FM / indexes

/usr/local/lib64/python3.6/site-packages/pandas/tests/indexes/

/usr/local/lib64/python3.6/site-packages/pandas/tests/indexes

Name	Size	Mode	Actions
base_class/	-	0755	rm
categorical/	-	0755	rm
datetimes/	-	0755	rm
interval/	-	0755	rm
multi/	-	0755	rm
numeric/	-	0755	rm
period/	-	0755	rm
ranges/	-	0755	rm
timedeltas/	-	0755	rm
__pycache__/	-	0755	rm
common.py	32027	0644	edit dl rm
conftest.py	726	0644	edit dl rm
datetimelike.py	3259	0644	edit dl rm
test_any_index.py	2375	0644	edit dl rm
test_base.py	93126	0644	edit dl rm
test_common.py	14453	0644	edit dl rm
test_engines.py	8656	0644	edit dl rm
test_frozen.py	3069	0644	edit dl rm
test_indexing.py	2856	0644	edit dl rm
test_index_new.py	4117	0644	edit dl rm
test_numeric.py	22982	0644	edit dl rm
test_numpy_compat.py	3362	0644	edit dl rm
test_setops.py	3608	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/indexes/test_index_new.py (4117B)

"""
Tests for the Index constructor conducting inference.
"""
import numpy as np
import pytest

from pandas.core.dtypes.common import is_unsigned_integer_dtype

from pandas import (
    NA,
    CategoricalIndex,
    DatetimeIndex,
    Index,
    Int64Index,
    MultiIndex,
    NaT,
    PeriodIndex,
    Series,
    TimedeltaIndex,
    Timestamp,
    UInt64Index,
    period_range,
)
import pandas._testing as tm

class TestIndexConstructorInference:
    @pytest.mark.parametrize("na_value", [None, np.nan])
    @pytest.mark.parametrize("vtype", [list, tuple, iter])
    def test_construction_list_tuples_nan(self, na_value, vtype):
        # GH#18505 : valid tuples containing NaN
        values = [(1, "two"), (3.0, na_value)]
        result = Index(vtype(values))
        expected = MultiIndex.from_tuples(values)
        tm.assert_index_equal(result, expected)

@pytest.mark.parametrize(
        "dtype",
        [int, "int64", "int32", "int16", "int8", "uint64", "uint32", "uint16", "uint8"],
    )
    def test_constructor_int_dtype_float(self, dtype):
        # GH#18400
        if is_unsigned_integer_dtype(dtype):
            index_type = UInt64Index
        else:
            index_type = Int64Index

expected = index_type([0, 1, 2, 3])
        result = Index([0.0, 1.0, 2.0, 3.0], dtype=dtype)
        tm.assert_index_equal(result, expected)

@pytest.mark.parametrize("cast_index", [True, False])
    @pytest.mark.parametrize(
        "vals", [[True, False, True], np.array([True, False, True], dtype=bool)]
    )
    def test_constructor_dtypes_to_object(self, cast_index, vals):
        if cast_index:
            index = Index(vals, dtype=bool)
        else:
            index = Index(vals)

assert type(index) is Index
        assert index.dtype == object

def test_constructor_categorical_to_object(self):
        # GH#32167 Categorical data and dtype=object should return object-dtype
        ci = CategoricalIndex(range(5))
        result = Index(ci, dtype=object)
        assert not isinstance(result, CategoricalIndex)

def test_constructor_infer_periodindex(self):
        xp = period_range("2012-1-1", freq="M", periods=3)
        rs = Index(xp)
        tm.assert_index_equal(rs, xp)
        assert isinstance(rs, PeriodIndex)

@pytest.mark.parametrize("pos", [0, 1])
    @pytest.mark.parametrize(
        "klass,dtype,ctor",
        [
            (DatetimeIndex, "datetime64[ns]", np.datetime64("nat")),
            (TimedeltaIndex, "timedelta64[ns]", np.timedelta64("nat")),
        ],
    )
    def test_constructor_infer_nat_dt_like(
        self, pos, klass, dtype, ctor, nulls_fixture, request
    ):
        expected = klass([NaT, NaT])
        assert expected.dtype == dtype
        data = [ctor]
        data.insert(pos, nulls_fixture)

if nulls_fixture is NA:
            expected = Index([NA, NaT])
            mark = pytest.mark.xfail(reason="Broken with np.NaT ctor; see GH 31884")
            request.node.add_marker(mark)

result = Index(data)
        tm.assert_index_equal(result, expected)

result = Index(np.array(data, dtype=object))
        tm.assert_index_equal(result, expected)

@pytest.mark.parametrize("swap_objs", [True, False])
    def test_constructor_mixed_nat_objs_infers_object(self, swap_objs):
        # mixed np.datetime64/timedelta64 nat results in object
        data = [np.datetime64("nat"), np.timedelta64("nat")]
        if swap_objs:
            data = data[::-1]

expected = Index(data, dtype=object)
        tm.assert_index_equal(Index(data), expected)
        tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)

class TestIndexConstructorUnwrapping:
    # Test passing different arraylike values to pd.Index

@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)

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