import numpy as np
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import pytest
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from pandas import (
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CategoricalIndex,
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DatetimeIndex,
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Index,
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PeriodIndex,
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TimedeltaIndex,
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isna,
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)
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import pandas._testing as tm
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from pandas.api.types import (
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is_complex_dtype,
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is_numeric_dtype,
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)
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from pandas.core.arrays import BooleanArray
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from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
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def test_numpy_ufuncs_out(index):
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result = index == index
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out = np.empty(index.shape, dtype=bool)
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np.equal(index, index, out=out)
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tm.assert_numpy_array_equal(out, result)
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if not index._is_multi:
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# same thing on the ExtensionArray
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out = np.empty(index.shape, dtype=bool)
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np.equal(index.array, index.array, out=out)
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tm.assert_numpy_array_equal(out, result)
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@pytest.mark.parametrize(
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"func",
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[
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np.exp,
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np.exp2,
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np.expm1,
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np.log,
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np.log2,
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np.log10,
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np.log1p,
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np.sqrt,
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np.sin,
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np.cos,
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np.tan,
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np.arcsin,
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np.arccos,
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np.arctan,
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np.sinh,
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np.cosh,
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np.tanh,
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np.arcsinh,
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np.arccosh,
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np.arctanh,
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np.deg2rad,
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np.rad2deg,
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],
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ids=lambda x: x.__name__,
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)
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def test_numpy_ufuncs_basic(index, func):
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# test ufuncs of numpy, see:
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# https://numpy.org/doc/stable/reference/ufuncs.html
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if isinstance(index, DatetimeIndexOpsMixin):
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with tm.external_error_raised((TypeError, AttributeError)):
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with np.errstate(all="ignore"):
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func(index)
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elif is_numeric_dtype(index) and not (
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is_complex_dtype(index) and func in [np.deg2rad, np.rad2deg]
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):
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# coerces to float (e.g. np.sin)
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with np.errstate(all="ignore"):
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result = func(index)
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arr_result = func(index.values)
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if arr_result.dtype == np.float16:
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arr_result = arr_result.astype(np.float32)
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exp = Index(arr_result, name=index.name)
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tm.assert_index_equal(result, exp)
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if isinstance(index.dtype, np.dtype) and is_numeric_dtype(index):
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if is_complex_dtype(index):
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assert result.dtype == index.dtype
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elif index.dtype in ["bool", "int8", "uint8"]:
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assert result.dtype in ["float16", "float32"]
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elif index.dtype in ["int16", "uint16", "float32"]:
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assert result.dtype == "float32"
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else:
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assert result.dtype == "float64"
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else:
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# e.g. np.exp with Int64 -> Float64
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assert type(result) is Index
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else:
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# raise AttributeError or TypeError
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if len(index) == 0:
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pass
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else:
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with tm.external_error_raised((TypeError, AttributeError)):
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with np.errstate(all="ignore"):
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func(index)
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@pytest.mark.parametrize(
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"func", [np.isfinite, np.isinf, np.isnan, np.signbit], ids=lambda x: x.__name__
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)
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def test_numpy_ufuncs_other(index, func):
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# test ufuncs of numpy, see:
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# https://numpy.org/doc/stable/reference/ufuncs.html
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if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
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if func in (np.isfinite, np.isinf, np.isnan):
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# numpy 1.18 changed isinf and isnan to not raise on dt64/td64
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result = func(index)
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assert isinstance(result, np.ndarray)
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out = np.empty(index.shape, dtype=bool)
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func(index, out=out)
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tm.assert_numpy_array_equal(out, result)
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else:
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with tm.external_error_raised(TypeError):
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func(index)
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elif isinstance(index, PeriodIndex):
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with tm.external_error_raised(TypeError):
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func(index)
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elif is_numeric_dtype(index) and not (
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is_complex_dtype(index) and func is np.signbit
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):
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# Results in bool array
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result = func(index)
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if not isinstance(index.dtype, np.dtype):
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# e.g. Int64 we expect to get BooleanArray back
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assert isinstance(result, BooleanArray)
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else:
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assert isinstance(result, np.ndarray)
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out = np.empty(index.shape, dtype=bool)
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func(index, out=out)
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if not isinstance(index.dtype, np.dtype):
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tm.assert_numpy_array_equal(out, result._data)
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else:
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tm.assert_numpy_array_equal(out, result)
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else:
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if len(index) == 0:
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pass
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else:
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with tm.external_error_raised(TypeError):
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func(index)
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@pytest.mark.parametrize("func", [np.maximum, np.minimum])
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def test_numpy_ufuncs_reductions(index, func, request):
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# TODO: overlap with tests.series.test_ufunc.test_reductions
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if len(index) == 0:
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return
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if isinstance(index, CategoricalIndex) and index.dtype.ordered is False:
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with pytest.raises(TypeError, match="is not ordered for"):
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func.reduce(index)
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return
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else:
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result = func.reduce(index)
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if func is np.maximum:
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expected = index.max(skipna=False)
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else:
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expected = index.min(skipna=False)
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# TODO: do we have cases both with and without NAs?
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assert type(result) is type(expected)
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if isna(result):
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assert isna(expected)
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else:
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assert result == expected
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@pytest.mark.parametrize("func", [np.bitwise_and, np.bitwise_or, np.bitwise_xor])
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def test_numpy_ufuncs_bitwise(func):
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# https://github.com/pandas-dev/pandas/issues/46769
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idx1 = Index([1, 2, 3, 4], dtype="int64")
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idx2 = Index([3, 4, 5, 6], dtype="int64")
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with tm.assert_produces_warning(None):
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result = func(idx1, idx2)
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expected = Index(func(idx1.values, idx2.values))
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tm.assert_index_equal(result, expected)
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