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ENH: implement FloatingArray.round() #38866

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ENH: implement FloatingArray.round() #38866

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1df9bf7
add test for nullable float
arw2019 Dec 31, 2020
192ebad
implement round method for FloatingArray
arw2019 Dec 31, 2020
6403026
use pytest fixture
arw2019 Dec 31, 2020
253d279
review: rewriting using _apply
arw2019 Jan 4, 2021
3e0d605
Merge branch 'master' of https://github.com/pandas-dev/pandas into GH…
arw2019 Jan 4, 2021
fe19597
remove explicit FloatingArray reference from _apply
arw2019 Jan 4, 2021
b557a50
move _apply, round to NumericArray
arw2019 Jan 4, 2021
85e6d4f
move _apply, round to NumericArray
arw2019 Jan 4, 2021
f79c0cd
typing, consistency with Series.round
arw2019 Jan 4, 2021
d1cd6d8
whatsnew
arw2019 Jan 4, 2021
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8 changes: 8 additions & 0 deletions pandas/core/arrays/floating.py
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Original file line number Diff line number Diff line change
Expand Up @@ -410,6 +410,14 @@ def max(self, *, skipna=True, **kwargs):
nv.validate_max((), kwargs)
return super()._reduce("max", skipna=skipna)

def round(self, decimals=0):
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should do this generically (and move to NumericArray), e.g. something like

def round(self, decimals=0):
     return self._apply(np.round, decimals=decimals)

where _apply handles the masking & recasting. I think we discussed similar in .to_numeric

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or event better

round = _apply_function(np.round, decimals=0, "nice doc-string here")

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If we're going with the second way (may as well) would _apply_function be method on NumericArray (versus a module level function)?

values = self._data[~self._mask]
values = np.round(values, decimals)

data = np.zeros(self._data.shape)
data[~self._mask] = values
return FloatingArray(data, self._mask)

def _maybe_mask_result(self, result, mask, other, op_name: str):
"""
Parameters
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20 changes: 14 additions & 6 deletions pandas/tests/series/methods/test_round.py
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Expand Up @@ -35,15 +35,23 @@ def test_round_numpy_with_nan(self):
expected = Series([2.0, np.nan, 0.0])
tm.assert_series_equal(result, expected)

def test_round_builtin(self):
ser = Series([1.123, 2.123, 3.123], index=range(3))
result = round(ser)
expected_rounded0 = Series([1.0, 2.0, 3.0], index=range(3))
def test_round_builtin(self, any_float_allowed_nullable_dtype):
ser = Series(
[1.123, 2.123, 3.123],
index=range(3),
dtype=any_float_allowed_nullable_dtype,
)
result = round(ser).astype(any_float_allowed_nullable_dtype)
expected_rounded0 = Series(
[1.0, 2.0, 3.0], index=range(3), dtype=any_float_allowed_nullable_dtype
)
tm.assert_series_equal(result, expected_rounded0)

decimals = 2
expected_rounded = Series([1.12, 2.12, 3.12], index=range(3))
result = round(ser, decimals)
expected_rounded = Series(
[1.12, 2.12, 3.12], index=range(3), dtype=any_float_allowed_nullable_dtype
)
result = round(ser, decimals).astype(any_float_allowed_nullable_dtype)
tm.assert_series_equal(result, expected_rounded)

@pytest.mark.parametrize("method", ["round", "floor", "ceil"])
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