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256 lines
6.9 KiB
Python
256 lines
6.9 KiB
Python
"""
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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 numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.api.types import (
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is_extension_array_dtype,
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is_integer_dtype,
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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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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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@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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]
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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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return pd.array(make_data(), dtype=dtype)
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@pytest.fixture
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def data_for_twos(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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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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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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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 na_value():
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return pd.NA
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@pytest.fixture
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def data_for_grouping(dtype):
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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 TestDtype(base.BaseDtypeTests):
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@pytest.mark.skip(reason="using multiple dtypes")
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def test_is_dtype_unboxes_dtype(self):
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# we have multiple dtypes, so skip
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pass
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class TestArithmeticOps(base.BaseArithmeticOpsTests):
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def check_opname(self, s, op_name, other, exc=None):
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# overwriting to indicate ops don't raise an error
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super().check_opname(s, op_name, other, exc=None)
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def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
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if exc is None:
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sdtype = tm.get_dtype(s)
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if sdtype.is_unsigned_integer and (op_name == "__rsub__"):
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# TODO see https://github.com/pandas-dev/pandas/issues/22023
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pytest.skip("unsigned subtraction gives negative values")
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if (
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hasattr(other, "dtype")
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and not is_extension_array_dtype(other.dtype)
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and is_integer_dtype(other.dtype)
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):
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# other is np.int64 and would therefore always result in
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# upcasting, so keeping other as same numpy_dtype
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other = other.astype(sdtype.numpy_dtype)
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result = op(s, other)
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expected = self._combine(s, other, op)
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if op_name in ("__rtruediv__", "__truediv__", "__div__"):
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expected = expected.fillna(np.nan).astype("Float64")
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elif op_name.startswith("__r"):
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# TODO reverse operators result in object dtype
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# see https://github.com/pandas-dev/pandas/issues/22024
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expected = expected.astype(sdtype)
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result = result.astype(sdtype)
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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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pass
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if (op_name == "__rpow__") and isinstance(other, pd.Series):
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# TODO pow on Int arrays gives different result with NA
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# see https://github.com/pandas-dev/pandas/issues/22022
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result = result.fillna(1)
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self.assert_equal(result, expected)
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else:
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with pytest.raises(exc):
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op(s, other)
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def _check_divmod_op(self, s, op, other, exc=None):
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super()._check_divmod_op(s, op, other, None)
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class TestComparisonOps(base.BaseComparisonOpsTests):
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def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
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if exc is None:
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result = op(s, other)
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# Override to do the astype to boolean
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expected = s.combine(other, op).astype("boolean")
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self.assert_series_equal(result, expected)
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else:
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with pytest.raises(exc):
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op(s, other)
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def check_opname(self, s, op_name, other, exc=None):
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super().check_opname(s, op_name, other, exc=None)
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def _compare_other(self, s, data, op_name, other):
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self.check_opname(s, op_name, other)
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class TestInterface(base.BaseInterfaceTests):
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pass
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class TestConstructors(base.BaseConstructorsTests):
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pass
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class TestReshaping(base.BaseReshapingTests):
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pass
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# for test_concat_mixed_dtypes test
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# concat of an Integer and Int coerces to object dtype
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# TODO(jreback) once integrated this would
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class TestGetitem(base.BaseGetitemTests):
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pass
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class TestSetitem(base.BaseSetitemTests):
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pass
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class TestMissing(base.BaseMissingTests):
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pass
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class TestMethods(base.BaseMethodsTests):
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@pytest.mark.skip(reason="uses nullable integer")
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def test_value_counts(self, all_data, dropna):
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all_data = all_data[:10]
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if dropna:
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other = np.array(all_data[~all_data.isna()])
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else:
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other = all_data
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result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
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expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
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expected.index = expected.index.astype(all_data.dtype)
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self.assert_series_equal(result, expected)
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@pytest.mark.skip(reason="uses nullable integer")
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def test_value_counts_with_normalize(self, data):
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pass
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class TestCasting(base.BaseCastingTests):
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pass
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class TestGroupby(base.BaseGroupbyTests):
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pass
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class TestNumericReduce(base.BaseNumericReduceTests):
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def check_reduce(self, s, op_name, skipna):
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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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result = getattr(s, op_name)(skipna=skipna)
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if not skipna and s.isna().any():
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expected = pd.NA
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else:
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expected = getattr(s.dropna().astype("int64"), op_name)(skipna=skipna)
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tm.assert_almost_equal(result, expected)
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class TestBooleanReduce(base.BaseBooleanReduceTests):
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pass
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class TestPrinting(base.BasePrintingTests):
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pass
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class TestParsing(base.BaseParsingTests):
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pass
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