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extension.py
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extension.py
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"""
Shared methods for Index subclasses backed by ExtensionArray.
"""
from __future__ import annotations
from typing import (
Hashable,
TypeVar,
)
import numpy as np
from pandas._typing import ArrayLike
from pandas.compat.numpy import function as nv
from pandas.util._decorators import (
cache_readonly,
doc,
)
from pandas.util._exceptions import rewrite_exception
from pandas.core.dtypes.common import (
is_dtype_equal,
is_object_dtype,
pandas_dtype,
)
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
from pandas.core.array_algos.putmask import validate_putmask
from pandas.core.arrays import (
Categorical,
DatetimeArray,
IntervalArray,
PeriodArray,
TimedeltaArray,
)
from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
from pandas.core.indexers import deprecate_ndim_indexing
from pandas.core.indexes.base import Index
from pandas.core.ops import get_op_result_name
_T = TypeVar("_T", bound="NDArrayBackedExtensionIndex")
def inherit_from_data(name: str, delegate, cache: bool = False, wrap: bool = False):
"""
Make an alias for a method of the underlying ExtensionArray.
Parameters
----------
name : str
Name of an attribute the class should inherit from its EA parent.
delegate : class
cache : bool, default False
Whether to convert wrapped properties into cache_readonly
wrap : bool, default False
Whether to wrap the inherited result in an Index.
Returns
-------
attribute, method, property, or cache_readonly
"""
attr = getattr(delegate, name)
if isinstance(attr, property) or type(attr).__name__ == "getset_descriptor":
# getset_descriptor i.e. property defined in cython class
if cache:
def cached(self):
return getattr(self._data, name)
cached.__name__ = name
cached.__doc__ = attr.__doc__
method = cache_readonly(cached)
else:
def fget(self):
result = getattr(self._data, name)
if wrap:
if isinstance(result, type(self._data)):
return type(self)._simple_new(result, name=self.name)
elif isinstance(result, ABCDataFrame):
return result.set_index(self)
return Index(result, name=self.name)
return result
def fset(self, value):
setattr(self._data, name, value)
fget.__name__ = name
fget.__doc__ = attr.__doc__
method = property(fget, fset)
elif not callable(attr):
# just a normal attribute, no wrapping
method = attr
else:
def method(self, *args, **kwargs):
result = attr(self._data, *args, **kwargs)
if wrap:
if isinstance(result, type(self._data)):
return type(self)._simple_new(result, name=self.name)
elif isinstance(result, ABCDataFrame):
return result.set_index(self)
return Index(result, name=self.name)
return result
method.__name__ = name
method.__doc__ = attr.__doc__
return method
def inherit_names(names: list[str], delegate, cache: bool = False, wrap: bool = False):
"""
Class decorator to pin attributes from an ExtensionArray to a Index subclass.
Parameters
----------
names : List[str]
delegate : class
cache : bool, default False
wrap : bool, default False
Whether to wrap the inherited result in an Index.
"""
def wrapper(cls):
for name in names:
meth = inherit_from_data(name, delegate, cache=cache, wrap=wrap)
setattr(cls, name, meth)
return cls
return wrapper
def _make_wrapped_comparison_op(opname: str):
"""
Create a comparison method that dispatches to ``._data``.
"""
def wrapper(self, other):
if isinstance(other, ABCSeries):
# the arrays defer to Series for comparison ops but the indexes
# don't, so we have to unwrap here.
other = other._values
other = _maybe_unwrap_index(other)
op = getattr(self._data, opname)
return op(other)
wrapper.__name__ = opname
return wrapper
def make_wrapped_arith_op(opname: str):
def method(self, other):
if (
isinstance(other, Index)
and is_object_dtype(other.dtype)
and type(other) is not Index
):
# We return NotImplemented for object-dtype index *subclasses* so they have
# a chance to implement ops before we unwrap them.
# See https://github.com/pandas-dev/pandas/issues/31109
return NotImplemented
meth = getattr(self._data, opname)
result = meth(_maybe_unwrap_index(other))
return _wrap_arithmetic_op(self, other, result)
method.__name__ = opname
return method
def _wrap_arithmetic_op(self, other, result):
if result is NotImplemented:
return NotImplemented
if isinstance(result, tuple):
# divmod, rdivmod
assert len(result) == 2
return (
_wrap_arithmetic_op(self, other, result[0]),
_wrap_arithmetic_op(self, other, result[1]),
)
if not isinstance(result, Index):
# Index.__new__ will choose appropriate subclass for dtype
result = Index(result)
res_name = get_op_result_name(self, other)
result.name = res_name
return result
def _maybe_unwrap_index(obj):
"""
If operating against another Index object, we need to unwrap the underlying
data before deferring to the DatetimeArray/TimedeltaArray/PeriodArray
implementation, otherwise we will incorrectly return NotImplemented.
Parameters
----------
obj : object
Returns
-------
unwrapped object
"""
if isinstance(obj, Index):
return obj._data
return obj
class ExtensionIndex(Index):
"""
Index subclass for indexes backed by ExtensionArray.
"""
# The base class already passes through to _data:
# size, __len__, dtype
_data: IntervalArray | NDArrayBackedExtensionArray
_data_cls: (
type[Categorical]
| type[DatetimeArray]
| type[TimedeltaArray]
| type[PeriodArray]
| type[IntervalArray]
)
@classmethod
def _simple_new(
cls,
array: IntervalArray | NDArrayBackedExtensionArray,
name: Hashable = None,
):
"""
Construct from an ExtensionArray of the appropriate type.
Parameters
----------
array : ExtensionArray
name : Label, default None
Attached as result.name
"""
assert isinstance(array, cls._data_cls), type(array)
result = object.__new__(cls)
result._data = array
result._name = name
result._cache = {}
result._reset_identity()
return result
__eq__ = _make_wrapped_comparison_op("__eq__")
__ne__ = _make_wrapped_comparison_op("__ne__")
__lt__ = _make_wrapped_comparison_op("__lt__")
__gt__ = _make_wrapped_comparison_op("__gt__")
__le__ = _make_wrapped_comparison_op("__le__")
__ge__ = _make_wrapped_comparison_op("__ge__")
@property
def _has_complex_internals(self) -> bool:
# used to avoid libreduction code paths, which raise or require conversion
return True
# ---------------------------------------------------------------------
# NDarray-Like Methods
def __getitem__(self, key):
result = self._data[key]
if isinstance(result, type(self._data)):
if result.ndim == 1:
return type(self)(result, name=self._name)
# Unpack to ndarray for MPL compat
result = result._ndarray
# Includes cases where we get a 2D ndarray back for MPL compat
deprecate_ndim_indexing(result)
return result
def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
# overriding IndexOpsMixin improves performance GH#38083
return self._data.searchsorted(value, side=side, sorter=sorter)
def putmask(self, mask, value) -> Index:
mask, noop = validate_putmask(self._data, mask)
if noop:
return self.copy()
try:
self._validate_fill_value(value)
except (ValueError, TypeError):
dtype = self._find_common_type_compat(value)
return self.astype(dtype).putmask(mask, value)
arr = self._data.copy()
arr.putmask(mask, value)
return type(self)._simple_new(arr, name=self.name)
# ---------------------------------------------------------------------
def _get_engine_target(self) -> np.ndarray:
return np.asarray(self._data)
def _from_join_target(self, result: np.ndarray) -> ArrayLike:
# ATM this is only for IntervalIndex, implicit assumption
# about _get_engine_target
return type(self._data)._from_sequence(result, dtype=self.dtype)
def delete(self, loc):
"""
Make new Index with passed location(-s) deleted
Returns
-------
new_index : Index
"""
arr = self._data.delete(loc)
return type(self)._simple_new(arr, name=self.name)
def repeat(self, repeats, axis=None):
nv.validate_repeat((), {"axis": axis})
result = self._data.repeat(repeats, axis=axis)
return type(self)._simple_new(result, name=self.name)
def insert(self, loc: int, item) -> Index:
"""
Make new Index inserting new item at location. Follows
Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
try:
result = self._data.insert(loc, item)
except (ValueError, TypeError):
# e.g. trying to insert an integer into a DatetimeIndex
# We cannot keep the same dtype, so cast to the (often object)
# minimal shared dtype before doing the insert.
dtype = self._find_common_type_compat(item)
return self.astype(dtype).insert(loc, item)
else:
return type(self)._simple_new(result, name=self.name)
def _validate_fill_value(self, value):
"""
Convert value to be insertable to underlying array.
"""
return self._data._validate_setitem_value(value)
def _get_unique_index(self):
if self.is_unique:
return self
result = self._data.unique()
return type(self)._simple_new(result, name=self.name)
@doc(Index.map)
def map(self, mapper, na_action=None):
# Try to run function on index first, and then on elements of index
# Especially important for group-by functionality
try:
result = mapper(self)
# Try to use this result if we can
if isinstance(result, np.ndarray):
result = Index(result)
if not isinstance(result, Index):
raise TypeError("The map function must return an Index object")
return result
except Exception:
return self.astype(object).map(mapper)
@doc(Index.astype)
def astype(self, dtype, copy: bool = True) -> Index:
dtype = pandas_dtype(dtype)
if is_dtype_equal(self.dtype, dtype):
if not copy:
# Ensure that self.astype(self.dtype) is self
return self
return self.copy()
# error: Non-overlapping equality check (left operand type: "dtype[Any]", right
# operand type: "Literal['M8[ns]']")
if (
isinstance(self.dtype, np.dtype)
and isinstance(dtype, np.dtype)
and dtype.kind == "M"
and dtype != "M8[ns]" # type: ignore[comparison-overlap]
):
# For now Datetime supports this by unwrapping ndarray, but DTI doesn't
raise TypeError(f"Cannot cast {type(self).__name__} to dtype")
with rewrite_exception(type(self._data).__name__, type(self).__name__):
new_values = self._data.astype(dtype, copy=copy)
# pass copy=False because any copying will be done in the
# _data.astype call above
return Index(new_values, dtype=new_values.dtype, name=self.name, copy=False)
@cache_readonly
def _isnan(self) -> np.ndarray:
# error: Incompatible return value type (got "ExtensionArray", expected
# "ndarray")
return self._data.isna() # type: ignore[return-value]
@doc(Index.equals)
def equals(self, other) -> bool:
# Dispatch to the ExtensionArray's .equals method.
if self.is_(other):
return True
if not isinstance(other, type(self)):
return False
return self._data.equals(other._data)
class NDArrayBackedExtensionIndex(ExtensionIndex):
"""
Index subclass for indexes backed by NDArrayBackedExtensionArray.
"""
_data: NDArrayBackedExtensionArray
@classmethod
def _simple_new(
cls,
values: NDArrayBackedExtensionArray,
name: Hashable = None,
):
result = super()._simple_new(values, name)
# For groupby perf. See note in indexes/base about _index_data
result._index_data = values._ndarray
return result
def _get_engine_target(self) -> np.ndarray:
return self._data._ndarray
def _from_join_target(self, result: np.ndarray) -> ArrayLike:
assert result.dtype == self._data._ndarray.dtype
return self._data._from_backing_data(result)