AutoAndroidController/py/venv/Lib/site-packages/pandas/tests/extension/test_string.py

"""
This file contains a minimal set of tests for compliance with the extension
array interface test suite, and should contain no other tests.
The test suite for the full functionality of the array is located in
`pandas/tests/arrays/`.

The tests in this file are inherited from the BaseExtensionTests, and only
minimal tweaks should be applied to get the tests passing (by overwriting a
parent method).

Additional tests should either be added to one of the BaseExtensionTests
classes (if they are relevant for the extension interface for all dtypes), or
be added to the array-specific tests in `pandas/tests/arrays/`.

"""
from __future__ import annotations

import string
from typing import cast

import numpy as np
import pytest

from pandas.compat import HAS_PYARROW

from pandas.core.dtypes.base import StorageExtensionDtype

import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_string_dtype
from pandas.core.arrays import ArrowStringArray
from pandas.core.arrays.string_ import StringDtype
from pandas.tests.arrays.string_.test_string import string_dtype_highest_priority
from pandas.tests.extension import base


def maybe_split_array(arr, chunked):
    if not chunked:
        return arr
    elif arr.dtype.storage != "pyarrow":
        return arr

    pa = pytest.importorskip("pyarrow")

    arrow_array = arr._pa_array
    split = len(arrow_array) // 2
    arrow_array = pa.chunked_array(
        [*arrow_array[:split].chunks, *arrow_array[split:].chunks]
    )
    assert arrow_array.num_chunks == 2
    return type(arr)(arrow_array)


@pytest.fixture(params=[True, False])
def chunked(request):
    return request.param


@pytest.fixture
def dtype(string_dtype_arguments):
    storage, na_value = string_dtype_arguments
    return StringDtype(storage=storage, na_value=na_value)


@pytest.fixture
def data(dtype, chunked):
    strings = np.random.default_rng(2).choice(list(string.ascii_letters), size=100)
    while strings[0] == strings[1]:
        strings = np.random.default_rng(2).choice(list(string.ascii_letters), size=100)

    arr = dtype.construct_array_type()._from_sequence(strings, dtype=dtype)
    return maybe_split_array(arr, chunked)


@pytest.fixture
def data_missing(dtype, chunked):
    """Length 2 array with [NA, Valid]"""
    arr = dtype.construct_array_type()._from_sequence([pd.NA, "A"], dtype=dtype)
    return maybe_split_array(arr, chunked)


@pytest.fixture
def data_for_sorting(dtype, chunked):
    arr = dtype.construct_array_type()._from_sequence(["B", "C", "A"], dtype=dtype)
    return maybe_split_array(arr, chunked)


@pytest.fixture
def data_missing_for_sorting(dtype, chunked):
    arr = dtype.construct_array_type()._from_sequence(["B", pd.NA, "A"], dtype=dtype)
    return maybe_split_array(arr, chunked)


@pytest.fixture
def data_for_grouping(dtype, chunked):
    arr = dtype.construct_array_type()._from_sequence(
        ["B", "B", pd.NA, pd.NA, "A", "A", "B", "C"], dtype=dtype
    )
    return maybe_split_array(arr, chunked)


class TestStringArray(base.ExtensionTests):
    def test_eq_with_str(self, dtype):
        super().test_eq_with_str(dtype)

        if dtype.na_value is pd.NA:
            # only the NA-variant supports parametrized string alias
            assert dtype == f"string[{dtype.storage}]"
        elif dtype.storage == "pyarrow":
            with tm.assert_produces_warning(FutureWarning):
                assert dtype == "string[pyarrow_numpy]"

    def test_is_not_string_type(self, dtype):
        # Different from BaseDtypeTests.test_is_not_string_type
        # because StringDtype is a string type
        assert is_string_dtype(dtype)

    def test_is_dtype_from_name(self, dtype, using_infer_string):
        if dtype.na_value is np.nan and not using_infer_string:
            result = type(dtype).is_dtype(dtype.name)
            assert result is False
        else:
            super().test_is_dtype_from_name(dtype)

    def test_construct_from_string_own_name(self, dtype, using_infer_string):
        if dtype.na_value is np.nan and not using_infer_string:
            with pytest.raises(TypeError, match="Cannot construct a 'StringDtype'"):
                dtype.construct_from_string(dtype.name)
        else:
            super().test_construct_from_string_own_name(dtype)

    def test_view(self, data):
        if data.dtype.storage == "pyarrow":
            pytest.skip(reason="2D support not implemented for ArrowStringArray")
        super().test_view(data)

    def test_from_dtype(self, data):
        # base test uses string representation of dtype
        pass

    def test_transpose(self, data):
        if data.dtype.storage == "pyarrow":
            pytest.skip(reason="2D support not implemented for ArrowStringArray")
        super().test_transpose(data)

    def test_setitem_preserves_views(self, data):
        if data.dtype.storage == "pyarrow":
            pytest.skip(reason="2D support not implemented for ArrowStringArray")
        super().test_setitem_preserves_views(data)

    def test_dropna_array(self, data_missing):
        result = data_missing.dropna()
        expected = data_missing[[1]]
        tm.assert_extension_array_equal(result, expected)

    def test_fillna_no_op_returns_copy(self, data):
        data = data[~data.isna()]

        valid = data[0]
        result = data.fillna(valid)
        assert result is not data
        tm.assert_extension_array_equal(result, data)

        result = data.fillna(method="backfill")
        assert result is not data
        tm.assert_extension_array_equal(result, data)

    def _get_expected_exception(
        self, op_name: str, obj, other
    ) -> type[Exception] | tuple[type[Exception], ...] | None:
        if op_name in [
            "__mod__",
            "__rmod__",
            "__divmod__",
            "__rdivmod__",
            "__pow__",
            "__rpow__",
        ]:
            return TypeError
        elif op_name in ["__mul__", "__rmul__"]:
            # Can only multiply strings by integers
            return TypeError
        elif op_name in [
            "__truediv__",
            "__rtruediv__",
            "__floordiv__",
            "__rfloordiv__",
            "__sub__",
            "__rsub__",
        ]:
            return TypeError

        return None

    def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
        return (
            op_name in ["min", "max", "sum"]
            or ser.dtype.na_value is np.nan  # type: ignore[union-attr]
            and op_name in ("any", "all")
        )

    def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool:
        assert isinstance(ser.dtype, StorageExtensionDtype)
        return op_name in ["cummin", "cummax", "cumsum"]

    def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
        dtype = cast(StringDtype, tm.get_dtype(obj))
        if op_name in ["__add__", "__radd__"]:
            cast_to = dtype
            dtype_other = tm.get_dtype(other) if not isinstance(other, str) else None
            if isinstance(dtype_other, StringDtype):
                cast_to = string_dtype_highest_priority(dtype, dtype_other)
        elif dtype.na_value is np.nan:
            cast_to = np.bool_  # type: ignore[assignment]
        elif dtype.storage == "pyarrow":
            cast_to = "bool[pyarrow]"  # type: ignore[assignment]
        else:
            cast_to = "boolean"  # type: ignore[assignment]
        return pointwise_result.astype(cast_to)

    def test_compare_scalar(self, data, comparison_op):
        ser = pd.Series(data)
        self._compare_other(ser, data, comparison_op, "abc")

    def test_combine_add(self, data_repeated, using_infer_string, request):
        dtype = next(data_repeated(1)).dtype
        if using_infer_string and (
            (dtype.na_value is pd.NA) and dtype.storage == "python"
        ):
            mark = pytest.mark.xfail(
                reason="The pointwise operation result will be inferred to "
                "string[nan, pyarrow], which does not match the input dtype"
            )
            request.applymarker(mark)
        super().test_combine_add(data_repeated)

    def test_arith_series_with_array(
        self, data, all_arithmetic_operators, using_infer_string, request
    ):
        dtype = data.dtype
        if (
            using_infer_string
            and all_arithmetic_operators == "__radd__"
            and dtype.na_value is pd.NA
            and (HAS_PYARROW or dtype.storage == "pyarrow")
        ):
            # TODO(infer_string)
            mark = pytest.mark.xfail(
                reason="The pointwise operation result will be inferred to "
                "string[nan, pyarrow], which does not match the input dtype"
            )
            request.applymarker(mark)
        super().test_arith_series_with_array(data, all_arithmetic_operators)


class Test2DCompat(base.Dim2CompatTests):
    @pytest.fixture(autouse=True)
    def arrow_not_supported(self, data):
        if isinstance(data, ArrowStringArray):
            pytest.skip(reason="2D support not implemented for ArrowStringArray")


def test_searchsorted_with_na_raises(data_for_sorting, as_series):
    # GH50447
    b, c, a = data_for_sorting
    arr = data_for_sorting.take([2, 0, 1])  # to get [a, b, c]
    arr[-1] = pd.NA

    if as_series:
        arr = pd.Series(arr)

    msg = (
        "searchsorted requires array to be sorted, "
        "which is impossible with NAs present."
    )
    with pytest.raises(ValueError, match=msg):
        arr.searchsorted(b)


def test_mixed_object_comparison(dtype):
    # GH#60228
    ser = pd.Series(["a", "b"], dtype=dtype)

    mixed = pd.Series([1, "b"], dtype=object)

    result = ser == mixed
    expected = pd.Series([False, True], dtype=bool)
    if dtype.storage == "python" and dtype.na_value is pd.NA:
        expected = expected.astype("boolean")
    elif dtype.storage == "pyarrow" and dtype.na_value is pd.NA:
        expected = expected.astype("bool[pyarrow]")

    tm.assert_series_equal(result, expected)