418 lines
14 KiB
Python
418 lines
14 KiB
Python
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"""
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This file contains a minimal set of tests for compliance with the extension
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array interface test suite, and should contain no other tests.
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The test suite for the full functionality of the array is located in
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`pandas/tests/arrays/`.
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The tests in this file are inherited from the BaseExtensionTests, and only
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minimal tweaks should be applied to get the tests passing (by overwriting a
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parent method).
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Additional tests should either be added to one of the BaseExtensionTests
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classes (if they are relevant for the extension interface for all dtypes), or
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be added to the array-specific tests in `pandas/tests/arrays/`.
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"""
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import warnings
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import numpy as np
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import pytest
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from pandas.compat import (
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IS64,
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is_platform_windows,
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)
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from pandas.compat.numpy import np_version_gt2
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from pandas.core.dtypes.common import (
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is_float_dtype,
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is_signed_integer_dtype,
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is_unsigned_integer_dtype,
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)
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays.boolean import BooleanDtype
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from pandas.core.arrays.floating import (
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Float32Dtype,
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Float64Dtype,
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)
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from pandas.core.arrays.integer import (
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Int8Dtype,
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Int16Dtype,
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Int32Dtype,
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Int64Dtype,
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UInt8Dtype,
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UInt16Dtype,
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UInt32Dtype,
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UInt64Dtype,
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)
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from pandas.tests.extension import base
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is_windows_or_32bit = (is_platform_windows() and not np_version_gt2) or not IS64
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pytestmark = [
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pytest.mark.filterwarnings(
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"ignore:invalid value encountered in divide:RuntimeWarning"
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),
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pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning"),
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# overflow only relevant for Floating dtype cases cases
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pytest.mark.filterwarnings("ignore:overflow encountered in reduce:RuntimeWarning"),
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]
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def make_data():
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return list(range(1, 9)) + [pd.NA] + list(range(10, 98)) + [pd.NA] + [99, 100]
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def make_float_data():
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return (
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list(np.arange(0.1, 0.9, 0.1))
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+ [pd.NA]
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+ list(np.arange(1, 9.8, 0.1))
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+ [pd.NA]
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+ [9.9, 10.0]
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)
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def make_bool_data():
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return [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False]
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@pytest.fixture(
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params=[
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Int8Dtype,
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Int16Dtype,
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Int32Dtype,
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Int64Dtype,
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UInt8Dtype,
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UInt16Dtype,
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UInt32Dtype,
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UInt64Dtype,
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Float32Dtype,
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Float64Dtype,
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BooleanDtype,
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]
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)
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def dtype(request):
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return request.param()
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@pytest.fixture
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def data(dtype):
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if dtype.kind == "f":
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data = make_float_data()
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elif dtype.kind == "b":
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data = make_bool_data()
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else:
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data = make_data()
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return pd.array(data, dtype=dtype)
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@pytest.fixture
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def data_for_twos(dtype):
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if dtype.kind == "b":
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return pd.array(np.ones(100), dtype=dtype)
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return pd.array(np.ones(100) * 2, dtype=dtype)
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@pytest.fixture
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def data_missing(dtype):
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if dtype.kind == "f":
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return pd.array([pd.NA, 0.1], dtype=dtype)
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elif dtype.kind == "b":
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return pd.array([np.nan, True], dtype=dtype)
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return pd.array([pd.NA, 1], dtype=dtype)
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@pytest.fixture
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def data_for_sorting(dtype):
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if dtype.kind == "f":
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return pd.array([0.1, 0.2, 0.0], dtype=dtype)
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elif dtype.kind == "b":
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return pd.array([True, True, False], dtype=dtype)
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return pd.array([1, 2, 0], dtype=dtype)
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@pytest.fixture
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def data_missing_for_sorting(dtype):
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if dtype.kind == "f":
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return pd.array([0.1, pd.NA, 0.0], dtype=dtype)
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elif dtype.kind == "b":
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return pd.array([True, np.nan, False], dtype=dtype)
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return pd.array([1, pd.NA, 0], dtype=dtype)
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@pytest.fixture
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def na_cmp():
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# we are pd.NA
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return lambda x, y: x is pd.NA and y is pd.NA
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@pytest.fixture
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def data_for_grouping(dtype):
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if dtype.kind == "f":
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b = 0.1
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a = 0.0
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c = 0.2
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elif dtype.kind == "b":
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b = True
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a = False
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c = b
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else:
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b = 1
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a = 0
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c = 2
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na = pd.NA
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return pd.array([b, b, na, na, a, a, b, c], dtype=dtype)
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class TestMaskedArrays(base.ExtensionTests):
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@pytest.mark.parametrize("na_action", [None, "ignore"])
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def test_map(self, data_missing, na_action):
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result = data_missing.map(lambda x: x, na_action=na_action)
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if data_missing.dtype == Float32Dtype():
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# map roundtrips through objects, which converts to float64
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expected = data_missing.to_numpy(dtype="float64", na_value=np.nan)
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else:
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expected = data_missing.to_numpy()
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tm.assert_numpy_array_equal(result, expected)
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def test_map_na_action_ignore(self, data_missing_for_sorting):
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zero = data_missing_for_sorting[2]
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result = data_missing_for_sorting.map(lambda x: zero, na_action="ignore")
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if data_missing_for_sorting.dtype.kind == "b":
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expected = np.array([False, pd.NA, False], dtype=object)
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else:
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expected = np.array([zero, np.nan, zero])
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tm.assert_numpy_array_equal(result, expected)
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def _get_expected_exception(self, op_name, obj, other):
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try:
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dtype = tm.get_dtype(obj)
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except AttributeError:
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# passed arguments reversed
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dtype = tm.get_dtype(other)
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if dtype.kind == "b":
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if op_name.strip("_").lstrip("r") in ["pow", "truediv", "floordiv"]:
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# match behavior with non-masked bool dtype
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return NotImplementedError
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elif op_name in ["__sub__", "__rsub__"]:
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# exception message would include "numpy boolean subtract""
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return TypeError
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return None
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return None
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def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
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sdtype = tm.get_dtype(obj)
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expected = pointwise_result
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if op_name in ("eq", "ne", "le", "ge", "lt", "gt"):
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return expected.astype("boolean")
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if sdtype.kind in "iu":
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if op_name in ("__rtruediv__", "__truediv__", "__div__"):
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with warnings.catch_warnings():
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warnings.filterwarnings(
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"ignore",
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"Downcasting object dtype arrays",
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category=FutureWarning,
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)
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filled = expected.fillna(np.nan)
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expected = filled.astype("Float64")
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else:
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# combine method result in 'biggest' (int64) dtype
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expected = expected.astype(sdtype)
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elif sdtype.kind == "b":
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if op_name in (
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"__floordiv__",
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"__rfloordiv__",
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"__pow__",
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"__rpow__",
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"__mod__",
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"__rmod__",
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):
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# combine keeps boolean type
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expected = expected.astype("Int8")
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elif op_name in ("__truediv__", "__rtruediv__"):
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# combine with bools does not generate the correct result
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# (numpy behaviour for div is to regard the bools as numeric)
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op = self.get_op_from_name(op_name)
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expected = self._combine(obj.astype(float), other, op)
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expected = expected.astype("Float64")
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if op_name == "__rpow__":
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# for rpow, combine does not propagate NaN
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result = getattr(obj, op_name)(other)
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expected[result.isna()] = np.nan
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else:
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# combine method result in 'biggest' (float64) dtype
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expected = expected.astype(sdtype)
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return expected
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def test_divmod_series_array(self, data, data_for_twos, request):
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if data.dtype.kind == "b":
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mark = pytest.mark.xfail(
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reason="Inconsistency between floordiv and divmod; we raise for "
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"floordiv but not for divmod. This matches what we do for "
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"non-masked bool dtype."
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)
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request.applymarker(mark)
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super().test_divmod_series_array(data, data_for_twos)
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def test_combine_le(self, data_repeated):
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# TODO: patching self is a bad pattern here
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orig_data1, orig_data2 = data_repeated(2)
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if orig_data1.dtype.kind == "b":
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self._combine_le_expected_dtype = "boolean"
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else:
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# TODO: can we make this boolean?
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self._combine_le_expected_dtype = object
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super().test_combine_le(data_repeated)
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def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
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if op_name in ["any", "all"] and ser.dtype.kind != "b":
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pytest.skip(reason="Tested in tests/reductions/test_reductions.py")
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return True
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def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
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# overwrite to ensure pd.NA is tested instead of np.nan
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# https://github.com/pandas-dev/pandas/issues/30958
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cmp_dtype = "int64"
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if ser.dtype.kind == "f":
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# Item "dtype[Any]" of "Union[dtype[Any], ExtensionDtype]" has
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# no attribute "numpy_dtype"
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cmp_dtype = ser.dtype.numpy_dtype # type: ignore[union-attr]
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elif ser.dtype.kind == "b":
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if op_name in ["min", "max"]:
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cmp_dtype = "bool"
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# TODO: prod with integer dtypes does *not* match the result we would
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# get if we used object for cmp_dtype. In that cae the object result
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# is a large integer while the non-object case overflows and returns 0
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alt = ser.dropna().astype(cmp_dtype)
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if op_name == "count":
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result = getattr(ser, op_name)()
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expected = getattr(alt, op_name)()
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else:
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result = getattr(ser, op_name)(skipna=skipna)
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expected = getattr(alt, op_name)(skipna=skipna)
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if not skipna and ser.isna().any() and op_name not in ["any", "all"]:
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expected = pd.NA
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tm.assert_almost_equal(result, expected)
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def _get_expected_reduction_dtype(self, arr, op_name: str, skipna: bool):
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if is_float_dtype(arr.dtype):
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cmp_dtype = arr.dtype.name
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elif op_name in ["mean", "median", "var", "std", "skew"]:
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cmp_dtype = "Float64"
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elif op_name in ["max", "min"]:
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cmp_dtype = arr.dtype.name
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elif arr.dtype in ["Int64", "UInt64"]:
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cmp_dtype = arr.dtype.name
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elif is_signed_integer_dtype(arr.dtype):
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# TODO: Why does Window Numpy 2.0 dtype depend on skipna?
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cmp_dtype = (
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"Int32"
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if (is_platform_windows() and (not np_version_gt2 or not skipna))
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or not IS64
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else "Int64"
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)
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elif is_unsigned_integer_dtype(arr.dtype):
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cmp_dtype = (
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"UInt32"
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if (is_platform_windows() and (not np_version_gt2 or not skipna))
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or not IS64
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else "UInt64"
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)
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elif arr.dtype.kind == "b":
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if op_name in ["mean", "median", "var", "std", "skew"]:
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cmp_dtype = "Float64"
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elif op_name in ["min", "max"]:
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cmp_dtype = "boolean"
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elif op_name in ["sum", "prod"]:
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cmp_dtype = (
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"Int32"
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if (is_platform_windows() and (not np_version_gt2 or not skipna))
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or not IS64
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else "Int64"
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)
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else:
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raise TypeError("not supposed to reach this")
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else:
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raise TypeError("not supposed to reach this")
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return cmp_dtype
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def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool:
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return True
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def check_accumulate(self, ser: pd.Series, op_name: str, skipna: bool):
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# overwrite to ensure pd.NA is tested instead of np.nan
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# https://github.com/pandas-dev/pandas/issues/30958
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length = 64
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if is_windows_or_32bit:
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# Item "ExtensionDtype" of "Union[dtype[Any], ExtensionDtype]" has
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# no attribute "itemsize"
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if not ser.dtype.itemsize == 8: # type: ignore[union-attr]
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length = 32
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|
|
|
||
|
|
if ser.dtype.name.startswith("U"):
|
||
|
|
expected_dtype = f"UInt{length}"
|
||
|
|
elif ser.dtype.name.startswith("I"):
|
||
|
|
expected_dtype = f"Int{length}"
|
||
|
|
elif ser.dtype.name.startswith("F"):
|
||
|
|
# Incompatible types in assignment (expression has type
|
||
|
|
# "Union[dtype[Any], ExtensionDtype]", variable has type "str")
|
||
|
|
expected_dtype = ser.dtype # type: ignore[assignment]
|
||
|
|
elif ser.dtype.kind == "b":
|
||
|
|
if op_name in ("cummin", "cummax"):
|
||
|
|
expected_dtype = "boolean"
|
||
|
|
else:
|
||
|
|
expected_dtype = f"Int{length}"
|
||
|
|
|
||
|
|
if expected_dtype == "Float32" and op_name == "cumprod" and skipna:
|
||
|
|
# TODO: xfail?
|
||
|
|
pytest.skip(
|
||
|
|
f"Float32 precision lead to large differences with op {op_name} "
|
||
|
|
f"and skipna={skipna}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if op_name == "cumsum":
|
||
|
|
result = getattr(ser, op_name)(skipna=skipna)
|
||
|
|
expected = pd.Series(
|
||
|
|
pd.array(
|
||
|
|
getattr(ser.astype("float64"), op_name)(skipna=skipna),
|
||
|
|
dtype=expected_dtype,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
elif op_name in ["cummax", "cummin"]:
|
||
|
|
result = getattr(ser, op_name)(skipna=skipna)
|
||
|
|
expected = pd.Series(
|
||
|
|
pd.array(
|
||
|
|
getattr(ser.astype("float64"), op_name)(skipna=skipna),
|
||
|
|
dtype=ser.dtype,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
elif op_name == "cumprod":
|
||
|
|
result = getattr(ser[:12], op_name)(skipna=skipna)
|
||
|
|
expected = pd.Series(
|
||
|
|
pd.array(
|
||
|
|
getattr(ser[:12].astype("float64"), op_name)(skipna=skipna),
|
||
|
|
dtype=expected_dtype,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise NotImplementedError(f"{op_name} not supported")
|
||
|
|
|
||
|
|
|
||
|
|
class Test2DCompat(base.Dim2CompatTests):
|
||
|
|
pass
|