REF/DEPR: DatetimeIndex constructor (pandas-dev#23675)

pandas/core/arrays/datetimes.py

@@ -1503,6 +1503,83 @@ def maybe_convert_dtype(data, copy):
     return data, copy
 
 
+def objects_to_datetime64ns(data, dayfirst, yearfirst,
+                            utc=False, errors="raise",
+                            require_iso8601=False, allow_object=False):
+    """
+    Convert data to array of timestamps.
+
+    Parameters
+    ----------
+    data : np.ndarray[object]
+    dayfirst : bool
+    yearfirst : bool
+    utc : bool, default False
+        Whether to convert timezone-aware timestamps to UTC
+    errors : {'raise', 'ignore', 'coerce'}
+    allow_object : bool
+        Whether to return an object-dtype ndarray instead of raising if the
+        data contains more than one timezone.
+
+    Returns
+    -------
+    result : ndarray
+        np.int64 dtype if returned values represent UTC timestamps
+        np.datetime64[ns] if returned values represent wall times
+        object if mixed timezones
+    inferred_tz : tzinfo or None
+
+    Raises
+    ------
+    ValueError : if data cannot be converted to datetimes
+    """
+    assert errors in ["raise", "ignore", "coerce"]
+
+    # if str-dtype, convert
+    data = np.array(data, copy=False, dtype=np.object_)
+
+    try:
+        result, tz_parsed = tslib.array_to_datetime(
+            data,
+            errors=errors,
+            utc=utc,
+            dayfirst=dayfirst,
+            yearfirst=yearfirst,
+            require_iso8601=require_iso8601
+        )
+    except ValueError as e:
+        try:
+            values, tz_parsed = conversion.datetime_to_datetime64(data)
+            # If tzaware, these values represent unix timestamps, so we
+            #  return them as i8 to distinguish from wall times
+            return values.view('i8'), tz_parsed
+        except (ValueError, TypeError):
+            raise e
+
+    if tz_parsed is not None:
+        # We can take a shortcut since the datetime64 numpy array
+        #  is in UTC
+        # Return i8 values to denote unix timestamps
+        return result.view('i8'), tz_parsed
+    elif is_datetime64_dtype(result):
+        # returning M8[ns] denotes wall-times; since tz is None
+        #  the distinction is a thin one
+        return result, tz_parsed
+    elif is_object_dtype(result):
+        # GH#23675 when called via `pd.to_datetime`, returning an object-dtype
+        #  array is allowed.  When called via `pd.DatetimeIndex`, we can
+        #  only accept datetime64 dtype, so raise TypeError if object-dtype
+        #  is returned, as that indicates the values can be recognized as
+        #  datetimes but they have conflicting timezones/awareness
+        if allow_object:
+            return result, tz_parsed
+        raise TypeError(result)
+    else:  # pragma: no cover
+        # GH#23675 this TypeError should never be hit, whereas the TypeError
+        #  in the object-dtype branch above is reachable.
+        raise TypeError(result)
+
+
 def _generate_regular_range(cls, start, end, periods, freq):
     """
     Generate a range of dates with the spans between dates described by

pandas/core/indexes/datetimes.py

@@ -17,16 +17,16 @@
 from pandas.core.dtypes.common import (
     _INT64_DTYPE, _NS_DTYPE, ensure_int64, is_datetime64_dtype,
     is_datetime64_ns_dtype, is_datetime64tz_dtype, is_dtype_equal, is_float,
-    is_integer, is_integer_dtype, is_list_like, is_period_dtype, is_scalar,
-    is_string_like, pandas_dtype)
+    is_integer, is_list_like, is_object_dtype, is_period_dtype, is_scalar,
+    is_string_dtype, is_string_like, pandas_dtype)
 import pandas.core.dtypes.concat as _concat
 from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core.arrays import datetimelike as dtl
 from pandas.core.arrays.datetimes import (
     DatetimeArrayMixin as DatetimeArray, _to_m8, maybe_convert_dtype,
-    maybe_infer_tz)
+    maybe_infer_tz, objects_to_datetime64ns)
 from pandas.core.base import _shared_docs
 import pandas.core.common as com
 from pandas.core.indexes.base import Index, _index_shared_docs
@@ -281,10 +281,19 @@ def __new__(cls, data=None,
 
         # By this point we are assured to have either a numpy array or Index
         data, copy = maybe_convert_dtype(data, copy)
-        if not (is_datetime64_dtype(data) or is_datetime64tz_dtype(data) or
-                is_integer_dtype(data) or lib.infer_dtype(data) == 'integer'):
-            data = tools.to_datetime(data, dayfirst=dayfirst,
-                                     yearfirst=yearfirst)
+
+        if is_object_dtype(data) or is_string_dtype(data):
+            # TODO: We do not have tests specific to string-dtypes,
+            #  also complex or categorical or other extension
+            copy = False
+            if lib.infer_dtype(data) == 'integer':
+                data = data.astype(np.int64)
+            else:
+                # data comes back here as either i8 to denote UTC timestamps
+                #  or M8[ns] to denote wall times
+                data, inferred_tz = objects_to_datetime64ns(
+                    data, dayfirst=dayfirst, yearfirst=yearfirst)
+                tz = maybe_infer_tz(tz, inferred_tz)
 
         if is_datetime64tz_dtype(data):
             tz = maybe_infer_tz(tz, data.tz)

pandas/core/tools/datetimes.py

@@ -171,7 +171,8 @@ def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
         - ndarray of Timestamps if box=False
     """
     from pandas import DatetimeIndex
-    from pandas.core.arrays.datetimes import maybe_convert_dtype
+    from pandas.core.arrays.datetimes import (
+        maybe_convert_dtype, objects_to_datetime64ns)
 
     if isinstance(arg, (list, tuple)):
         arg = np.array(arg, dtype='O')
@@ -233,8 +234,9 @@ def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
 
     tz_parsed = None
     result = None
-    try:
-        if format is not None:
+
+    if format is not None:
+        try:
             # shortcut formatting here
             if format == '%Y%m%d':
                 try:
@@ -266,24 +268,22 @@ def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
                         if errors == 'raise':
                             raise
                         result = arg
-
-        if result is None:
-            assert format is None or infer_datetime_format
-            result, tz_parsed = tslib.array_to_datetime(
-                arg,
-                errors=errors,
-                utc=tz == 'utc',
-                dayfirst=dayfirst,
-                yearfirst=yearfirst,
-                require_iso8601=require_iso8601
-            )
-    except ValueError as e:
-        # Fallback to try to convert datetime objects
-        try:
-            values, tz = conversion.datetime_to_datetime64(arg)
-            return DatetimeIndex._simple_new(values, name=name, tz=tz)
-        except (ValueError, TypeError):
-            raise e
+        except ValueError as e:
+            # Fallback to try to convert datetime objects if timezone-aware
+            #  datetime objects are found without passing `utc=True`
+            try:
+                values, tz = conversion.datetime_to_datetime64(arg)
+                return DatetimeIndex._simple_new(values, name=name, tz=tz)
+            except (ValueError, TypeError):
+                raise e
+
+    if result is None:
+        assert format is None or infer_datetime_format
+        utc = tz == 'utc'
+        result, tz_parsed = objects_to_datetime64ns(
+            arg, dayfirst=dayfirst, yearfirst=yearfirst,
+            utc=utc, errors=errors, require_iso8601=require_iso8601,
+            allow_object=True)
 
     if tz_parsed is not None:
         if box: