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CLN: remove methods of ExtensionIndex that duplicate base Index #34163

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May 15, 2020
Merged

CLN: remove methods of ExtensionIndex that duplicate base Index #34163

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4d897f8
CLN: remove methods of ExtensionIndex that duplicate base Index
jorisvandenbossche May 13, 2020
244ddc7
check the values instead of hasattr
jorisvandenbossche May 14, 2020
cb05175
reuse values
jorisvandenbossche May 15, 2020
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42 changes: 1 addition & 41 deletions pandas/core/indexes/extension.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,7 @@
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly, doc

from pandas.core.dtypes.common import (
ensure_platform_int,
is_dtype_equal,
is_object_dtype,
)
from pandas.core.dtypes.common import is_dtype_equal, is_object_dtype
from pandas.core.dtypes.generic import ABCSeries

from pandas.core.arrays import ExtensionArray
Expand Down Expand Up @@ -223,29 +219,14 @@ def __getitem__(self, key):
deprecate_ndim_indexing(result)
return result

def __iter__(self):
return self._data.__iter__()
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Should be equivalent to

pandas/pandas/core/base.py

Lines 1034 to 1051 in 9c08fe1

def __iter__(self):
"""
Return an iterator of the values.
These are each a scalar type, which is a Python scalar
(for str, int, float) or a pandas scalar
(for Timestamp/Timedelta/Interval/Period)
Returns
-------
iterator
"""
# We are explicitly making element iterators.
if not isinstance(self._values, np.ndarray):
# Check type instead of dtype to catch DTA/TDA
return iter(self._values)
else:
return map(self._values.item, range(self._values.size))

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Sure (alternatively could remove L1047-1049 from the base class implementation.

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alternatively could remove L1047-1049 from the base class implementation

No, because Series also uses that


# ---------------------------------------------------------------------

def __array__(self, dtype=None) -> np.ndarray:
return np.asarray(self._data, dtype=dtype)
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This is identical with the Index one


def _get_engine_target(self) -> np.ndarray:
# NB: _values_for_argsort happens to match the desired engine targets
# for all of our existing EA-backed indexes, but in general
# cannot be relied upon to exist.
return self._data._values_for_argsort()

@doc(Index.dropna)
def dropna(self, how="any"):
if how not in ("any", "all"):
raise ValueError(f"invalid how option: {how}")

if self.hasnans:
return self._shallow_copy(self._data[~self._isnan])
return self._shallow_copy()
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The only difference here with the Index one is the use of self._data vs self._values, which as far as I know shouldn't matter?

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not sure if it matters for this method, but the distinction would matter for MultiIndex, which does not have _data.

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In this case, MultiIndex actually overrides dropna, so that shouldn't even matter here.

But indeed, in general it's only for MI that using _values vs _data matters, for all the others it's the same (I think?), which I suppose is the reason in the base class there is more usage of _values.


def repeat(self, repeats, axis=None):
nv.validate_repeat(tuple(), dict(axis=axis))
result = self._data.repeat(repeats, axis=axis)
Expand All @@ -259,27 +240,6 @@ def _concat_same_dtype(self, to_concat, name):
arr = type(self._data)._concat_same_type(to_concat)
return type(self)._simple_new(arr, name=name)

@doc(Index.take)
def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
nv.validate_take(tuple(), kwargs)
indices = ensure_platform_int(indices)

taken = self._assert_take_fillable(
self._data,
indices,
allow_fill=allow_fill,
fill_value=fill_value,
na_value=self._na_value,
)
return type(self)(taken, name=self.name)
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Same here ( self._data vs self._values), and the base class just has an extra if not self._can_hold_na: branch

pandas/pandas/core/indexes/base.py

Lines 688 to 703 in 9c08fe1

if self._can_hold_na:
taken = self._assert_take_fillable(
self._values,
indices,
allow_fill=allow_fill,
fill_value=fill_value,
na_value=self._na_value,
)
else:
if allow_fill and fill_value is not None:
cls_name = type(self).__name__
raise ValueError(
f"Unable to fill values because {cls_name} cannot contain NA"
)
taken = self._values.take(indices)
return self._shallow_copy(taken)


def unique(self, level=None):
if level is not None:
self._validate_index_level(level)

result = self._data.unique()
return self._shallow_copy(result)
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The base class ends up dispatching to IndexOpsMixin unique:

pandas/pandas/core/base.py

Lines 1257 to 1270 in 9c08fe1

def unique(self):
values = self._values
if hasattr(values, "unique"):
result = values.unique()
if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries):
# GH#31182 Series._values returns EA, unpack for backward-compat
if getattr(self.dtype, "tz", None) is None:
result = np.asarray(result)
else:
result = unique1d(values)
return result

The hasattr(values, "unique") could probably be made more explicit / cleaner to check for EA, but basically this should also be the same

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could probably be made more explicit / cleaner

sounds worthwhile, yah


def _get_unique_index(self, dropna=False):
if self.is_unique and not dropna:
return self
Expand Down