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omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py

@@ -0,0 +1,79 @@
+import numpy as np
+
+from pandas.core.dtypes.cast import can_hold_element
+
+
+def test_can_hold_element_range(any_int_numpy_dtype):
+    # GH#44261
+    dtype = np.dtype(any_int_numpy_dtype)
+    arr = np.array([], dtype=dtype)
+
+    rng = range(2, 127)
+    assert can_hold_element(arr, rng)
+
+    # negatives -> can't be held by uint dtypes
+    rng = range(-2, 127)
+    if dtype.kind == "i":
+        assert can_hold_element(arr, rng)
+    else:
+        assert not can_hold_element(arr, rng)
+
+    rng = range(2, 255)
+    if dtype == "int8":
+        assert not can_hold_element(arr, rng)
+    else:
+        assert can_hold_element(arr, rng)
+
+    rng = range(-255, 65537)
+    if dtype.kind == "u":
+        assert not can_hold_element(arr, rng)
+    elif dtype.itemsize < 4:
+        assert not can_hold_element(arr, rng)
+    else:
+        assert can_hold_element(arr, rng)
+
+    # empty
+    rng = range(-(10**10), -(10**10))
+    assert len(rng) == 0
+    # assert can_hold_element(arr, rng)
+
+    rng = range(10**10, 10**10)
+    assert len(rng) == 0
+    assert can_hold_element(arr, rng)
+
+
+def test_can_hold_element_int_values_float_ndarray():
+    arr = np.array([], dtype=np.int64)
+
+    element = np.array([1.0, 2.0])
+    assert can_hold_element(arr, element)
+
+    assert not can_hold_element(arr, element + 0.5)
+
+    # integer but not losslessly castable to int64
+    element = np.array([3, 2**65], dtype=np.float64)
+    assert not can_hold_element(arr, element)
+
+
+def test_can_hold_element_int8_int():
+    arr = np.array([], dtype=np.int8)
+
+    element = 2
+    assert can_hold_element(arr, element)
+    assert can_hold_element(arr, np.int8(element))
+    assert can_hold_element(arr, np.uint8(element))
+    assert can_hold_element(arr, np.int16(element))
+    assert can_hold_element(arr, np.uint16(element))
+    assert can_hold_element(arr, np.int32(element))
+    assert can_hold_element(arr, np.uint32(element))
+    assert can_hold_element(arr, np.int64(element))
+    assert can_hold_element(arr, np.uint64(element))
+
+    element = 2**9
+    assert not can_hold_element(arr, element)
+    assert not can_hold_element(arr, np.int16(element))
+    assert not can_hold_element(arr, np.uint16(element))
+    assert not can_hold_element(arr, np.int32(element))
+    assert not can_hold_element(arr, np.uint32(element))
+    assert not can_hold_element(arr, np.int64(element))
+    assert not can_hold_element(arr, np.uint64(element))

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py

@@ -0,0 +1,55 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas import (
+    Categorical,
+    Timedelta,
+)
+import pandas._testing as tm
+
+
+def test_cast_1d_array_like_from_scalar_categorical():
+    # see gh-19565
+    #
+    # Categorical result from scalar did not maintain
+    # categories and ordering of the passed dtype.
+    cats = ["a", "b", "c"]
+    cat_type = CategoricalDtype(categories=cats, ordered=False)
+    expected = Categorical(["a", "a"], categories=cats)
+
+    result = construct_1d_arraylike_from_scalar("a", len(expected), cat_type)
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_cast_1d_array_like_from_timestamp(fixed_now_ts):
+    # check we dont lose nanoseconds
+    ts = fixed_now_ts + Timedelta(1)
+    res = construct_1d_arraylike_from_scalar(ts, 2, np.dtype("M8[ns]"))
+    assert res[0] == ts
+
+
+def test_cast_1d_array_like_from_timedelta():
+    # check we dont lose nanoseconds
+    td = Timedelta(1)
+    res = construct_1d_arraylike_from_scalar(td, 2, np.dtype("m8[ns]"))
+    assert res[0] == td
+
+
+def test_cast_1d_array_like_mismatched_datetimelike():
+    td = np.timedelta64("NaT", "ns")
+    dt = np.datetime64("NaT", "ns")
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(td, 2, dt.dtype)
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(np.timedelta64(4, "ns"), 2, dt.dtype)
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(dt, 2, td.dtype)
+
+    with pytest.raises(TypeError, match="Cannot cast"):
+        construct_1d_arraylike_from_scalar(np.datetime64(4, "ns"), 2, td.dtype)

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py

@@ -0,0 +1,36 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.construction import sanitize_array
+
+
+@pytest.mark.parametrize(
+    "values, dtype, expected",
+    [
+        ([1, 2, 3], None, np.array([1, 2, 3], dtype=np.int64)),
+        (np.array([1, 2, 3]), None, np.array([1, 2, 3])),
+        (["1", "2", None], None, np.array(["1", "2", None])),
+        (["1", "2", None], np.dtype("str"), np.array(["1", "2", None])),
+        ([1, 2, None], np.dtype("str"), np.array(["1", "2", None])),
+    ],
+)
+def test_construct_1d_ndarray_preserving_na(
+    values, dtype, expected, using_infer_string
+):
+    result = sanitize_array(values, index=None, dtype=dtype)
+    if using_infer_string and expected.dtype == object and dtype is None:
+        tm.assert_extension_array_equal(result, pd.array(expected))
+    else:
+        tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]"])
+def test_construct_1d_ndarray_preserving_na_datetimelike(dtype):
+    arr = np.arange(5, dtype=np.int64).view(dtype)
+    expected = np.array(list(arr), dtype=object)
+    assert all(isinstance(x, type(arr[0])) for x in expected)
+
+    result = sanitize_array(arr, index=None, dtype=np.dtype(object))
+    tm.assert_numpy_array_equal(result, expected)

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py

@@ -0,0 +1,20 @@
+import pytest
+
+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
+
+
+@pytest.mark.parametrize("datum1", [1, 2.0, "3", (4, 5), [6, 7], None])
+@pytest.mark.parametrize("datum2", [8, 9.0, "10", (11, 12), [13, 14], None])
+def test_cast_1d_array(datum1, datum2):
+    data = [datum1, datum2]
+    result = construct_1d_object_array_from_listlike(data)
+
+    # Direct comparison fails: https://github.com/numpy/numpy/issues/10218
+    assert result.dtype == "object"
+    assert list(result) == data
+
+
+@pytest.mark.parametrize("val", [1, 2.0, None])
+def test_cast_1d_array_invalid_scalar(val):
+    with pytest.raises(TypeError, match="has no len()"):
+        construct_1d_object_array_from_listlike(val)

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py

@@ -0,0 +1,14 @@
+import numpy as np
+
+from pandas.core.dtypes.cast import dict_compat
+
+from pandas import Timestamp
+
+
+def test_dict_compat():
+    data_datetime64 = {np.datetime64("1990-03-15"): 1, np.datetime64("2015-03-15"): 2}
+    data_unchanged = {1: 2, 3: 4, 5: 6}
+    expected = {Timestamp("1990-3-15"): 1, Timestamp("2015-03-15"): 2}
+    assert dict_compat(data_datetime64) == expected
+    assert dict_compat(expected) == expected
+    assert dict_compat(data_unchanged) == data_unchanged

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py

@@ -0,0 +1,175 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+
+from pandas import (
+    Categorical,
+    Index,
+)
+
+
+@pytest.mark.parametrize(
+    "source_dtypes,expected_common_dtype",
+    [
+        ((np.int64,), np.int64),
+        ((np.uint64,), np.uint64),
+        ((np.float32,), np.float32),
+        ((object,), object),
+        # Into ints.
+        ((np.int16, np.int64), np.int64),
+        ((np.int32, np.uint32), np.int64),
+        ((np.uint16, np.uint64), np.uint64),
+        # Into floats.
+        ((np.float16, np.float32), np.float32),
+        ((np.float16, np.int16), np.float32),
+        ((np.float32, np.int16), np.float32),
+        ((np.uint64, np.int64), np.float64),
+        ((np.int16, np.float64), np.float64),
+        ((np.float16, np.int64), np.float64),
+        # Into others.
+        ((np.complex128, np.int32), np.complex128),
+        ((object, np.float32), object),
+        ((object, np.int16), object),
+        # Bool with int.
+        ((np.dtype("bool"), np.int64), object),
+        ((np.dtype("bool"), np.int32), object),
+        ((np.dtype("bool"), np.int16), object),
+        ((np.dtype("bool"), np.int8), object),
+        ((np.dtype("bool"), np.uint64), object),
+        ((np.dtype("bool"), np.uint32), object),
+        ((np.dtype("bool"), np.uint16), object),
+        ((np.dtype("bool"), np.uint8), object),
+        # Bool with float.
+        ((np.dtype("bool"), np.float64), object),
+        ((np.dtype("bool"), np.float32), object),
+        (
+            (np.dtype("datetime64[ns]"), np.dtype("datetime64[ns]")),
+            np.dtype("datetime64[ns]"),
+        ),
+        (
+            (np.dtype("timedelta64[ns]"), np.dtype("timedelta64[ns]")),
+            np.dtype("timedelta64[ns]"),
+        ),
+        (
+            (np.dtype("datetime64[ns]"), np.dtype("datetime64[ms]")),
+            np.dtype("datetime64[ns]"),
+        ),
+        (
+            (np.dtype("timedelta64[ms]"), np.dtype("timedelta64[ns]")),
+            np.dtype("timedelta64[ns]"),
+        ),
+        ((np.dtype("datetime64[ns]"), np.dtype("timedelta64[ns]")), object),
+        ((np.dtype("datetime64[ns]"), np.int64), object),
+    ],
+)
+def test_numpy_dtypes(source_dtypes, expected_common_dtype):
+    source_dtypes = [pandas_dtype(x) for x in source_dtypes]
+    assert find_common_type(source_dtypes) == expected_common_dtype
+
+
+def test_raises_empty_input():
+    with pytest.raises(ValueError, match="no types given"):
+        find_common_type([])
+
+
+@pytest.mark.parametrize(
+    "dtypes,exp_type",
+    [
+        ([CategoricalDtype()], "category"),
+        ([object, CategoricalDtype()], object),
+        ([CategoricalDtype(), CategoricalDtype()], "category"),
+    ],
+)
+def test_categorical_dtype(dtypes, exp_type):
+    assert find_common_type(dtypes) == exp_type
+
+
+def test_datetimetz_dtype_match():
+    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
+    assert find_common_type([dtype, dtype]) == "datetime64[ns, US/Eastern]"
+
+
+@pytest.mark.parametrize(
+    "dtype2",
+    [
+        DatetimeTZDtype(unit="ns", tz="Asia/Tokyo"),
+        np.dtype("datetime64[ns]"),
+        object,
+        np.int64,
+    ],
+)
+def test_datetimetz_dtype_mismatch(dtype2):
+    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
+    assert find_common_type([dtype, dtype2]) == object
+    assert find_common_type([dtype2, dtype]) == object
+
+
+def test_period_dtype_match():
+    dtype = PeriodDtype(freq="D")
+    assert find_common_type([dtype, dtype]) == "period[D]"
+
+
+@pytest.mark.parametrize(
+    "dtype2",
+    [
+        DatetimeTZDtype(unit="ns", tz="Asia/Tokyo"),
+        PeriodDtype(freq="2D"),
+        PeriodDtype(freq="h"),
+        np.dtype("datetime64[ns]"),
+        object,
+        np.int64,
+    ],
+)
+def test_period_dtype_mismatch(dtype2):
+    dtype = PeriodDtype(freq="D")
+    assert find_common_type([dtype, dtype2]) == object
+    assert find_common_type([dtype2, dtype]) == object
+
+
+interval_dtypes = [
+    IntervalDtype(np.int64, "right"),
+    IntervalDtype(np.float64, "right"),
+    IntervalDtype(np.uint64, "right"),
+    IntervalDtype(DatetimeTZDtype(unit="ns", tz="US/Eastern"), "right"),
+    IntervalDtype("M8[ns]", "right"),
+    IntervalDtype("m8[ns]", "right"),
+]
+
+
+@pytest.mark.parametrize("left", interval_dtypes)
+@pytest.mark.parametrize("right", interval_dtypes)
+def test_interval_dtype(left, right):
+    result = find_common_type([left, right])
+
+    if left is right:
+        assert result is left
+
+    elif left.subtype.kind in ["i", "u", "f"]:
+        # i.e. numeric
+        if right.subtype.kind in ["i", "u", "f"]:
+            # both numeric -> common numeric subtype
+            expected = IntervalDtype(np.float64, "right")
+            assert result == expected
+        else:
+            assert result == object
+
+    else:
+        assert result == object
+
+
+@pytest.mark.parametrize("dtype", interval_dtypes)
+def test_interval_dtype_with_categorical(dtype):
+    obj = Index([], dtype=dtype)
+
+    cat = Categorical([], categories=obj)
+
+    result = find_common_type([dtype, cat.dtype])
+    assert result == dtype

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -0,0 +1,216 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import (
+    infer_dtype_from,
+    infer_dtype_from_array,
+    infer_dtype_from_scalar,
+)
+from pandas.core.dtypes.common import is_dtype_equal
+
+from pandas import (
+    Categorical,
+    Interval,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+
+
+def test_infer_dtype_from_int_scalar(any_int_numpy_dtype):
+    # Test that infer_dtype_from_scalar is
+    # returning correct dtype for int and float.
+    data = np.dtype(any_int_numpy_dtype).type(12)
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == type(data)
+
+
+def test_infer_dtype_from_float_scalar(float_numpy_dtype):
+    float_numpy_dtype = np.dtype(float_numpy_dtype).type
+    data = float_numpy_dtype(12)
+
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == float_numpy_dtype
+
+
+@pytest.mark.parametrize(
+    "data,exp_dtype", [(12, np.int64), (np.float64(12), np.float64)]
+)
+def test_infer_dtype_from_python_scalar(data, exp_dtype):
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == exp_dtype
+
+
+@pytest.mark.parametrize("bool_val", [True, False])
+def test_infer_dtype_from_boolean(bool_val):
+    dtype, val = infer_dtype_from_scalar(bool_val)
+    assert dtype == np.bool_
+
+
+def test_infer_dtype_from_complex(complex_dtype):
+    data = np.dtype(complex_dtype).type(1)
+    dtype, val = infer_dtype_from_scalar(data)
+    assert dtype == np.complex128
+
+
+def test_infer_dtype_from_datetime():
+    dt64 = np.datetime64(1, "ns")
+    dtype, val = infer_dtype_from_scalar(dt64)
+    assert dtype == "M8[ns]"
+
+    ts = Timestamp(1)
+    dtype, val = infer_dtype_from_scalar(ts)
+    assert dtype == "M8[ns]"
+
+    dt = datetime(2000, 1, 1, 0, 0)
+    dtype, val = infer_dtype_from_scalar(dt)
+    assert dtype == "M8[us]"
+
+
+def test_infer_dtype_from_timedelta():
+    td64 = np.timedelta64(1, "ns")
+    dtype, val = infer_dtype_from_scalar(td64)
+    assert dtype == "m8[ns]"
+
+    pytd = timedelta(1)
+    dtype, val = infer_dtype_from_scalar(pytd)
+    assert dtype == "m8[us]"
+
+    td = Timedelta(1)
+    dtype, val = infer_dtype_from_scalar(td)
+    assert dtype == "m8[ns]"
+
+
+@pytest.mark.parametrize("freq", ["M", "D"])
+def test_infer_dtype_from_period(freq):
+    p = Period("2011-01-01", freq=freq)
+    dtype, val = infer_dtype_from_scalar(p)
+
+    exp_dtype = f"period[{freq}]"
+
+    assert dtype == exp_dtype
+    assert val == p
+
+
+def test_infer_dtype_misc():
+    dt = date(2000, 1, 1)
+    dtype, val = infer_dtype_from_scalar(dt)
+    assert dtype == np.object_
+
+    ts = Timestamp(1, tz="US/Eastern")
+    dtype, val = infer_dtype_from_scalar(ts)
+    assert dtype == "datetime64[ns, US/Eastern]"
+
+
+@pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo"])
+def test_infer_from_scalar_tz(tz):
+    dt = Timestamp(1, tz=tz)
+    dtype, val = infer_dtype_from_scalar(dt)
+
+    exp_dtype = f"datetime64[ns, {tz}]"
+
+    assert dtype == exp_dtype
+    assert val == dt
+
+
+@pytest.mark.parametrize(
+    "left, right, subtype",
+    [
+        (0, 1, "int64"),
+        (0.0, 1.0, "float64"),
+        (Timestamp(0), Timestamp(1), "datetime64[ns]"),
+        (Timestamp(0, tz="UTC"), Timestamp(1, tz="UTC"), "datetime64[ns, UTC]"),
+        (Timedelta(0), Timedelta(1), "timedelta64[ns]"),
+    ],
+)
+def test_infer_from_interval(left, right, subtype, closed):
+    # GH 30337
+    interval = Interval(left, right, closed)
+    result_dtype, result_value = infer_dtype_from_scalar(interval)
+    expected_dtype = f"interval[{subtype}, {closed}]"
+    assert result_dtype == expected_dtype
+    assert result_value == interval
+
+
+def test_infer_dtype_from_scalar_errors():
+    msg = "invalid ndarray passed to infer_dtype_from_scalar"
+
+    with pytest.raises(ValueError, match=msg):
+        infer_dtype_from_scalar(np.array([1]))
+
+
+@pytest.mark.parametrize(
+    "value, expected",
+    [
+        ("foo", np.object_),
+        (b"foo", np.object_),
+        (1, np.int64),
+        (1.5, np.float64),
+        (np.datetime64("2016-01-01"), np.dtype("M8[s]")),
+        (Timestamp("20160101"), np.dtype("M8[s]")),
+        (Timestamp("20160101", tz="UTC"), "datetime64[s, UTC]"),
+    ],
+)
+def test_infer_dtype_from_scalar(value, expected, using_infer_string):
+    dtype, _ = infer_dtype_from_scalar(value)
+    if using_infer_string and value == "foo":
+        expected = "string"
+    assert is_dtype_equal(dtype, expected)
+
+    with pytest.raises(TypeError, match="must be list-like"):
+        infer_dtype_from_array(value)
+
+
+@pytest.mark.parametrize(
+    "arr, expected",
+    [
+        ([1], np.dtype(int)),
+        (np.array([1], dtype=np.int64), np.int64),
+        ([np.nan, 1, ""], np.object_),
+        (np.array([[1.0, 2.0]]), np.float64),
+        (Categorical(list("aabc")), "category"),
+        (Categorical([1, 2, 3]), "category"),
+        (date_range("20160101", periods=3), np.dtype("=M8[ns]")),
+        (
+            date_range("20160101", periods=3, tz="US/Eastern"),
+            "datetime64[ns, US/Eastern]",
+        ),
+        (Series([1.0, 2, 3]), np.float64),
+        (Series(list("abc")), np.object_),
+        (
+            Series(date_range("20160101", periods=3, tz="US/Eastern")),
+            "datetime64[ns, US/Eastern]",
+        ),
+    ],
+)
+def test_infer_dtype_from_array(arr, expected, using_infer_string):
+    dtype, _ = infer_dtype_from_array(arr)
+    if (
+        using_infer_string
+        and isinstance(arr, Series)
+        and arr.tolist() == ["a", "b", "c"]
+    ):
+        expected = "string"
+    assert is_dtype_equal(dtype, expected)
+
+
+@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
+def test_infer_dtype_from_scalar_zerodim_datetimelike(cls):
+    # ndarray.item() can incorrectly return int instead of td64/dt64
+    val = cls(1234, "ns")
+    arr = np.array(val)
+
+    dtype, res = infer_dtype_from_scalar(arr)
+    assert dtype.type is cls
+    assert isinstance(res, cls)
+
+    dtype, res = infer_dtype_from(arr)
+    assert dtype.type is cls

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py

@@ -0,0 +1,40 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import maybe_box_native
+
+from pandas import (
+    Interval,
+    Period,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "obj,expected_dtype",
+    [
+        (b"\x00\x10", bytes),
+        (int(4), int),
+        (np.uint(4), int),
+        (np.int32(-4), int),
+        (np.uint8(4), int),
+        (float(454.98), float),
+        (np.float16(0.4), float),
+        (np.float64(1.4), float),
+        (np.bool_(False), bool),
+        (datetime(2005, 2, 25), datetime),
+        (np.datetime64("2005-02-25"), Timestamp),
+        (Timestamp("2005-02-25"), Timestamp),
+        (np.timedelta64(1, "D"), Timedelta),
+        (Timedelta(1, "D"), Timedelta),
+        (Interval(0, 1), Interval),
+        (Period("4Q2005"), Period),
+    ],
+)
+def test_maybe_box_native(obj, expected_dtype):
+    boxed_obj = maybe_box_native(obj)
+    result_dtype = type(boxed_obj)
+    assert result_dtype is expected_dtype

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py

@@ -0,0 +1,530 @@
+"""
+These test the method maybe_promote from core/dtypes/cast.py
+"""
+
+import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import NaT
+
+from pandas.core.dtypes.cast import maybe_promote
+from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+
+import pandas as pd
+
+
+def _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar=None):
+    """
+    Auxiliary function to unify testing of scalar/array promotion.
+
+    Parameters
+    ----------
+    dtype : dtype
+        The value to pass on as the first argument to maybe_promote.
+    fill_value : scalar
+        The value to pass on as the second argument to maybe_promote as
+        a scalar.
+    expected_dtype : dtype
+        The expected dtype returned by maybe_promote (by design this is the
+        same regardless of whether fill_value was passed as a scalar or in an
+        array!).
+    exp_val_for_scalar : scalar
+        The expected value for the (potentially upcast) fill_value returned by
+        maybe_promote.
+    """
+    assert is_scalar(fill_value)
+
+    # here, we pass on fill_value as a scalar directly; the expected value
+    # returned from maybe_promote is fill_value, potentially upcast to the
+    # returned dtype.
+    result_dtype, result_fill_value = maybe_promote(dtype, fill_value)
+    expected_fill_value = exp_val_for_scalar
+
+    assert result_dtype == expected_dtype
+    _assert_match(result_fill_value, expected_fill_value)
+
+
+def _assert_match(result_fill_value, expected_fill_value):
+    # GH#23982/25425 require the same type in addition to equality/NA-ness
+    res_type = type(result_fill_value)
+    ex_type = type(expected_fill_value)
+
+    if hasattr(result_fill_value, "dtype"):
+        # Compare types in a way that is robust to platform-specific
+        #  idiosyncrasies where e.g. sometimes we get "ulonglong" as an alias
+        #  for "uint64" or "intc" as an alias for "int32"
+        assert result_fill_value.dtype.kind == expected_fill_value.dtype.kind
+        assert result_fill_value.dtype.itemsize == expected_fill_value.dtype.itemsize
+    else:
+        # On some builds, type comparison fails, e.g. np.int32 != np.int32
+        assert res_type == ex_type or res_type.__name__ == ex_type.__name__
+
+    match_value = result_fill_value == expected_fill_value
+    if match_value is pd.NA:
+        match_value = False
+
+    # Note: type check above ensures that we have the _same_ NA value
+    # for missing values, None == None (which is checked
+    # through match_value above), but np.nan != np.nan and pd.NaT != pd.NaT
+    match_missing = isna(result_fill_value) and isna(expected_fill_value)
+
+    assert match_value or match_missing
+
+
+@pytest.mark.parametrize(
+    "dtype, fill_value, expected_dtype",
+    [
+        # size 8
+        ("int8", 1, "int8"),
+        ("int8", np.iinfo("int8").max + 1, "int16"),
+        ("int8", np.iinfo("int16").max + 1, "int32"),
+        ("int8", np.iinfo("int32").max + 1, "int64"),
+        ("int8", np.iinfo("int64").max + 1, "object"),
+        ("int8", -1, "int8"),
+        ("int8", np.iinfo("int8").min - 1, "int16"),
+        ("int8", np.iinfo("int16").min - 1, "int32"),
+        ("int8", np.iinfo("int32").min - 1, "int64"),
+        ("int8", np.iinfo("int64").min - 1, "object"),
+        # keep signed-ness as long as possible
+        ("uint8", 1, "uint8"),
+        ("uint8", np.iinfo("int8").max + 1, "uint8"),
+        ("uint8", np.iinfo("uint8").max + 1, "uint16"),
+        ("uint8", np.iinfo("int16").max + 1, "uint16"),
+        ("uint8", np.iinfo("uint16").max + 1, "uint32"),
+        ("uint8", np.iinfo("int32").max + 1, "uint32"),
+        ("uint8", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint8", np.iinfo("int64").max + 1, "uint64"),
+        ("uint8", np.iinfo("uint64").max + 1, "object"),
+        # max of uint8 cannot be contained in int8
+        ("uint8", -1, "int16"),
+        ("uint8", np.iinfo("int8").min - 1, "int16"),
+        ("uint8", np.iinfo("int16").min - 1, "int32"),
+        ("uint8", np.iinfo("int32").min - 1, "int64"),
+        ("uint8", np.iinfo("int64").min - 1, "object"),
+        # size 16
+        ("int16", 1, "int16"),
+        ("int16", np.iinfo("int8").max + 1, "int16"),
+        ("int16", np.iinfo("int16").max + 1, "int32"),
+        ("int16", np.iinfo("int32").max + 1, "int64"),
+        ("int16", np.iinfo("int64").max + 1, "object"),
+        ("int16", -1, "int16"),
+        ("int16", np.iinfo("int8").min - 1, "int16"),
+        ("int16", np.iinfo("int16").min - 1, "int32"),
+        ("int16", np.iinfo("int32").min - 1, "int64"),
+        ("int16", np.iinfo("int64").min - 1, "object"),
+        ("uint16", 1, "uint16"),
+        ("uint16", np.iinfo("int8").max + 1, "uint16"),
+        ("uint16", np.iinfo("uint8").max + 1, "uint16"),
+        ("uint16", np.iinfo("int16").max + 1, "uint16"),
+        ("uint16", np.iinfo("uint16").max + 1, "uint32"),
+        ("uint16", np.iinfo("int32").max + 1, "uint32"),
+        ("uint16", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint16", np.iinfo("int64").max + 1, "uint64"),
+        ("uint16", np.iinfo("uint64").max + 1, "object"),
+        ("uint16", -1, "int32"),
+        ("uint16", np.iinfo("int8").min - 1, "int32"),
+        ("uint16", np.iinfo("int16").min - 1, "int32"),
+        ("uint16", np.iinfo("int32").min - 1, "int64"),
+        ("uint16", np.iinfo("int64").min - 1, "object"),
+        # size 32
+        ("int32", 1, "int32"),
+        ("int32", np.iinfo("int8").max + 1, "int32"),
+        ("int32", np.iinfo("int16").max + 1, "int32"),
+        ("int32", np.iinfo("int32").max + 1, "int64"),
+        ("int32", np.iinfo("int64").max + 1, "object"),
+        ("int32", -1, "int32"),
+        ("int32", np.iinfo("int8").min - 1, "int32"),
+        ("int32", np.iinfo("int16").min - 1, "int32"),
+        ("int32", np.iinfo("int32").min - 1, "int64"),
+        ("int32", np.iinfo("int64").min - 1, "object"),
+        ("uint32", 1, "uint32"),
+        ("uint32", np.iinfo("int8").max + 1, "uint32"),
+        ("uint32", np.iinfo("uint8").max + 1, "uint32"),
+        ("uint32", np.iinfo("int16").max + 1, "uint32"),
+        ("uint32", np.iinfo("uint16").max + 1, "uint32"),
+        ("uint32", np.iinfo("int32").max + 1, "uint32"),
+        ("uint32", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint32", np.iinfo("int64").max + 1, "uint64"),
+        ("uint32", np.iinfo("uint64").max + 1, "object"),
+        ("uint32", -1, "int64"),
+        ("uint32", np.iinfo("int8").min - 1, "int64"),
+        ("uint32", np.iinfo("int16").min - 1, "int64"),
+        ("uint32", np.iinfo("int32").min - 1, "int64"),
+        ("uint32", np.iinfo("int64").min - 1, "object"),
+        # size 64
+        ("int64", 1, "int64"),
+        ("int64", np.iinfo("int8").max + 1, "int64"),
+        ("int64", np.iinfo("int16").max + 1, "int64"),
+        ("int64", np.iinfo("int32").max + 1, "int64"),
+        ("int64", np.iinfo("int64").max + 1, "object"),
+        ("int64", -1, "int64"),
+        ("int64", np.iinfo("int8").min - 1, "int64"),
+        ("int64", np.iinfo("int16").min - 1, "int64"),
+        ("int64", np.iinfo("int32").min - 1, "int64"),
+        ("int64", np.iinfo("int64").min - 1, "object"),
+        ("uint64", 1, "uint64"),
+        ("uint64", np.iinfo("int8").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint8").max + 1, "uint64"),
+        ("uint64", np.iinfo("int16").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint16").max + 1, "uint64"),
+        ("uint64", np.iinfo("int32").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint32").max + 1, "uint64"),
+        ("uint64", np.iinfo("int64").max + 1, "uint64"),
+        ("uint64", np.iinfo("uint64").max + 1, "object"),
+        ("uint64", -1, "object"),
+        ("uint64", np.iinfo("int8").min - 1, "object"),
+        ("uint64", np.iinfo("int16").min - 1, "object"),
+        ("uint64", np.iinfo("int32").min - 1, "object"),
+        ("uint64", np.iinfo("int64").min - 1, "object"),
+    ],
+)
+def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
+    dtype = np.dtype(dtype)
+    expected_dtype = np.dtype(expected_dtype)
+
+    # output is not a generic int, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_int_with_float(any_int_numpy_dtype, float_numpy_dtype):
+    dtype = np.dtype(any_int_numpy_dtype)
+    fill_dtype = np.dtype(float_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling int with float always upcasts to float64
+    expected_dtype = np.float64
+    # fill_value can be different float type
+    exp_val_for_scalar = np.float64(fill_value)
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_float_with_int(float_numpy_dtype, any_int_numpy_dtype):
+    dtype = np.dtype(float_numpy_dtype)
+    fill_dtype = np.dtype(any_int_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling float with int always keeps float dtype
+    # because: np.finfo('float32').max > np.iinfo('uint64').max
+    expected_dtype = dtype
+    # output is not a generic float, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "dtype, fill_value, expected_dtype",
+    [
+        # float filled with float
+        ("float32", 1, "float32"),
+        ("float32", float(np.finfo("float32").max) * 1.1, "float64"),
+        ("float64", 1, "float64"),
+        ("float64", float(np.finfo("float32").max) * 1.1, "float64"),
+        # complex filled with float
+        ("complex64", 1, "complex64"),
+        ("complex64", float(np.finfo("float32").max) * 1.1, "complex128"),
+        ("complex128", 1, "complex128"),
+        ("complex128", float(np.finfo("float32").max) * 1.1, "complex128"),
+        # float filled with complex
+        ("float32", 1 + 1j, "complex64"),
+        ("float32", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+        ("float64", 1 + 1j, "complex128"),
+        ("float64", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+        # complex filled with complex
+        ("complex64", 1 + 1j, "complex64"),
+        ("complex64", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+        ("complex128", 1 + 1j, "complex128"),
+        ("complex128", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"),
+    ],
+)
+def test_maybe_promote_float_with_float(dtype, fill_value, expected_dtype):
+    dtype = np.dtype(dtype)
+    expected_dtype = np.dtype(expected_dtype)
+
+    # output is not a generic float, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_bool_with_any(any_numpy_dtype):
+    dtype = np.dtype(bool)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling bool with anything but bool casts to object
+    expected_dtype = np.dtype(object) if fill_dtype != bool else fill_dtype
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_bool(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+    fill_value = True
+
+    # filling anything but bool with bool casts to object
+    expected_dtype = np.dtype(object) if dtype != bool else dtype
+    # output is not a generic bool, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_bytes_with_any(bytes_dtype, any_numpy_dtype):
+    dtype = np.dtype(bytes_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # we never use bytes dtype internally, always promote to object
+    expected_dtype = np.dtype(np.object_)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_bytes(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype
+    fill_value = b"abc"
+
+    # we never use bytes dtype internally, always promote to object
+    expected_dtype = np.dtype(np.object_)
+    # output is not a generic bytes, but corresponds to expected_dtype
+    exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_datetime64_with_any(datetime64_dtype, any_numpy_dtype):
+    dtype = np.dtype(datetime64_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling datetime with anything but datetime casts to object
+    if fill_dtype.kind == "M":
+        expected_dtype = dtype
+        # for datetime dtypes, scalar values get cast to to_datetime64
+        exp_val_for_scalar = pd.Timestamp(fill_value).to_datetime64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        pd.Timestamp("now"),
+        np.datetime64("now"),
+        datetime.datetime.now(),
+        datetime.date.today(),
+    ],
+    ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
+)
+def test_maybe_promote_any_with_datetime64(any_numpy_dtype, fill_value):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # filling datetime with anything but datetime casts to object
+    if dtype.kind == "M":
+        expected_dtype = dtype
+        # for datetime dtypes, scalar values get cast to pd.Timestamp.value
+        exp_val_for_scalar = pd.Timestamp(fill_value).to_datetime64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    if type(fill_value) is datetime.date and dtype.kind == "M":
+        # Casting date to dt64 is deprecated, in 2.0 enforced to cast to object
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "fill_value",
+    [
+        pd.Timestamp(2023, 1, 1),
+        np.datetime64("2023-01-01"),
+        datetime.datetime(2023, 1, 1),
+        datetime.date(2023, 1, 1),
+    ],
+    ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
+)
+def test_maybe_promote_any_numpy_dtype_with_datetimetz(
+    any_numpy_dtype, tz_aware_fixture, fill_value
+):
+    dtype = np.dtype(any_numpy_dtype)
+    fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture)
+
+    fill_value = pd.Series([fill_value], dtype=fill_dtype)[0]
+
+    # filling any numpy dtype with datetimetz casts to object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype):
+    dtype = np.dtype(timedelta64_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling timedelta with anything but timedelta casts to object
+    if fill_dtype.kind == "m":
+        expected_dtype = dtype
+        # for timedelta dtypes, scalar values get cast to pd.Timedelta.value
+        exp_val_for_scalar = pd.Timedelta(fill_value).to_timedelta64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+@pytest.mark.parametrize(
+    "fill_value",
+    [pd.Timedelta(days=1), np.timedelta64(24, "h"), datetime.timedelta(1)],
+    ids=["pd.Timedelta", "np.timedelta64", "datetime.timedelta"],
+)
+def test_maybe_promote_any_with_timedelta64(any_numpy_dtype, fill_value):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # filling anything but timedelta with timedelta casts to object
+    if dtype.kind == "m":
+        expected_dtype = dtype
+        # for timedelta dtypes, scalar values get cast to pd.Timedelta.value
+        exp_val_for_scalar = pd.Timedelta(fill_value).to_timedelta64()
+    else:
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_string_with_any(string_dtype, any_numpy_dtype):
+    dtype = np.dtype(string_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling string with anything casts to object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_string(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype
+    fill_value = "abc"
+
+    # filling anything with a string casts to object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_object_with_any(object_dtype, any_numpy_dtype):
+    dtype = np.dtype(object_dtype)
+    fill_dtype = np.dtype(any_numpy_dtype)
+
+    # create array of given dtype; casts "1" to correct dtype
+    fill_value = np.array([1], dtype=fill_dtype)[0]
+
+    # filling object with anything stays object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_with_object(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
+
+    # create array of object dtype from a scalar value (i.e. passing
+    # dtypes.common.is_scalar), which can however not be cast to int/float etc.
+    fill_value = pd.DateOffset(1)
+
+    # filling object with anything stays object
+    expected_dtype = np.dtype(object)
+    exp_val_for_scalar = fill_value
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+
+
+def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype, nulls_fixture):
+    fill_value = nulls_fixture
+    dtype = np.dtype(any_numpy_dtype)
+
+    if isinstance(fill_value, Decimal):
+        # Subject to change, but ATM (When Decimal(NAN) is being added to nulls_fixture)
+        #  this is the existing behavior in maybe_promote,
+        #  hinges on is_valid_na_for_dtype
+        if dtype.kind in "iufc":
+            if dtype.kind in "iu":
+                expected_dtype = np.dtype(np.float64)
+            else:
+                expected_dtype = dtype
+            exp_val_for_scalar = np.nan
+        else:
+            expected_dtype = np.dtype(object)
+            exp_val_for_scalar = fill_value
+    elif dtype.kind in "iu" and fill_value is not NaT:
+        # integer + other missing value (np.nan / None) casts to float
+        expected_dtype = np.float64
+        exp_val_for_scalar = np.nan
+    elif dtype == object and fill_value is NaT:
+        # inserting into object does not cast the value
+        # but *does* cast None to np.nan
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
+    elif dtype.kind in "mM":
+        # datetime / timedelta cast all missing values to dtyped-NaT
+        expected_dtype = dtype
+        exp_val_for_scalar = dtype.type("NaT", "ns")
+    elif fill_value is NaT:
+        # NaT upcasts everything that's not datetime/timedelta to object
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = NaT
+    elif dtype.kind in "fc":
+        # float / complex + missing value (!= NaT) stays the same
+        expected_dtype = dtype
+        exp_val_for_scalar = np.nan
+    else:
+        # all other cases cast to object, and use np.nan as missing value
+        expected_dtype = np.dtype(object)
+        if fill_value is pd.NA:
+            exp_val_for_scalar = pd.NA
+        else:
+            exp_val_for_scalar = np.nan
+
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)

omnilmm/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py

@@ -0,0 +1,2047 @@
+"""
+These the test the public routines exposed in types/common.py
+related to inference and not otherwise tested in types/test_common.py
+
+"""
+import collections
+from collections import namedtuple
+from collections.abc import Iterator
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from decimal import Decimal
+from fractions import Fraction
+from io import StringIO
+import itertools
+from numbers import Number
+import re
+import sys
+from typing import (
+    Generic,
+    TypeVar,
+)
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs import (
+    lib,
+    missing as libmissing,
+    ops as libops,
+)
+from pandas.compat.numpy import np_version_gt2
+
+from pandas.core.dtypes import inference
+from pandas.core.dtypes.cast import find_result_type
+from pandas.core.dtypes.common import (
+    ensure_int32,
+    is_bool,
+    is_complex,
+    is_datetime64_any_dtype,
+    is_datetime64_dtype,
+    is_datetime64_ns_dtype,
+    is_datetime64tz_dtype,
+    is_float,
+    is_integer,
+    is_number,
+    is_scalar,
+    is_scipy_sparse,
+    is_timedelta64_dtype,
+    is_timedelta64_ns_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DateOffset,
+    DatetimeIndex,
+    Index,
+    Interval,
+    Period,
+    PeriodIndex,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    BooleanArray,
+    FloatingArray,
+    IntegerArray,
+)
+
+
+@pytest.fixture(params=[True, False], ids=str)
+def coerce(request):
+    return request.param
+
+
+class MockNumpyLikeArray:
+    """
+    A class which is numpy-like (e.g. Pint's Quantity) but not actually numpy
+
+    The key is that it is not actually a numpy array so
+    ``util.is_array(mock_numpy_like_array_instance)`` returns ``False``. Other
+    important properties are that the class defines a :meth:`__iter__` method
+    (so that ``isinstance(abc.Iterable)`` returns ``True``) and has a
+    :meth:`ndim` property, as pandas special-cases 0-dimensional arrays in some
+    cases.
+
+    We expect pandas to behave with respect to such duck arrays exactly as
+    with real numpy arrays. In particular, a 0-dimensional duck array is *NOT*
+    a scalar (`is_scalar(np.array(1)) == False`), but it is not list-like either.
+    """
+
+    def __init__(self, values) -> None:
+        self._values = values
+
+    def __iter__(self) -> Iterator:
+        iter_values = iter(self._values)
+
+        def it_outer():
+            yield from iter_values
+
+        return it_outer()
+
+    def __len__(self) -> int:
+        return len(self._values)
+
+    def __array__(self, dtype=None, copy=None):
+        return np.asarray(self._values, dtype=dtype)
+
+    @property
+    def ndim(self):
+        return self._values.ndim
+
+    @property
+    def dtype(self):
+        return self._values.dtype
+
+    @property
+    def size(self):
+        return self._values.size
+
+    @property
+    def shape(self):
+        return self._values.shape
+
+
+# collect all objects to be tested for list-like-ness; use tuples of objects,
+# whether they are list-like or not (special casing for sets), and their ID
+ll_params = [
+    ([1], True, "list"),
+    ([], True, "list-empty"),
+    ((1,), True, "tuple"),
+    ((), True, "tuple-empty"),
+    ({"a": 1}, True, "dict"),
+    ({}, True, "dict-empty"),
+    ({"a", 1}, "set", "set"),
+    (set(), "set", "set-empty"),
+    (frozenset({"a", 1}), "set", "frozenset"),
+    (frozenset(), "set", "frozenset-empty"),
+    (iter([1, 2]), True, "iterator"),
+    (iter([]), True, "iterator-empty"),
+    ((x for x in [1, 2]), True, "generator"),
+    ((_ for _ in []), True, "generator-empty"),
+    (Series([1]), True, "Series"),
+    (Series([], dtype=object), True, "Series-empty"),
+    # Series.str will still raise a TypeError if iterated
+    (Series(["a"]).str, True, "StringMethods"),
+    (Series([], dtype="O").str, True, "StringMethods-empty"),
+    (Index([1]), True, "Index"),
+    (Index([]), True, "Index-empty"),
+    (DataFrame([[1]]), True, "DataFrame"),
+    (DataFrame(), True, "DataFrame-empty"),
+    (np.ndarray((2,) * 1), True, "ndarray-1d"),
+    (np.array([]), True, "ndarray-1d-empty"),
+    (np.ndarray((2,) * 2), True, "ndarray-2d"),
+    (np.array([[]]), True, "ndarray-2d-empty"),
+    (np.ndarray((2,) * 3), True, "ndarray-3d"),
+    (np.array([[[]]]), True, "ndarray-3d-empty"),
+    (np.ndarray((2,) * 4), True, "ndarray-4d"),
+    (np.array([[[[]]]]), True, "ndarray-4d-empty"),
+    (np.array(2), False, "ndarray-0d"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 1)), True, "duck-ndarray-1d"),
+    (MockNumpyLikeArray(np.array([])), True, "duck-ndarray-1d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 2)), True, "duck-ndarray-2d"),
+    (MockNumpyLikeArray(np.array([[]])), True, "duck-ndarray-2d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 3)), True, "duck-ndarray-3d"),
+    (MockNumpyLikeArray(np.array([[[]]])), True, "duck-ndarray-3d-empty"),
+    (MockNumpyLikeArray(np.ndarray((2,) * 4)), True, "duck-ndarray-4d"),
+    (MockNumpyLikeArray(np.array([[[[]]]])), True, "duck-ndarray-4d-empty"),
+    (MockNumpyLikeArray(np.array(2)), False, "duck-ndarray-0d"),
+    (1, False, "int"),
+    (b"123", False, "bytes"),
+    (b"", False, "bytes-empty"),
+    ("123", False, "string"),
+    ("", False, "string-empty"),
+    (str, False, "string-type"),
+    (object(), False, "object"),
+    (np.nan, False, "NaN"),
+    (None, False, "None"),
+]
+objs, expected, ids = zip(*ll_params)
+
+
+@pytest.fixture(params=zip(objs, expected), ids=ids)
+def maybe_list_like(request):
+    return request.param
+
+
+def test_is_list_like(maybe_list_like):
+    obj, expected = maybe_list_like
+    expected = True if expected == "set" else expected
+    assert inference.is_list_like(obj) == expected
+
+
+def test_is_list_like_disallow_sets(maybe_list_like):
+    obj, expected = maybe_list_like
+    expected = False if expected == "set" else expected
+    assert inference.is_list_like(obj, allow_sets=False) == expected
+
+
+def test_is_list_like_recursion():
+    # GH 33721
+    # interpreter would crash with SIGABRT
+    def list_like():
+        inference.is_list_like([])
+        list_like()
+
+    rec_limit = sys.getrecursionlimit()
+    try:
+        # Limit to avoid stack overflow on Windows CI
+        sys.setrecursionlimit(100)
+        with tm.external_error_raised(RecursionError):
+            list_like()
+    finally:
+        sys.setrecursionlimit(rec_limit)
+
+
+def test_is_list_like_iter_is_none():
+    # GH 43373
+    # is_list_like was yielding false positives with __iter__ == None
+    class NotListLike:
+        def __getitem__(self, item):
+            return self
+
+        __iter__ = None
+
+    assert not inference.is_list_like(NotListLike())
+
+
+def test_is_list_like_generic():
+    # GH 49649
+    # is_list_like was yielding false positives for Generic classes in python 3.11
+    T = TypeVar("T")
+
+    class MyDataFrame(DataFrame, Generic[T]):
+        ...
+
+    tstc = MyDataFrame[int]
+    tst = MyDataFrame[int]({"x": [1, 2, 3]})
+
+    assert not inference.is_list_like(tstc)
+    assert isinstance(tst, DataFrame)
+    assert inference.is_list_like(tst)
+
+
+def test_is_sequence():
+    is_seq = inference.is_sequence
+    assert is_seq((1, 2))
+    assert is_seq([1, 2])
+    assert not is_seq("abcd")
+    assert not is_seq(np.int64)
+
+    class A:
+        def __getitem__(self, item):
+            return 1
+
+    assert not is_seq(A())
+
+
+def test_is_array_like():
+    assert inference.is_array_like(Series([], dtype=object))
+    assert inference.is_array_like(Series([1, 2]))
+    assert inference.is_array_like(np.array(["a", "b"]))
+    assert inference.is_array_like(Index(["2016-01-01"]))
+    assert inference.is_array_like(np.array([2, 3]))
+    assert inference.is_array_like(MockNumpyLikeArray(np.array([2, 3])))
+
+    class DtypeList(list):
+        dtype = "special"
+
+    assert inference.is_array_like(DtypeList())
+
+    assert not inference.is_array_like([1, 2, 3])
+    assert not inference.is_array_like(())
+    assert not inference.is_array_like("foo")
+    assert not inference.is_array_like(123)
+
+
+@pytest.mark.parametrize(
+    "inner",
+    [
+        [],
+        [1],
+        (1,),
+        (1, 2),
+        {"a": 1},
+        {1, "a"},
+        Series([1]),
+        Series([], dtype=object),
+        Series(["a"]).str,
+        (x for x in range(5)),
+    ],
+)
+@pytest.mark.parametrize("outer", [list, Series, np.array, tuple])
+def test_is_nested_list_like_passes(inner, outer):
+    result = outer([inner for _ in range(5)])
+    assert inference.is_list_like(result)
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        "abc",
+        [],
+        [1],
+        (1,),
+        ["a"],
+        "a",
+        {"a"},
+        [1, 2, 3],
+        Series([1]),
+        DataFrame({"A": [1]}),
+        ([1, 2] for _ in range(5)),
+    ],
+)
+def test_is_nested_list_like_fails(obj):
+    assert not inference.is_nested_list_like(obj)
+
+
+@pytest.mark.parametrize("ll", [{}, {"A": 1}, Series([1]), collections.defaultdict()])
+def test_is_dict_like_passes(ll):
+    assert inference.is_dict_like(ll)
+
+
+@pytest.mark.parametrize(
+    "ll",
+    [
+        "1",
+        1,
+        [1, 2],
+        (1, 2),
+        range(2),
+        Index([1]),
+        dict,
+        collections.defaultdict,
+        Series,
+    ],
+)
+def test_is_dict_like_fails(ll):
+    assert not inference.is_dict_like(ll)
+
+
+@pytest.mark.parametrize("has_keys", [True, False])
+@pytest.mark.parametrize("has_getitem", [True, False])
+@pytest.mark.parametrize("has_contains", [True, False])
+def test_is_dict_like_duck_type(has_keys, has_getitem, has_contains):
+    class DictLike:
+        def __init__(self, d) -> None:
+            self.d = d
+
+        if has_keys:
+
+            def keys(self):
+                return self.d.keys()
+
+        if has_getitem:
+
+            def __getitem__(self, key):
+                return self.d.__getitem__(key)
+
+        if has_contains:
+
+            def __contains__(self, key) -> bool:
+                return self.d.__contains__(key)
+
+    d = DictLike({1: 2})
+    result = inference.is_dict_like(d)
+    expected = has_keys and has_getitem and has_contains
+
+    assert result is expected
+
+
+def test_is_file_like():
+    class MockFile:
+        pass
+
+    is_file = inference.is_file_like
+
+    data = StringIO("data")
+    assert is_file(data)
+
+    # No read / write attributes
+    # No iterator attributes
+    m = MockFile()
+    assert not is_file(m)
+
+    MockFile.write = lambda self: 0
+
+    # Write attribute but not an iterator
+    m = MockFile()
+    assert not is_file(m)
+
+    # gh-16530: Valid iterator just means we have the
+    # __iter__ attribute for our purposes.
+    MockFile.__iter__ = lambda self: self
+
+    # Valid write-only file
+    m = MockFile()
+    assert is_file(m)
+
+    del MockFile.write
+    MockFile.read = lambda self: 0
+
+    # Valid read-only file
+    m = MockFile()
+    assert is_file(m)
+
+    # Iterator but no read / write attributes
+    data = [1, 2, 3]
+    assert not is_file(data)
+
+
+test_tuple = collections.namedtuple("test_tuple", ["a", "b", "c"])
+
+
+@pytest.mark.parametrize("ll", [test_tuple(1, 2, 3)])
+def test_is_names_tuple_passes(ll):
+    assert inference.is_named_tuple(ll)
+
+
+@pytest.mark.parametrize("ll", [(1, 2, 3), "a", Series({"pi": 3.14})])
+def test_is_names_tuple_fails(ll):
+    assert not inference.is_named_tuple(ll)
+
+
+def test_is_hashable():
+    # all new-style classes are hashable by default
+    class HashableClass:
+        pass
+
+    class UnhashableClass1:
+        __hash__ = None
+
+    class UnhashableClass2:
+        def __hash__(self):
+            raise TypeError("Not hashable")
+
+    hashable = (1, 3.14, np.float64(3.14), "a", (), (1,), HashableClass())
+    not_hashable = ([], UnhashableClass1())
+    abc_hashable_not_really_hashable = (([],), UnhashableClass2())
+
+    for i in hashable:
+        assert inference.is_hashable(i)
+    for i in not_hashable:
+        assert not inference.is_hashable(i)
+    for i in abc_hashable_not_really_hashable:
+        assert not inference.is_hashable(i)
+
+    # numpy.array is no longer collections.abc.Hashable as of
+    # https://github.com/numpy/numpy/pull/5326, just test
+    # is_hashable()
+    assert not inference.is_hashable(np.array([]))
+
+
+@pytest.mark.parametrize("ll", [re.compile("ad")])
+def test_is_re_passes(ll):
+    assert inference.is_re(ll)
+
+
+@pytest.mark.parametrize("ll", ["x", 2, 3, object()])
+def test_is_re_fails(ll):
+    assert not inference.is_re(ll)
+
+
+@pytest.mark.parametrize(
+    "ll", [r"a", "x", r"asdf", re.compile("adsf"), r"\u2233\s*", re.compile(r"")]
+)
+def test_is_recompilable_passes(ll):
+    assert inference.is_re_compilable(ll)
+
+
+@pytest.mark.parametrize("ll", [1, [], object()])
+def test_is_recompilable_fails(ll):
+    assert not inference.is_re_compilable(ll)
+
+
+class TestInference:
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array(list("abc"), dtype="S1"),
+            np.array(list("abc"), dtype="S1").astype(object),
+            [b"a", np.nan, b"c"],
+        ],
+    )
+    def test_infer_dtype_bytes(self, arr):
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "bytes"
+
+    @pytest.mark.parametrize(
+        "value, expected",
+        [
+            (float("inf"), True),
+            (np.inf, True),
+            (-np.inf, False),
+            (1, False),
+            ("a", False),
+        ],
+    )
+    def test_isposinf_scalar(self, value, expected):
+        # GH 11352
+        result = libmissing.isposinf_scalar(value)
+        assert result is expected
+
+    @pytest.mark.parametrize(
+        "value, expected",
+        [
+            (float("-inf"), True),
+            (-np.inf, True),
+            (np.inf, False),
+            (1, False),
+            ("a", False),
+        ],
+    )
+    def test_isneginf_scalar(self, value, expected):
+        result = libmissing.isneginf_scalar(value)
+        assert result is expected
+
+    @pytest.mark.parametrize(
+        "convert_to_masked_nullable, exp",
+        [
+            (
+                True,
+                BooleanArray(
+                    np.array([True, False], dtype="bool"), np.array([False, True])
+                ),
+            ),
+            (False, np.array([True, np.nan], dtype="object")),
+        ],
+    )
+    def test_maybe_convert_nullable_boolean(self, convert_to_masked_nullable, exp):
+        # GH 40687
+        arr = np.array([True, np.nan], dtype=object)
+        result = libops.maybe_convert_bool(
+            arr, set(), convert_to_masked_nullable=convert_to_masked_nullable
+        )
+        if convert_to_masked_nullable:
+            tm.assert_extension_array_equal(BooleanArray(*result), exp)
+        else:
+            result = result[0]
+            tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    @pytest.mark.parametrize("coerce_numeric", [True, False])
+    @pytest.mark.parametrize(
+        "infinity", ["inf", "inF", "iNf", "Inf", "iNF", "InF", "INf", "INF"]
+    )
+    @pytest.mark.parametrize("prefix", ["", "-", "+"])
+    def test_maybe_convert_numeric_infinities(
+        self, coerce_numeric, infinity, prefix, convert_to_masked_nullable
+    ):
+        # see gh-13274
+        result, _ = lib.maybe_convert_numeric(
+            np.array([prefix + infinity], dtype=object),
+            na_values={"", "NULL", "nan"},
+            coerce_numeric=coerce_numeric,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        expected = np.array([np.inf if prefix in ["", "+"] else -np.inf])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_maybe_convert_numeric_infinities_raises(self, convert_to_masked_nullable):
+        msg = "Unable to parse string"
+        with pytest.raises(ValueError, match=msg):
+            lib.maybe_convert_numeric(
+                np.array(["foo_inf"], dtype=object),
+                na_values={"", "NULL", "nan"},
+                coerce_numeric=False,
+                convert_to_masked_nullable=convert_to_masked_nullable,
+            )
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_maybe_convert_numeric_post_floatify_nan(
+        self, coerce, convert_to_masked_nullable
+    ):
+        # see gh-13314
+        data = np.array(["1.200", "-999.000", "4.500"], dtype=object)
+        expected = np.array([1.2, np.nan, 4.5], dtype=np.float64)
+        nan_values = {-999, -999.0}
+
+        out = lib.maybe_convert_numeric(
+            data,
+            nan_values,
+            coerce,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        if convert_to_masked_nullable:
+            expected = FloatingArray(expected, np.isnan(expected))
+            tm.assert_extension_array_equal(expected, FloatingArray(*out))
+        else:
+            out = out[0]
+            tm.assert_numpy_array_equal(out, expected)
+
+    def test_convert_infs(self):
+        arr = np.array(["inf", "inf", "inf"], dtype="O")
+        result, _ = lib.maybe_convert_numeric(arr, set(), False)
+        assert result.dtype == np.float64
+
+        arr = np.array(["-inf", "-inf", "-inf"], dtype="O")
+        result, _ = lib.maybe_convert_numeric(arr, set(), False)
+        assert result.dtype == np.float64
+
+    def test_scientific_no_exponent(self):
+        # See PR 12215
+        arr = np.array(["42E", "2E", "99e", "6e"], dtype="O")
+        result, _ = lib.maybe_convert_numeric(arr, set(), False, True)
+        assert np.all(np.isnan(result))
+
+    def test_convert_non_hashable(self):
+        # GH13324
+        # make sure that we are handing non-hashables
+        arr = np.array([[10.0, 2], 1.0, "apple"], dtype=object)
+        result, _ = lib.maybe_convert_numeric(arr, set(), False, True)
+        tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan]))
+
+    def test_convert_numeric_uint64(self):
+        arr = np.array([2**63], dtype=object)
+        exp = np.array([2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp)
+
+        arr = np.array([str(2**63)], dtype=object)
+        exp = np.array([2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp)
+
+        arr = np.array([np.uint64(2**63)], dtype=object)
+        exp = np.array([2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp)
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([2**63, np.nan], dtype=object),
+            np.array([str(2**63), np.nan], dtype=object),
+            np.array([np.nan, 2**63], dtype=object),
+            np.array([np.nan, str(2**63)], dtype=object),
+        ],
+    )
+    def test_convert_numeric_uint64_nan(self, coerce, arr):
+        expected = arr.astype(float) if coerce else arr.copy()
+        result, _ = lib.maybe_convert_numeric(arr, set(), coerce_numeric=coerce)
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_convert_numeric_uint64_nan_values(
+        self, coerce, convert_to_masked_nullable
+    ):
+        arr = np.array([2**63, 2**63 + 1], dtype=object)
+        na_values = {2**63}
+
+        expected = (
+            np.array([np.nan, 2**63 + 1], dtype=float) if coerce else arr.copy()
+        )
+        result = lib.maybe_convert_numeric(
+            arr,
+            na_values,
+            coerce_numeric=coerce,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        if convert_to_masked_nullable and coerce:
+            expected = IntegerArray(
+                np.array([0, 2**63 + 1], dtype="u8"),
+                np.array([True, False], dtype="bool"),
+            )
+            result = IntegerArray(*result)
+        else:
+            result = result[0]  # discard mask
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "case",
+        [
+            np.array([2**63, -1], dtype=object),
+            np.array([str(2**63), -1], dtype=object),
+            np.array([str(2**63), str(-1)], dtype=object),
+            np.array([-1, 2**63], dtype=object),
+            np.array([-1, str(2**63)], dtype=object),
+            np.array([str(-1), str(2**63)], dtype=object),
+        ],
+    )
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_convert_numeric_int64_uint64(
+        self, case, coerce, convert_to_masked_nullable
+    ):
+        expected = case.astype(float) if coerce else case.copy()
+        result, _ = lib.maybe_convert_numeric(
+            case,
+            set(),
+            coerce_numeric=coerce,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+
+        tm.assert_almost_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_to_masked_nullable", [True, False])
+    def test_convert_numeric_string_uint64(self, convert_to_masked_nullable):
+        # GH32394
+        result = lib.maybe_convert_numeric(
+            np.array(["uint64"], dtype=object),
+            set(),
+            coerce_numeric=True,
+            convert_to_masked_nullable=convert_to_masked_nullable,
+        )
+        if convert_to_masked_nullable:
+            result = FloatingArray(*result)
+        else:
+            result = result[0]
+        assert np.isnan(result)
+
+    @pytest.mark.parametrize("value", [-(2**63) - 1, 2**64])
+    def test_convert_int_overflow(self, value):
+        # see gh-18584
+        arr = np.array([value], dtype=object)
+        result = lib.maybe_convert_objects(arr)
+        tm.assert_numpy_array_equal(arr, result)
+
+    @pytest.mark.parametrize("val", [None, np.nan, float("nan")])
+    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+    def test_maybe_convert_objects_nat_inference(self, val, dtype):
+        dtype = np.dtype(dtype)
+        vals = np.array([pd.NaT, val], dtype=object)
+        result = lib.maybe_convert_objects(
+            vals,
+            convert_non_numeric=True,
+            dtype_if_all_nat=dtype,
+        )
+        assert result.dtype == dtype
+        assert np.isnat(result).all()
+
+        result = lib.maybe_convert_objects(
+            vals[::-1],
+            convert_non_numeric=True,
+            dtype_if_all_nat=dtype,
+        )
+        assert result.dtype == dtype
+        assert np.isnat(result).all()
+
+    @pytest.mark.parametrize(
+        "value, expected_dtype",
+        [
+            # see gh-4471
+            ([2**63], np.uint64),
+            # NumPy bug: can't compare uint64 to int64, as that
+            # results in both casting to float64, so we should
+            # make sure that this function is robust against it
+            ([np.uint64(2**63)], np.uint64),
+            ([2, -1], np.int64),
+            ([2**63, -1], object),
+            # GH#47294
+            ([np.uint8(1)], np.uint8),
+            ([np.uint16(1)], np.uint16),
+            ([np.uint32(1)], np.uint32),
+            ([np.uint64(1)], np.uint64),
+            ([np.uint8(2), np.uint16(1)], np.uint16),
+            ([np.uint32(2), np.uint16(1)], np.uint32),
+            ([np.uint32(2), -1], object),
+            ([np.uint32(2), 1], np.uint64),
+            ([np.uint32(2), np.int32(1)], object),
+        ],
+    )
+    def test_maybe_convert_objects_uint(self, value, expected_dtype):
+        arr = np.array(value, dtype=object)
+        exp = np.array(value, dtype=expected_dtype)
+        tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp)
+
+    def test_maybe_convert_objects_datetime(self):
+        # GH27438
+        arr = np.array(
+            [np.datetime64("2000-01-01"), np.timedelta64(1, "s")], dtype=object
+        )
+        exp = arr.copy()
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+        arr = np.array([pd.NaT, np.timedelta64(1, "s")], dtype=object)
+        exp = np.array([np.timedelta64("NaT"), np.timedelta64(1, "s")], dtype="m8[ns]")
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+        # with convert_non_numeric=True, the nan is a valid NA value for td64
+        arr = np.array([np.timedelta64(1, "s"), np.nan], dtype=object)
+        exp = exp[::-1]
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+    def test_maybe_convert_objects_dtype_if_all_nat(self):
+        arr = np.array([pd.NaT, pd.NaT], dtype=object)
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        # no dtype_if_all_nat passed -> we dont guess
+        tm.assert_numpy_array_equal(out, arr)
+
+        out = lib.maybe_convert_objects(
+            arr,
+            convert_non_numeric=True,
+            dtype_if_all_nat=np.dtype("timedelta64[ns]"),
+        )
+        exp = np.array(["NaT", "NaT"], dtype="timedelta64[ns]")
+        tm.assert_numpy_array_equal(out, exp)
+
+        out = lib.maybe_convert_objects(
+            arr,
+            convert_non_numeric=True,
+            dtype_if_all_nat=np.dtype("datetime64[ns]"),
+        )
+        exp = np.array(["NaT", "NaT"], dtype="datetime64[ns]")
+        tm.assert_numpy_array_equal(out, exp)
+
+    def test_maybe_convert_objects_dtype_if_all_nat_invalid(self):
+        # we accept datetime64[ns], timedelta64[ns], and EADtype
+        arr = np.array([pd.NaT, pd.NaT], dtype=object)
+
+        with pytest.raises(ValueError, match="int64"):
+            lib.maybe_convert_objects(
+                arr,
+                convert_non_numeric=True,
+                dtype_if_all_nat=np.dtype("int64"),
+            )
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_maybe_convert_objects_datetime_overflow_safe(self, dtype):
+        stamp = datetime(2363, 10, 4)  # Enterprise-D launch date
+        if dtype == "timedelta64[ns]":
+            stamp = stamp - datetime(1970, 1, 1)
+        arr = np.array([stamp], dtype=object)
+
+        out = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        # no OutOfBoundsDatetime/OutOfBoundsTimedeltas
+        tm.assert_numpy_array_equal(out, arr)
+
+    def test_maybe_convert_objects_mixed_datetimes(self):
+        ts = Timestamp("now")
+        vals = [ts, ts.to_pydatetime(), ts.to_datetime64(), pd.NaT, np.nan, None]
+
+        for data in itertools.permutations(vals):
+            data = np.array(list(data), dtype=object)
+            expected = DatetimeIndex(data)._data._ndarray
+            result = lib.maybe_convert_objects(data, convert_non_numeric=True)
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_maybe_convert_objects_timedelta64_nat(self):
+        obj = np.timedelta64("NaT", "ns")
+        arr = np.array([obj], dtype=object)
+        assert arr[0] is obj
+
+        result = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+
+        expected = np.array([obj], dtype="m8[ns]")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "exp",
+        [
+            IntegerArray(np.array([2, 0], dtype="i8"), np.array([False, True])),
+            IntegerArray(np.array([2, 0], dtype="int64"), np.array([False, True])),
+        ],
+    )
+    def test_maybe_convert_objects_nullable_integer(self, exp):
+        # GH27335
+        arr = np.array([2, np.nan], dtype=object)
+        result = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+
+        tm.assert_extension_array_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "dtype, val", [("int64", 1), ("uint64", np.iinfo(np.int64).max + 1)]
+    )
+    def test_maybe_convert_objects_nullable_none(self, dtype, val):
+        # GH#50043
+        arr = np.array([val, None, 3], dtype="object")
+        result = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        expected = IntegerArray(
+            np.array([val, 0, 3], dtype=dtype), np.array([False, True, False])
+        )
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "convert_to_masked_nullable, exp",
+        [
+            (True, IntegerArray(np.array([2, 0], dtype="i8"), np.array([False, True]))),
+            (False, np.array([2, np.nan], dtype="float64")),
+        ],
+    )
+    def test_maybe_convert_numeric_nullable_integer(
+        self, convert_to_masked_nullable, exp
+    ):
+        # GH 40687
+        arr = np.array([2, np.nan], dtype=object)
+        result = lib.maybe_convert_numeric(
+            arr, set(), convert_to_masked_nullable=convert_to_masked_nullable
+        )
+        if convert_to_masked_nullable:
+            result = IntegerArray(*result)
+            tm.assert_extension_array_equal(result, exp)
+        else:
+            result = result[0]
+            tm.assert_numpy_array_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "convert_to_masked_nullable, exp",
+        [
+            (
+                True,
+                FloatingArray(
+                    np.array([2.0, 0.0], dtype="float64"), np.array([False, True])
+                ),
+            ),
+            (False, np.array([2.0, np.nan], dtype="float64")),
+        ],
+    )
+    def test_maybe_convert_numeric_floating_array(
+        self, convert_to_masked_nullable, exp
+    ):
+        # GH 40687
+        arr = np.array([2.0, np.nan], dtype=object)
+        result = lib.maybe_convert_numeric(
+            arr, set(), convert_to_masked_nullable=convert_to_masked_nullable
+        )
+        if convert_to_masked_nullable:
+            tm.assert_extension_array_equal(FloatingArray(*result), exp)
+        else:
+            result = result[0]
+            tm.assert_numpy_array_equal(result, exp)
+
+    def test_maybe_convert_objects_bool_nan(self):
+        # GH32146
+        ind = Index([True, False, np.nan], dtype=object)
+        exp = np.array([True, False, np.nan], dtype=object)
+        out = lib.maybe_convert_objects(ind.values, safe=1)
+        tm.assert_numpy_array_equal(out, exp)
+
+    def test_maybe_convert_objects_nullable_boolean(self):
+        # GH50047
+        arr = np.array([True, False], dtype=object)
+        exp = np.array([True, False])
+        out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+        arr = np.array([True, False, pd.NaT], dtype=object)
+        exp = np.array([True, False, pd.NaT], dtype=object)
+        out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        tm.assert_numpy_array_equal(out, exp)
+
+    @pytest.mark.parametrize("val", [None, np.nan])
+    def test_maybe_convert_objects_nullable_boolean_na(self, val):
+        # GH50047
+        arr = np.array([True, False, val], dtype=object)
+        exp = BooleanArray(
+            np.array([True, False, False]), np.array([False, False, True])
+        )
+        out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True)
+        tm.assert_extension_array_equal(out, exp)
+
+    @pytest.mark.parametrize(
+        "data0",
+        [
+            True,
+            1,
+            1.0,
+            1.0 + 1.0j,
+            np.int8(1),
+            np.int16(1),
+            np.int32(1),
+            np.int64(1),
+            np.float16(1),
+            np.float32(1),
+            np.float64(1),
+            np.complex64(1),
+            np.complex128(1),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "data1",
+        [
+            True,
+            1,
+            1.0,
+            1.0 + 1.0j,
+            np.int8(1),
+            np.int16(1),
+            np.int32(1),
+            np.int64(1),
+            np.float16(1),
+            np.float32(1),
+            np.float64(1),
+            np.complex64(1),
+            np.complex128(1),
+        ],
+    )
+    def test_maybe_convert_objects_itemsize(self, data0, data1):
+        # GH 40908
+        data = [data0, data1]
+        arr = np.array(data, dtype="object")
+
+        common_kind = np.result_type(type(data0), type(data1)).kind
+        kind0 = "python" if not hasattr(data0, "dtype") else data0.dtype.kind
+        kind1 = "python" if not hasattr(data1, "dtype") else data1.dtype.kind
+        if kind0 != "python" and kind1 != "python":
+            kind = common_kind
+            itemsize = max(data0.dtype.itemsize, data1.dtype.itemsize)
+        elif is_bool(data0) or is_bool(data1):
+            kind = "bool" if (is_bool(data0) and is_bool(data1)) else "object"
+            itemsize = ""
+        elif is_complex(data0) or is_complex(data1):
+            kind = common_kind
+            itemsize = 16
+        else:
+            kind = common_kind
+            itemsize = 8
+
+        expected = np.array(data, dtype=f"{kind}{itemsize}")
+        result = lib.maybe_convert_objects(arr)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_mixed_dtypes_remain_object_array(self):
+        # GH14956
+        arr = np.array([datetime(2015, 1, 1, tzinfo=pytz.utc), 1], dtype=object)
+        result = lib.maybe_convert_objects(arr, convert_non_numeric=True)
+        tm.assert_numpy_array_equal(result, arr)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            pd.IntervalIndex.from_breaks(range(5), closed="both"),
+            pd.period_range("2016-01-01", periods=3, freq="D"),
+        ],
+    )
+    def test_maybe_convert_objects_ea(self, idx):
+        result = lib.maybe_convert_objects(
+            np.array(idx, dtype=object),
+            convert_non_numeric=True,
+        )
+        tm.assert_extension_array_equal(result, idx._data)
+
+
+class TestTypeInference:
+    # Dummy class used for testing with Python objects
+    class Dummy:
+        pass
+
+    def test_inferred_dtype_fixture(self, any_skipna_inferred_dtype):
+        # see pandas/conftest.py
+        inferred_dtype, values = any_skipna_inferred_dtype
+
+        # make sure the inferred dtype of the fixture is as requested
+        assert inferred_dtype == lib.infer_dtype(values, skipna=True)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_length_zero(self, skipna):
+        result = lib.infer_dtype(np.array([], dtype="i4"), skipna=skipna)
+        assert result == "integer"
+
+        result = lib.infer_dtype([], skipna=skipna)
+        assert result == "empty"
+
+        # GH 18004
+        arr = np.array([np.array([], dtype=object), np.array([], dtype=object)])
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "empty"
+
+    def test_integers(self):
+        arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "integer"
+
+        arr = np.array([1, 2, 3, np.int64(4), np.int32(5), "foo"], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed-integer"
+
+        arr = np.array([1, 2, 3, 4, 5], dtype="i4")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "integer"
+
+    @pytest.mark.parametrize(
+        "arr, skipna",
+        [
+            (np.array([1, 2, np.nan, np.nan, 3], dtype="O"), False),
+            (np.array([1, 2, np.nan, np.nan, 3], dtype="O"), True),
+            (np.array([1, 2, 3, np.int64(4), np.int32(5), np.nan], dtype="O"), False),
+            (np.array([1, 2, 3, np.int64(4), np.int32(5), np.nan], dtype="O"), True),
+        ],
+    )
+    def test_integer_na(self, arr, skipna):
+        # GH 27392
+        result = lib.infer_dtype(arr, skipna=skipna)
+        expected = "integer" if skipna else "integer-na"
+        assert result == expected
+
+    def test_infer_dtype_skipna_default(self):
+        # infer_dtype `skipna` default deprecated in GH#24050,
+        #  changed to True in GH#29876
+        arr = np.array([1, 2, 3, np.nan], dtype=object)
+
+        result = lib.infer_dtype(arr)
+        assert result == "integer"
+
+    def test_bools(self):
+        arr = np.array([True, False, True, True, True], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        arr = np.array([np.bool_(True), np.bool_(False)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        arr = np.array([True, False, True, "foo"], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed"
+
+        arr = np.array([True, False, True], dtype=bool)
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        arr = np.array([True, np.nan, False], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "boolean"
+
+        result = lib.infer_dtype(arr, skipna=False)
+        assert result == "mixed"
+
+    def test_floats(self):
+        arr = np.array([1.0, 2.0, 3.0, np.float64(4), np.float32(5)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "floating"
+
+        arr = np.array([1, 2, 3, np.float64(4), np.float32(5), "foo"], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed-integer"
+
+        arr = np.array([1, 2, 3, 4, 5], dtype="f4")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "floating"
+
+        arr = np.array([1, 2, 3, 4, 5], dtype="f8")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "floating"
+
+    def test_decimals(self):
+        # GH15690
+        arr = np.array([Decimal(1), Decimal(2), Decimal(3)])
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "decimal"
+
+        arr = np.array([1.0, 2.0, Decimal(3)])
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed"
+
+        result = lib.infer_dtype(arr[::-1], skipna=True)
+        assert result == "mixed"
+
+        arr = np.array([Decimal(1), Decimal("NaN"), Decimal(3)])
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "decimal"
+
+        arr = np.array([Decimal(1), np.nan, Decimal(3)], dtype="O")
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "decimal"
+
+    # complex is compatible with nan, so skipna has no effect
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_complex(self, skipna):
+        # gets cast to complex on array construction
+        arr = np.array([1.0, 2.0, 1 + 1j])
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+        arr = np.array([1.0, 2.0, 1 + 1j], dtype="O")
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "mixed"
+
+        result = lib.infer_dtype(arr[::-1], skipna=skipna)
+        assert result == "mixed"
+
+        # gets cast to complex on array construction
+        arr = np.array([1, np.nan, 1 + 1j])
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+        arr = np.array([1.0, np.nan, 1 + 1j], dtype="O")
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "mixed"
+
+        # complex with nans stays complex
+        arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype="O")
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+        # test smaller complex dtype; will pass through _try_infer_map fastpath
+        arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype=np.complex64)
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == "complex"
+
+    def test_string(self):
+        pass
+
+    def test_unicode(self):
+        arr = ["a", np.nan, "c"]
+        result = lib.infer_dtype(arr, skipna=False)
+        # This currently returns "mixed", but it's not clear that's optimal.
+        # This could also return "string" or "mixed-string"
+        assert result == "mixed"
+
+        # even though we use skipna, we are only skipping those NAs that are
+        #  considered matching by is_string_array
+        arr = ["a", np.nan, "c"]
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "string"
+
+        arr = ["a", pd.NA, "c"]
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "string"
+
+        arr = ["a", pd.NaT, "c"]
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "mixed"
+
+        arr = ["a", "c"]
+        result = lib.infer_dtype(arr, skipna=False)
+        assert result == "string"
+
+    @pytest.mark.parametrize(
+        "dtype, missing, skipna, expected",
+        [
+            (float, np.nan, False, "floating"),
+            (float, np.nan, True, "floating"),
+            (object, np.nan, False, "floating"),
+            (object, np.nan, True, "empty"),
+            (object, None, False, "mixed"),
+            (object, None, True, "empty"),
+        ],
+    )
+    @pytest.mark.parametrize("box", [Series, np.array])
+    def test_object_empty(self, box, missing, dtype, skipna, expected):
+        # GH 23421
+        arr = box([missing, missing], dtype=dtype)
+
+        result = lib.infer_dtype(arr, skipna=skipna)
+        assert result == expected
+
+    def test_datetime(self):
+        dates = [datetime(2012, 1, x) for x in range(1, 20)]
+        index = Index(dates)
+        assert index.inferred_type == "datetime64"
+
+    def test_infer_dtype_datetime64(self):
+        arr = np.array(
+            [np.datetime64("2011-01-01"), np.datetime64("2011-01-01")], dtype=object
+        )
+        assert lib.infer_dtype(arr, skipna=True) == "datetime64"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    def test_infer_dtype_datetime64_with_na(self, na_value):
+        # starts with nan
+        arr = np.array([na_value, np.datetime64("2011-01-02")])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime64"
+
+        arr = np.array([na_value, np.datetime64("2011-01-02"), na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime64"
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array(
+                [np.timedelta64("nat"), np.datetime64("2011-01-02")], dtype=object
+            ),
+            np.array(
+                [np.datetime64("2011-01-02"), np.timedelta64("nat")], dtype=object
+            ),
+            np.array([np.datetime64("2011-01-01"), Timestamp("2011-01-02")]),
+            np.array([Timestamp("2011-01-02"), np.datetime64("2011-01-01")]),
+            np.array([np.nan, Timestamp("2011-01-02"), 1.1]),
+            np.array([np.nan, "2011-01-01", Timestamp("2011-01-02")], dtype=object),
+            np.array([np.datetime64("nat"), np.timedelta64(1, "D")], dtype=object),
+            np.array([np.timedelta64(1, "D"), np.datetime64("nat")], dtype=object),
+        ],
+    )
+    def test_infer_datetimelike_dtype_mixed(self, arr):
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+    def test_infer_dtype_mixed_integer(self):
+        arr = np.array([np.nan, Timestamp("2011-01-02"), 1])
+        assert lib.infer_dtype(arr, skipna=True) == "mixed-integer"
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([Timestamp("2011-01-01"), Timestamp("2011-01-02")]),
+            np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]),
+            np.array([datetime(2011, 1, 1), Timestamp("2011-01-02")]),
+        ],
+    )
+    def test_infer_dtype_datetime(self, arr):
+        assert lib.infer_dtype(arr, skipna=True) == "datetime"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    @pytest.mark.parametrize(
+        "time_stamp", [Timestamp("2011-01-01"), datetime(2011, 1, 1)]
+    )
+    def test_infer_dtype_datetime_with_na(self, na_value, time_stamp):
+        # starts with nan
+        arr = np.array([na_value, time_stamp])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime"
+
+        arr = np.array([na_value, time_stamp, na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "datetime"
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([Timedelta("1 days"), Timedelta("2 days")]),
+            np.array([np.timedelta64(1, "D"), np.timedelta64(2, "D")], dtype=object),
+            np.array([timedelta(1), timedelta(2)]),
+        ],
+    )
+    def test_infer_dtype_timedelta(self, arr):
+        assert lib.infer_dtype(arr, skipna=True) == "timedelta"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    @pytest.mark.parametrize(
+        "delta", [Timedelta("1 days"), np.timedelta64(1, "D"), timedelta(1)]
+    )
+    def test_infer_dtype_timedelta_with_na(self, na_value, delta):
+        # starts with nan
+        arr = np.array([na_value, delta])
+        assert lib.infer_dtype(arr, skipna=True) == "timedelta"
+
+        arr = np.array([na_value, delta, na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "timedelta"
+
+    def test_infer_dtype_period(self):
+        # GH 13664
+        arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="D")])
+        assert lib.infer_dtype(arr, skipna=True) == "period"
+
+        # non-homogeneous freqs -> mixed
+        arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="M")])
+        assert lib.infer_dtype(arr, skipna=True) == "mixed"
+
+    @pytest.mark.parametrize("klass", [pd.array, Series, Index])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_infer_dtype_period_array(self, klass, skipna):
+        # https://github.com/pandas-dev/pandas/issues/23553
+        values = klass(
+            [
+                Period("2011-01-01", freq="D"),
+                Period("2011-01-02", freq="D"),
+                pd.NaT,
+            ]
+        )
+        assert lib.infer_dtype(values, skipna=skipna) == "period"
+
+        # periods but mixed freq
+        values = klass(
+            [
+                Period("2011-01-01", freq="D"),
+                Period("2011-01-02", freq="M"),
+                pd.NaT,
+            ]
+        )
+        # with pd.array this becomes NumpyExtensionArray which ends up
+        #  as "unknown-array"
+        exp = "unknown-array" if klass is pd.array else "mixed"
+        assert lib.infer_dtype(values, skipna=skipna) == exp
+
+    def test_infer_dtype_period_mixed(self):
+        arr = np.array(
+            [Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object
+        )
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array(
+            [np.datetime64("nat"), Period("2011-01", freq="M")], dtype=object
+        )
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+    @pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
+    def test_infer_dtype_period_with_na(self, na_value):
+        # starts with nan
+        arr = np.array([na_value, Period("2011-01", freq="D")])
+        assert lib.infer_dtype(arr, skipna=True) == "period"
+
+        arr = np.array([na_value, Period("2011-01", freq="D"), na_value])
+        assert lib.infer_dtype(arr, skipna=True) == "period"
+
+    def test_infer_dtype_all_nan_nat_like(self):
+        arr = np.array([np.nan, np.nan])
+        assert lib.infer_dtype(arr, skipna=True) == "floating"
+
+        # nan and None mix are result in mixed
+        arr = np.array([np.nan, np.nan, None])
+        assert lib.infer_dtype(arr, skipna=True) == "empty"
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([None, np.nan, np.nan])
+        assert lib.infer_dtype(arr, skipna=True) == "empty"
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        # pd.NaT
+        arr = np.array([pd.NaT])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([pd.NaT, np.nan])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([np.nan, pd.NaT])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([np.nan, pd.NaT, np.nan])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        arr = np.array([None, pd.NaT, None])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime"
+
+        # np.datetime64(nat)
+        arr = np.array([np.datetime64("nat")])
+        assert lib.infer_dtype(arr, skipna=False) == "datetime64"
+
+        for n in [np.nan, pd.NaT, None]:
+            arr = np.array([n, np.datetime64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "datetime64"
+
+            arr = np.array([pd.NaT, n, np.datetime64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "datetime64"
+
+        arr = np.array([np.timedelta64("nat")], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "timedelta"
+
+        for n in [np.nan, pd.NaT, None]:
+            arr = np.array([n, np.timedelta64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "timedelta"
+
+            arr = np.array([pd.NaT, n, np.timedelta64("nat"), n])
+            assert lib.infer_dtype(arr, skipna=False) == "timedelta"
+
+        # datetime / timedelta mixed
+        arr = np.array([pd.NaT, np.datetime64("nat"), np.timedelta64("nat"), np.nan])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([np.timedelta64("nat"), np.datetime64("nat")], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+    def test_is_datetimelike_array_all_nan_nat_like(self):
+        arr = np.array([np.nan, pd.NaT, np.datetime64("nat")])
+        assert lib.is_datetime_array(arr)
+        assert lib.is_datetime64_array(arr)
+        assert not lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, pd.NaT, np.timedelta64("nat")])
+        assert not lib.is_datetime_array(arr)
+        assert not lib.is_datetime64_array(arr)
+        assert lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, pd.NaT, np.datetime64("nat"), np.timedelta64("nat")])
+        assert not lib.is_datetime_array(arr)
+        assert not lib.is_datetime64_array(arr)
+        assert not lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, pd.NaT])
+        assert lib.is_datetime_array(arr)
+        assert lib.is_datetime64_array(arr)
+        assert lib.is_timedelta_or_timedelta64_array(arr)
+
+        arr = np.array([np.nan, np.nan], dtype=object)
+        assert not lib.is_datetime_array(arr)
+        assert not lib.is_datetime64_array(arr)
+        assert not lib.is_timedelta_or_timedelta64_array(arr)
+
+        assert lib.is_datetime_with_singletz_array(
+            np.array(
+                [
+                    Timestamp("20130101", tz="US/Eastern"),
+                    Timestamp("20130102", tz="US/Eastern"),
+                ],
+                dtype=object,
+            )
+        )
+        assert not lib.is_datetime_with_singletz_array(
+            np.array(
+                [
+                    Timestamp("20130101", tz="US/Eastern"),
+                    Timestamp("20130102", tz="CET"),
+                ],
+                dtype=object,
+            )
+        )
+
+    @pytest.mark.parametrize(
+        "func",
+        [
+            "is_datetime_array",
+            "is_datetime64_array",
+            "is_bool_array",
+            "is_timedelta_or_timedelta64_array",
+            "is_date_array",
+            "is_time_array",
+            "is_interval_array",
+        ],
+    )
+    def test_other_dtypes_for_array(self, func):
+        func = getattr(lib, func)
+        arr = np.array(["foo", "bar"])
+        assert not func(arr)
+        assert not func(arr.reshape(2, 1))
+
+        arr = np.array([1, 2])
+        assert not func(arr)
+        assert not func(arr.reshape(2, 1))
+
+    def test_date(self):
+        dates = [date(2012, 1, day) for day in range(1, 20)]
+        index = Index(dates)
+        assert index.inferred_type == "date"
+
+        dates = [date(2012, 1, day) for day in range(1, 20)] + [np.nan]
+        result = lib.infer_dtype(dates, skipna=False)
+        assert result == "mixed"
+
+        result = lib.infer_dtype(dates, skipna=True)
+        assert result == "date"
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [date(2020, 1, 1), Timestamp("2020-01-01")],
+            [Timestamp("2020-01-01"), date(2020, 1, 1)],
+            [date(2020, 1, 1), pd.NaT],
+            [pd.NaT, date(2020, 1, 1)],
+        ],
+    )
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_infer_dtype_date_order_invariant(self, values, skipna):
+        # https://github.com/pandas-dev/pandas/issues/33741
+        result = lib.infer_dtype(values, skipna=skipna)
+        assert result == "date"
+
+    def test_is_numeric_array(self):
+        assert lib.is_float_array(np.array([1, 2.0]))
+        assert lib.is_float_array(np.array([1, 2.0, np.nan]))
+        assert not lib.is_float_array(np.array([1, 2]))
+
+        assert lib.is_integer_array(np.array([1, 2]))
+        assert not lib.is_integer_array(np.array([1, 2.0]))
+
+    def test_is_string_array(self):
+        # We should only be accepting pd.NA, np.nan,
+        # other floating point nans e.g. float('nan')]
+        # when skipna is True.
+        assert lib.is_string_array(np.array(["foo", "bar"]))
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", pd.NA], dtype=object), skipna=False
+        )
+        assert lib.is_string_array(
+            np.array(["foo", "bar", pd.NA], dtype=object), skipna=True
+        )
+        # we allow NaN/None in the StringArray constructor, so its allowed here
+        assert lib.is_string_array(
+            np.array(["foo", "bar", None], dtype=object), skipna=True
+        )
+        assert lib.is_string_array(
+            np.array(["foo", "bar", np.nan], dtype=object), skipna=True
+        )
+        # But not e.g. datetimelike or Decimal NAs
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", pd.NaT], dtype=object), skipna=True
+        )
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", np.datetime64("NaT")], dtype=object), skipna=True
+        )
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", Decimal("NaN")], dtype=object), skipna=True
+        )
+
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", None], dtype=object), skipna=False
+        )
+        assert not lib.is_string_array(
+            np.array(["foo", "bar", np.nan], dtype=object), skipna=False
+        )
+        assert not lib.is_string_array(np.array([1, 2]))
+
+    def test_to_object_array_tuples(self):
+        r = (5, 6)
+        values = [r]
+        lib.to_object_array_tuples(values)
+
+        # make sure record array works
+        record = namedtuple("record", "x y")
+        r = record(5, 6)
+        values = [r]
+        lib.to_object_array_tuples(values)
+
+    def test_object(self):
+        # GH 7431
+        # cannot infer more than this as only a single element
+        arr = np.array([None], dtype="O")
+        result = lib.infer_dtype(arr, skipna=False)
+        assert result == "mixed"
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "empty"
+
+    def test_to_object_array_width(self):
+        # see gh-13320
+        rows = [[1, 2, 3], [4, 5, 6]]
+
+        expected = np.array(rows, dtype=object)
+        out = lib.to_object_array(rows)
+        tm.assert_numpy_array_equal(out, expected)
+
+        expected = np.array(rows, dtype=object)
+        out = lib.to_object_array(rows, min_width=1)
+        tm.assert_numpy_array_equal(out, expected)
+
+        expected = np.array(
+            [[1, 2, 3, None, None], [4, 5, 6, None, None]], dtype=object
+        )
+        out = lib.to_object_array(rows, min_width=5)
+        tm.assert_numpy_array_equal(out, expected)
+
+    def test_is_period(self):
+        # GH#55264
+        msg = "is_period is deprecated and will be removed in a future version"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert lib.is_period(Period("2011-01", freq="M"))
+            assert not lib.is_period(PeriodIndex(["2011-01"], freq="M"))
+            assert not lib.is_period(Timestamp("2011-01"))
+            assert not lib.is_period(1)
+            assert not lib.is_period(np.nan)
+
+    def test_is_interval(self):
+        # GH#55264
+        msg = "is_interval is deprecated and will be removed in a future version"
+        item = Interval(1, 2)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert lib.is_interval(item)
+            assert not lib.is_interval(pd.IntervalIndex([item]))
+            assert not lib.is_interval(pd.IntervalIndex([item])._engine)
+
+    def test_categorical(self):
+        # GH 8974
+        arr = Categorical(list("abc"))
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "categorical"
+
+        result = lib.infer_dtype(Series(arr), skipna=True)
+        assert result == "categorical"
+
+        arr = Categorical(list("abc"), categories=["cegfab"], ordered=True)
+        result = lib.infer_dtype(arr, skipna=True)
+        assert result == "categorical"
+
+        result = lib.infer_dtype(Series(arr), skipna=True)
+        assert result == "categorical"
+
+    @pytest.mark.parametrize("asobject", [True, False])
+    def test_interval(self, asobject):
+        idx = pd.IntervalIndex.from_breaks(range(5), closed="both")
+        if asobject:
+            idx = idx.astype(object)
+
+        inferred = lib.infer_dtype(idx, skipna=False)
+        assert inferred == "interval"
+
+        inferred = lib.infer_dtype(idx._data, skipna=False)
+        assert inferred == "interval"
+
+        inferred = lib.infer_dtype(Series(idx, dtype=idx.dtype), skipna=False)
+        assert inferred == "interval"
+
+    @pytest.mark.parametrize("value", [Timestamp(0), Timedelta(0), 0, 0.0])
+    def test_interval_mismatched_closed(self, value):
+        first = Interval(value, value, closed="left")
+        second = Interval(value, value, closed="right")
+
+        # if closed match, we should infer "interval"
+        arr = np.array([first, first], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "interval"
+
+        # if closed dont match, we should _not_ get "interval"
+        arr2 = np.array([first, second], dtype=object)
+        assert lib.infer_dtype(arr2, skipna=False) == "mixed"
+
+    def test_interval_mismatched_subtype(self):
+        first = Interval(0, 1, closed="left")
+        second = Interval(Timestamp(0), Timestamp(1), closed="left")
+        third = Interval(Timedelta(0), Timedelta(1), closed="left")
+
+        arr = np.array([first, second])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([second, third])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        arr = np.array([first, third])
+        assert lib.infer_dtype(arr, skipna=False) == "mixed"
+
+        # float vs int subdtype are compatible
+        flt_interval = Interval(1.5, 2.5, closed="left")
+        arr = np.array([first, flt_interval], dtype=object)
+        assert lib.infer_dtype(arr, skipna=False) == "interval"
+
+    @pytest.mark.parametrize("klass", [pd.array, Series])
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("data", [["a", "b", "c"], ["a", "b", pd.NA]])
+    def test_string_dtype(self, data, skipna, klass, nullable_string_dtype):
+        # StringArray
+        val = klass(data, dtype=nullable_string_dtype)
+        inferred = lib.infer_dtype(val, skipna=skipna)
+        assert inferred == "string"
+
+    @pytest.mark.parametrize("klass", [pd.array, Series])
+    @pytest.mark.parametrize("skipna", [True, False])
+    @pytest.mark.parametrize("data", [[True, False, True], [True, False, pd.NA]])
+    def test_boolean_dtype(self, data, skipna, klass):
+        # BooleanArray
+        val = klass(data, dtype="boolean")
+        inferred = lib.infer_dtype(val, skipna=skipna)
+        assert inferred == "boolean"
+
+
+class TestNumberScalar:
+    def test_is_number(self):
+        assert is_number(True)
+        assert is_number(1)
+        assert is_number(1.1)
+        assert is_number(1 + 3j)
+        assert is_number(np.int64(1))
+        assert is_number(np.float64(1.1))
+        assert is_number(np.complex128(1 + 3j))
+        assert is_number(np.nan)
+
+        assert not is_number(None)
+        assert not is_number("x")
+        assert not is_number(datetime(2011, 1, 1))
+        assert not is_number(np.datetime64("2011-01-01"))
+        assert not is_number(Timestamp("2011-01-01"))
+        assert not is_number(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_number(timedelta(1000))
+        assert not is_number(Timedelta("1 days"))
+
+        # questionable
+        assert not is_number(np.bool_(False))
+        assert is_number(np.timedelta64(1, "D"))
+
+    def test_is_bool(self):
+        assert is_bool(True)
+        assert is_bool(False)
+        assert is_bool(np.bool_(False))
+
+        assert not is_bool(1)
+        assert not is_bool(1.1)
+        assert not is_bool(1 + 3j)
+        assert not is_bool(np.int64(1))
+        assert not is_bool(np.float64(1.1))
+        assert not is_bool(np.complex128(1 + 3j))
+        assert not is_bool(np.nan)
+        assert not is_bool(None)
+        assert not is_bool("x")
+        assert not is_bool(datetime(2011, 1, 1))
+        assert not is_bool(np.datetime64("2011-01-01"))
+        assert not is_bool(Timestamp("2011-01-01"))
+        assert not is_bool(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_bool(timedelta(1000))
+        assert not is_bool(np.timedelta64(1, "D"))
+        assert not is_bool(Timedelta("1 days"))
+
+    def test_is_integer(self):
+        assert is_integer(1)
+        assert is_integer(np.int64(1))
+
+        assert not is_integer(True)
+        assert not is_integer(1.1)
+        assert not is_integer(1 + 3j)
+        assert not is_integer(False)
+        assert not is_integer(np.bool_(False))
+        assert not is_integer(np.float64(1.1))
+        assert not is_integer(np.complex128(1 + 3j))
+        assert not is_integer(np.nan)
+        assert not is_integer(None)
+        assert not is_integer("x")
+        assert not is_integer(datetime(2011, 1, 1))
+        assert not is_integer(np.datetime64("2011-01-01"))
+        assert not is_integer(Timestamp("2011-01-01"))
+        assert not is_integer(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_integer(timedelta(1000))
+        assert not is_integer(Timedelta("1 days"))
+        assert not is_integer(np.timedelta64(1, "D"))
+
+    def test_is_float(self):
+        assert is_float(1.1)
+        assert is_float(np.float64(1.1))
+        assert is_float(np.nan)
+
+        assert not is_float(True)
+        assert not is_float(1)
+        assert not is_float(1 + 3j)
+        assert not is_float(False)
+        assert not is_float(np.bool_(False))
+        assert not is_float(np.int64(1))
+        assert not is_float(np.complex128(1 + 3j))
+        assert not is_float(None)
+        assert not is_float("x")
+        assert not is_float(datetime(2011, 1, 1))
+        assert not is_float(np.datetime64("2011-01-01"))
+        assert not is_float(Timestamp("2011-01-01"))
+        assert not is_float(Timestamp("2011-01-01", tz="US/Eastern"))
+        assert not is_float(timedelta(1000))
+        assert not is_float(np.timedelta64(1, "D"))
+        assert not is_float(Timedelta("1 days"))
+
+    def test_is_datetime_dtypes(self):
+        ts = pd.date_range("20130101", periods=3)
+        tsa = pd.date_range("20130101", periods=3, tz="US/Eastern")
+
+        msg = "is_datetime64tz_dtype is deprecated"
+
+        assert is_datetime64_dtype("datetime64")
+        assert is_datetime64_dtype("datetime64[ns]")
+        assert is_datetime64_dtype(ts)
+        assert not is_datetime64_dtype(tsa)
+
+        assert not is_datetime64_ns_dtype("datetime64")
+        assert is_datetime64_ns_dtype("datetime64[ns]")
+        assert is_datetime64_ns_dtype(ts)
+        assert is_datetime64_ns_dtype(tsa)
+
+        assert is_datetime64_any_dtype("datetime64")
+        assert is_datetime64_any_dtype("datetime64[ns]")
+        assert is_datetime64_any_dtype(ts)
+        assert is_datetime64_any_dtype(tsa)
+
+        with tm.assert_produces_warning(DeprecationWarning, match=msg):
+            assert not is_datetime64tz_dtype("datetime64")
+            assert not is_datetime64tz_dtype("datetime64[ns]")
+            assert not is_datetime64tz_dtype(ts)
+            assert is_datetime64tz_dtype(tsa)
+
+    @pytest.mark.parametrize("tz", ["US/Eastern", "UTC"])
+    def test_is_datetime_dtypes_with_tz(self, tz):
+        dtype = f"datetime64[ns, {tz}]"
+        assert not is_datetime64_dtype(dtype)
+
+        msg = "is_datetime64tz_dtype is deprecated"
+        with tm.assert_produces_warning(DeprecationWarning, match=msg):
+            assert is_datetime64tz_dtype(dtype)
+        assert is_datetime64_ns_dtype(dtype)
+        assert is_datetime64_any_dtype(dtype)
+
+    def test_is_timedelta(self):
+        assert is_timedelta64_dtype("timedelta64")
+        assert is_timedelta64_dtype("timedelta64[ns]")
+        assert not is_timedelta64_ns_dtype("timedelta64")
+        assert is_timedelta64_ns_dtype("timedelta64[ns]")
+
+        tdi = TimedeltaIndex([1e14, 2e14], dtype="timedelta64[ns]")
+        assert is_timedelta64_dtype(tdi)
+        assert is_timedelta64_ns_dtype(tdi)
+        assert is_timedelta64_ns_dtype(tdi.astype("timedelta64[ns]"))
+
+        assert not is_timedelta64_ns_dtype(Index([], dtype=np.float64))
+        assert not is_timedelta64_ns_dtype(Index([], dtype=np.int64))
+
+
+class TestIsScalar:
+    def test_is_scalar_builtin_scalars(self):
+        assert is_scalar(None)
+        assert is_scalar(True)
+        assert is_scalar(False)
+        assert is_scalar(Fraction())
+        assert is_scalar(0.0)
+        assert is_scalar(1)
+        assert is_scalar(complex(2))
+        assert is_scalar(float("NaN"))
+        assert is_scalar(np.nan)
+        assert is_scalar("foobar")
+        assert is_scalar(b"foobar")
+        assert is_scalar(datetime(2014, 1, 1))
+        assert is_scalar(date(2014, 1, 1))
+        assert is_scalar(time(12, 0))
+        assert is_scalar(timedelta(hours=1))
+        assert is_scalar(pd.NaT)
+        assert is_scalar(pd.NA)
+
+    def test_is_scalar_builtin_nonscalars(self):
+        assert not is_scalar({})
+        assert not is_scalar([])
+        assert not is_scalar([1])
+        assert not is_scalar(())
+        assert not is_scalar((1,))
+        assert not is_scalar(slice(None))
+        assert not is_scalar(Ellipsis)
+
+    def test_is_scalar_numpy_array_scalars(self):
+        assert is_scalar(np.int64(1))
+        assert is_scalar(np.float64(1.0))
+        assert is_scalar(np.int32(1))
+        assert is_scalar(np.complex64(2))
+        assert is_scalar(np.object_("foobar"))
+        assert is_scalar(np.str_("foobar"))
+        assert is_scalar(np.bytes_(b"foobar"))
+        assert is_scalar(np.datetime64("2014-01-01"))
+        assert is_scalar(np.timedelta64(1, "h"))
+
+    @pytest.mark.parametrize(
+        "zerodim",
+        [
+            np.array(1),
+            np.array("foobar"),
+            np.array(np.datetime64("2014-01-01")),
+            np.array(np.timedelta64(1, "h")),
+            np.array(np.datetime64("NaT")),
+        ],
+    )
+    def test_is_scalar_numpy_zerodim_arrays(self, zerodim):
+        assert not is_scalar(zerodim)
+        assert is_scalar(lib.item_from_zerodim(zerodim))
+
+    @pytest.mark.parametrize("arr", [np.array([]), np.array([[]])])
+    def test_is_scalar_numpy_arrays(self, arr):
+        assert not is_scalar(arr)
+        assert not is_scalar(MockNumpyLikeArray(arr))
+
+    def test_is_scalar_pandas_scalars(self):
+        assert is_scalar(Timestamp("2014-01-01"))
+        assert is_scalar(Timedelta(hours=1))
+        assert is_scalar(Period("2014-01-01"))
+        assert is_scalar(Interval(left=0, right=1))
+        assert is_scalar(DateOffset(days=1))
+        assert is_scalar(pd.offsets.Minute(3))
+
+    def test_is_scalar_pandas_containers(self):
+        assert not is_scalar(Series(dtype=object))
+        assert not is_scalar(Series([1]))
+        assert not is_scalar(DataFrame())
+        assert not is_scalar(DataFrame([[1]]))
+        assert not is_scalar(Index([]))
+        assert not is_scalar(Index([1]))
+        assert not is_scalar(Categorical([]))
+        assert not is_scalar(DatetimeIndex([])._data)
+        assert not is_scalar(TimedeltaIndex([])._data)
+        assert not is_scalar(DatetimeIndex([])._data.to_period("D"))
+        assert not is_scalar(pd.array([1, 2, 3]))
+
+    def test_is_scalar_number(self):
+        # Number() is not recognied by PyNumber_Check, so by extension
+        #  is not recognized by is_scalar, but instances of non-abstract
+        #  subclasses are.
+
+        class Numeric(Number):
+            def __init__(self, value) -> None:
+                self.value = value
+
+            def __int__(self) -> int:
+                return self.value
+
+        num = Numeric(1)
+        assert is_scalar(num)
+
+
+@pytest.mark.parametrize("unit", ["ms", "us", "ns"])
+def test_datetimeindex_from_empty_datetime64_array(unit):
+    idx = DatetimeIndex(np.array([], dtype=f"datetime64[{unit}]"))
+    assert len(idx) == 0
+
+
+def test_nan_to_nat_conversions():
+    df = DataFrame(
+        {"A": np.asarray(range(10), dtype="float64"), "B": Timestamp("20010101")}
+    )
+    df.iloc[3:6, :] = np.nan
+    result = df.loc[4, "B"]
+    assert result is pd.NaT
+
+    s = df["B"].copy()
+    s[8:9] = np.nan
+    assert s[8] is pd.NaT
+
+
+@pytest.mark.filterwarnings("ignore::PendingDeprecationWarning")
+def test_is_scipy_sparse(spmatrix):
+    pytest.importorskip("scipy")
+    assert is_scipy_sparse(spmatrix([[0, 1]]))
+    assert not is_scipy_sparse(np.array([1]))
+
+
+def test_ensure_int32():
+    values = np.arange(10, dtype=np.int32)
+    result = ensure_int32(values)
+    assert result.dtype == np.int32
+
+    values = np.arange(10, dtype=np.int64)
+    result = ensure_int32(values)
+    assert result.dtype == np.int32
+
+
+@pytest.mark.parametrize(
+    "right,result",
+    [
+        (0, np.uint8),
+        (-1, np.int16),
+        (300, np.uint16),
+        # For floats, we just upcast directly to float64 instead of trying to
+        # find a smaller floating dtype
+        (300.0, np.uint16),  # for integer floats, we convert them to ints
+        (300.1, np.float64),
+        (np.int16(300), np.int16 if np_version_gt2 else np.uint16),
+    ],
+)
+def test_find_result_type_uint_int(right, result):
+    left_dtype = np.dtype("uint8")
+    assert find_result_type(left_dtype, right) == result
+
+
+@pytest.mark.parametrize(
+    "right,result",
+    [
+        (0, np.int8),
+        (-1, np.int8),
+        (300, np.int16),
+        # For floats, we just upcast directly to float64 instead of trying to
+        # find a smaller floating dtype
+        (300.0, np.int16),  # for integer floats, we convert them to ints
+        (300.1, np.float64),
+        (np.int16(300), np.int16),
+    ],
+)
+def test_find_result_type_int_int(right, result):
+    left_dtype = np.dtype("int8")
+    assert find_result_type(left_dtype, right) == result
+
+
+@pytest.mark.parametrize(
+    "right,result",
+    [
+        (300.0, np.float64),
+        (np.float32(300), np.float32),
+    ],
+)
+def test_find_result_type_floats(right, result):
+    left_dtype = np.dtype("float16")
+    assert find_result_type(left_dtype, right) == result

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_arithmetic.py

@@ -0,0 +1,192 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Interval,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestIntervalArithmetic:
+    def test_interval_add(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(1, 2, closed=closed)
+
+        result = interval + 1
+        assert result == expected
+
+        result = 1 + interval
+        assert result == expected
+
+        result = interval
+        result += 1
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for \+"
+        with pytest.raises(TypeError, match=msg):
+            interval + interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval + "foo"
+
+    def test_interval_sub(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(-1, 0, closed=closed)
+
+        result = interval - 1
+        assert result == expected
+
+        result = interval
+        result -= 1
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for -"
+        with pytest.raises(TypeError, match=msg):
+            interval - interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval - "foo"
+
+    def test_interval_mult(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(0, 2, closed=closed)
+
+        result = interval * 2
+        assert result == expected
+
+        result = 2 * interval
+        assert result == expected
+
+        result = interval
+        result *= 2
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for \*"
+        with pytest.raises(TypeError, match=msg):
+            interval * interval
+
+        msg = r"can\'t multiply sequence by non-int"
+        with pytest.raises(TypeError, match=msg):
+            interval * "foo"
+
+    def test_interval_div(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(0, 0.5, closed=closed)
+
+        result = interval / 2.0
+        assert result == expected
+
+        result = interval
+        result /= 2.0
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for /"
+        with pytest.raises(TypeError, match=msg):
+            interval / interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval / "foo"
+
+    def test_interval_floordiv(self, closed):
+        interval = Interval(1, 2, closed=closed)
+        expected = Interval(0, 1, closed=closed)
+
+        result = interval // 2
+        assert result == expected
+
+        result = interval
+        result //= 2
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for //"
+        with pytest.raises(TypeError, match=msg):
+            interval // interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval // "foo"
+
+    @pytest.mark.parametrize("method", ["__add__", "__sub__"])
+    @pytest.mark.parametrize(
+        "interval",
+        [
+            Interval(
+                Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")
+            ),
+            Interval(Timedelta(days=7), Timedelta(days=14)),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")]
+    )
+    def test_time_interval_add_subtract_timedelta(self, interval, delta, method):
+        # https://github.com/pandas-dev/pandas/issues/32023
+        result = getattr(interval, method)(delta)
+        left = getattr(interval.left, method)(delta)
+        right = getattr(interval.right, method)(delta)
+        expected = Interval(left, right)
+
+        assert result == expected
+
+    @pytest.mark.parametrize("interval", [Interval(1, 2), Interval(1.0, 2.0)])
+    @pytest.mark.parametrize(
+        "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")]
+    )
+    def test_numeric_interval_add_timedelta_raises(self, interval, delta):
+        # https://github.com/pandas-dev/pandas/issues/32023
+        msg = "|".join(
+            [
+                "unsupported operand",
+                "cannot use operands",
+                "Only numeric, Timestamp and Timedelta endpoints are allowed",
+            ]
+        )
+        with pytest.raises((TypeError, ValueError), match=msg):
+            interval + delta
+
+        with pytest.raises((TypeError, ValueError), match=msg):
+            delta + interval
+
+    @pytest.mark.parametrize("klass", [timedelta, np.timedelta64, Timedelta])
+    def test_timedelta_add_timestamp_interval(self, klass):
+        delta = klass(0)
+        expected = Interval(Timestamp("2020-01-01"), Timestamp("2020-02-01"))
+
+        result = delta + expected
+        assert result == expected
+
+        result = expected + delta
+        assert result == expected
+
+
+class TestIntervalComparisons:
+    def test_interval_equal(self):
+        assert Interval(0, 1) == Interval(0, 1, closed="right")
+        assert Interval(0, 1) != Interval(0, 1, closed="left")
+        assert Interval(0, 1) != 0
+
+    def test_interval_comparison(self):
+        msg = (
+            "'<' not supported between instances of "
+            "'pandas._libs.interval.Interval' and 'int'"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Interval(0, 1) < 2
+
+        assert Interval(0, 1) < Interval(1, 2)
+        assert Interval(0, 1) < Interval(0, 2)
+        assert Interval(0, 1) < Interval(0.5, 1.5)
+        assert Interval(0, 1) <= Interval(0, 1)
+        assert Interval(0, 1) > Interval(-1, 2)
+        assert Interval(0, 1) >= Interval(0, 1)
+
+    def test_equality_comparison_broadcasts_over_array(self):
+        # https://github.com/pandas-dev/pandas/issues/35931
+        interval = Interval(0, 1)
+        arr = np.array([interval, interval])
+        result = interval == arr
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_constructors.py

@@ -0,0 +1,51 @@
+import pytest
+
+from pandas import (
+    Interval,
+    Period,
+    Timestamp,
+)
+
+
+class TestIntervalConstructors:
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            ("a", "z"),
+            (("a", "b"), ("c", "d")),
+            (list("AB"), list("ab")),
+            (Interval(0, 1), Interval(1, 2)),
+            (Period("2018Q1", freq="Q"), Period("2018Q1", freq="Q")),
+        ],
+    )
+    def test_construct_errors(self, left, right):
+        # GH#23013
+        msg = "Only numeric, Timestamp and Timedelta endpoints are allowed"
+        with pytest.raises(ValueError, match=msg):
+            Interval(left, right)
+
+    def test_constructor_errors(self):
+        msg = "invalid option for 'closed': foo"
+        with pytest.raises(ValueError, match=msg):
+            Interval(0, 1, closed="foo")
+
+        msg = "left side of interval must be <= right side"
+        with pytest.raises(ValueError, match=msg):
+            Interval(1, 0)
+
+    @pytest.mark.parametrize(
+        "tz_left, tz_right", [(None, "UTC"), ("UTC", None), ("UTC", "US/Eastern")]
+    )
+    def test_constructor_errors_tz(self, tz_left, tz_right):
+        # GH#18538
+        left = Timestamp("2017-01-01", tz=tz_left)
+        right = Timestamp("2017-01-02", tz=tz_right)
+
+        if tz_left is None or tz_right is None:
+            error = TypeError
+            msg = "Cannot compare tz-naive and tz-aware timestamps"
+        else:
+            error = ValueError
+            msg = "left and right must have the same time zone"
+        with pytest.raises(error, match=msg):
+            Interval(left, right)

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_contains.py

@@ -0,0 +1,73 @@
+import pytest
+
+from pandas import (
+    Interval,
+    Timedelta,
+    Timestamp,
+)
+
+
+class TestContains:
+    def test_contains(self):
+        interval = Interval(0, 1)
+        assert 0.5 in interval
+        assert 1 in interval
+        assert 0 not in interval
+
+        interval_both = Interval(0, 1, "both")
+        assert 0 in interval_both
+        assert 1 in interval_both
+
+        interval_neither = Interval(0, 1, closed="neither")
+        assert 0 not in interval_neither
+        assert 0.5 in interval_neither
+        assert 1 not in interval_neither
+
+    def test_contains_interval(self, inclusive_endpoints_fixture):
+        interval1 = Interval(0, 1, "both")
+        interval2 = Interval(0, 1, inclusive_endpoints_fixture)
+        assert interval1 in interval1
+        assert interval2 in interval2
+        assert interval2 in interval1
+        assert interval1 not in interval2 or inclusive_endpoints_fixture == "both"
+
+    def test_contains_infinite_length(self):
+        interval1 = Interval(0, 1, "both")
+        interval2 = Interval(float("-inf"), float("inf"), "neither")
+        assert interval1 in interval2
+        assert interval2 not in interval1
+
+    def test_contains_zero_length(self):
+        interval1 = Interval(0, 1, "both")
+        interval2 = Interval(-1, -1, "both")
+        interval3 = Interval(0.5, 0.5, "both")
+        assert interval2 not in interval1
+        assert interval3 in interval1
+        assert interval2 not in interval3 and interval3 not in interval2
+        assert interval1 not in interval2 and interval1 not in interval3
+
+    @pytest.mark.parametrize(
+        "type1",
+        [
+            (0, 1),
+            (Timestamp(2000, 1, 1, 0), Timestamp(2000, 1, 1, 1)),
+            (Timedelta("0h"), Timedelta("1h")),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "type2",
+        [
+            (0, 1),
+            (Timestamp(2000, 1, 1, 0), Timestamp(2000, 1, 1, 1)),
+            (Timedelta("0h"), Timedelta("1h")),
+        ],
+    )
+    def test_contains_mixed_types(self, type1, type2):
+        interval1 = Interval(*type1)
+        interval2 = Interval(*type2)
+        if type1 == type2:
+            assert interval1 in interval2
+        else:
+            msg = "^'<=' not supported between instances of"
+            with pytest.raises(TypeError, match=msg):
+                interval1 in interval2

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_formats.py

@@ -0,0 +1,11 @@
+from pandas import Interval
+
+
+def test_interval_repr():
+    interval = Interval(0, 1)
+    assert repr(interval) == "Interval(0, 1, closed='right')"
+    assert str(interval) == "(0, 1]"
+
+    interval_left = Interval(0, 1, closed="left")
+    assert repr(interval_left) == "Interval(0, 1, closed='left')"
+    assert str(interval_left) == "[0, 1)"

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_interval.py

@@ -0,0 +1,87 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Interval,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.fixture
+def interval():
+    return Interval(0, 1)
+
+
+class TestInterval:
+    def test_properties(self, interval):
+        assert interval.closed == "right"
+        assert interval.left == 0
+        assert interval.right == 1
+        assert interval.mid == 0.5
+
+    def test_hash(self, interval):
+        # should not raise
+        hash(interval)
+
+    @pytest.mark.parametrize(
+        "left, right, expected",
+        [
+            (0, 5, 5),
+            (-2, 5.5, 7.5),
+            (10, 10, 0),
+            (10, np.inf, np.inf),
+            (-np.inf, -5, np.inf),
+            (-np.inf, np.inf, np.inf),
+            (Timedelta("0 days"), Timedelta("5 days"), Timedelta("5 days")),
+            (Timedelta("10 days"), Timedelta("10 days"), Timedelta("0 days")),
+            (Timedelta("1h10min"), Timedelta("5h5min"), Timedelta("3h55min")),
+            (Timedelta("5s"), Timedelta("1h"), Timedelta("59min55s")),
+        ],
+    )
+    def test_length(self, left, right, expected):
+        # GH 18789
+        iv = Interval(left, right)
+        result = iv.length
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "left, right, expected",
+        [
+            ("2017-01-01", "2017-01-06", "5 days"),
+            ("2017-01-01", "2017-01-01 12:00:00", "12 hours"),
+            ("2017-01-01 12:00", "2017-01-01 12:00:00", "0 days"),
+            ("2017-01-01 12:01", "2017-01-05 17:31:00", "4 days 5 hours 30 min"),
+        ],
+    )
+    @pytest.mark.parametrize("tz", (None, "UTC", "CET", "US/Eastern"))
+    def test_length_timestamp(self, tz, left, right, expected):
+        # GH 18789
+        iv = Interval(Timestamp(left, tz=tz), Timestamp(right, tz=tz))
+        result = iv.length
+        expected = Timedelta(expected)
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            (0, 1),
+            (Timedelta("0 days"), Timedelta("1 day")),
+            (Timestamp("2018-01-01"), Timestamp("2018-01-02")),
+            (
+                Timestamp("2018-01-01", tz="US/Eastern"),
+                Timestamp("2018-01-02", tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_is_empty(self, left, right, closed):
+        # GH27219
+        # non-empty always return False
+        iv = Interval(left, right, closed)
+        assert iv.is_empty is False
+
+        # same endpoint is empty except when closed='both' (contains one point)
+        iv = Interval(left, left, closed)
+        result = iv.is_empty
+        expected = closed != "both"
+        assert result is expected

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_overlaps.py

@@ -0,0 +1,67 @@
+import pytest
+
+from pandas import (
+    Interval,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.fixture(
+    params=[
+        (Timedelta("0 days"), Timedelta("1 day")),
+        (Timestamp("2018-01-01"), Timedelta("1 day")),
+        (0, 1),
+    ],
+    ids=lambda x: type(x[0]).__name__,
+)
+def start_shift(request):
+    """
+    Fixture for generating intervals of types from a start value and a shift
+    value that can be added to start to generate an endpoint
+    """
+    return request.param
+
+
+class TestOverlaps:
+    def test_overlaps_self(self, start_shift, closed):
+        start, shift = start_shift
+        interval = Interval(start, start + shift, closed)
+        assert interval.overlaps(interval)
+
+    def test_overlaps_nested(self, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval1 = Interval(start, start + 3 * shift, other_closed)
+        interval2 = Interval(start + shift, start + 2 * shift, closed)
+
+        # nested intervals should always overlap
+        assert interval1.overlaps(interval2)
+
+    def test_overlaps_disjoint(self, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval1 = Interval(start, start + shift, other_closed)
+        interval2 = Interval(start + 2 * shift, start + 3 * shift, closed)
+
+        # disjoint intervals should never overlap
+        assert not interval1.overlaps(interval2)
+
+    def test_overlaps_endpoint(self, start_shift, closed, other_closed):
+        start, shift = start_shift
+        interval1 = Interval(start, start + shift, other_closed)
+        interval2 = Interval(start + shift, start + 2 * shift, closed)
+
+        # overlap if shared endpoint is closed for both (overlap at a point)
+        result = interval1.overlaps(interval2)
+        expected = interval1.closed_right and interval2.closed_left
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "other",
+        [10, True, "foo", Timedelta("1 day"), Timestamp("2018-01-01")],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_overlaps_invalid_type(self, other):
+        interval = Interval(0, 1)
+        msg = f"`other` must be an Interval, got {type(other).__name__}"
+        with pytest.raises(TypeError, match=msg):
+            interval.overlaps(other)

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/test_na_scalar.py

@@ -0,0 +1,316 @@
+from datetime import (
+    date,
+    time,
+    timedelta,
+)
+import pickle
+
+import numpy as np
+import pytest
+
+from pandas._libs.missing import NA
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_singleton():
+    assert NA is NA
+    new_NA = type(NA)()
+    assert new_NA is NA
+
+
+def test_repr():
+    assert repr(NA) == "<NA>"
+    assert str(NA) == "<NA>"
+
+
+def test_format():
+    # GH-34740
+    assert format(NA) == "<NA>"
+    assert format(NA, ">10") == "      <NA>"
+    assert format(NA, "xxx") == "<NA>"  # NA is flexible, accept any format spec
+
+    assert f"{NA}" == "<NA>"
+    assert f"{NA:>10}" == "      <NA>"
+    assert f"{NA:xxx}" == "<NA>"
+
+
+def test_truthiness():
+    msg = "boolean value of NA is ambiguous"
+
+    with pytest.raises(TypeError, match=msg):
+        bool(NA)
+
+    with pytest.raises(TypeError, match=msg):
+        not NA
+
+
+def test_hashable():
+    assert hash(NA) == hash(NA)
+    d = {NA: "test"}
+    assert d[NA] == "test"
+
+
+@pytest.mark.parametrize(
+    "other", [NA, 1, 1.0, "a", b"a", np.int64(1), np.nan], ids=repr
+)
+def test_arithmetic_ops(all_arithmetic_functions, other):
+    op = all_arithmetic_functions
+
+    if op.__name__ in ("pow", "rpow", "rmod") and isinstance(other, (str, bytes)):
+        pytest.skip(reason=f"{op.__name__} with NA and {other} not defined.")
+    if op.__name__ in ("divmod", "rdivmod"):
+        assert op(NA, other) is (NA, NA)
+    else:
+        if op.__name__ == "rpow":
+            # avoid special case
+            other += 1
+        assert op(NA, other) is NA
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        NA,
+        1,
+        1.0,
+        "a",
+        b"a",
+        np.int64(1),
+        np.nan,
+        np.bool_(True),
+        time(0),
+        date(1, 2, 3),
+        timedelta(1),
+        pd.NaT,
+    ],
+)
+def test_comparison_ops(comparison_op, other):
+    assert comparison_op(NA, other) is NA
+    assert comparison_op(other, NA) is NA
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        0,
+        0.0,
+        -0,
+        -0.0,
+        False,
+        np.bool_(False),
+        np.int_(0),
+        np.float64(0),
+        np.int_(-0),
+        np.float64(-0),
+    ],
+)
+@pytest.mark.parametrize("asarray", [True, False])
+def test_pow_special(value, asarray):
+    if asarray:
+        value = np.array([value])
+    result = NA**value
+
+    if asarray:
+        result = result[0]
+    else:
+        # this assertion isn't possible for ndarray.
+        assert isinstance(result, type(value))
+    assert result == 1
+
+
+@pytest.mark.parametrize(
+    "value", [1, 1.0, True, np.bool_(True), np.int_(1), np.float64(1)]
+)
+@pytest.mark.parametrize("asarray", [True, False])
+def test_rpow_special(value, asarray):
+    if asarray:
+        value = np.array([value])
+    result = value**NA
+
+    if asarray:
+        result = result[0]
+    elif not isinstance(value, (np.float64, np.bool_, np.int_)):
+        # this assertion isn't possible with asarray=True
+        assert isinstance(result, type(value))
+
+    assert result == value
+
+
+@pytest.mark.parametrize("value", [-1, -1.0, np.int_(-1), np.float64(-1)])
+@pytest.mark.parametrize("asarray", [True, False])
+def test_rpow_minus_one(value, asarray):
+    if asarray:
+        value = np.array([value])
+    result = value**NA
+
+    if asarray:
+        result = result[0]
+
+    assert pd.isna(result)
+
+
+def test_unary_ops():
+    assert +NA is NA
+    assert -NA is NA
+    assert abs(NA) is NA
+    assert ~NA is NA
+
+
+def test_logical_and():
+    assert NA & True is NA
+    assert True & NA is NA
+    assert NA & False is False
+    assert False & NA is False
+    assert NA & NA is NA
+
+    msg = "unsupported operand type"
+    with pytest.raises(TypeError, match=msg):
+        NA & 5
+
+
+def test_logical_or():
+    assert NA | True is True
+    assert True | NA is True
+    assert NA | False is NA
+    assert False | NA is NA
+    assert NA | NA is NA
+
+    msg = "unsupported operand type"
+    with pytest.raises(TypeError, match=msg):
+        NA | 5
+
+
+def test_logical_xor():
+    assert NA ^ True is NA
+    assert True ^ NA is NA
+    assert NA ^ False is NA
+    assert False ^ NA is NA
+    assert NA ^ NA is NA
+
+    msg = "unsupported operand type"
+    with pytest.raises(TypeError, match=msg):
+        NA ^ 5
+
+
+def test_logical_not():
+    assert ~NA is NA
+
+
+@pytest.mark.parametrize("shape", [(3,), (3, 3), (1, 2, 3)])
+def test_arithmetic_ndarray(shape, all_arithmetic_functions):
+    op = all_arithmetic_functions
+    a = np.zeros(shape)
+    if op.__name__ == "pow":
+        a += 5
+    result = op(NA, a)
+    expected = np.full(a.shape, NA, dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_is_scalar():
+    assert is_scalar(NA) is True
+
+
+def test_isna():
+    assert pd.isna(NA) is True
+    assert pd.notna(NA) is False
+
+
+def test_series_isna():
+    s = pd.Series([1, NA], dtype=object)
+    expected = pd.Series([False, True])
+    tm.assert_series_equal(s.isna(), expected)
+
+
+def test_ufunc():
+    assert np.log(NA) is NA
+    assert np.add(NA, 1) is NA
+    result = np.divmod(NA, 1)
+    assert result[0] is NA and result[1] is NA
+
+    result = np.frexp(NA)
+    assert result[0] is NA and result[1] is NA
+
+
+def test_ufunc_raises():
+    msg = "ufunc method 'at'"
+    with pytest.raises(ValueError, match=msg):
+        np.log.at(NA, 0)
+
+
+def test_binary_input_not_dunder():
+    a = np.array([1, 2, 3])
+    expected = np.array([NA, NA, NA], dtype=object)
+    result = np.logaddexp(a, NA)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = np.logaddexp(NA, a)
+    tm.assert_numpy_array_equal(result, expected)
+
+    # all NA, multiple inputs
+    assert np.logaddexp(NA, NA) is NA
+
+    result = np.modf(NA, NA)
+    assert len(result) == 2
+    assert all(x is NA for x in result)
+
+
+def test_divmod_ufunc():
+    # binary in, binary out.
+    a = np.array([1, 2, 3])
+    expected = np.array([NA, NA, NA], dtype=object)
+
+    result = np.divmod(a, NA)
+    assert isinstance(result, tuple)
+    for arr in result:
+        tm.assert_numpy_array_equal(arr, expected)
+        tm.assert_numpy_array_equal(arr, expected)
+
+    result = np.divmod(NA, a)
+    for arr in result:
+        tm.assert_numpy_array_equal(arr, expected)
+        tm.assert_numpy_array_equal(arr, expected)
+
+
+def test_integer_hash_collision_dict():
+    # GH 30013
+    result = {NA: "foo", hash(NA): "bar"}
+
+    assert result[NA] == "foo"
+    assert result[hash(NA)] == "bar"
+
+
+def test_integer_hash_collision_set():
+    # GH 30013
+    result = {NA, hash(NA)}
+
+    assert len(result) == 2
+    assert NA in result
+    assert hash(NA) in result
+
+
+def test_pickle_roundtrip():
+    # https://github.com/pandas-dev/pandas/issues/31847
+    result = pickle.loads(pickle.dumps(NA))
+    assert result is NA
+
+
+def test_pickle_roundtrip_pandas():
+    result = tm.round_trip_pickle(NA)
+    assert result is NA
+
+
+@pytest.mark.parametrize(
+    "values, dtype", [([1, 2, NA], "Int64"), (["A", "B", NA], "string")]
+)
+@pytest.mark.parametrize("as_frame", [True, False])
+def test_pickle_roundtrip_containers(as_frame, values, dtype):
+    s = pd.Series(pd.array(values, dtype=dtype))
+    if as_frame:
+        s = s.to_frame(name="A")
+    result = tm.round_trip_pickle(s)
+    tm.assert_equal(result, s)

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/test_nat.py

@@ -0,0 +1,709 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+import operator
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs import iNaT
+from pandas.compat.numpy import np_version_gte1p24p3
+
+from pandas import (
+    DatetimeIndex,
+    DatetimeTZDtype,
+    Index,
+    NaT,
+    Period,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    isna,
+    offsets,
+)
+import pandas._testing as tm
+from pandas.core import roperator
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+
+
+class TestNaTFormatting:
+    def test_repr(self):
+        assert repr(NaT) == "NaT"
+
+    def test_str(self):
+        assert str(NaT) == "NaT"
+
+    def test_isoformat(self):
+        assert NaT.isoformat() == "NaT"
+
+
+@pytest.mark.parametrize(
+    "nat,idx",
+    [
+        (Timestamp("NaT"), DatetimeArray),
+        (Timedelta("NaT"), TimedeltaArray),
+        (Period("NaT", freq="M"), PeriodArray),
+    ],
+)
+def test_nat_fields(nat, idx):
+    for field in idx._field_ops:
+        # weekday is a property of DTI, but a method
+        # on NaT/Timestamp for compat with datetime
+        if field == "weekday":
+            continue
+
+        result = getattr(NaT, field)
+        assert np.isnan(result)
+
+        result = getattr(nat, field)
+        assert np.isnan(result)
+
+    for field in idx._bool_ops:
+        result = getattr(NaT, field)
+        assert result is False
+
+        result = getattr(nat, field)
+        assert result is False
+
+
+def test_nat_vector_field_access():
+    idx = DatetimeIndex(["1/1/2000", None, None, "1/4/2000"])
+
+    for field in DatetimeArray._field_ops:
+        # weekday is a property of DTI, but a method
+        # on NaT/Timestamp for compat with datetime
+        if field == "weekday":
+            continue
+
+        result = getattr(idx, field)
+        expected = Index([getattr(x, field) for x in idx])
+        tm.assert_index_equal(result, expected)
+
+    ser = Series(idx)
+
+    for field in DatetimeArray._field_ops:
+        # weekday is a property of DTI, but a method
+        # on NaT/Timestamp for compat with datetime
+        if field == "weekday":
+            continue
+
+        result = getattr(ser.dt, field)
+        expected = [getattr(x, field) for x in idx]
+        tm.assert_series_equal(result, Series(expected))
+
+    for field in DatetimeArray._bool_ops:
+        result = getattr(ser.dt, field)
+        expected = [getattr(x, field) for x in idx]
+        tm.assert_series_equal(result, Series(expected))
+
+
+@pytest.mark.parametrize("klass", [Timestamp, Timedelta, Period])
+@pytest.mark.parametrize(
+    "value", [None, np.nan, iNaT, float("nan"), NaT, "NaT", "nat", "", "NAT"]
+)
+def test_identity(klass, value):
+    assert klass(value) is NaT
+
+
+@pytest.mark.parametrize("klass", [Timestamp, Timedelta])
+@pytest.mark.parametrize("method", ["round", "floor", "ceil"])
+@pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"])
+def test_round_nat(klass, method, freq):
+    # see gh-14940
+    ts = klass("nat")
+
+    round_method = getattr(ts, method)
+    assert round_method(freq) is ts
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        "astimezone",
+        "combine",
+        "ctime",
+        "dst",
+        "fromordinal",
+        "fromtimestamp",
+        "fromisocalendar",
+        "isocalendar",
+        "strftime",
+        "strptime",
+        "time",
+        "timestamp",
+        "timetuple",
+        "timetz",
+        "toordinal",
+        "tzname",
+        "utcfromtimestamp",
+        "utcnow",
+        "utcoffset",
+        "utctimetuple",
+        "timestamp",
+    ],
+)
+def test_nat_methods_raise(method):
+    # see gh-9513, gh-17329
+    msg = f"NaTType does not support {method}"
+
+    with pytest.raises(ValueError, match=msg):
+        getattr(NaT, method)()
+
+
+@pytest.mark.parametrize("method", ["weekday", "isoweekday"])
+def test_nat_methods_nan(method):
+    # see gh-9513, gh-17329
+    assert np.isnan(getattr(NaT, method)())
+
+
+@pytest.mark.parametrize(
+    "method", ["date", "now", "replace", "today", "tz_convert", "tz_localize"]
+)
+def test_nat_methods_nat(method):
+    # see gh-8254, gh-9513, gh-17329
+    assert getattr(NaT, method)() is NaT
+
+
+@pytest.mark.parametrize(
+    "get_nat", [lambda x: NaT, lambda x: Timedelta(x), lambda x: Timestamp(x)]
+)
+def test_nat_iso_format(get_nat):
+    # see gh-12300
+    assert get_nat("NaT").isoformat() == "NaT"
+    assert get_nat("NaT").isoformat(timespec="nanoseconds") == "NaT"
+
+
+@pytest.mark.parametrize(
+    "klass,expected",
+    [
+        (Timestamp, ["normalize", "to_julian_date", "to_period", "unit"]),
+        (
+            Timedelta,
+            [
+                "components",
+                "resolution_string",
+                "to_pytimedelta",
+                "to_timedelta64",
+                "unit",
+                "view",
+            ],
+        ),
+    ],
+)
+def test_missing_public_nat_methods(klass, expected):
+    # see gh-17327
+    #
+    # NaT should have *most* of the Timestamp and Timedelta methods.
+    # Here, we check which public methods NaT does not have. We
+    # ignore any missing private methods.
+    nat_names = dir(NaT)
+    klass_names = dir(klass)
+
+    missing = [x for x in klass_names if x not in nat_names and not x.startswith("_")]
+    missing.sort()
+
+    assert missing == expected
+
+
+def _get_overlap_public_nat_methods(klass, as_tuple=False):
+    """
+    Get overlapping public methods between NaT and another class.
+
+    Parameters
+    ----------
+    klass : type
+        The class to compare with NaT
+    as_tuple : bool, default False
+        Whether to return a list of tuples of the form (klass, method).
+
+    Returns
+    -------
+    overlap : list
+    """
+    nat_names = dir(NaT)
+    klass_names = dir(klass)
+
+    overlap = [
+        x
+        for x in nat_names
+        if x in klass_names and not x.startswith("_") and callable(getattr(klass, x))
+    ]
+
+    # Timestamp takes precedence over Timedelta in terms of overlap.
+    if klass is Timedelta:
+        ts_names = dir(Timestamp)
+        overlap = [x for x in overlap if x not in ts_names]
+
+    if as_tuple:
+        overlap = [(klass, method) for method in overlap]
+
+    overlap.sort()
+    return overlap
+
+
+@pytest.mark.parametrize(
+    "klass,expected",
+    [
+        (
+            Timestamp,
+            [
+                "as_unit",
+                "astimezone",
+                "ceil",
+                "combine",
+                "ctime",
+                "date",
+                "day_name",
+                "dst",
+                "floor",
+                "fromisocalendar",
+                "fromisoformat",
+                "fromordinal",
+                "fromtimestamp",
+                "isocalendar",
+                "isoformat",
+                "isoweekday",
+                "month_name",
+                "now",
+                "replace",
+                "round",
+                "strftime",
+                "strptime",
+                "time",
+                "timestamp",
+                "timetuple",
+                "timetz",
+                "to_datetime64",
+                "to_numpy",
+                "to_pydatetime",
+                "today",
+                "toordinal",
+                "tz_convert",
+                "tz_localize",
+                "tzname",
+                "utcfromtimestamp",
+                "utcnow",
+                "utcoffset",
+                "utctimetuple",
+                "weekday",
+            ],
+        ),
+        (Timedelta, ["total_seconds"]),
+    ],
+)
+def test_overlap_public_nat_methods(klass, expected):
+    # see gh-17327
+    #
+    # NaT should have *most* of the Timestamp and Timedelta methods.
+    # In case when Timestamp, Timedelta, and NaT are overlap, the overlap
+    # is considered to be with Timestamp and NaT, not Timedelta.
+    assert _get_overlap_public_nat_methods(klass) == expected
+
+
+@pytest.mark.parametrize(
+    "compare",
+    (
+        _get_overlap_public_nat_methods(Timestamp, True)
+        + _get_overlap_public_nat_methods(Timedelta, True)
+    ),
+    ids=lambda x: f"{x[0].__name__}.{x[1]}",
+)
+def test_nat_doc_strings(compare):
+    # see gh-17327
+    #
+    # The docstrings for overlapping methods should match.
+    klass, method = compare
+    klass_doc = getattr(klass, method).__doc__
+
+    if klass == Timestamp and method == "isoformat":
+        pytest.skip(
+            "Ignore differences with Timestamp.isoformat() as they're intentional"
+        )
+
+    if method == "to_numpy":
+        # GH#44460 can return either dt64 or td64 depending on dtype,
+        #  different docstring is intentional
+        pytest.skip(f"different docstring for {method} is intentional")
+
+    nat_doc = getattr(NaT, method).__doc__
+    assert klass_doc == nat_doc
+
+
+_ops = {
+    "left_plus_right": lambda a, b: a + b,
+    "right_plus_left": lambda a, b: b + a,
+    "left_minus_right": lambda a, b: a - b,
+    "right_minus_left": lambda a, b: b - a,
+    "left_times_right": lambda a, b: a * b,
+    "right_times_left": lambda a, b: b * a,
+    "left_div_right": lambda a, b: a / b,
+    "right_div_left": lambda a, b: b / a,
+}
+
+
+@pytest.mark.parametrize("op_name", list(_ops.keys()))
+@pytest.mark.parametrize(
+    "value,val_type",
+    [
+        (2, "scalar"),
+        (1.5, "floating"),
+        (np.nan, "floating"),
+        ("foo", "str"),
+        (timedelta(3600), "timedelta"),
+        (Timedelta("5s"), "timedelta"),
+        (datetime(2014, 1, 1), "timestamp"),
+        (Timestamp("2014-01-01"), "timestamp"),
+        (Timestamp("2014-01-01", tz="UTC"), "timestamp"),
+        (Timestamp("2014-01-01", tz="US/Eastern"), "timestamp"),
+        (pytz.timezone("Asia/Tokyo").localize(datetime(2014, 1, 1)), "timestamp"),
+    ],
+)
+def test_nat_arithmetic_scalar(op_name, value, val_type):
+    # see gh-6873
+    invalid_ops = {
+        "scalar": {"right_div_left"},
+        "floating": {
+            "right_div_left",
+            "left_minus_right",
+            "right_minus_left",
+            "left_plus_right",
+            "right_plus_left",
+        },
+        "str": set(_ops.keys()),
+        "timedelta": {"left_times_right", "right_times_left"},
+        "timestamp": {
+            "left_times_right",
+            "right_times_left",
+            "left_div_right",
+            "right_div_left",
+        },
+    }
+
+    op = _ops[op_name]
+
+    if op_name in invalid_ops.get(val_type, set()):
+        if (
+            val_type == "timedelta"
+            and "times" in op_name
+            and isinstance(value, Timedelta)
+        ):
+            typs = "(Timedelta|NaTType)"
+            msg = rf"unsupported operand type\(s\) for \*: '{typs}' and '{typs}'"
+        elif val_type == "str":
+            # un-specific check here because the message comes from str
+            #  and varies by method
+            msg = "|".join(
+                [
+                    "can only concatenate str",
+                    "unsupported operand type",
+                    "can't multiply sequence",
+                    "Can't convert 'NaTType'",
+                    "must be str, not NaTType",
+                ]
+            )
+        else:
+            msg = "unsupported operand type"
+
+        with pytest.raises(TypeError, match=msg):
+            op(NaT, value)
+    else:
+        if val_type == "timedelta" and "div" in op_name:
+            expected = np.nan
+        else:
+            expected = NaT
+
+        assert op(NaT, value) is expected
+
+
+@pytest.mark.parametrize(
+    "val,expected", [(np.nan, NaT), (NaT, np.nan), (np.timedelta64("NaT"), np.nan)]
+)
+def test_nat_rfloordiv_timedelta(val, expected):
+    # see gh-#18846
+    #
+    # See also test_timedelta.TestTimedeltaArithmetic.test_floordiv
+    td = Timedelta(hours=3, minutes=4)
+    assert td // val is expected
+
+
+@pytest.mark.parametrize(
+    "op_name",
+    ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"],
+)
+@pytest.mark.parametrize(
+    "value",
+    [
+        DatetimeIndex(["2011-01-01", "2011-01-02"], name="x"),
+        DatetimeIndex(["2011-01-01", "2011-01-02"], tz="US/Eastern", name="x"),
+        DatetimeArray._from_sequence(["2011-01-01", "2011-01-02"], dtype="M8[ns]"),
+        DatetimeArray._from_sequence(
+            ["2011-01-01", "2011-01-02"], dtype=DatetimeTZDtype(tz="US/Pacific")
+        ),
+        TimedeltaIndex(["1 day", "2 day"], name="x"),
+    ],
+)
+def test_nat_arithmetic_index(op_name, value):
+    # see gh-11718
+    exp_name = "x"
+    exp_data = [NaT] * 2
+
+    if value.dtype.kind == "M" and "plus" in op_name:
+        expected = DatetimeIndex(exp_data, tz=value.tz, name=exp_name)
+    else:
+        expected = TimedeltaIndex(exp_data, name=exp_name)
+    expected = expected.as_unit(value.unit)
+
+    if not isinstance(value, Index):
+        expected = expected.array
+
+    op = _ops[op_name]
+    result = op(NaT, value)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op_name",
+    ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"],
+)
+@pytest.mark.parametrize("box", [TimedeltaIndex, Series, TimedeltaArray._from_sequence])
+def test_nat_arithmetic_td64_vector(op_name, box):
+    # see gh-19124
+    vec = box(["1 day", "2 day"], dtype="timedelta64[ns]")
+    box_nat = box([NaT, NaT], dtype="timedelta64[ns]")
+    tm.assert_equal(_ops[op_name](vec, NaT), box_nat)
+
+
+@pytest.mark.parametrize(
+    "dtype,op,out_dtype",
+    [
+        ("datetime64[ns]", operator.add, "datetime64[ns]"),
+        ("datetime64[ns]", roperator.radd, "datetime64[ns]"),
+        ("datetime64[ns]", operator.sub, "timedelta64[ns]"),
+        ("datetime64[ns]", roperator.rsub, "timedelta64[ns]"),
+        ("timedelta64[ns]", operator.add, "datetime64[ns]"),
+        ("timedelta64[ns]", roperator.radd, "datetime64[ns]"),
+        ("timedelta64[ns]", operator.sub, "datetime64[ns]"),
+        ("timedelta64[ns]", roperator.rsub, "timedelta64[ns]"),
+    ],
+)
+def test_nat_arithmetic_ndarray(dtype, op, out_dtype):
+    other = np.arange(10).astype(dtype)
+    result = op(NaT, other)
+
+    expected = np.empty(other.shape, dtype=out_dtype)
+    expected.fill("NaT")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_nat_pinned_docstrings():
+    # see gh-17327
+    assert NaT.ctime.__doc__ == Timestamp.ctime.__doc__
+
+
+def test_to_numpy_alias():
+    # GH 24653: alias .to_numpy() for scalars
+    expected = NaT.to_datetime64()
+    result = NaT.to_numpy()
+
+    assert isna(expected) and isna(result)
+
+    # GH#44460
+    result = NaT.to_numpy("M8[s]")
+    assert isinstance(result, np.datetime64)
+    assert result.dtype == "M8[s]"
+
+    result = NaT.to_numpy("m8[ns]")
+    assert isinstance(result, np.timedelta64)
+    assert result.dtype == "m8[ns]"
+
+    result = NaT.to_numpy("m8[s]")
+    assert isinstance(result, np.timedelta64)
+    assert result.dtype == "m8[s]"
+
+    with pytest.raises(ValueError, match="NaT.to_numpy dtype must be a "):
+        NaT.to_numpy(np.int64)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        Timedelta(0),
+        Timedelta(0).to_pytimedelta(),
+        pytest.param(
+            Timedelta(0).to_timedelta64(),
+            marks=pytest.mark.xfail(
+                not np_version_gte1p24p3,
+                reason="td64 doesn't return NotImplemented, see numpy#17017",
+                # When this xfail is fixed, test_nat_comparisons_numpy
+                #  can be removed.
+            ),
+        ),
+        Timestamp(0),
+        Timestamp(0).to_pydatetime(),
+        pytest.param(
+            Timestamp(0).to_datetime64(),
+            marks=pytest.mark.xfail(
+                not np_version_gte1p24p3,
+                reason="dt64 doesn't return NotImplemented, see numpy#17017",
+            ),
+        ),
+        Timestamp(0).tz_localize("UTC"),
+        NaT,
+    ],
+)
+def test_nat_comparisons(compare_operators_no_eq_ne, other):
+    # GH 26039
+    opname = compare_operators_no_eq_ne
+
+    assert getattr(NaT, opname)(other) is False
+
+    op = getattr(operator, opname.strip("_"))
+    assert op(NaT, other) is False
+    assert op(other, NaT) is False
+
+
+@pytest.mark.parametrize("other", [np.timedelta64(0, "ns"), np.datetime64("now", "ns")])
+def test_nat_comparisons_numpy(other):
+    # Once numpy#17017 is fixed and the xfailed cases in test_nat_comparisons
+    #  pass, this test can be removed
+    assert not NaT == other
+    assert NaT != other
+    assert not NaT < other
+    assert not NaT > other
+    assert not NaT <= other
+    assert not NaT >= other
+
+
+@pytest.mark.parametrize("other_and_type", [("foo", "str"), (2, "int"), (2.0, "float")])
+@pytest.mark.parametrize(
+    "symbol_and_op",
+    [("<=", operator.le), ("<", operator.lt), (">=", operator.ge), (">", operator.gt)],
+)
+def test_nat_comparisons_invalid(other_and_type, symbol_and_op):
+    # GH#35585
+    other, other_type = other_and_type
+    symbol, op = symbol_and_op
+
+    assert not NaT == other
+    assert not other == NaT
+
+    assert NaT != other
+    assert other != NaT
+
+    msg = f"'{symbol}' not supported between instances of 'NaTType' and '{other_type}'"
+    with pytest.raises(TypeError, match=msg):
+        op(NaT, other)
+
+    msg = f"'{symbol}' not supported between instances of '{other_type}' and 'NaTType'"
+    with pytest.raises(TypeError, match=msg):
+        op(other, NaT)
+
+
+@pytest.mark.parametrize(
+    "other",
+    [
+        np.array(["foo"] * 2, dtype=object),
+        np.array([2, 3], dtype="int64"),
+        np.array([2.0, 3.5], dtype="float64"),
+    ],
+    ids=["str", "int", "float"],
+)
+def test_nat_comparisons_invalid_ndarray(other):
+    # GH#40722
+    expected = np.array([False, False])
+    result = NaT == other
+    tm.assert_numpy_array_equal(result, expected)
+    result = other == NaT
+    tm.assert_numpy_array_equal(result, expected)
+
+    expected = np.array([True, True])
+    result = NaT != other
+    tm.assert_numpy_array_equal(result, expected)
+    result = other != NaT
+    tm.assert_numpy_array_equal(result, expected)
+
+    for symbol, op in [
+        ("<=", operator.le),
+        ("<", operator.lt),
+        (">=", operator.ge),
+        (">", operator.gt),
+    ]:
+        msg = f"'{symbol}' not supported between"
+
+        with pytest.raises(TypeError, match=msg):
+            op(NaT, other)
+
+        if other.dtype == np.dtype("object"):
+            # uses the reverse operator, so symbol changes
+            msg = None
+        with pytest.raises(TypeError, match=msg):
+            op(other, NaT)
+
+
+def test_compare_date(fixed_now_ts):
+    # GH#39151 comparing NaT with date object is deprecated
+    # See also: tests.scalar.timestamps.test_comparisons::test_compare_date
+
+    dt = fixed_now_ts.to_pydatetime().date()
+
+    msg = "Cannot compare NaT with datetime.date object"
+    for left, right in [(NaT, dt), (dt, NaT)]:
+        assert not left == right
+        assert left != right
+
+        with pytest.raises(TypeError, match=msg):
+            left < right
+        with pytest.raises(TypeError, match=msg):
+            left <= right
+        with pytest.raises(TypeError, match=msg):
+            left > right
+        with pytest.raises(TypeError, match=msg):
+            left >= right
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        offsets.YearEnd(2),
+        offsets.YearBegin(2),
+        offsets.MonthBegin(1),
+        offsets.MonthEnd(2),
+        offsets.MonthEnd(12),
+        offsets.Day(2),
+        offsets.Day(5),
+        offsets.Hour(24),
+        offsets.Hour(3),
+        offsets.Minute(),
+        np.timedelta64(3, "h"),
+        np.timedelta64(4, "h"),
+        np.timedelta64(3200, "s"),
+        np.timedelta64(3600, "s"),
+        np.timedelta64(3600 * 24, "s"),
+        np.timedelta64(2, "D"),
+        np.timedelta64(365, "D"),
+        timedelta(-2),
+        timedelta(365),
+        timedelta(minutes=120),
+        timedelta(days=4, minutes=180),
+        timedelta(hours=23),
+        timedelta(hours=23, minutes=30),
+        timedelta(hours=48),
+    ],
+)
+def test_nat_addsub_tdlike_scalar(obj):
+    assert NaT + obj is NaT
+    assert obj + NaT is NaT
+    assert NaT - obj is NaT
+
+
+def test_pickle():
+    # GH#4606
+    p = tm.round_trip_pickle(NaT)
+    assert p is NaT

omnilmm/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_constructors.py

@@ -0,0 +1,698 @@
+from datetime import timedelta
+from itertools import product
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import OutOfBoundsTimedelta
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+
+from pandas import (
+    Index,
+    NaT,
+    Timedelta,
+    TimedeltaIndex,
+    offsets,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaConstructorUnitKeyword:
+    @pytest.mark.parametrize("unit", ["Y", "y", "M"])
+    def test_unit_m_y_raises(self, unit):
+        msg = "Units 'M', 'Y', and 'y' are no longer supported"
+
+        with pytest.raises(ValueError, match=msg):
+            Timedelta(10, unit)
+
+        with pytest.raises(ValueError, match=msg):
+            to_timedelta(10, unit)
+
+        with pytest.raises(ValueError, match=msg):
+            to_timedelta([1, 2], unit)
+
+    @pytest.mark.parametrize(
+        "unit,unit_depr",
+        [
+            ("h", "H"),
+            ("min", "T"),
+            ("s", "S"),
+            ("ms", "L"),
+            ("ns", "N"),
+            ("us", "U"),
+        ],
+    )
+    def test_units_H_T_S_L_N_U_deprecated(self, unit, unit_depr):
+        # GH#52536
+        msg = f"'{unit_depr}' is deprecated and will be removed in a future version."
+
+        expected = Timedelta(1, unit=unit)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = Timedelta(1, unit=unit_depr)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "unit, np_unit",
+        [(value, "W") for value in ["W", "w"]]
+        + [(value, "D") for value in ["D", "d", "days", "day", "Days", "Day"]]
+        + [
+            (value, "m")
+            for value in [
+                "m",
+                "minute",
+                "min",
+                "minutes",
+                "Minute",
+                "Min",
+                "Minutes",
+            ]
+        ]
+        + [
+            (value, "s")
+            for value in [
+                "s",
+                "seconds",
+                "sec",
+                "second",
+                "Seconds",
+                "Sec",
+                "Second",
+            ]
+        ]
+        + [
+            (value, "ms")
+            for value in [
+                "ms",
+                "milliseconds",
+                "millisecond",
+                "milli",
+                "millis",
+                "MS",
+                "Milliseconds",
+                "Millisecond",
+                "Milli",
+                "Millis",
+            ]
+        ]
+        + [
+            (value, "us")
+            for value in [
+                "us",
+                "microseconds",
+                "microsecond",
+                "micro",
+                "micros",
+                "u",
+                "US",
+                "Microseconds",
+                "Microsecond",
+                "Micro",
+                "Micros",
+                "U",
+            ]
+        ]
+        + [
+            (value, "ns")
+            for value in [
+                "ns",
+                "nanoseconds",
+                "nanosecond",
+                "nano",
+                "nanos",
+                "n",
+                "NS",
+                "Nanoseconds",
+                "Nanosecond",
+                "Nano",
+                "Nanos",
+                "N",
+            ]
+        ],
+    )
+    @pytest.mark.parametrize("wrapper", [np.array, list, Index])
+    def test_unit_parser(self, unit, np_unit, wrapper):
+        # validate all units, GH 6855, GH 21762
+        # array-likes
+        expected = TimedeltaIndex(
+            [np.timedelta64(i, np_unit) for i in np.arange(5).tolist()],
+            dtype="m8[ns]",
+        )
+        # TODO(2.0): the desired output dtype may have non-nano resolution
+        msg = f"'{unit}' is deprecated and will be removed in a future version."
+
+        if (unit, np_unit) in (("u", "us"), ("U", "us"), ("n", "ns"), ("N", "ns")):
+            warn = FutureWarning
+        else:
+            warn = FutureWarning
+            msg = "The 'unit' keyword in TimedeltaIndex construction is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = to_timedelta(wrapper(range(5)), unit=unit)
+            tm.assert_index_equal(result, expected)
+            result = TimedeltaIndex(wrapper(range(5)), unit=unit)
+            tm.assert_index_equal(result, expected)
+
+            str_repr = [f"{x}{unit}" for x in np.arange(5)]
+            result = to_timedelta(wrapper(str_repr))
+            tm.assert_index_equal(result, expected)
+            result = to_timedelta(wrapper(str_repr))
+            tm.assert_index_equal(result, expected)
+
+            # scalar
+            expected = Timedelta(np.timedelta64(2, np_unit).astype("timedelta64[ns]"))
+            result = to_timedelta(2, unit=unit)
+            assert result == expected
+            result = Timedelta(2, unit=unit)
+            assert result == expected
+
+            result = to_timedelta(f"2{unit}")
+            assert result == expected
+            result = Timedelta(f"2{unit}")
+            assert result == expected
+
+
+def test_construct_from_kwargs_overflow():
+    # GH#55503
+    msg = "seconds=86400000000000000000, milliseconds=0, microseconds=0, nanoseconds=0"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(days=10**6)
+    msg = "seconds=60000000000000000000, milliseconds=0, microseconds=0, nanoseconds=0"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(minutes=10**9)
+
+
+def test_construct_with_weeks_unit_overflow():
+    # GH#47268 don't silently wrap around
+    with pytest.raises(OutOfBoundsTimedelta, match="without overflow"):
+        Timedelta(1000000000000000000, unit="W")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="without overflow"):
+        Timedelta(1000000000000000000.0, unit="W")
+
+
+def test_construct_from_td64_with_unit():
+    # ignore the unit, as it may cause silently overflows leading to incorrect
+    #  results, and in non-overflow cases is irrelevant GH#46827
+    obj = np.timedelta64(123456789000000000, "h")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"):
+        Timedelta(obj, unit="ps")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"):
+        Timedelta(obj, unit="ns")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"):
+        Timedelta(obj)
+
+
+def test_from_td64_retain_resolution():
+    # case where we retain millisecond resolution
+    obj = np.timedelta64(12345, "ms")
+
+    td = Timedelta(obj)
+    assert td._value == obj.view("i8")
+    assert td._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+    # Case where we cast to nearest-supported reso
+    obj2 = np.timedelta64(1234, "D")
+    td2 = Timedelta(obj2)
+    assert td2._creso == NpyDatetimeUnit.NPY_FR_s.value
+    assert td2 == obj2
+    assert td2.days == 1234
+
+    # Case that _would_ overflow if we didn't support non-nano
+    obj3 = np.timedelta64(1000000000000000000, "us")
+    td3 = Timedelta(obj3)
+    assert td3.total_seconds() == 1000000000000
+    assert td3._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+
+def test_from_pytimedelta_us_reso():
+    # pytimedelta has microsecond resolution, so Timedelta(pytd) inherits that
+    td = timedelta(days=4, minutes=3)
+    result = Timedelta(td)
+    assert result.to_pytimedelta() == td
+    assert result._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+
+def test_from_tick_reso():
+    tick = offsets.Nano()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    tick = offsets.Micro()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+    tick = offsets.Milli()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+    tick = offsets.Second()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+    # everything above Second gets cast to the closest supported reso: second
+    tick = offsets.Minute()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+    tick = offsets.Hour()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+    tick = offsets.Day()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+
+def test_construction():
+    expected = np.timedelta64(10, "D").astype("m8[ns]").view("i8")
+    assert Timedelta(10, unit="d")._value == expected
+    assert Timedelta(10.0, unit="d")._value == expected
+    assert Timedelta("10 days")._value == expected
+    assert Timedelta(days=10)._value == expected
+    assert Timedelta(days=10.0)._value == expected
+
+    expected += np.timedelta64(10, "s").astype("m8[ns]").view("i8")
+    assert Timedelta("10 days 00:00:10")._value == expected
+    assert Timedelta(days=10, seconds=10)._value == expected
+    assert Timedelta(days=10, milliseconds=10 * 1000)._value == expected
+    assert Timedelta(days=10, microseconds=10 * 1000 * 1000)._value == expected
+
+    # rounding cases
+    assert Timedelta(82739999850000)._value == 82739999850000
+    assert "0 days 22:58:59.999850" in str(Timedelta(82739999850000))
+    assert Timedelta(123072001000000)._value == 123072001000000
+    assert "1 days 10:11:12.001" in str(Timedelta(123072001000000))
+
+    # string conversion with/without leading zero
+    # GH#9570
+    assert Timedelta("0:00:00") == timedelta(hours=0)
+    assert Timedelta("00:00:00") == timedelta(hours=0)
+    assert Timedelta("-1:00:00") == -timedelta(hours=1)
+    assert Timedelta("-01:00:00") == -timedelta(hours=1)
+
+    # more strings & abbrevs
+    # GH#8190
+    assert Timedelta("1 h") == timedelta(hours=1)
+    assert Timedelta("1 hour") == timedelta(hours=1)
+    assert Timedelta("1 hr") == timedelta(hours=1)
+    assert Timedelta("1 hours") == timedelta(hours=1)
+    assert Timedelta("-1 hours") == -timedelta(hours=1)
+    assert Timedelta("1 m") == timedelta(minutes=1)
+    assert Timedelta("1.5 m") == timedelta(seconds=90)
+    assert Timedelta("1 minute") == timedelta(minutes=1)
+    assert Timedelta("1 minutes") == timedelta(minutes=1)
+    assert Timedelta("1 s") == timedelta(seconds=1)
+    assert Timedelta("1 second") == timedelta(seconds=1)
+    assert Timedelta("1 seconds") == timedelta(seconds=1)
+    assert Timedelta("1 ms") == timedelta(milliseconds=1)
+    assert Timedelta("1 milli") == timedelta(milliseconds=1)
+    assert Timedelta("1 millisecond") == timedelta(milliseconds=1)
+    assert Timedelta("1 us") == timedelta(microseconds=1)
+    assert Timedelta("1 µs") == timedelta(microseconds=1)
+    assert Timedelta("1 micros") == timedelta(microseconds=1)
+    assert Timedelta("1 microsecond") == timedelta(microseconds=1)
+    assert Timedelta("1.5 microsecond") == Timedelta("00:00:00.000001500")
+    assert Timedelta("1 ns") == Timedelta("00:00:00.000000001")
+    assert Timedelta("1 nano") == Timedelta("00:00:00.000000001")
+    assert Timedelta("1 nanosecond") == Timedelta("00:00:00.000000001")
+
+    # combos
+    assert Timedelta("10 days 1 hour") == timedelta(days=10, hours=1)
+    assert Timedelta("10 days 1 h") == timedelta(days=10, hours=1)
+    assert Timedelta("10 days 1 h 1m 1s") == timedelta(
+        days=10, hours=1, minutes=1, seconds=1
+    )
+    assert Timedelta("-10 days 1 h 1m 1s") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=1
+    )
+    assert Timedelta("-10 days 1 h 1m 1s") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=1
+    )
+    assert Timedelta("-10 days 1 h 1m 1s 3us") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=1, microseconds=3
+    )
+    assert Timedelta("-10 days 1 h 1.5m 1s 3us") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=31, microseconds=3
+    )
+
+    # Currently invalid as it has a - on the hh:mm:dd part
+    # (only allowed on the days)
+    msg = "only leading negative signs are allowed"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("-10 days -1 h 1.5m 1s 3us")
+
+    # only leading neg signs are allowed
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("10 days -1 h 1.5m 1s 3us")
+
+    # no units specified
+    msg = "no units specified"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("3.1415")
+
+    # invalid construction
+    msg = "cannot construct a Timedelta"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta()
+
+    msg = "unit abbreviation w/o a number"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("foo")
+
+    msg = (
+        "cannot construct a Timedelta from "
+        "the passed arguments, allowed keywords are "
+    )
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(day=10)
+
+    # floats
+    expected = np.timedelta64(10, "s").astype("m8[ns]").view("i8") + np.timedelta64(
+        500, "ms"
+    ).astype("m8[ns]").view("i8")
+    assert Timedelta(10.5, unit="s")._value == expected
+
+    # offset
+    assert to_timedelta(offsets.Hour(2)) == Timedelta(hours=2)
+    assert Timedelta(offsets.Hour(2)) == Timedelta(hours=2)
+    assert Timedelta(offsets.Second(2)) == Timedelta(seconds=2)
+
+    # GH#11995: unicode
+    expected = Timedelta("1h")
+    result = Timedelta("1h")
+    assert result == expected
+    assert to_timedelta(offsets.Hour(2)) == Timedelta("0 days, 02:00:00")
+
+    msg = "unit abbreviation w/o a number"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("foo bar")
+
+
+@pytest.mark.parametrize(
+    "item",
+    list(
+        {
+            "days": "D",
+            "seconds": "s",
+            "microseconds": "us",
+            "milliseconds": "ms",
+            "minutes": "m",
+            "hours": "h",
+            "weeks": "W",
+        }.items()
+    ),
+)
+@pytest.mark.parametrize(
+    "npdtype", [np.int64, np.int32, np.int16, np.float64, np.float32, np.float16]
+)
+def test_td_construction_with_np_dtypes(npdtype, item):
+    # GH#8757: test construction with np dtypes
+    pykwarg, npkwarg = item
+    expected = np.timedelta64(1, npkwarg).astype("m8[ns]").view("i8")
+    assert Timedelta(**{pykwarg: npdtype(1)})._value == expected
+
+
+@pytest.mark.parametrize(
+    "val",
+    [
+        "1s",
+        "-1s",
+        "1us",
+        "-1us",
+        "1 day",
+        "-1 day",
+        "-23:59:59.999999",
+        "-1 days +23:59:59.999999",
+        "-1ns",
+        "1ns",
+        "-23:59:59.999999999",
+    ],
+)
+def test_td_from_repr_roundtrip(val):
+    # round-trip both for string and value
+    td = Timedelta(val)
+    assert Timedelta(td._value) == td
+
+    assert Timedelta(str(td)) == td
+    assert Timedelta(td._repr_base(format="all")) == td
+    assert Timedelta(td._repr_base()) == td
+
+
+def test_overflow_on_construction():
+    # GH#3374
+    value = Timedelta("1day")._value * 20169940
+    msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(value)
+
+    # xref GH#17637
+    msg = "Cannot cast 139993 from D to 'ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(7 * 19999, unit="D")
+
+    # used to overflow before non-ns support
+    td = Timedelta(timedelta(days=13 * 19999))
+    assert td._creso == NpyDatetimeUnit.NPY_FR_us.value
+    assert td.days == 13 * 19999
+
+
+@pytest.mark.parametrize(
+    "val, unit",
+    [
+        (15251, "W"),  # 1
+        (106752, "D"),  # change from previous:
+        (2562048, "h"),  # 0 hours
+        (153722868, "m"),  # 13 minutes
+        (9223372037, "s"),  # 44 seconds
+    ],
+)
+def test_construction_out_of_bounds_td64ns(val, unit):
+    # TODO: parametrize over units just above/below the implementation bounds
+    #  once GH#38964 is resolved
+
+    # Timedelta.max is just under 106752 days
+    td64 = np.timedelta64(val, unit)
+    assert td64.astype("m8[ns]").view("i8") < 0  # i.e. naive astype will be wrong
+
+    td = Timedelta(td64)
+    if unit != "M":
+        # with unit="M" the conversion to "s" is poorly defined
+        #  (and numpy issues DeprecationWarning)
+        assert td.asm8 == td64
+    assert td.asm8.dtype == "m8[s]"
+    msg = r"Cannot cast 1067\d\d days .* to unit='ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        td.as_unit("ns")
+
+    # But just back in bounds and we are OK
+    assert Timedelta(td64 - 1) == td64 - 1
+
+    td64 *= -1
+    assert td64.astype("m8[ns]").view("i8") > 0  # i.e. naive astype will be wrong
+
+    td2 = Timedelta(td64)
+    msg = r"Cannot cast -1067\d\d days .* to unit='ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        td2.as_unit("ns")
+
+    # But just back in bounds and we are OK
+    assert Timedelta(td64 + 1) == td64 + 1
+
+
+@pytest.mark.parametrize(
+    "val, unit",
+    [
+        (15251 * 10**9, "W"),
+        (106752 * 10**9, "D"),
+        (2562048 * 10**9, "h"),
+        (153722868 * 10**9, "m"),
+    ],
+)
+def test_construction_out_of_bounds_td64s(val, unit):
+    td64 = np.timedelta64(val, unit)
+    with pytest.raises(OutOfBoundsTimedelta, match=str(td64)):
+        Timedelta(td64)
+
+    # But just back in bounds and we are OK
+    assert Timedelta(td64 - 10**9) == td64 - 10**9
+
+
+@pytest.mark.parametrize(
+    "fmt,exp",
+    [
+        (
+            "P6DT0H50M3.010010012S",
+            Timedelta(
+                days=6,
+                minutes=50,
+                seconds=3,
+                milliseconds=10,
+                microseconds=10,
+                nanoseconds=12,
+            ),
+        ),
+        (
+            "P-6DT0H50M3.010010012S",
+            Timedelta(
+                days=-6,
+                minutes=50,
+                seconds=3,
+                milliseconds=10,
+                microseconds=10,
+                nanoseconds=12,
+            ),
+        ),
+        ("P4DT12H30M5S", Timedelta(days=4, hours=12, minutes=30, seconds=5)),
+        ("P0DT0H0M0.000000123S", Timedelta(nanoseconds=123)),
+        ("P0DT0H0M0.00001S", Timedelta(microseconds=10)),
+        ("P0DT0H0M0.001S", Timedelta(milliseconds=1)),
+        ("P0DT0H1M0S", Timedelta(minutes=1)),
+        ("P1DT25H61M61S", Timedelta(days=1, hours=25, minutes=61, seconds=61)),
+        ("PT1S", Timedelta(seconds=1)),
+        ("PT0S", Timedelta(seconds=0)),
+        ("P1WT0S", Timedelta(days=7, seconds=0)),
+        ("P1D", Timedelta(days=1)),
+        ("P1DT1H", Timedelta(days=1, hours=1)),
+        ("P1W", Timedelta(days=7)),
+        ("PT300S", Timedelta(seconds=300)),
+        ("P1DT0H0M00000000000S", Timedelta(days=1)),
+        ("PT-6H3M", Timedelta(hours=-6, minutes=3)),
+        ("-PT6H3M", Timedelta(hours=-6, minutes=-3)),
+        ("-PT-6H+3M", Timedelta(hours=6, minutes=-3)),
+    ],
+)
+def test_iso_constructor(fmt, exp):
+    assert Timedelta(fmt) == exp
+
+
+@pytest.mark.parametrize(
+    "fmt",
+    [
+        "PPPPPPPPPPPP",
+        "PDTHMS",
+        "P0DT999H999M999S",
+        "P1DT0H0M0.0000000000000S",
+        "P1DT0H0M0.S",
+        "P",
+        "-P",
+    ],
+)
+def test_iso_constructor_raises(fmt):
+    msg = f"Invalid ISO 8601 Duration format - {fmt}"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(fmt)
+
+
+@pytest.mark.parametrize(
+    "constructed_td, conversion",
+    [
+        (Timedelta(nanoseconds=100), "100ns"),
+        (
+            Timedelta(
+                days=1,
+                hours=1,
+                minutes=1,
+                weeks=1,
+                seconds=1,
+                milliseconds=1,
+                microseconds=1,
+                nanoseconds=1,
+            ),
+            694861001001001,
+        ),
+        (Timedelta(microseconds=1) + Timedelta(nanoseconds=1), "1us1ns"),
+        (Timedelta(microseconds=1) - Timedelta(nanoseconds=1), "999ns"),
+        (Timedelta(microseconds=1) + 5 * Timedelta(nanoseconds=-2), "990ns"),
+    ],
+)
+def test_td_constructor_on_nanoseconds(constructed_td, conversion):
+    # GH#9273
+    assert constructed_td == Timedelta(conversion)
+
+
+def test_td_constructor_value_error():
+    msg = "Invalid type <class 'str'>. Must be int or float."
+    with pytest.raises(TypeError, match=msg):
+        Timedelta(nanoseconds="abc")
+
+
+def test_timedelta_constructor_identity():
+    # Test for #30543
+    expected = Timedelta(np.timedelta64(1, "s"))
+    result = Timedelta(expected)
+    assert result is expected
+
+
+def test_timedelta_pass_td_and_kwargs_raises():
+    # don't silently ignore the kwargs GH#48898
+    td = Timedelta(days=1)
+    msg = (
+        "Cannot pass both a Timedelta input and timedelta keyword arguments, "
+        r"got \['days'\]"
+    )
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(td, days=2)
+
+
+@pytest.mark.parametrize(
+    "constructor, value, unit, expectation",
+    [
+        (Timedelta, "10s", "ms", (ValueError, "unit must not be specified")),
+        (to_timedelta, "10s", "ms", (ValueError, "unit must not be specified")),
+        (to_timedelta, ["1", 2, 3], "s", (ValueError, "unit must not be specified")),
+    ],
+)
+def test_string_with_unit(constructor, value, unit, expectation):
+    exp, match = expectation
+    with pytest.raises(exp, match=match):
+        _ = constructor(value, unit=unit)
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        "".join(elements)
+        for repetition in (1, 2)
+        for elements in product("+-, ", repeat=repetition)
+    ],
+)
+def test_string_without_numbers(value):
+    # GH39710 Timedelta input string with only symbols and no digits raises an error
+    msg = (
+        "symbols w/o a number"
+        if value != "--"
+        else "only leading negative signs are allowed"
+    )
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(value)
+
+
+def test_timedelta_new_npnat():
+    # GH#48898
+    nat = np.timedelta64("NaT", "h")
+    assert Timedelta(nat) is NaT
+
+
+def test_subclass_respected():
+    # GH#49579
+    class MyCustomTimedelta(Timedelta):
+        pass
+
+    td = MyCustomTimedelta("1 minute")
+    assert isinstance(td, MyCustomTimedelta)
+
+
+def test_non_nano_value():
+    # https://github.com/pandas-dev/pandas/issues/49076
+    result = Timedelta(10, unit="D").as_unit("s").value
+    # `.value` shows nanoseconds, even though unit is 's'
+    assert result == 864000000000000
+
+    # out-of-nanoseconds-bounds `.value` raises informative message
+    msg = (
+        r"Cannot convert Timedelta to nanoseconds without overflow. "
+        r"Use `.asm8.view\('i8'\)` to cast represent Timedelta in its "
+        r"own unit \(here, s\).$"
+    )
+    td = Timedelta(1_000, "D").as_unit("s") * 1_000
+    with pytest.raises(OverflowError, match=msg):
+        td.value
+    # check that the suggested workaround actually works
+    result = td.asm8.view("i8")
+    assert result == 86400000000