from contextlib import nullcontext
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import copy
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import numpy as np
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import pytest
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from pandas._libs.missing import is_matching_na
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from pandas.compat import is_numpy_dev
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from pandas.core.dtypes.common import is_float
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from pandas import (
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Index,
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MultiIndex,
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Series,
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)
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import pandas._testing as tm
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@pytest.mark.parametrize(
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"arr, idx",
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[
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([1, 2, 3, 4], [0, 2, 1, 3]),
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([1, np.nan, 3, np.nan], [0, 2, 1, 3]),
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(
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[1, np.nan, 3, np.nan],
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MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c"), (3, "c")]),
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),
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],
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)
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def test_equals(arr, idx):
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s1 = Series(arr, index=idx)
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s2 = s1.copy()
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assert s1.equals(s2)
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s1[1] = 9
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assert not s1.equals(s2)
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@pytest.mark.parametrize(
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"val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None]
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)
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def test_equals_list_array(val):
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# GH20676 Verify equals operator for list of Numpy arrays
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arr = np.array([1, 2])
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s1 = Series([arr, arr])
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s2 = s1.copy()
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assert s1.equals(s2)
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s1[1] = val
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cm = (
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tm.assert_produces_warning(FutureWarning, check_stacklevel=False)
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if isinstance(val, str) and not is_numpy_dev
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else nullcontext()
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)
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with cm:
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assert not s1.equals(s2)
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def test_equals_false_negative():
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# GH8437 Verify false negative behavior of equals function for dtype object
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arr = [False, np.nan]
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s1 = Series(arr)
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s2 = s1.copy()
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s3 = Series(index=range(2), dtype=object)
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s4 = s3.copy()
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s5 = s3.copy()
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s6 = s3.copy()
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s3[:-1] = s4[:-1] = s5[0] = s6[0] = False
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assert s1.equals(s1)
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assert s1.equals(s2)
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assert s1.equals(s3)
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assert s1.equals(s4)
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assert s1.equals(s5)
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assert s5.equals(s6)
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def test_equals_matching_nas():
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# matching but not identical NAs
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left = Series([np.datetime64("NaT")], dtype=object)
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right = Series([np.datetime64("NaT")], dtype=object)
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assert left.equals(right)
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assert Index(left).equals(Index(right))
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assert left.array.equals(right.array)
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left = Series([np.timedelta64("NaT")], dtype=object)
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right = Series([np.timedelta64("NaT")], dtype=object)
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assert left.equals(right)
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assert Index(left).equals(Index(right))
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assert left.array.equals(right.array)
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left = Series([np.float64("NaN")], dtype=object)
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right = Series([np.float64("NaN")], dtype=object)
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assert left.equals(right)
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assert Index(left, dtype=left.dtype).equals(Index(right, dtype=right.dtype))
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assert left.array.equals(right.array)
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def test_equals_mismatched_nas(nulls_fixture, nulls_fixture2):
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# GH#39650
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left = nulls_fixture
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right = nulls_fixture2
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if hasattr(right, "copy"):
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right = right.copy()
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else:
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right = copy.copy(right)
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ser = Series([left], dtype=object)
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ser2 = Series([right], dtype=object)
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if is_matching_na(left, right):
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assert ser.equals(ser2)
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elif (left is None and is_float(right)) or (right is None and is_float(left)):
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assert ser.equals(ser2)
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else:
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assert not ser.equals(ser2)
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def test_equals_none_vs_nan():
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# GH#39650
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ser = Series([1, None], dtype=object)
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ser2 = Series([1, np.nan], dtype=object)
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assert ser.equals(ser2)
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assert Index(ser, dtype=ser.dtype).equals(Index(ser2, dtype=ser2.dtype))
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assert ser.array.equals(ser2.array)
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def test_equals_None_vs_float():
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# GH#44190
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left = Series([-np.inf, np.nan, -1.0, 0.0, 1.0, 10 / 3, np.inf], dtype=object)
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right = Series([None] * len(left))
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# these series were found to be equal due to a bug, check that they are correctly
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# found to not equal
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assert not left.equals(right)
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assert not right.equals(left)
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assert not left.to_frame().equals(right.to_frame())
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assert not right.to_frame().equals(left.to_frame())
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assert not Index(left, dtype="object").equals(Index(right, dtype="object"))
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assert not Index(right, dtype="object").equals(Index(left, dtype="object"))
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