针对pulse-transit的工具

dist/client/pandas/tests/tools/test_to_numeric.py

@@ -0,0 +1,791 @@
+import decimal
+
+import numpy as np
+from numpy import iinfo
+import pytest
+
+from pandas.compat import is_platform_arm
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    to_numeric,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[None, "ignore", "raise", "coerce"])
+def errors(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def signed(request):
+    return request.param
+
+
+@pytest.fixture(params=[lambda x: x, str], ids=["identity", "str"])
+def transform(request):
+    return request.param
+
+
+@pytest.fixture(params=[47393996303418497800, 100000000000000000000])
+def large_val(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def multiple_elts(request):
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        (lambda x: Index(x, name="idx"), tm.assert_index_equal),
+        (lambda x: Series(x, name="ser"), tm.assert_series_equal),
+        (lambda x: np.array(Index(x).values), tm.assert_numpy_array_equal),
+    ]
+)
+def transform_assert_equal(request):
+    return request.param
+
+
+@pytest.mark.parametrize(
+    "input_kwargs,result_kwargs",
+    [
+        ({}, {"dtype": np.int64}),
+        ({"errors": "coerce", "downcast": "integer"}, {"dtype": np.int8}),
+    ],
+)
+def test_empty(input_kwargs, result_kwargs):
+    # see gh-16302
+    ser = Series([], dtype=object)
+    result = to_numeric(ser, **input_kwargs)
+
+    expected = Series([], **result_kwargs)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("last_val", ["7", 7])
+def test_series(last_val):
+    ser = Series(["1", "-3.14", last_val])
+    result = to_numeric(ser)
+
+    expected = Series([1, -3.14, 7])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [1, 3, 4, 5],
+        [1.0, 3.0, 4.0, 5.0],
+        # Bool is regarded as numeric.
+        [True, False, True, True],
+    ],
+)
+def test_series_numeric(data):
+    ser = Series(data, index=list("ABCD"), name="EFG")
+
+    result = to_numeric(ser)
+    tm.assert_series_equal(result, ser)
+
+
+@pytest.mark.parametrize(
+    "data,msg",
+    [
+        ([1, -3.14, "apple"], 'Unable to parse string "apple" at position 2'),
+        (
+            ["orange", 1, -3.14, "apple"],
+            'Unable to parse string "orange" at position 0',
+        ),
+    ],
+)
+def test_error(data, msg):
+    ser = Series(data)
+
+    with pytest.raises(ValueError, match=msg):
+        to_numeric(ser, errors="raise")
+
+
+@pytest.mark.parametrize(
+    "errors,exp_data", [("ignore", [1, -3.14, "apple"]), ("coerce", [1, -3.14, np.nan])]
+)
+def test_ignore_error(errors, exp_data):
+    ser = Series([1, -3.14, "apple"])
+    result = to_numeric(ser, errors=errors)
+
+    expected = Series(exp_data)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "errors,exp",
+    [
+        ("raise", 'Unable to parse string "apple" at position 2'),
+        ("ignore", [True, False, "apple"]),
+        # Coerces to float.
+        ("coerce", [1.0, 0.0, np.nan]),
+    ],
+)
+def test_bool_handling(errors, exp):
+    ser = Series([True, False, "apple"])
+
+    if isinstance(exp, str):
+        with pytest.raises(ValueError, match=exp):
+            to_numeric(ser, errors=errors)
+    else:
+        result = to_numeric(ser, errors=errors)
+        expected = Series(exp)
+
+        tm.assert_series_equal(result, expected)
+
+
+def test_list():
+    ser = ["1", "-3.14", "7"]
+    res = to_numeric(ser)
+
+    expected = np.array([1, -3.14, 7])
+    tm.assert_numpy_array_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "data,arr_kwargs",
+    [
+        ([1, 3, 4, 5], {"dtype": np.int64}),
+        ([1.0, 3.0, 4.0, 5.0], {}),
+        # Boolean is regarded as numeric.
+        ([True, False, True, True], {}),
+    ],
+)
+def test_list_numeric(data, arr_kwargs):
+    result = to_numeric(data)
+    expected = np.array(data, **arr_kwargs)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("kwargs", [{"dtype": "O"}, {}])
+def test_numeric(kwargs):
+    data = [1, -3.14, 7]
+
+    ser = Series(data, **kwargs)
+    result = to_numeric(ser)
+
+    expected = Series(data)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "columns",
+    [
+        # One column.
+        "a",
+        # Multiple columns.
+        ["a", "b"],
+    ],
+)
+def test_numeric_df_columns(columns):
+    # see gh-14827
+    df = DataFrame(
+        {
+            "a": [1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), "0.1"],
+            "b": [1.0, 2.0, 3.0, 4.0],
+        }
+    )
+
+    expected = DataFrame({"a": [1.2, 3.14, np.inf, 0.1], "b": [1.0, 2.0, 3.0, 4.0]})
+
+    df_copy = df.copy()
+    df_copy[columns] = df_copy[columns].apply(to_numeric)
+
+    tm.assert_frame_equal(df_copy, expected)
+
+
+@pytest.mark.parametrize(
+    "data,exp_data",
+    [
+        (
+            [[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1],
+            [[3.14, 1.0], 1.6, 0.1],
+        ),
+        ([np.array([decimal.Decimal(3.14), 1.0]), 0.1], [[3.14, 1.0], 0.1]),
+    ],
+)
+def test_numeric_embedded_arr_likes(data, exp_data):
+    # Test to_numeric with embedded lists and arrays
+    df = DataFrame({"a": data})
+    df["a"] = df["a"].apply(to_numeric)
+
+    expected = DataFrame({"a": exp_data})
+    tm.assert_frame_equal(df, expected)
+
+
+def test_all_nan():
+    ser = Series(["a", "b", "c"])
+    result = to_numeric(ser, errors="coerce")
+
+    expected = Series([np.nan, np.nan, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_type_check(errors):
+    # see gh-11776
+    df = DataFrame({"a": [1, -3.14, 7], "b": ["4", "5", "6"]})
+    kwargs = {"errors": errors} if errors is not None else {}
+    with pytest.raises(TypeError, match="1-d array"):
+        to_numeric(df, **kwargs)
+
+
+@pytest.mark.parametrize("val", [1, 1.1, 20001])
+def test_scalar(val, signed, transform):
+    val = -val if signed else val
+    assert to_numeric(transform(val)) == float(val)
+
+
+def test_really_large_scalar(large_val, signed, transform, errors):
+    # see gh-24910
+    kwargs = {"errors": errors} if errors is not None else {}
+    val = -large_val if signed else large_val
+
+    val = transform(val)
+    val_is_string = isinstance(val, str)
+
+    if val_is_string and errors in (None, "raise"):
+        msg = "Integer out of range. at position 0"
+        with pytest.raises(ValueError, match=msg):
+            to_numeric(val, **kwargs)
+    else:
+        expected = float(val) if (errors == "coerce" and val_is_string) else val
+        tm.assert_almost_equal(to_numeric(val, **kwargs), expected)
+
+
+def test_really_large_in_arr(large_val, signed, transform, multiple_elts, errors):
+    # see gh-24910
+    kwargs = {"errors": errors} if errors is not None else {}
+    val = -large_val if signed else large_val
+    val = transform(val)
+
+    extra_elt = "string"
+    arr = [val] + multiple_elts * [extra_elt]
+
+    val_is_string = isinstance(val, str)
+    coercing = errors == "coerce"
+
+    if errors in (None, "raise") and (val_is_string or multiple_elts):
+        if val_is_string:
+            msg = "Integer out of range. at position 0"
+        else:
+            msg = 'Unable to parse string "string" at position 1'
+
+        with pytest.raises(ValueError, match=msg):
+            to_numeric(arr, **kwargs)
+    else:
+        result = to_numeric(arr, **kwargs)
+
+        exp_val = float(val) if (coercing and val_is_string) else val
+        expected = [exp_val]
+
+        if multiple_elts:
+            if coercing:
+                expected.append(np.nan)
+                exp_dtype = float
+            else:
+                expected.append(extra_elt)
+                exp_dtype = object
+        else:
+            exp_dtype = float if isinstance(exp_val, (int, float)) else object
+
+        tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
+
+
+def test_really_large_in_arr_consistent(large_val, signed, multiple_elts, errors):
+    # see gh-24910
+    #
+    # Even if we discover that we have to hold float, does not mean
+    # we should be lenient on subsequent elements that fail to be integer.
+    kwargs = {"errors": errors} if errors is not None else {}
+    arr = [str(-large_val if signed else large_val)]
+
+    if multiple_elts:
+        arr.insert(0, large_val)
+
+    if errors in (None, "raise"):
+        index = int(multiple_elts)
+        msg = f"Integer out of range. at position {index}"
+
+        with pytest.raises(ValueError, match=msg):
+            to_numeric(arr, **kwargs)
+    else:
+        result = to_numeric(arr, **kwargs)
+
+        if errors == "coerce":
+            expected = [float(i) for i in arr]
+            exp_dtype = float
+        else:
+            expected = arr
+            exp_dtype = object
+
+        tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
+
+
+@pytest.mark.parametrize(
+    "errors,checker",
+    [
+        ("raise", 'Unable to parse string "fail" at position 0'),
+        ("ignore", lambda x: x == "fail"),
+        ("coerce", lambda x: np.isnan(x)),
+    ],
+)
+def test_scalar_fail(errors, checker):
+    scalar = "fail"
+
+    if isinstance(checker, str):
+        with pytest.raises(ValueError, match=checker):
+            to_numeric(scalar, errors=errors)
+    else:
+        assert checker(to_numeric(scalar, errors=errors))
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, np.nan, 3, np.nan]])
+def test_numeric_dtypes(data, transform_assert_equal):
+    transform, assert_equal = transform_assert_equal
+    data = transform(data)
+
+    result = to_numeric(data)
+    assert_equal(result, data)
+
+
+@pytest.mark.parametrize(
+    "data,exp",
+    [
+        (["1", "2", "3"], np.array([1, 2, 3], dtype="int64")),
+        (["1.5", "2.7", "3.4"], np.array([1.5, 2.7, 3.4])),
+    ],
+)
+def test_str(data, exp, transform_assert_equal):
+    transform, assert_equal = transform_assert_equal
+    result = to_numeric(transform(data))
+
+    expected = transform(exp)
+    assert_equal(result, expected)
+
+
+def test_datetime_like(tz_naive_fixture, transform_assert_equal):
+    transform, assert_equal = transform_assert_equal
+    idx = pd.date_range("20130101", periods=3, tz=tz_naive_fixture)
+
+    result = to_numeric(transform(idx))
+    expected = transform(idx.asi8)
+    assert_equal(result, expected)
+
+
+def test_timedelta(transform_assert_equal):
+    transform, assert_equal = transform_assert_equal
+    idx = pd.timedelta_range("1 days", periods=3, freq="D")
+
+    result = to_numeric(transform(idx))
+    expected = transform(idx.asi8)
+    assert_equal(result, expected)
+
+
+def test_period(transform_assert_equal):
+    transform, assert_equal = transform_assert_equal
+
+    idx = pd.period_range("2011-01", periods=3, freq="M", name="")
+    inp = transform(idx)
+
+    if isinstance(inp, Index):
+        result = to_numeric(inp)
+        expected = transform(idx.asi8)
+        assert_equal(result, expected)
+    else:
+        # TODO: PeriodDtype, so support it in to_numeric.
+        pytest.skip("Missing PeriodDtype support in to_numeric")
+
+
+@pytest.mark.parametrize(
+    "errors,expected",
+    [
+        ("raise", "Invalid object type at position 0"),
+        ("ignore", Series([[10.0, 2], 1.0, "apple"])),
+        ("coerce", Series([np.nan, 1.0, np.nan])),
+    ],
+)
+def test_non_hashable(errors, expected):
+    # see gh-13324
+    ser = Series([[10.0, 2], 1.0, "apple"])
+
+    if isinstance(expected, str):
+        with pytest.raises(TypeError, match=expected):
+            to_numeric(ser, errors=errors)
+    else:
+        result = to_numeric(ser, errors=errors)
+        tm.assert_series_equal(result, expected)
+
+
+def test_downcast_invalid_cast():
+    # see gh-13352
+    data = ["1", 2, 3]
+    invalid_downcast = "unsigned-integer"
+    msg = "invalid downcasting method provided"
+
+    with pytest.raises(ValueError, match=msg):
+        to_numeric(data, downcast=invalid_downcast)
+
+
+def test_errors_invalid_value():
+    # see gh-26466
+    data = ["1", 2, 3]
+    invalid_error_value = "invalid"
+    msg = "invalid error value specified"
+
+    with pytest.raises(ValueError, match=msg):
+        to_numeric(data, errors=invalid_error_value)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["1", 2, 3],
+        [1, 2, 3],
+        np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
+    ],
+)
+@pytest.mark.parametrize(
+    "kwargs,exp_dtype",
+    [
+        # Basic function tests.
+        ({}, np.int64),
+        ({"downcast": None}, np.int64),
+        # Support below np.float32 is rare and far between.
+        ({"downcast": "float"}, np.dtype(np.float32).char),
+        # Basic dtype support.
+        ({"downcast": "unsigned"}, np.dtype(np.typecodes["UnsignedInteger"][0])),
+    ],
+)
+def test_downcast_basic(data, kwargs, exp_dtype):
+    # see gh-13352
+    result = to_numeric(data, **kwargs)
+    expected = np.array([1, 2, 3], dtype=exp_dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("signed_downcast", ["integer", "signed"])
+@pytest.mark.parametrize(
+    "data",
+    [
+        ["1", 2, 3],
+        [1, 2, 3],
+        np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
+    ],
+)
+def test_signed_downcast(data, signed_downcast):
+    # see gh-13352
+    smallest_int_dtype = np.dtype(np.typecodes["Integer"][0])
+    expected = np.array([1, 2, 3], dtype=smallest_int_dtype)
+
+    res = to_numeric(data, downcast=signed_downcast)
+    tm.assert_numpy_array_equal(res, expected)
+
+
+def test_ignore_downcast_invalid_data():
+    # If we can't successfully cast the given
+    # data to a numeric dtype, do not bother
+    # with the downcast parameter.
+    data = ["foo", 2, 3]
+    expected = np.array(data, dtype=object)
+
+    res = to_numeric(data, errors="ignore", downcast="unsigned")
+    tm.assert_numpy_array_equal(res, expected)
+
+
+def test_ignore_downcast_neg_to_unsigned():
+    # Cannot cast to an unsigned integer
+    # because we have a negative number.
+    data = ["-1", 2, 3]
+    expected = np.array([-1, 2, 3], dtype=np.int64)
+
+    res = to_numeric(data, downcast="unsigned")
+    tm.assert_numpy_array_equal(res, expected)
+
+
+@pytest.mark.parametrize("downcast", ["integer", "signed", "unsigned"])
+@pytest.mark.parametrize(
+    "data,expected",
+    [
+        (["1.1", 2, 3], np.array([1.1, 2, 3], dtype=np.float64)),
+        (
+            [10000.0, 20000, 3000, 40000.36, 50000, 50000.00],
+            np.array(
+                [10000.0, 20000, 3000, 40000.36, 50000, 50000.00], dtype=np.float64
+            ),
+        ),
+    ],
+)
+def test_ignore_downcast_cannot_convert_float(data, expected, downcast):
+    # Cannot cast to an integer (signed or unsigned)
+    # because we have a float number.
+    res = to_numeric(data, downcast=downcast)
+    tm.assert_numpy_array_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "downcast,expected_dtype",
+    [("integer", np.int16), ("signed", np.int16), ("unsigned", np.uint16)],
+)
+def test_downcast_not8bit(downcast, expected_dtype):
+    # the smallest integer dtype need not be np.(u)int8
+    data = ["256", 257, 258]
+
+    expected = np.array([256, 257, 258], dtype=expected_dtype)
+    res = to_numeric(data, downcast=downcast)
+    tm.assert_numpy_array_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype,downcast,min_max",
+    [
+        ("int8", "integer", [iinfo(np.int8).min, iinfo(np.int8).max]),
+        ("int16", "integer", [iinfo(np.int16).min, iinfo(np.int16).max]),
+        ("int32", "integer", [iinfo(np.int32).min, iinfo(np.int32).max]),
+        ("int64", "integer", [iinfo(np.int64).min, iinfo(np.int64).max]),
+        ("uint8", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max]),
+        ("uint16", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max]),
+        ("uint32", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max]),
+        ("uint64", "unsigned", [iinfo(np.uint64).min, iinfo(np.uint64).max]),
+        ("int16", "integer", [iinfo(np.int8).min, iinfo(np.int8).max + 1]),
+        ("int32", "integer", [iinfo(np.int16).min, iinfo(np.int16).max + 1]),
+        ("int64", "integer", [iinfo(np.int32).min, iinfo(np.int32).max + 1]),
+        ("int16", "integer", [iinfo(np.int8).min - 1, iinfo(np.int16).max]),
+        ("int32", "integer", [iinfo(np.int16).min - 1, iinfo(np.int32).max]),
+        ("int64", "integer", [iinfo(np.int32).min - 1, iinfo(np.int64).max]),
+        ("uint16", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max + 1]),
+        ("uint32", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max + 1]),
+        ("uint64", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max + 1]),
+    ],
+)
+def test_downcast_limits(dtype, downcast, min_max):
+    # see gh-14404: test the limits of each downcast.
+    series = to_numeric(Series(min_max), downcast=downcast)
+    assert series.dtype == dtype
+
+
+@pytest.mark.parametrize(
+    "ser,expected",
+    [
+        (
+            Series([0, 9223372036854775808]),
+            Series([0, 9223372036854775808], dtype=np.uint64),
+        )
+    ],
+)
+def test_downcast_uint64(ser, expected):
+    # see gh-14422:
+    # BUG: to_numeric doesn't work uint64 numbers
+
+    result = to_numeric(ser, downcast="unsigned")
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data,exp_data",
+    [
+        (
+            [200, 300, "", "NaN", 30000000000000000000],
+            [200, 300, np.nan, np.nan, 30000000000000000000],
+        ),
+        (
+            ["12345678901234567890", "1234567890", "ITEM"],
+            [12345678901234567890, 1234567890, np.nan],
+        ),
+    ],
+)
+def test_coerce_uint64_conflict(data, exp_data):
+    # see gh-17007 and gh-17125
+    #
+    # Still returns float despite the uint64-nan conflict,
+    # which would normally force the casting to object.
+    result = to_numeric(Series(data), errors="coerce")
+    expected = Series(exp_data, dtype=float)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "errors,exp",
+    [
+        ("ignore", Series(["12345678901234567890", "1234567890", "ITEM"])),
+        ("raise", "Unable to parse string"),
+    ],
+)
+def test_non_coerce_uint64_conflict(errors, exp):
+    # see gh-17007 and gh-17125
+    #
+    # For completeness.
+    ser = Series(["12345678901234567890", "1234567890", "ITEM"])
+
+    if isinstance(exp, str):
+        with pytest.raises(ValueError, match=exp):
+            to_numeric(ser, errors=errors)
+    else:
+        result = to_numeric(ser, errors=errors)
+        tm.assert_series_equal(result, ser)
+
+
+@pytest.mark.parametrize("dc1", ["integer", "float", "unsigned"])
+@pytest.mark.parametrize("dc2", ["integer", "float", "unsigned"])
+def test_downcast_empty(dc1, dc2):
+    # GH32493
+
+    tm.assert_numpy_array_equal(
+        to_numeric([], downcast=dc1),
+        to_numeric([], downcast=dc2),
+        check_dtype=False,
+    )
+
+
+def test_failure_to_convert_uint64_string_to_NaN():
+    # GH 32394
+    result = to_numeric("uint64", errors="coerce")
+    assert np.isnan(result)
+
+    ser = Series([32, 64, np.nan])
+    result = to_numeric(Series(["32", "64", "uint64"]), errors="coerce")
+    tm.assert_series_equal(result, ser)
+
+
+@pytest.mark.parametrize(
+    "strrep",
+    [
+        "243.164",
+        "245.968",
+        "249.585",
+        "259.745",
+        "265.742",
+        "272.567",
+        "279.196",
+        "280.366",
+        "275.034",
+        "271.351",
+        "272.889",
+        "270.627",
+        "280.828",
+        "290.383",
+        "308.153",
+        "319.945",
+        "336.0",
+        "344.09",
+        "351.385",
+        "356.178",
+        "359.82",
+        "361.03",
+        "367.701",
+        "380.812",
+        "387.98",
+        "391.749",
+        "391.171",
+        "385.97",
+        "385.345",
+        "386.121",
+        "390.996",
+        "399.734",
+        "413.073",
+        "421.532",
+        "430.221",
+        "437.092",
+        "439.746",
+        "446.01",
+        "451.191",
+        "460.463",
+        "469.779",
+        "472.025",
+        "479.49",
+        "474.864",
+        "467.54",
+        "471.978",
+    ],
+)
+def test_precision_float_conversion(strrep):
+    # GH 31364
+    result = to_numeric(strrep)
+
+    assert result == float(strrep)
+
+
+@pytest.mark.parametrize(
+    "values, expected",
+    [
+        (["1", "2", None], Series([1, 2, np.nan])),
+        (["1", "2", "3"], Series([1, 2, 3])),
+        (["1", "2", 3], Series([1, 2, 3])),
+        (["1", "2", 3.5], Series([1, 2, 3.5])),
+        (["1", None, 3.5], Series([1, np.nan, 3.5])),
+        (["1", "2", "3.5"], Series([1, 2, 3.5])),
+    ],
+)
+def test_to_numeric_from_nullable_string(values, nullable_string_dtype, expected):
+    # https://github.com/pandas-dev/pandas/issues/37262
+    s = Series(values, dtype=nullable_string_dtype)
+    result = to_numeric(s)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, input_dtype, downcast, expected_dtype",
+    (
+        ([1, 1], "Int64", "integer", "Int8"),
+        ([1.0, pd.NA], "Float64", "integer", "Int8"),
+        ([1.0, 1.1], "Float64", "integer", "Float64"),
+        ([1, pd.NA], "Int64", "integer", "Int8"),
+        ([450, 300], "Int64", "integer", "Int16"),
+        ([1, 1], "Float64", "integer", "Int8"),
+        ([np.iinfo(np.int64).max - 1, 1], "Int64", "integer", "Int64"),
+        ([1, 1], "Int64", "signed", "Int8"),
+        ([1.0, 1.0], "Float32", "signed", "Int8"),
+        ([1.0, 1.1], "Float64", "signed", "Float64"),
+        ([1, pd.NA], "Int64", "signed", "Int8"),
+        ([450, -300], "Int64", "signed", "Int16"),
+        pytest.param(
+            [np.iinfo(np.uint64).max - 1, 1],
+            "UInt64",
+            "signed",
+            "UInt64",
+            marks=pytest.mark.xfail(not is_platform_arm(), reason="GH38798"),
+        ),
+        ([1, 1], "Int64", "unsigned", "UInt8"),
+        ([1.0, 1.0], "Float32", "unsigned", "UInt8"),
+        ([1.0, 1.1], "Float64", "unsigned", "Float64"),
+        ([1, pd.NA], "Int64", "unsigned", "UInt8"),
+        ([450, -300], "Int64", "unsigned", "Int64"),
+        ([-1, -1], "Int32", "unsigned", "Int32"),
+        ([1, 1], "Float64", "float", "Float32"),
+        ([1, 1.1], "Float64", "float", "Float32"),
+    ),
+)
+def test_downcast_nullable_numeric(data, input_dtype, downcast, expected_dtype):
+    arr = pd.array(data, dtype=input_dtype)
+    result = to_numeric(arr, downcast=downcast)
+    expected = pd.array(data, dtype=expected_dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_downcast_nullable_mask_is_copied():
+    # GH38974
+
+    arr = pd.array([1, 2, pd.NA], dtype="Int64")
+
+    result = to_numeric(arr, downcast="integer")
+    expected = pd.array([1, 2, pd.NA], dtype="Int8")
+    tm.assert_extension_array_equal(result, expected)
+
+    arr[1] = pd.NA  # should not modify result
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_numeric_scientific_notation():
+    # GH 15898
+    result = to_numeric("1.7e+308")
+    expected = np.float64(1.7e308)
+    assert result == expected