PyCTBN/venv/lib/python3.9/site-packages/pandas/tests/extension/arrow/arrays.py

"""
Rudimentary Apache Arrow-backed ExtensionArray.

At the moment, just a boolean array / type is implemented.
Eventually, we'll want to parametrize the type and support
multiple dtypes. Not all methods are implemented yet, and the
current implementation is not efficient.
"""
import copy
import itertools
import operator
from typing import Type

import numpy as np
import pyarrow as pa

import pandas as pd
from pandas.api.extensions import (
    ExtensionArray,
    ExtensionDtype,
    register_extension_dtype,
    take,
)


@register_extension_dtype
class ArrowBoolDtype(ExtensionDtype):

    type = np.bool_
    kind = "b"
    name = "arrow_bool"
    na_value = pa.NULL

    @classmethod
    def construct_array_type(cls) -> Type["ArrowBoolArray"]:
        """
        Return the array type associated with this dtype.

        Returns
        -------
        type
        """
        return ArrowBoolArray

    @property
    def _is_boolean(self) -> bool:
        return True


@register_extension_dtype
class ArrowStringDtype(ExtensionDtype):

    type = str
    kind = "U"
    name = "arrow_string"
    na_value = pa.NULL

    @classmethod
    def construct_array_type(cls) -> Type["ArrowStringArray"]:
        """
        Return the array type associated with this dtype.

        Returns
        -------
        type
        """
        return ArrowStringArray


class ArrowExtensionArray(ExtensionArray):
    @classmethod
    def from_scalars(cls, values):
        arr = pa.chunked_array([pa.array(np.asarray(values))])
        return cls(arr)

    @classmethod
    def from_array(cls, arr):
        assert isinstance(arr, pa.Array)
        return cls(pa.chunked_array([arr]))

    @classmethod
    def _from_sequence(cls, scalars, dtype=None, copy=False):
        return cls.from_scalars(scalars)

    def __repr__(self):
        return f"{type(self).__name__}({repr(self._data)})"

    def __getitem__(self, item):
        if pd.api.types.is_scalar(item):
            return self._data.to_pandas()[item]
        else:
            vals = self._data.to_pandas()[item]
            return type(self).from_scalars(vals)

    def __len__(self):
        return len(self._data)

    def astype(self, dtype, copy=True):
        # needed to fix this astype for the Series constructor.
        if isinstance(dtype, type(self.dtype)) and dtype == self.dtype:
            if copy:
                return self.copy()
            return self
        return super().astype(dtype, copy)

    @property
    def dtype(self):
        return self._dtype

    def _boolean_op(self, other, op):
        if not isinstance(other, type(self)):
            raise NotImplementedError()

        result = op(np.array(self._data), np.array(other._data))
        return ArrowBoolArray(
            pa.chunked_array([pa.array(result, mask=pd.isna(self._data.to_pandas()))])
        )

    def __eq__(self, other):
        if not isinstance(other, type(self)):
            return False

        return self._boolean_op(other, operator.eq)

    def __and__(self, other):
        return self._boolean_op(other, operator.and_)

    def __or__(self, other):
        return self._boolean_op(other, operator.or_)

    @property
    def nbytes(self):
        return sum(
            x.size
            for chunk in self._data.chunks
            for x in chunk.buffers()
            if x is not None
        )

    def isna(self):
        nas = pd.isna(self._data.to_pandas())
        return type(self).from_scalars(nas)

    def take(self, indices, allow_fill=False, fill_value=None):
        data = self._data.to_pandas()

        if allow_fill and fill_value is None:
            fill_value = self.dtype.na_value

        result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill)
        return self._from_sequence(result, dtype=self.dtype)

    def copy(self):
        return type(self)(copy.copy(self._data))

    @classmethod
    def _concat_same_type(cls, to_concat):
        chunks = list(itertools.chain.from_iterable(x._data.chunks for x in to_concat))
        arr = pa.chunked_array(chunks)
        return cls(arr)

    def __invert__(self):
        return type(self).from_scalars(~self._data.to_pandas())

    def _reduce(self, name: str, skipna: bool = True, **kwargs):
        if skipna:
            arr = self[~self.isna()]
        else:
            arr = self

        try:
            op = getattr(arr, name)
        except AttributeError as err:
            raise TypeError from err
        return op(**kwargs)

    def any(self, axis=0, out=None):
        # Explicitly return a plain bool to reproduce GH-34660
        return bool(self._data.to_pandas().any())

    def all(self, axis=0, out=None):
        # Explicitly return a plain bool to reproduce GH-34660
        return bool(self._data.to_pandas().all())


class ArrowBoolArray(ArrowExtensionArray):
    def __init__(self, values):
        if not isinstance(values, pa.ChunkedArray):
            raise ValueError

        assert values.type == pa.bool_()
        self._data = values
        self._dtype = ArrowBoolDtype()


class ArrowStringArray(ArrowExtensionArray):
    def __init__(self, values):
        if not isinstance(values, pa.ChunkedArray):
            raise ValueError

        assert values.type == pa.string()
        self._data = values
        self._dtype = ArrowStringDtype()