156 lines
5.0 KiB
Python
156 lines
5.0 KiB
Python
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"""
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Tests shared by MaskedArray subclasses.
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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.tests.extension.base import BaseOpsUtil
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class ComparisonOps(BaseOpsUtil):
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def _compare_other(self, data, op, other):
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# array
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result = pd.Series(op(data, other))
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expected = pd.Series(op(data._data, other), dtype="boolean")
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# fill the nan locations
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expected[data._mask] = pd.NA
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tm.assert_series_equal(result, expected)
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# series
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ser = pd.Series(data)
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result = op(ser, other)
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expected = op(pd.Series(data._data), other)
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# fill the nan locations
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expected[data._mask] = pd.NA
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expected = expected.astype("boolean")
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tm.assert_series_equal(result, expected)
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# subclass will override to parametrize 'other'
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def test_scalar(self, other, comparison_op, dtype):
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op = comparison_op
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left = pd.array([1, 0, None], dtype=dtype)
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result = op(left, other)
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if other is pd.NA:
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expected = pd.array([None, None, None], dtype="boolean")
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else:
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values = op(left._data, other)
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expected = pd.arrays.BooleanArray(values, left._mask, copy=True)
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tm.assert_extension_array_equal(result, expected)
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# ensure we haven't mutated anything inplace
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result[0] = pd.NA
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tm.assert_extension_array_equal(left, pd.array([1, 0, None], dtype=dtype))
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class NumericOps:
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# Shared by IntegerArray and FloatingArray, not BooleanArray
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def test_searchsorted_nan(self, dtype):
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# The base class casts to object dtype, for which searchsorted returns
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# 0 from the left and 10 from the right.
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arr = pd.array(range(10), dtype=dtype)
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assert arr.searchsorted(np.nan, side="left") == 10
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assert arr.searchsorted(np.nan, side="right") == 10
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def test_no_shared_mask(self, data):
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result = data + 1
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assert not tm.shares_memory(result, data)
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def test_array(self, comparison_op, dtype):
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op = comparison_op
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left = pd.array([0, 1, 2, None, None, None], dtype=dtype)
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right = pd.array([0, 1, None, 0, 1, None], dtype=dtype)
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result = op(left, right)
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values = op(left._data, right._data)
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mask = left._mask | right._mask
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expected = pd.arrays.BooleanArray(values, mask)
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tm.assert_extension_array_equal(result, expected)
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# ensure we haven't mutated anything inplace
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result[0] = pd.NA
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tm.assert_extension_array_equal(
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left, pd.array([0, 1, 2, None, None, None], dtype=dtype)
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)
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tm.assert_extension_array_equal(
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right, pd.array([0, 1, None, 0, 1, None], dtype=dtype)
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)
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def test_compare_with_booleanarray(self, comparison_op, dtype):
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op = comparison_op
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left = pd.array([True, False, None] * 3, dtype="boolean")
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right = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype=dtype)
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other = pd.array([False] * 3 + [True] * 3 + [None] * 3, dtype="boolean")
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expected = op(left, other)
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result = op(left, right)
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tm.assert_extension_array_equal(result, expected)
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# reversed op
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expected = op(other, left)
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result = op(right, left)
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tm.assert_extension_array_equal(result, expected)
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def test_compare_to_string(self, dtype):
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# GH#28930
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ser = pd.Series([1, None], dtype=dtype)
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result = ser == "a"
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expected = pd.Series([False, pd.NA], dtype="boolean")
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self.assert_series_equal(result, expected)
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def test_ufunc_with_out(self, dtype):
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arr = pd.array([1, 2, 3], dtype=dtype)
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arr2 = pd.array([1, 2, pd.NA], dtype=dtype)
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mask = arr == arr
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mask2 = arr2 == arr2
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result = np.zeros(3, dtype=bool)
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result |= mask
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# If MaskedArray.__array_ufunc__ handled "out" appropriately,
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# `result` should still be an ndarray.
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assert isinstance(result, np.ndarray)
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assert result.all()
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# result |= mask worked because mask could be cast losslessly to
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# boolean ndarray. mask2 can't, so this raises
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result = np.zeros(3, dtype=bool)
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msg = "Specify an appropriate 'na_value' for this dtype"
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with pytest.raises(ValueError, match=msg):
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result |= mask2
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# addition
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res = np.add(arr, arr2)
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expected = pd.array([2, 4, pd.NA], dtype=dtype)
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tm.assert_extension_array_equal(res, expected)
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# when passing out=arr, we will modify 'arr' inplace.
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res = np.add(arr, arr2, out=arr)
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assert res is arr
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tm.assert_extension_array_equal(res, expected)
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tm.assert_extension_array_equal(arr, expected)
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def test_mul_td64_array(self, dtype):
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# GH#45622
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arr = pd.array([1, 2, pd.NA], dtype=dtype)
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other = np.arange(3, dtype=np.int64).view("m8[ns]")
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result = arr * other
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expected = pd.array([pd.Timedelta(0), pd.Timedelta(2), pd.NaT])
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tm.assert_extension_array_equal(result, expected)
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