REF: Delegate more methods for DTI/TDI/PI (#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):