401 lines
13 KiB
Python
401 lines
13 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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from datetime import (
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datetime,
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timedelta,
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)
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from decimal import Decimal
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import numpy as np
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import pytest
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from pandas import (
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NA,
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Categorical,
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CategoricalIndex,
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DatetimeIndex,
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Index,
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IntervalIndex,
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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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array,
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date_range,
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period_range,
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timedelta_range,
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)
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import pandas._testing as tm
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class TestIndexConstructorInference:
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def test_object_all_bools(self):
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# GH#49594 match Series behavior on ndarray[object] of all bools
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arr = np.array([True, False], dtype=object)
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res = Index(arr)
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assert res.dtype == object
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# since the point is matching Series behavior, let's double check
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assert Series(arr).dtype == object
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def test_object_all_complex(self):
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# GH#49594 match Series behavior on ndarray[object] of all complex
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arr = np.array([complex(1), complex(2)], dtype=object)
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res = Index(arr)
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assert res.dtype == object
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# since the point is matching Series behavior, let's double check
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assert Series(arr).dtype == object
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@pytest.mark.parametrize("val", [NaT, None, np.nan, float("nan")])
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def test_infer_nat(self, val):
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# GH#49340 all NaT/None/nan and at least 1 NaT -> datetime64[ns],
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# matching Series behavior
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values = [NaT, val]
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idx = Index(values)
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assert idx.dtype == "datetime64[ns]" and idx.isna().all()
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idx = Index(values[::-1])
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assert idx.dtype == "datetime64[ns]" and idx.isna().all()
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idx = Index(np.array(values, dtype=object))
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assert idx.dtype == "datetime64[ns]" and idx.isna().all()
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idx = Index(np.array(values, dtype=object)[::-1])
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assert idx.dtype == "datetime64[ns]" and idx.isna().all()
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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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expected = Index([0, 1, 2, 3], dtype=dtype)
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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 == bool
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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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def test_from_list_of_periods(self):
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rng = period_range("1/1/2000", periods=20, freq="D")
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periods = list(rng)
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result = Index(periods)
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assert isinstance(result, 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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if isinstance(nulls_fixture, Decimal):
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# We dont cast these to datetime64/timedelta64
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return
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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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@pytest.mark.parametrize("swap_objs", [True, False])
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def test_constructor_datetime_and_datetime64(self, swap_objs):
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data = [Timestamp(2021, 6, 8, 9, 42), np.datetime64("now")]
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if swap_objs:
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data = data[::-1]
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expected = DatetimeIndex(data)
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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 TestDtypeEnforced:
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# check we don't silently ignore the dtype keyword
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def test_constructor_object_dtype_with_ea_data(self, any_numeric_ea_dtype):
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# GH#45206
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arr = array([0], dtype=any_numeric_ea_dtype)
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idx = Index(arr, dtype=object)
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assert idx.dtype == object
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@pytest.mark.parametrize("dtype", [object, "float64", "uint64", "category"])
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def test_constructor_range_values_mismatched_dtype(self, dtype):
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rng = Index(range(5))
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result = Index(rng, dtype=dtype)
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assert result.dtype == dtype
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result = Index(range(5), dtype=dtype)
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assert result.dtype == dtype
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@pytest.mark.parametrize("dtype", [object, "float64", "uint64", "category"])
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def test_constructor_categorical_values_mismatched_non_ea_dtype(self, dtype):
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cat = Categorical([1, 2, 3])
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result = Index(cat, dtype=dtype)
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assert result.dtype == dtype
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def test_constructor_categorical_values_mismatched_dtype(self):
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dti = date_range("2016-01-01", periods=3)
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cat = Categorical(dti)
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result = Index(cat, dti.dtype)
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tm.assert_index_equal(result, dti)
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dti2 = dti.tz_localize("Asia/Tokyo")
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cat2 = Categorical(dti2)
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result = Index(cat2, dti2.dtype)
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tm.assert_index_equal(result, dti2)
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ii = IntervalIndex.from_breaks(range(5))
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cat3 = Categorical(ii)
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result = Index(cat3, dtype=ii.dtype)
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tm.assert_index_equal(result, ii)
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def test_constructor_ea_values_mismatched_categorical_dtype(self):
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dti = date_range("2016-01-01", periods=3)
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result = Index(dti, dtype="category")
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expected = CategoricalIndex(dti)
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tm.assert_index_equal(result, expected)
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dti2 = date_range("2016-01-01", periods=3, tz="US/Pacific")
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result = Index(dti2, dtype="category")
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expected = CategoricalIndex(dti2)
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tm.assert_index_equal(result, expected)
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def test_constructor_period_values_mismatched_dtype(self):
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pi = period_range("2016-01-01", periods=3, freq="D")
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result = Index(pi, dtype="category")
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expected = CategoricalIndex(pi)
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tm.assert_index_equal(result, expected)
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def test_constructor_timedelta64_values_mismatched_dtype(self):
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# check we don't silently ignore the dtype keyword
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tdi = timedelta_range("4 Days", periods=5)
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result = Index(tdi, dtype="category")
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expected = CategoricalIndex(tdi)
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tm.assert_index_equal(result, expected)
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def test_constructor_interval_values_mismatched_dtype(self):
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dti = date_range("2016-01-01", periods=3)
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ii = IntervalIndex.from_breaks(dti)
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result = Index(ii, dtype="category")
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expected = CategoricalIndex(ii)
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tm.assert_index_equal(result, expected)
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def test_constructor_datetime64_values_mismatched_period_dtype(self):
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dti = date_range("2016-01-01", periods=3)
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result = Index(dti, dtype="Period[D]")
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expected = dti.to_period("D")
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize("dtype", ["int64", "uint64"])
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def test_constructor_int_dtype_nan_raises(self, dtype):
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# see GH#15187
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data = [np.nan]
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msg = "cannot convert"
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with pytest.raises(ValueError, match=msg):
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Index(data, dtype=dtype)
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@pytest.mark.parametrize(
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"vals",
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[
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[1, 2, 3],
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np.array([1, 2, 3]),
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np.array([1, 2, 3], dtype=int),
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# below should coerce
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[1.0, 2.0, 3.0],
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np.array([1.0, 2.0, 3.0], dtype=float),
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],
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)
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def test_constructor_dtypes_to_int(self, vals, any_int_numpy_dtype):
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dtype = any_int_numpy_dtype
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index = Index(vals, dtype=dtype)
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assert index.dtype == dtype
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@pytest.mark.parametrize(
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"vals",
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[
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[1, 2, 3],
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[1.0, 2.0, 3.0],
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np.array([1.0, 2.0, 3.0]),
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np.array([1, 2, 3], dtype=int),
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np.array([1.0, 2.0, 3.0], dtype=float),
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],
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)
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def test_constructor_dtypes_to_float(self, vals, float_numpy_dtype):
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dtype = float_numpy_dtype
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index = Index(vals, dtype=dtype)
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assert index.dtype == dtype
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@pytest.mark.parametrize(
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"vals",
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[
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[1, 2, 3],
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np.array([1, 2, 3], dtype=int),
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np.array(["2011-01-01", "2011-01-02"], dtype="datetime64[ns]"),
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[datetime(2011, 1, 1), datetime(2011, 1, 2)],
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],
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)
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def test_constructor_dtypes_to_categorical(self, vals):
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index = Index(vals, dtype="category")
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assert isinstance(index, CategoricalIndex)
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@pytest.mark.parametrize("cast_index", [True, False])
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@pytest.mark.parametrize(
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"vals",
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[
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Index(np.array([np.datetime64("2011-01-01"), np.datetime64("2011-01-02")])),
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Index([datetime(2011, 1, 1), datetime(2011, 1, 2)]),
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],
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)
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def test_constructor_dtypes_to_datetime(self, cast_index, vals):
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if cast_index:
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index = Index(vals, dtype=object)
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assert isinstance(index, Index)
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assert index.dtype == object
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else:
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index = Index(vals)
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assert isinstance(index, DatetimeIndex)
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@pytest.mark.parametrize("cast_index", [True, False])
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@pytest.mark.parametrize(
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"vals",
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[
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np.array([np.timedelta64(1, "D"), np.timedelta64(1, "D")]),
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[timedelta(1), timedelta(1)],
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],
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)
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def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
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if cast_index:
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index = Index(vals, dtype=object)
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assert isinstance(index, Index)
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assert index.dtype == object
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else:
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index = Index(vals)
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assert isinstance(index, TimedeltaIndex)
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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])
|
||
|
|
def test_constructor_from_series_dt64(self, klass):
|
||
|
|
stamps = [Timestamp("20110101"), Timestamp("20120101"), Timestamp("20130101")]
|
||
|
|
expected = DatetimeIndex(stamps)
|
||
|
|
ser = Series(stamps)
|
||
|
|
result = klass(ser)
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
def test_constructor_no_pandas_array(self):
|
||
|
|
ser = Series([1, 2, 3])
|
||
|
|
result = Index(ser.array)
|
||
|
|
expected = Index([1, 2, 3])
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"array",
|
||
|
|
[
|
||
|
|
np.arange(5),
|
||
|
|
np.array(["a", "b", "c"]),
|
||
|
|
date_range("2000-01-01", periods=3).values,
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_constructor_ndarray_like(self, array):
|
||
|
|
# GH#5460#issuecomment-44474502
|
||
|
|
# it should be possible to convert any object that satisfies the numpy
|
||
|
|
# ndarray interface directly into an Index
|
||
|
|
class ArrayLike:
|
||
|
|
def __init__(self, array) -> None:
|
||
|
|
self.array = array
|
||
|
|
|
||
|
|
def __array__(self, dtype=None) -> np.ndarray:
|
||
|
|
return self.array
|
||
|
|
|
||
|
|
expected = Index(array)
|
||
|
|
result = Index(ArrayLike(array))
|
||
|
|
tm.assert_index_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestIndexConstructionErrors:
|
||
|
|
def test_constructor_overflow_int64(self):
|
||
|
|
# see GH#15832
|
||
|
|
msg = (
|
||
|
|
"The elements provided in the data cannot "
|
||
|
|
"all be casted to the dtype int64"
|
||
|
|
)
|
||
|
|
with pytest.raises(OverflowError, match=msg):
|
||
|
|
Index([np.iinfo(np.uint64).max - 1], dtype="int64")
|