173 lines
5.1 KiB
Python
173 lines
5.1 KiB
Python
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"""
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Shared methods for Index subclasses backed by ExtensionArray.
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"""
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from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Callable,
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TypeVar,
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)
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from pandas.util._decorators import cache_readonly
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from pandas.core.dtypes.generic import ABCDataFrame
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from pandas.core.indexes.base import Index
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if TYPE_CHECKING:
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import numpy as np
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from pandas._typing import (
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ArrayLike,
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npt,
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)
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from pandas.core.arrays import IntervalArray
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from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
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_ExtensionIndexT = TypeVar("_ExtensionIndexT", bound="ExtensionIndex")
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def _inherit_from_data(
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name: str, delegate: type, cache: bool = False, wrap: bool = False
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):
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"""
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Make an alias for a method of the underlying ExtensionArray.
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Parameters
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----------
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name : str
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Name of an attribute the class should inherit from its EA parent.
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delegate : class
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cache : bool, default False
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Whether to convert wrapped properties into cache_readonly
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wrap : bool, default False
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Whether to wrap the inherited result in an Index.
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Returns
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-------
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attribute, method, property, or cache_readonly
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"""
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attr = getattr(delegate, name)
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if isinstance(attr, property) or type(attr).__name__ == "getset_descriptor":
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# getset_descriptor i.e. property defined in cython class
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if cache:
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def cached(self):
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return getattr(self._data, name)
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cached.__name__ = name
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cached.__doc__ = attr.__doc__
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method = cache_readonly(cached)
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else:
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def fget(self):
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result = getattr(self._data, name)
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if wrap:
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if isinstance(result, type(self._data)):
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return type(self)._simple_new(result, name=self.name)
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elif isinstance(result, ABCDataFrame):
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return result.set_index(self)
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return Index(result, name=self.name)
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return result
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def fset(self, value) -> None:
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setattr(self._data, name, value)
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fget.__name__ = name
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fget.__doc__ = attr.__doc__
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method = property(fget, fset)
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elif not callable(attr):
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# just a normal attribute, no wrapping
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method = attr
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else:
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# error: Incompatible redefinition (redefinition with type "Callable[[Any,
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# VarArg(Any), KwArg(Any)], Any]", original type "property")
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def method(self, *args, **kwargs): # type: ignore[misc]
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if "inplace" in kwargs:
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raise ValueError(f"cannot use inplace with {type(self).__name__}")
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result = attr(self._data, *args, **kwargs)
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if wrap:
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if isinstance(result, type(self._data)):
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return type(self)._simple_new(result, name=self.name)
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elif isinstance(result, ABCDataFrame):
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return result.set_index(self)
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return Index(result, name=self.name)
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return result
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# error: "property" has no attribute "__name__"
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method.__name__ = name # type: ignore[attr-defined]
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method.__doc__ = attr.__doc__
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return method
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def inherit_names(
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names: list[str], delegate: type, cache: bool = False, wrap: bool = False
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) -> Callable[[type[_ExtensionIndexT]], type[_ExtensionIndexT]]:
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"""
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Class decorator to pin attributes from an ExtensionArray to a Index subclass.
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Parameters
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----------
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names : List[str]
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delegate : class
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cache : bool, default False
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wrap : bool, default False
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Whether to wrap the inherited result in an Index.
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"""
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def wrapper(cls: type[_ExtensionIndexT]) -> type[_ExtensionIndexT]:
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for name in names:
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meth = _inherit_from_data(name, delegate, cache=cache, wrap=wrap)
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setattr(cls, name, meth)
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return cls
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return wrapper
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class ExtensionIndex(Index):
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"""
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Index subclass for indexes backed by ExtensionArray.
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"""
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# The base class already passes through to _data:
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# size, __len__, dtype
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_data: IntervalArray | NDArrayBackedExtensionArray
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# ---------------------------------------------------------------------
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def _validate_fill_value(self, value):
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"""
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Convert value to be insertable to underlying array.
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"""
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return self._data._validate_setitem_value(value)
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@cache_readonly
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def _isnan(self) -> npt.NDArray[np.bool_]:
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# error: Incompatible return value type (got "ExtensionArray", expected
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# "ndarray")
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return self._data.isna() # type: ignore[return-value]
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class NDArrayBackedExtensionIndex(ExtensionIndex):
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"""
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Index subclass for indexes backed by NDArrayBackedExtensionArray.
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"""
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_data: NDArrayBackedExtensionArray
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def _get_engine_target(self) -> np.ndarray:
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return self._data._ndarray
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def _from_join_target(self, result: np.ndarray) -> ArrayLike:
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assert result.dtype == self._data._ndarray.dtype
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return self._data._from_backing_data(result)
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