@
     DT64NS_DTYPE,
     INT64_DTYPE,
     is_bool_dtype,
-    is_categorical_dtype,
     is_datetime64_any_dtype,
     is_datetime64_dtype,
     is_datetime64_ns_dtype,
@@ -69,13 +68,9 @@
     pandas_dtype,
 )
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
-from pandas.core.dtypes.generic import ABCMultiIndex
 from pandas.core.dtypes.missing import isna
 
-from pandas.core.arrays import (
-    ExtensionArray,
-    datetimelike as dtl,
-)
+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
@@ -2064,23 +2059,9 @@ def _sequence_to_dt64ns(
     # if dtype has an embedded tz, capture it
     tz = validate_tz_from_dtype(dtype, tz)
 
-    if not hasattr(data, "dtype"):
-        # e.g. list, tuple
-        if np.ndim(data) == 0:
-            # i.e. generator
-            data = list(data)
-        data = np.asarray(data)
-        copy = False
-    elif isinstance(data, ABCMultiIndex):
-        raise TypeError("Cannot create a DatetimeArray from a MultiIndex.")
-    else:
-        data = extract_array(data, extract_numpy=True)
-
-    if isinstance(data, IntegerArray):
-        data = data.to_numpy("int64", na_value=iNaT)
-    elif not isinstance(data, (np.ndarray, ExtensionArray)):
-        # GH#24539 e.g. xarray, dask object
-        data = np.asarray(data)
+    data, copy = dtl.ensure_arraylike_for_datetimelike(
+        data, copy, cls_name="DatetimeArray"
+    )
 
     if isinstance(data, DatetimeArray):
         inferred_freq = data.freq
@@ -2320,13 +2301,6 @@ def maybe_convert_dtype(data, copy: bool, tz: tzinfo | None = None):
             "Passing PeriodDtype data is invalid. Use `data.to_timestamp()` instead"
         )
 
-    elif is_categorical_dtype(data.dtype):
-        # GH#18664 preserve tz in going DTI->Categorical->DTI
-        # TODO: cases where we need to do another pass through this func,
-        #  e.g. the categories are timedelta64s
-        data = data.categories.take(data.codes, fill_value=NaT)._values
-        copy = False
-
     elif is_extension_array_dtype(data.dtype) and not is_datetime64tz_dtype(data.dtype):
         # TODO: We have no tests for these
         data = np.array(data, dtype=np.object_)

pandas/core/arrays/timedeltas.py

@@ -51,15 +51,10 @@
     is_timedelta64_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.generic import (
-    ABCCategorical,
-    ABCMultiIndex,
-)
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import nanops
 from pandas.core.arrays import (
-    ExtensionArray,
     IntegerArray,
     datetimelike as dtl,
 )
@@ -936,26 +931,9 @@ def sequence_to_td64ns(
     if unit is not None:
         unit = parse_timedelta_unit(unit)
 
-    # Unwrap whatever we have into a np.ndarray
-    if not hasattr(data, "dtype"):
-        # e.g. list, tuple
-        if np.ndim(data) == 0:
-            # i.e. generator
-            data = list(data)
-        data = np.array(data, copy=False)
-    elif isinstance(data, ABCMultiIndex):
-        raise TypeError("Cannot create a TimedeltaArray from a MultiIndex.")
-    else:
-        data = extract_array(data, extract_numpy=True)
-
-    if isinstance(data, IntegerArray):
-        data = data.to_numpy("int64", na_value=iNaT)
-    elif not isinstance(data, (np.ndarray, ExtensionArray)):
-        # GH#24539 e.g. xarray, dask object
-        data = np.asarray(data)
-    elif isinstance(data, ABCCategorical):
-        data = data.categories.take(data.codes, fill_value=NaT)._values
-        copy = False
+    data, copy = dtl.ensure_arraylike_for_datetimelike(
+        data, copy, cls_name="TimedeltaArray"
+    )
 
     if isinstance(data, TimedeltaArray):
         inferred_freq = data.freq