import numpy as np
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from pandas import (
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Series,
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interval_range,
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)
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import pandas._testing as tm
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class TestInferObjects:
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def test_copy(self, index_or_series):
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# GH#50096
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# case where we don't need to do inference because it is already non-object
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obj = index_or_series(np.array([1, 2, 3], dtype="int64"))
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result = obj.infer_objects(copy=False)
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assert tm.shares_memory(result, obj)
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# case where we try to do inference but can't do better than object
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obj2 = index_or_series(np.array(["foo", 2], dtype=object))
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result2 = obj2.infer_objects(copy=False)
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assert tm.shares_memory(result2, obj2)
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def test_infer_objects_series(self, index_or_series):
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# GH#11221
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actual = index_or_series(np.array([1, 2, 3], dtype="O")).infer_objects()
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expected = index_or_series([1, 2, 3])
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tm.assert_equal(actual, expected)
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actual = index_or_series(np.array([1, 2, 3, None], dtype="O")).infer_objects()
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expected = index_or_series([1.0, 2.0, 3.0, np.nan])
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tm.assert_equal(actual, expected)
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# only soft conversions, unconvertable pass thru unchanged
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obj = index_or_series(np.array([1, 2, 3, None, "a"], dtype="O"))
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actual = obj.infer_objects()
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expected = index_or_series([1, 2, 3, None, "a"], dtype=object)
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assert actual.dtype == "object"
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tm.assert_equal(actual, expected)
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def test_infer_objects_interval(self, index_or_series):
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# GH#50090
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ii = interval_range(1, 10)
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obj = index_or_series(ii)
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result = obj.astype(object).infer_objects()
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tm.assert_equal(result, obj)
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def test_infer_objects_bytes(self):
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# GH#49650
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ser = Series([b"a"], dtype="bytes")
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expected = ser.copy()
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result = ser.infer_objects()
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tm.assert_series_equal(result, expected)
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