REF: Delegate more methods for DTI/TDI/PI (pandas-dev#30607)

Author: jbrockmendel

pandas/core/arrays/timedeltas.py

@@ -445,7 +445,7 @@ def _formatter(self, boxed=False):
 
         return _get_format_timedelta64(self, box=True)
 
-    def _format_native_types(self, na_rep="NaT", date_format=None):
+    def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
         from pandas.io.formats.format import _get_format_timedelta64
 
         formatter = _get_format_timedelta64(self._data, na_rep)

pandas/core/indexes/datetimes.py

@@ -1,5 +1,6 @@
-from datetime import datetime, time, timedelta
+from datetime import datetime, time, timedelta, tzinfo
 import operator
+from typing import Optional
 import warnings
 
 import numpy as np
@@ -66,8 +67,13 @@ class DatetimeDelegateMixin(DatetimelikeDelegateMixin):
     # We also have a few "extra" attrs, which may or may not be raw,
     # which we we dont' want to expose in the .dt accessor.
     _extra_methods = ["to_period", "to_perioddelta", "to_julian_date", "strftime"]
-    _extra_raw_methods = ["to_pydatetime", "_local_timestamps", "_has_same_tz"]
-    _extra_raw_properties = ["_box_func", "tz", "tzinfo"]
+    _extra_raw_methods = [
+        "to_pydatetime",
+        "_local_timestamps",
+        "_has_same_tz",
+        "_format_native_types",
+    ]
+    _extra_raw_properties = ["_box_func", "tz", "tzinfo", "dtype"]
     _delegated_properties = DatetimeArray._datetimelike_ops + _extra_raw_properties
     _delegated_methods = (
         DatetimeArray._datetimelike_methods + _extra_methods + _extra_raw_methods
@@ -88,7 +94,7 @@ class DatetimeDelegateMixin(DatetimelikeDelegateMixin):
     DatetimeArray,
     DatetimeDelegateMixin._delegated_methods,
     typ="method",
-    overwrite=False,
+    overwrite=True,
 )
 class DatetimeIndex(DatetimeTimedeltaMixin, DatetimeDelegateMixin):
     """
@@ -197,8 +203,6 @@ class DatetimeIndex(DatetimeTimedeltaMixin, DatetimeDelegateMixin):
     _engine_type = libindex.DatetimeEngine
     _supports_partial_string_indexing = True
 
-    _tz = None
-    _freq = None
     _comparables = ["name", "freqstr", "tz"]
     _attributes = ["name", "tz", "freq"]
 
@@ -214,6 +218,8 @@ class DatetimeIndex(DatetimeTimedeltaMixin, DatetimeDelegateMixin):
     _datetimelike_ops = DatetimeArray._datetimelike_ops
     _datetimelike_methods = DatetimeArray._datetimelike_methods
 
+    tz: Optional[tzinfo]
+
     # --------------------------------------------------------------------
     # Constructors
 
@@ -310,25 +316,6 @@ def __array__(self, dtype=None):
             dtype = "M8[ns]"
         return np.asarray(self._data, dtype=dtype)
 
-    @property
-    def dtype(self):
-        return self._data.dtype
-
-    @property
-    def tz(self):
-        # GH 18595
-        return self._data.tz
-
-    @tz.setter
-    def tz(self, value):
-        # GH 3746: Prevent localizing or converting the index by setting tz
-        raise AttributeError(
-            "Cannot directly set timezone. Use tz_localize() "
-            "or tz_convert() as appropriate"
-        )
-
-    tzinfo = tz
-
     @cache_readonly
     def _is_dates_only(self) -> bool:
         """
@@ -401,15 +388,6 @@ def _mpl_repr(self):
         # how to represent ourselves to matplotlib
         return libts.ints_to_pydatetime(self.asi8, self.tz)
 
-    def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
-        from pandas.io.formats.format import _get_format_datetime64_from_values
-
-        fmt = _get_format_datetime64_from_values(self, date_format)
-
-        return libts.format_array_from_datetime(
-            self.asi8, tz=self.tz, format=fmt, na_rep=na_rep
-        )
-
     @property
     def _formatter_func(self):
         from pandas.io.formats.format import _get_format_datetime64
@@ -999,10 +977,6 @@ def __getitem__(self, key):
             return result
         return type(self)(result, name=self.name)
 
-    @property
-    def _box_func(self):
-        return lambda x: Timestamp(x, tz=self.tz)
-
     # --------------------------------------------------------------------
 
     @Substitution(klass="DatetimeIndex")

pandas/core/indexes/period.py

@@ -66,12 +66,11 @@ class PeriodDelegateMixin(DatetimelikeDelegateMixin):
     """
 
     _delegate_class = PeriodArray
-    _delegated_properties = PeriodArray._datetimelike_ops
-    _delegated_methods = set(PeriodArray._datetimelike_methods) | {
-        "_addsub_int_array",
-        "strftime",
-    }
-    _raw_properties = {"is_leap_year"}
+    _raw_methods = {"_format_native_types"}
+    _raw_properties = {"is_leap_year", "freq"}
+
+    _delegated_properties = PeriodArray._datetimelike_ops + list(_raw_properties)
+    _delegated_methods = set(PeriodArray._datetimelike_methods) | _raw_methods
 
 
 @delegate_names(PeriodArray, PeriodDelegateMixin._delegated_properties, typ="property")
@@ -262,10 +261,6 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs):
     def values(self):
         return np.asarray(self)
 
-    @property
-    def freq(self) -> DateOffset:
-        return self._data.freq
-
     def _shallow_copy(self, values=None, **kwargs):
         # TODO: simplify, figure out type of values
         if values is None:
@@ -363,10 +358,6 @@ def _maybe_convert_timedelta(self, other):
     # ------------------------------------------------------------------------
     # Rendering Methods
 
-    def _format_native_types(self, na_rep="NaT", quoting=None, **kwargs):
-        # just dispatch, return ndarray
-        return self._data._format_native_types(na_rep=na_rep, quoting=quoting, **kwargs)
-
     def _mpl_repr(self):
         # how to represent ourselves to matplotlib
         return self.astype(object).values

pandas/core/indexes/timedeltas.py

@@ -41,18 +41,15 @@ class TimedeltaDelegateMixin(DatetimelikeDelegateMixin):
     # We also have a few "extra" attrs, which may or may not be raw,
     # which we don't want to expose in the .dt accessor.
     _delegate_class = TimedeltaArray
-    _delegated_properties = TimedeltaArray._datetimelike_ops + ["components"]
-    _delegated_methods = TimedeltaArray._datetimelike_methods + [
-        "_box_values",
-        "__neg__",
-        "__pos__",
-        "__abs__",
-        "sum",
-        "std",
-        "median",
-    ]
-    _raw_properties = {"components"}
-    _raw_methods = {"to_pytimedelta", "sum", "std", "median"}
+    _raw_properties = {"components", "_box_func"}
+    _raw_methods = {"to_pytimedelta", "sum", "std", "median", "_format_native_types"}
+
+    _delegated_properties = TimedeltaArray._datetimelike_ops + list(_raw_properties)
+    _delegated_methods = (
+        TimedeltaArray._datetimelike_methods
+        + list(_raw_methods)
+        + ["_box_values", "__neg__", "__pos__", "__abs__"]
+    )
 
 
 @delegate_names(
@@ -225,22 +222,9 @@ def _formatter_func(self):
 
         return _get_format_timedelta64(self, box=True)
 
-    def _format_native_types(self, na_rep="NaT", date_format=None, **kwargs):
-        from pandas.io.formats.format import Timedelta64Formatter
-
-        return np.asarray(
-            Timedelta64Formatter(
-                values=self, nat_rep=na_rep, justify="all"
-            ).get_result()
-        )
-
     # -------------------------------------------------------------------
     # Wrapping TimedeltaArray
 
-    @property
-    def _box_func(self):
-        return lambda x: Timedelta(x, unit="ns")
-
     def __getitem__(self, key):
         result = self._data.__getitem__(key)
         if is_scalar(result):