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REF: Delegate more methods for DTI/TDI/PI #30607

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merged 3 commits into from
Jan 2, 2020
Merged

REF: Delegate more methods for DTI/TDI/PI #30607

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f7247d2
REF: delegate more methods for DTA/TDA
jbrockmendel Jan 1, 2020
c8d6b59
delegate _format_native_types
jbrockmendel Jan 1, 2020
739953a
Merge branch 'master' of https://github.com/pandas-dev/pandas into cl…
jbrockmendel Jan 1, 2020
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2 changes: 1 addition & 1 deletion pandas/core/arrays/timedeltas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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):
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do we actually use these kwargs (i know they are on the method you are removing)?

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yes, without this we get a test failure bc float_format is passed

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ahh i c, as these are fairly generic, ok then.

from pandas.io.formats.format import _get_format_timedelta64

formatter = _get_format_timedelta64(self._data, na_rep)
Expand Down
50 changes: 12 additions & 38 deletions pandas/core/indexes/datetimes.py
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@@ -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
Expand Down Expand Up @@ -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 = [
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"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
Expand All @@ -88,7 +94,7 @@ class DatetimeDelegateMixin(DatetimelikeDelegateMixin):
DatetimeArray,
DatetimeDelegateMixin._delegated_methods,
typ="method",
overwrite=False,
overwrite=True,
)
class DatetimeIndex(DatetimeTimedeltaMixin, DatetimeDelegateMixin):
"""
Expand Down Expand Up @@ -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"]

Expand All @@ -214,6 +218,8 @@ class DatetimeIndex(DatetimeTimedeltaMixin, DatetimeDelegateMixin):
_datetimelike_ops = DatetimeArray._datetimelike_ops
_datetimelike_methods = DatetimeArray._datetimelike_methods

tz: Optional[tzinfo]

# --------------------------------------------------------------------
# Constructors

Expand Down Expand Up @@ -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:
"""
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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")
Expand Down
19 changes: 5 additions & 14 deletions pandas/core/indexes/period.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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")
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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
Expand Down
34 changes: 9 additions & 25 deletions pandas/core/indexes/timedeltas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down Expand Up @@ -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):
Expand Down