venv

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/conftest.py

@@ -0,0 +1,48 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+
+
+@pytest.fixture(params=[Float32Dtype, Float64Dtype])
+def dtype(request):
+    """Parametrized fixture returning a float 'dtype'"""
+    return request.param()
+
+
+@pytest.fixture
+def data(dtype):
+    """Fixture returning 'data' array according to parametrized float 'dtype'"""
+    return pd.array(
+        list(np.arange(0.1, 0.9, 0.1))
+        + [pd.NA]
+        + list(np.arange(1, 9.8, 0.1))
+        + [pd.NA]
+        + [9.9, 10.0],
+        dtype=dtype,
+    )
+
+
+@pytest.fixture
+def data_missing(dtype):
+    """
+    Fixture returning array with missing data according to parametrized float
+    'dtype'.
+    """
+    return pd.array([np.nan, 0.1], dtype=dtype)
+
+
+@pytest.fixture(params=["data", "data_missing"])
+def all_data(request, data, data_missing):
+    """Parametrized fixture returning 'data' or 'data_missing' float arrays.
+
+    Used to test dtype conversion with and without missing values.
+    """
+    if request.param == "data":
+        return data
+    elif request.param == "data_missing":
+        return data_missing

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_arithmetic.py

@@ -0,0 +1,244 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+
+# Basic test for the arithmetic array ops
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "opname, exp",
+    [
+        ("add", [1.1, 2.2, None, None, 5.5]),
+        ("mul", [0.1, 0.4, None, None, 2.5]),
+        ("sub", [0.9, 1.8, None, None, 4.5]),
+        ("truediv", [10.0, 10.0, None, None, 10.0]),
+        ("floordiv", [9.0, 9.0, None, None, 10.0]),
+        ("mod", [0.1, 0.2, None, None, 0.0]),
+    ],
+    ids=["add", "mul", "sub", "div", "floordiv", "mod"],
+)
+def test_array_op(dtype, opname, exp):
+    a = pd.array([1.0, 2.0, None, 4.0, 5.0], dtype=dtype)
+    b = pd.array([0.1, 0.2, 0.3, None, 0.5], dtype=dtype)
+
+    op = getattr(operator, opname)
+
+    result = op(a, b)
+    expected = pd.array(exp, dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)])
+def test_divide_by_zero(dtype, zero, negative):
+    # TODO pending NA/NaN discussion
+    # https://github.com/pandas-dev/pandas/issues/32265/
+    a = pd.array([0, 1, -1, None], dtype=dtype)
+    result = a / zero
+    expected = FloatingArray(
+        np.array([np.nan, np.inf, -np.inf, np.nan], dtype=dtype.numpy_dtype),
+        np.array([False, False, False, True]),
+    )
+    if negative:
+        expected *= -1
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_pow_scalar(dtype):
+    a = pd.array([-1, 0, 1, None, 2], dtype=dtype)
+    result = a**0
+    expected = pd.array([1, 1, 1, 1, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**1
+    expected = pd.array([-1, 0, 1, None, 2], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**pd.NA
+    expected = pd.array([None, None, 1, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = a**np.nan
+    # TODO np.nan should be converted to pd.NA / missing before operation?
+    expected = FloatingArray(
+        np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype=dtype.numpy_dtype),
+        mask=a._mask,
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    # reversed
+    a = a[1:]  # Can't raise integers to negative powers.
+
+    result = 0**a
+    expected = pd.array([1, 0, None, 0], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = 1**a
+    expected = pd.array([1, 1, 1, 1], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = pd.NA**a
+    expected = pd.array([1, None, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = np.nan**a
+    expected = FloatingArray(
+        np.array([1, np.nan, np.nan, np.nan], dtype=dtype.numpy_dtype), mask=a._mask
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_pow_array(dtype):
+    a = pd.array([0, 0, 0, 1, 1, 1, None, None, None], dtype=dtype)
+    b = pd.array([0, 1, None, 0, 1, None, 0, 1, None], dtype=dtype)
+    result = a**b
+    expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None], dtype=dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_rpow_one_to_na():
+    # https://github.com/pandas-dev/pandas/issues/22022
+    # https://github.com/pandas-dev/pandas/issues/29997
+    arr = pd.array([np.nan, np.nan], dtype="Float64")
+    result = np.array([1.0, 2.0]) ** arr
+    expected = pd.array([1.0, np.nan], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("other", [0, 0.5])
+def test_arith_zero_dim_ndarray(other):
+    arr = pd.array([1, None, 2], dtype="Float64")
+    result = arr + np.array(other)
+    expected = arr + other
+    tm.assert_equal(result, expected)
+
+
+# Test generic characteristics / errors
+# -----------------------------------------------------------------------------
+
+
+def test_error_invalid_values(data, all_arithmetic_operators, using_infer_string):
+    op = all_arithmetic_operators
+    s = pd.Series(data)
+    ops = getattr(s, op)
+
+    if using_infer_string:
+        import pyarrow as pa
+
+        errs = (TypeError, pa.lib.ArrowNotImplementedError, NotImplementedError)
+    else:
+        errs = TypeError
+
+    # invalid scalars
+    msg = "|".join(
+        [
+            r"can only perform ops with numeric values",
+            r"FloatingArray cannot perform the operation mod",
+            "unsupported operand type",
+            "not all arguments converted during string formatting",
+            "can't multiply sequence by non-int of type 'float'",
+            "ufunc 'subtract' cannot use operands with types dtype",
+            r"can only concatenate str \(not \"float\"\) to str",
+            "ufunc '.*' not supported for the input types, and the inputs could not",
+            "ufunc '.*' did not contain a loop with signature matching types",
+            "Concatenation operation is not implemented for NumPy arrays",
+            "has no kernel",
+            "not implemented",
+        ]
+    )
+    with pytest.raises(errs, match=msg):
+        ops("foo")
+    with pytest.raises(errs, match=msg):
+        ops(pd.Timestamp("20180101"))
+
+    # invalid array-likes
+    with pytest.raises(errs, match=msg):
+        ops(pd.Series("foo", index=s.index))
+
+    msg = "|".join(
+        [
+            "can only perform ops with numeric values",
+            "cannot perform .* with this index type: DatetimeArray",
+            "Addition/subtraction of integers and integer-arrays "
+            "with DatetimeArray is no longer supported. *",
+            "unsupported operand type",
+            "not all arguments converted during string formatting",
+            "can't multiply sequence by non-int of type 'float'",
+            "ufunc 'subtract' cannot use operands with types dtype",
+            (
+                "ufunc 'add' cannot use operands with types "
+                rf"dtype\('{tm.ENDIAN}M8\[ns\]'\)"
+            ),
+            r"ufunc 'add' cannot use operands with types dtype\('float\d{2}'\)",
+            "cannot subtract DatetimeArray from ndarray",
+            "has no kernel",
+            "not implemented",
+        ]
+    )
+    with pytest.raises(errs, match=msg):
+        ops(pd.Series(pd.date_range("20180101", periods=len(s))))
+
+
+# Various
+# -----------------------------------------------------------------------------
+
+
+def test_cross_type_arithmetic():
+    df = pd.DataFrame(
+        {
+            "A": pd.array([1, 2, np.nan], dtype="Float64"),
+            "B": pd.array([1, np.nan, 3], dtype="Float32"),
+            "C": np.array([1, 2, 3], dtype="float64"),
+        }
+    )
+
+    result = df.A + df.C
+    expected = pd.Series([2, 4, np.nan], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+    result = (df.A + df.C) * 3 == 12
+    expected = pd.Series([False, True, None], dtype="boolean")
+    tm.assert_series_equal(result, expected)
+
+    result = df.A + df.B
+    expected = pd.Series([2, np.nan, np.nan], dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "source, neg_target, abs_target",
+    [
+        ([1.1, 2.2, 3.3], [-1.1, -2.2, -3.3], [1.1, 2.2, 3.3]),
+        ([1.1, 2.2, None], [-1.1, -2.2, None], [1.1, 2.2, None]),
+        ([-1.1, 0.0, 1.1], [1.1, 0.0, -1.1], [1.1, 0.0, 1.1]),
+    ],
+)
+def test_unary_float_operators(float_ea_dtype, source, neg_target, abs_target):
+    # GH38794
+    dtype = float_ea_dtype
+    arr = pd.array(source, dtype=dtype)
+    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
+    neg_target = pd.array(neg_target, dtype=dtype)
+    abs_target = pd.array(abs_target, dtype=dtype)
+
+    tm.assert_extension_array_equal(neg_result, neg_target)
+    tm.assert_extension_array_equal(pos_result, arr)
+    assert not tm.shares_memory(pos_result, arr)
+    tm.assert_extension_array_equal(abs_result, abs_target)
+
+
+def test_bitwise(dtype):
+    left = pd.array([1, None, 3, 4], dtype=dtype)
+    right = pd.array([None, 3, 5, 4], dtype=dtype)
+
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left | right
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left & right
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        left ^ right

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_astype.py

@@ -0,0 +1,128 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_astype():
+    # with missing values
+    arr = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    with pytest.raises(ValueError, match="cannot convert NA to integer"):
+        arr.astype("int64")
+
+    with pytest.raises(ValueError, match="cannot convert float NaN to bool"):
+        arr.astype("bool")
+
+    result = arr.astype("float64")
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    # no missing values
+    arr = pd.array([0.0, 1.0, 0.5], dtype="Float64")
+    result = arr.astype("int64")
+    expected = np.array([0, 1, 0], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("bool")
+    expected = np.array([False, True, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_to_floating_array():
+    # astype to FloatingArray
+    arr = pd.array([0.0, 1.0, None], dtype="Float64")
+
+    result = arr.astype("Float64")
+    tm.assert_extension_array_equal(result, arr)
+    result = arr.astype(pd.Float64Dtype())
+    tm.assert_extension_array_equal(result, arr)
+    result = arr.astype("Float32")
+    expected = pd.array([0.0, 1.0, None], dtype="Float32")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_to_boolean_array():
+    # astype to BooleanArray
+    arr = pd.array([0.0, 1.0, None], dtype="Float64")
+
+    result = arr.astype("boolean")
+    expected = pd.array([False, True, None], dtype="boolean")
+    tm.assert_extension_array_equal(result, expected)
+    result = arr.astype(pd.BooleanDtype())
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_to_integer_array():
+    # astype to IntegerArray
+    arr = pd.array([0.0, 1.5, None], dtype="Float64")
+
+    result = arr.astype("Int64")
+    expected = pd.array([0, 1, None], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_astype_str():
+    a = pd.array([0.1, 0.2, None], dtype="Float64")
+    expected = np.array(["0.1", "0.2", "<NA>"], dtype="U32")
+
+    tm.assert_numpy_array_equal(a.astype(str), expected)
+    tm.assert_numpy_array_equal(a.astype("str"), expected)
+
+
+def test_astype_copy():
+    arr = pd.array([0.1, 0.2, None], dtype="Float64")
+    orig = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    # copy=True -> ensure both data and mask are actual copies
+    result = arr.astype("Float64", copy=True)
+    assert result is not arr
+    assert not tm.shares_memory(result, arr)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, orig)
+    result[0] = pd.NA
+    tm.assert_extension_array_equal(arr, orig)
+
+    # copy=False
+    result = arr.astype("Float64", copy=False)
+    assert result is arr
+    assert np.shares_memory(result._data, arr._data)
+    assert np.shares_memory(result._mask, arr._mask)
+    result[0] = 10
+    assert arr[0] == 10
+    result[0] = pd.NA
+    assert arr[0] is pd.NA
+
+    # astype to different dtype -> always needs a copy -> even with copy=False
+    # we need to ensure that also the mask is actually copied
+    arr = pd.array([0.1, 0.2, None], dtype="Float64")
+    orig = pd.array([0.1, 0.2, None], dtype="Float64")
+
+    result = arr.astype("Float32", copy=False)
+    assert not tm.shares_memory(result, arr)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, orig)
+    result[0] = pd.NA
+    tm.assert_extension_array_equal(arr, orig)
+
+
+def test_astype_object(dtype):
+    arr = pd.array([1.0, pd.NA], dtype=dtype)
+
+    result = arr.astype(object)
+    expected = np.array([1.0, pd.NA], dtype=object)
+    tm.assert_numpy_array_equal(result, expected)
+    # check exact element types
+    assert isinstance(result[0], float)
+    assert result[1] is pd.NA
+
+
+def test_Float64_conversion():
+    # GH#40729
+    testseries = pd.Series(["1", "2", "3", "4"], dtype="object")
+    result = testseries.astype(pd.Float64Dtype())
+
+    expected = pd.Series([1.0, 2.0, 3.0, 4.0], dtype=pd.Float64Dtype())
+
+    tm.assert_series_equal(result, expected)

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_comparison.py

@@ -0,0 +1,65 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+from pandas.tests.arrays.masked_shared import (
+    ComparisonOps,
+    NumericOps,
+)
+
+
+class TestComparisonOps(NumericOps, ComparisonOps):
+    @pytest.mark.parametrize("other", [True, False, pd.NA, -1.0, 0.0, 1])
+    def test_scalar(self, other, comparison_op, dtype):
+        ComparisonOps.test_scalar(self, other, comparison_op, dtype)
+
+    def test_compare_with_integerarray(self, comparison_op):
+        op = comparison_op
+        a = pd.array([0, 1, None] * 3, dtype="Int64")
+        b = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype="Float64")
+        other = b.astype("Int64")
+        expected = op(a, other)
+        result = op(a, b)
+        tm.assert_extension_array_equal(result, expected)
+        expected = op(other, a)
+        result = op(b, a)
+        tm.assert_extension_array_equal(result, expected)
+
+
+def test_equals():
+    # GH-30652
+    # equals is generally tested in /tests/extension/base/methods, but this
+    # specifically tests that two arrays of the same class but different dtype
+    # do not evaluate equal
+    a1 = pd.array([1, 2, None], dtype="Float64")
+    a2 = pd.array([1, 2, None], dtype="Float32")
+    assert a1.equals(a2) is False
+
+
+def test_equals_nan_vs_na():
+    # GH#44382
+
+    mask = np.zeros(3, dtype=bool)
+    data = np.array([1.0, np.nan, 3.0], dtype=np.float64)
+
+    left = FloatingArray(data, mask)
+    assert left.equals(left)
+    tm.assert_extension_array_equal(left, left)
+
+    assert left.equals(left.copy())
+    assert left.equals(FloatingArray(data.copy(), mask.copy()))
+
+    mask2 = np.array([False, True, False], dtype=bool)
+    data2 = np.array([1.0, 2.0, 3.0], dtype=np.float64)
+    right = FloatingArray(data2, mask2)
+    assert right.equals(right)
+    tm.assert_extension_array_equal(right, right)
+
+    assert not left.equals(right)
+
+    # with mask[1] = True, the only difference is data[1], which should
+    #  not matter for equals
+    mask[1] = True
+    assert left.equals(right)

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_concat.py

@@ -0,0 +1,20 @@
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "to_concat_dtypes, result_dtype",
+    [
+        (["Float64", "Float64"], "Float64"),
+        (["Float32", "Float64"], "Float64"),
+        (["Float32", "Float32"], "Float32"),
+    ],
+)
+def test_concat_series(to_concat_dtypes, result_dtype):
+    result = pd.concat([pd.Series([1, 2, pd.NA], dtype=t) for t in to_concat_dtypes])
+    expected = pd.concat([pd.Series([1, 2, pd.NA], dtype=object)] * 2).astype(
+        result_dtype
+    )
+    tm.assert_series_equal(result, expected)

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_construction.py

@@ -0,0 +1,204 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+
+
+def test_uses_pandas_na():
+    a = pd.array([1, None], dtype=Float64Dtype())
+    assert a[1] is pd.NA
+
+
+def test_floating_array_constructor():
+    values = np.array([1, 2, 3, 4], dtype="float64")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = FloatingArray(values, mask)
+    expected = pd.array([1, 2, 3, np.nan], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+    tm.assert_numpy_array_equal(result._data, values)
+    tm.assert_numpy_array_equal(result._mask, mask)
+
+    msg = r".* should be .* numpy array. Use the 'pd.array' function instead"
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values.tolist(), mask)
+
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values, mask.tolist())
+
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values.astype(int), mask)
+
+    msg = r"__init__\(\) missing 1 required positional argument: 'mask'"
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(values)
+
+
+def test_floating_array_disallows_float16():
+    # GH#44715
+    arr = np.array([1, 2], dtype=np.float16)
+    mask = np.array([False, False])
+
+    msg = "FloatingArray does not support np.float16 dtype"
+    with pytest.raises(TypeError, match=msg):
+        FloatingArray(arr, mask)
+
+
+def test_floating_array_disallows_Float16_dtype(request):
+    # GH#44715
+    with pytest.raises(TypeError, match="data type 'Float16' not understood"):
+        pd.array([1.0, 2.0], dtype="Float16")
+
+
+def test_floating_array_constructor_copy():
+    values = np.array([1, 2, 3, 4], dtype="float64")
+    mask = np.array([False, False, False, True], dtype="bool")
+
+    result = FloatingArray(values, mask)
+    assert result._data is values
+    assert result._mask is mask
+
+    result = FloatingArray(values, mask, copy=True)
+    assert result._data is not values
+    assert result._mask is not mask
+
+
+def test_to_array():
+    result = pd.array([0.1, 0.2, 0.3, 0.4])
+    expected = pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "a, b",
+    [
+        ([1, None], [1, pd.NA]),
+        ([None], [pd.NA]),
+        ([None, np.nan], [pd.NA, pd.NA]),
+        ([1, np.nan], [1, pd.NA]),
+        ([np.nan], [pd.NA]),
+    ],
+)
+def test_to_array_none_is_nan(a, b):
+    result = pd.array(a, dtype="Float64")
+    expected = pd.array(b, dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_to_array_mixed_integer_float():
+    result = pd.array([1, 2.0])
+    expected = pd.array([1.0, 2.0], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = pd.array([1, None, 2.0])
+    expected = pd.array([1.0, None, 2.0], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        ["foo", "bar"],
+        "foo",
+        1,
+        1.0,
+        pd.date_range("20130101", periods=2),
+        np.array(["foo"]),
+        [[1, 2], [3, 4]],
+        [np.nan, {"a": 1}],
+        # GH#44514 all-NA case used to get quietly swapped out before checking ndim
+        np.array([pd.NA] * 6, dtype=object).reshape(3, 2),
+    ],
+)
+def test_to_array_error(values):
+    # error in converting existing arrays to FloatingArray
+    msg = "|".join(
+        [
+            "cannot be converted to FloatingDtype",
+            "values must be a 1D list-like",
+            "Cannot pass scalar",
+            r"float\(\) argument must be a string or a (real )?number, not 'dict'",
+            "could not convert string to float: 'foo'",
+            r"could not convert string to float: np\.str_\('foo'\)",
+        ]
+    )
+    with pytest.raises((TypeError, ValueError), match=msg):
+        pd.array(values, dtype="Float64")
+
+
+@pytest.mark.parametrize("values", [["1", "2", None], ["1.5", "2", None]])
+def test_construct_from_float_strings(values):
+    # see also test_to_integer_array_str
+    expected = pd.array([float(values[0]), 2, None], dtype="Float64")
+
+    res = pd.array(values, dtype="Float64")
+    tm.assert_extension_array_equal(res, expected)
+
+    res = FloatingArray._from_sequence(values)
+    tm.assert_extension_array_equal(res, expected)
+
+
+def test_to_array_inferred_dtype():
+    # if values has dtype -> respect it
+    result = pd.array(np.array([1, 2], dtype="float32"))
+    assert result.dtype == Float32Dtype()
+
+    # if values have no dtype -> always float64
+    result = pd.array([1.0, 2.0])
+    assert result.dtype == Float64Dtype()
+
+
+def test_to_array_dtype_keyword():
+    result = pd.array([1, 2], dtype="Float32")
+    assert result.dtype == Float32Dtype()
+
+    # if values has dtype -> override it
+    result = pd.array(np.array([1, 2], dtype="float32"), dtype="Float64")
+    assert result.dtype == Float64Dtype()
+
+
+def test_to_array_integer():
+    result = pd.array([1, 2], dtype="Float64")
+    expected = pd.array([1.0, 2.0], dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # for integer dtypes, the itemsize is not preserved
+    # TODO can we specify "floating" in general?
+    result = pd.array(np.array([1, 2], dtype="int32"), dtype="Float64")
+    assert result.dtype == Float64Dtype()
+
+
+@pytest.mark.parametrize(
+    "bool_values, values, target_dtype, expected_dtype",
+    [
+        ([False, True], [0, 1], Float64Dtype(), Float64Dtype()),
+        ([False, True], [0, 1], "Float64", Float64Dtype()),
+        ([False, True, np.nan], [0, 1, np.nan], Float64Dtype(), Float64Dtype()),
+    ],
+)
+def test_to_array_bool(bool_values, values, target_dtype, expected_dtype):
+    result = pd.array(bool_values, dtype=target_dtype)
+    assert result.dtype == expected_dtype
+    expected = pd.array(values, dtype=target_dtype)
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_series_from_float(data):
+    # construct from our dtype & string dtype
+    dtype = data.dtype
+
+    # from float
+    expected = pd.Series(data)
+    result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype))
+    tm.assert_series_equal(result, expected)
+
+    # from list
+    expected = pd.Series(data)
+    result = pd.Series(np.array(data).tolist(), dtype=str(dtype))
+    tm.assert_series_equal(result, expected)

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_contains.py

@@ -0,0 +1,12 @@
+import numpy as np
+
+import pandas as pd
+
+
+def test_contains_nan():
+    # GH#52840
+    arr = pd.array(range(5)) / 0
+
+    assert np.isnan(arr._data[0])
+    assert not arr.isna()[0]
+    assert np.nan in arr

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_function.py

@@ -0,0 +1,194 @@
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("ufunc", [np.abs, np.sign])
+# np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
+@pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning")
+def test_ufuncs_single(ufunc):
+    a = pd.array([1, 2, -3, np.nan], dtype="Float64")
+    result = ufunc(a)
+    expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    result = ufunc(s)
+    expected = pd.Series(expected)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
+def test_ufuncs_single_float(ufunc):
+    a = pd.array([1.0, 0.2, 3.0, np.nan], dtype="Float64")
+    with np.errstate(invalid="ignore"):
+        result = ufunc(a)
+        expected = pd.array(ufunc(a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    s = pd.Series(a)
+    with np.errstate(invalid="ignore"):
+        result = ufunc(s)
+        expected = pd.Series(ufunc(s.astype(float)), dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ufunc", [np.add, np.subtract])
+def test_ufuncs_binary_float(ufunc):
+    # two FloatingArrays
+    a = pd.array([1, 0.2, -3, np.nan], dtype="Float64")
+    result = ufunc(a, a)
+    expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # FloatingArray with numpy array
+    arr = np.array([1, 2, 3, 4])
+    result = ufunc(a, arr)
+    expected = pd.array(ufunc(a.astype(float), arr), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(arr, a)
+    expected = pd.array(ufunc(arr, a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    # FloatingArray with scalar
+    result = ufunc(a, 1)
+    expected = pd.array(ufunc(a.astype(float), 1), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = ufunc(1, a)
+    expected = pd.array(ufunc(1, a.astype(float)), dtype="Float64")
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("values", [[0, 1], [0, None]])
+def test_ufunc_reduce_raises(values):
+    arr = pd.array(values, dtype="Float64")
+
+    res = np.add.reduce(arr)
+    expected = arr.sum(skipna=False)
+    tm.assert_almost_equal(res, expected)
+
+
+@pytest.mark.skipif(not IS64, reason="GH 36579: fail on 32-bit system")
+@pytest.mark.parametrize(
+    "pandasmethname, kwargs",
+    [
+        ("var", {"ddof": 0}),
+        ("var", {"ddof": 1}),
+        ("std", {"ddof": 0}),
+        ("std", {"ddof": 1}),
+        ("kurtosis", {}),
+        ("skew", {}),
+        ("sem", {}),
+    ],
+)
+def test_stat_method(pandasmethname, kwargs):
+    s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan], dtype="Float64")
+    pandasmeth = getattr(s, pandasmethname)
+    result = pandasmeth(**kwargs)
+    s2 = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64")
+    pandasmeth = getattr(s2, pandasmethname)
+    expected = pandasmeth(**kwargs)
+    assert expected == result
+
+
+def test_value_counts_na():
+    arr = pd.array([0.1, 0.2, 0.1, pd.NA], dtype="Float64")
+    result = arr.value_counts(dropna=False)
+    idx = pd.Index([0.1, 0.2, pd.NA], dtype=arr.dtype)
+    assert idx.dtype == arr.dtype
+    expected = pd.Series([2, 1, 1], index=idx, dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+    result = arr.value_counts(dropna=True)
+    expected = pd.Series([2, 1], index=idx[:-1], dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_empty():
+    ser = pd.Series([], dtype="Float64")
+    result = ser.value_counts()
+    idx = pd.Index([], dtype="Float64")
+    assert idx.dtype == "Float64"
+    expected = pd.Series([], index=idx, dtype="Int64", name="count")
+    tm.assert_series_equal(result, expected)
+
+
+def test_value_counts_with_normalize():
+    ser = pd.Series([0.1, 0.2, 0.1, pd.NA], dtype="Float64")
+    result = ser.value_counts(normalize=True)
+    expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3
+    assert expected.index.dtype == ser.dtype
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [0, 4])
+def test_floating_array_sum(skipna, min_count, dtype):
+    arr = pd.array([1, 2, 3, None], dtype=dtype)
+    result = arr.sum(skipna=skipna, min_count=min_count)
+    if skipna and min_count == 0:
+        assert result == 6.0
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize(
+    "values, expected", [([1, 2, 3], 6.0), ([1, 2, 3, None], 6.0), ([None], 0.0)]
+)
+def test_floating_array_numpy_sum(values, expected):
+    arr = pd.array(values, dtype="Float64")
+    result = np.sum(arr)
+    assert result == expected
+
+
+@pytest.mark.parametrize("op", ["sum", "min", "max", "prod"])
+def test_preserve_dtypes(op):
+    df = pd.DataFrame(
+        {
+            "A": ["a", "b", "b"],
+            "B": [1, None, 3],
+            "C": pd.array([0.1, None, 3.0], dtype="Float64"),
+        }
+    )
+
+    # op
+    result = getattr(df.C, op)()
+    assert isinstance(result, np.float64)
+
+    # groupby
+    result = getattr(df.groupby("A"), op)()
+
+    expected = pd.DataFrame(
+        {"B": np.array([1.0, 3.0]), "C": pd.array([0.1, 3], dtype="Float64")},
+        index=pd.Index(["a", "b"], name="A"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("method", ["min", "max"])
+def test_floating_array_min_max(skipna, method, dtype):
+    arr = pd.array([0.0, 1.0, None], dtype=dtype)
+    func = getattr(arr, method)
+    result = func(skipna=skipna)
+    if skipna:
+        assert result == (0 if method == "min" else 1)
+    else:
+        assert result is pd.NA
+
+
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("min_count", [0, 9])
+def test_floating_array_prod(skipna, min_count, dtype):
+    arr = pd.array([1.0, 2.0, None], dtype=dtype)
+    result = arr.prod(skipna=skipna, min_count=min_count)
+    if skipna and min_count == 0:
+        assert result == 2
+    else:
+        assert result is pd.NA

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_repr.py

@@ -0,0 +1,47 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.arrays.floating import (
+    Float32Dtype,
+    Float64Dtype,
+)
+
+
+def test_dtypes(dtype):
+    # smoke tests on auto dtype construction
+
+    np.dtype(dtype.type).kind == "f"
+    assert dtype.name is not None
+
+
+@pytest.mark.parametrize(
+    "dtype, expected",
+    [(Float32Dtype(), "Float32Dtype()"), (Float64Dtype(), "Float64Dtype()")],
+)
+def test_repr_dtype(dtype, expected):
+    assert repr(dtype) == expected
+
+
+def test_repr_array():
+    result = repr(pd.array([1.0, None, 3.0]))
+    expected = "<FloatingArray>\n[1.0, <NA>, 3.0]\nLength: 3, dtype: Float64"
+    assert result == expected
+
+
+def test_repr_array_long():
+    data = pd.array([1.0, 2.0, None] * 1000)
+    expected = """<FloatingArray>
+[ 1.0,  2.0, <NA>,  1.0,  2.0, <NA>,  1.0,  2.0, <NA>,  1.0,
+ ...
+ <NA>,  1.0,  2.0, <NA>,  1.0,  2.0, <NA>,  1.0,  2.0, <NA>]
+Length: 3000, dtype: Float64"""
+    result = repr(data)
+    assert result == expected
+
+
+def test_frame_repr(data_missing):
+    df = pd.DataFrame({"A": data_missing})
+    result = repr(df)
+    expected = "      A\n0  <NA>\n1   0.1"
+    assert result == expected

venv/lib/python3.12/site-packages/pandas/tests/arrays/floating/test_to_numpy.py

@@ -0,0 +1,132 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import FloatingArray
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy(box):
+    con = pd.Series if box else pd.array
+
+    # default (with or without missing values) -> object dtype
+    arr = con([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy()
+    expected = np.array([0.1, 0.2, 0.3], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([0.1, 0.2, None], dtype="Float64")
+    result = arr.to_numpy()
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_float(box):
+    con = pd.Series if box else pd.array
+
+    # no missing values -> can convert to float, otherwise raises
+    arr = con([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy(dtype="float64")
+    expected = np.array([0.1, 0.2, 0.3], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([0.1, 0.2, None], dtype="Float64")
+    result = arr.to_numpy(dtype="float64")
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype="float64", na_value=np.nan)
+    expected = np.array([0.1, 0.2, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_int(box):
+    con = pd.Series if box else pd.array
+
+    # no missing values -> can convert to int, otherwise raises
+    arr = con([1.0, 2.0, 3.0], dtype="Float64")
+    result = arr.to_numpy(dtype="int64")
+    expected = np.array([1, 2, 3], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    arr = con([1.0, 2.0, None], dtype="Float64")
+    with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"):
+        result = arr.to_numpy(dtype="int64")
+
+    # automatic casting (floors the values)
+    arr = con([0.1, 0.9, 1.1], dtype="Float64")
+    result = arr.to_numpy(dtype="int64")
+    expected = np.array([0, 0, 1], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_na_value(box):
+    con = pd.Series if box else pd.array
+
+    arr = con([0.0, 1.0, None], dtype="Float64")
+    result = arr.to_numpy(dtype=object, na_value=None)
+    expected = np.array([0.0, 1.0, None], dtype="object")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype=bool, na_value=False)
+    expected = np.array([False, True, False], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.to_numpy(dtype="int64", na_value=-99)
+    expected = np.array([0, 1, -99], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_na_value_with_nan():
+    # array with both NaN and NA -> only fill NA with `na_value`
+    arr = FloatingArray(np.array([0.0, np.nan, 0.0]), np.array([False, False, True]))
+    result = arr.to_numpy(dtype="float64", na_value=-1)
+    expected = np.array([0.0, np.nan, -1.0], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "float32", "int32", "int64", "bool"])
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_dtype(box, dtype):
+    con = pd.Series if box else pd.array
+    arr = con([0.0, 1.0], dtype="Float64")
+
+    result = arr.to_numpy(dtype=dtype)
+    expected = np.array([0, 1], dtype=dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int32", "int64", "bool"])
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_na_raises(box, dtype):
+    con = pd.Series if box else pd.array
+    arr = con([0.0, 1.0, None], dtype="Float64")
+    with pytest.raises(ValueError, match=dtype):
+        arr.to_numpy(dtype=dtype)
+
+
+@pytest.mark.parametrize("box", [True, False], ids=["series", "array"])
+def test_to_numpy_string(box, dtype):
+    con = pd.Series if box else pd.array
+    arr = con([0.0, 1.0, None], dtype="Float64")
+
+    result = arr.to_numpy(dtype="str")
+    expected = np.array([0.0, 1.0, pd.NA], dtype=f"{tm.ENDIAN}U32")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_copy():
+    # to_numpy can be zero-copy if no missing values
+    arr = pd.array([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy(dtype="float64")
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, pd.array([10, 0.2, 0.3], dtype="Float64"))
+
+    arr = pd.array([0.1, 0.2, 0.3], dtype="Float64")
+    result = arr.to_numpy(dtype="float64", copy=True)
+    result[0] = 10
+    tm.assert_extension_array_equal(arr, pd.array([0.1, 0.2, 0.3], dtype="Float64"))