keshe/.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

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.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_storage):
    return StringDtype(storage=string_storage)


@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):
        assert dtype == f"string[{dtype.storage}]"
        super().test_eq_with_str(dtype)

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

    def test_setitem_preserves_views(self, data, request, arrow_string_storage):
        if data.dtype.storage in arrow_string_storage:
            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] | None:
        if op_name in ["__divmod__", "__rdivmod__"]:
            if isinstance(obj, pd.Series) and cast(
                StringDtype, tm.get_dtype(obj)
            ).storage in [
                "pyarrow",
                "pyarrow_numpy",
            ]:
                # TODO: re-raise as TypeError?
                return NotImplementedError
            elif isinstance(other, pd.Series) and cast(
                StringDtype, tm.get_dtype(other)
            ).storage in [
                "pyarrow",
                "pyarrow_numpy",
            ]:
                # TODO: re-raise as TypeError?
                return NotImplementedError
            return TypeError
        elif op_name in ["__mod__", "__rmod__", "__pow__", "__rpow__"]:
            if cast(StringDtype, tm.get_dtype(obj)).storage in [
                "pyarrow",
                "pyarrow_numpy",
            ]:
                return NotImplementedError
            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__",
        ]:
            if cast(StringDtype, tm.get_dtype(obj)).storage in [
                "pyarrow",
                "pyarrow_numpy",
            ]:
                import pyarrow as pa

                # TODO: better to re-raise as TypeError?
                return pa.ArrowNotImplementedError
            return TypeError

        return None

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

    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
        elif dtype.storage == "pyarrow":
            cast_to = "boolean[pyarrow]"  # type: ignore[assignment]
        elif dtype.storage == "pyarrow_numpy":
            cast_to = np.bool_  # 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")

    @pytest.mark.filterwarnings("ignore:Falling back:pandas.errors.PerformanceWarning")
    def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
        super().test_groupby_extension_apply(data_for_grouping, groupby_apply_op)


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)