135 lines
4.4 KiB
Python
135 lines
4.4 KiB
Python
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"""
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Tests for the Index constructor conducting inference.
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"""
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_unsigned_integer_dtype
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from pandas import (
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NA,
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CategoricalIndex,
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DatetimeIndex,
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Index,
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Int64Index,
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MultiIndex,
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NaT,
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PeriodIndex,
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Series,
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TimedeltaIndex,
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Timestamp,
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UInt64Index,
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period_range,
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)
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import pandas._testing as tm
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class TestIndexConstructorInference:
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@pytest.mark.parametrize("na_value", [None, np.nan])
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@pytest.mark.parametrize("vtype", [list, tuple, iter])
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def test_construction_list_tuples_nan(self, na_value, vtype):
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# GH#18505 : valid tuples containing NaN
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values = [(1, "two"), (3.0, na_value)]
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result = Index(vtype(values))
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expected = MultiIndex.from_tuples(values)
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype",
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[int, "int64", "int32", "int16", "int8", "uint64", "uint32", "uint16", "uint8"],
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)
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def test_constructor_int_dtype_float(self, dtype):
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# GH#18400
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if is_unsigned_integer_dtype(dtype):
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index_type = UInt64Index
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else:
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index_type = Int64Index
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expected = index_type([0, 1, 2, 3])
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result = Index([0.0, 1.0, 2.0, 3.0], dtype=dtype)
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize("cast_index", [True, False])
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@pytest.mark.parametrize(
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"vals", [[True, False, True], np.array([True, False, True], dtype=bool)]
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)
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def test_constructor_dtypes_to_object(self, cast_index, vals):
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if cast_index:
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index = Index(vals, dtype=bool)
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else:
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index = Index(vals)
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assert type(index) is Index
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assert index.dtype == object
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def test_constructor_categorical_to_object(self):
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# GH#32167 Categorical data and dtype=object should return object-dtype
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ci = CategoricalIndex(range(5))
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result = Index(ci, dtype=object)
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assert not isinstance(result, CategoricalIndex)
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def test_constructor_infer_periodindex(self):
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xp = period_range("2012-1-1", freq="M", periods=3)
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rs = Index(xp)
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tm.assert_index_equal(rs, xp)
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assert isinstance(rs, PeriodIndex)
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@pytest.mark.parametrize("pos", [0, 1])
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@pytest.mark.parametrize(
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"klass,dtype,ctor",
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[
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(DatetimeIndex, "datetime64[ns]", np.datetime64("nat")),
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(TimedeltaIndex, "timedelta64[ns]", np.timedelta64("nat")),
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],
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)
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def test_constructor_infer_nat_dt_like(
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self, pos, klass, dtype, ctor, nulls_fixture, request
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):
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expected = klass([NaT, NaT])
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assert expected.dtype == dtype
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data = [ctor]
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data.insert(pos, nulls_fixture)
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warn = None
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if nulls_fixture is NA:
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expected = Index([NA, NaT])
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mark = pytest.mark.xfail(reason="Broken with np.NaT ctor; see GH 31884")
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request.node.add_marker(mark)
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# GH#35942 numpy will emit a DeprecationWarning within the
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# assert_index_equal calls. Since we can't do anything
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# about it until GH#31884 is fixed, we suppress that warning.
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warn = DeprecationWarning
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result = Index(data)
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with tm.assert_produces_warning(warn):
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tm.assert_index_equal(result, expected)
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result = Index(np.array(data, dtype=object))
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with tm.assert_produces_warning(warn):
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize("swap_objs", [True, False])
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def test_constructor_mixed_nat_objs_infers_object(self, swap_objs):
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# mixed np.datetime64/timedelta64 nat results in object
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data = [np.datetime64("nat"), np.timedelta64("nat")]
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if swap_objs:
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data = data[::-1]
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expected = Index(data, dtype=object)
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tm.assert_index_equal(Index(data), expected)
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tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)
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class TestIndexConstructorUnwrapping:
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# Test passing different arraylike values to pd.Index
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@pytest.mark.parametrize("klass", [Index, DatetimeIndex])
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def test_constructor_from_series_dt64(self, klass):
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stamps = [Timestamp("20110101"), Timestamp("20120101"), Timestamp("20130101")]
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expected = DatetimeIndex(stamps)
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ser = Series(stamps)
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result = klass(ser)
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tm.assert_index_equal(result, expected)
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