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CLN: simplify interpolate_2d and callers #36624

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Merged
merged 3 commits into from
Sep 26, 2020
Merged

CLN: simplify interpolate_2d and callers #36624

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CLN: simplify interpolate_2d and callers
jbrockmendel Sep 25, 2020
5734f11
Merge branch 'master' of https://github.com/pandas-dev/pandas into re…
jbrockmendel Sep 25, 2020
437be12
add test
jbrockmendel Sep 25, 2020
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2 changes: 1 addition & 1 deletion pandas/core/arrays/categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1643,7 +1643,7 @@ def fillna(self, value=None, method=None, limit=None):

# TODO: dispatch when self.categories is EA-dtype
values = np.asarray(self).reshape(-1, len(self))
values = interpolate_2d(values, method, 0, None, value).astype(
values = interpolate_2d(values, method, 0, None).astype(
self.categories.dtype
)[0]
codes = _get_codes_for_values(values, self.categories)
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11 changes: 4 additions & 7 deletions pandas/core/internals/blocks.py
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Expand Up @@ -1181,12 +1181,15 @@ def interpolate(
m = None

if m is not None:
if fill_value is not None:
# similar to validate_fillna_kwargs
raise ValueError("Cannot pass both fill_value and method")
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Can you add a test for codecov?


return self._interpolate_with_fill(
method=m,
axis=axis,
inplace=inplace,
limit=limit,
fill_value=fill_value,
coerce=coerce,
downcast=downcast,
)
Expand Down Expand Up @@ -1214,7 +1217,6 @@ def _interpolate_with_fill(
axis: int = 0,
inplace: bool = False,
limit: Optional[int] = None,
fill_value: Optional[Any] = None,
coerce: bool = False,
downcast: Optional[str] = None,
) -> List["Block"]:
Expand All @@ -1232,16 +1234,11 @@ def _interpolate_with_fill(

values = self.values if inplace else self.values.copy()

# We only get here for non-ExtensionBlock
fill_value = convert_scalar_for_putitemlike(fill_value, self.values.dtype)

values = missing.interpolate_2d(
values,
method=method,
axis=axis,
limit=limit,
fill_value=fill_value,
dtype=self.dtype,
)

blocks = [self.make_block_same_class(values, ndim=self.ndim)]
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13 changes: 2 additions & 11 deletions pandas/core/missing.py
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Expand Up @@ -545,8 +545,6 @@ def interpolate_2d(
method="pad",
axis=0,
limit=None,
fill_value=None,
dtype: Optional[DtypeObj] = None,
):
"""
Perform an actual interpolation of values, values will be make 2-d if
Expand All @@ -563,18 +561,11 @@ def interpolate_2d(
raise AssertionError("cannot interpolate on a ndim == 1 with axis != 0")
values = values.reshape(tuple((1,) + values.shape))

if fill_value is None:
mask = None
else: # todo create faster fill func without masking
mask = mask_missing(transf(values), fill_value)

method = clean_fill_method(method)
if method == "pad":
values = transf(pad_2d(transf(values), limit=limit, mask=mask, dtype=dtype))
values = transf(pad_2d(transf(values), limit=limit))
else:
values = transf(
backfill_2d(transf(values), limit=limit, mask=mask, dtype=dtype)
)
values = transf(backfill_2d(transf(values), limit=limit))

# reshape back
if ndim == 1:
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8 changes: 8 additions & 0 deletions pandas/tests/series/methods/test_interpolate.py
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Expand Up @@ -340,6 +340,14 @@ def test_interp_invalid_method(self, invalid_method):
with pytest.raises(ValueError, match=msg):
s.interpolate(method=invalid_method, limit=-1)

def test_interp_invalid_method_and_value(self):
# GH#36624
ser = Series([1, 3, np.nan, 12, np.nan, 25])

msg = "Cannot pass both fill_value and method"
with pytest.raises(ValueError, match=msg):
ser.interpolate(fill_value=3, method="pad")
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fill_value is not in the Series.interpolate api. This is accepted through kwargs from the public api? Then fill_value is accepted and passed along internally? I assume none of the scipy interpolate functions accept fill_value? or won't in the future!

do we really need the ValueError if fill_value is passed?

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do we really need the ValueError if fill_value is passed?

In master:

>>> ser = pd.Series([1, 2, np.nan, 4])
>>> ser.interpolate(method="pad", fill_value=99)
0    1.0
1    2.0
2    NaN
3    4.0
dtype: float64

I'm pretty confident this isn't intentional.

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but on master and this PR

>>> pd.__version__
'1.2.0.dev0+499.g3b12293cea'
>>> ser = pd.Series([1, 2, np.nan, 4])
>>> ser.interpolate(fill_value=99)
0    1.0
1    2.0
2    3.0
3    4.0
dtype: float64

>>> ser.interpolate(humpty_dumpty=99)
0    1.0
1    2.0
2    3.0
3    4.0
dtype: float64

why the ValueError when method passed?

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  1. it may be that we can disallow fill_value at the top of Series.interpolate. making **kwargs explicit would be nice. But i haven't thoroughly checked if thats possible.
  2. BlockManager.interpolate also gets called from fillna, so we need to rule out fill_value at the place where this PR does it.

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this looks ok to me; i think we are accepting too much here accidently.

@jbrockmendel can you add a whatsnew note in any case (just saying passing invalid keyword args to .interpolate() will raise (or can do it after when all non-named keyword args are banned.

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so a ValueError may be appropriate for fill_na, but the test is for Series.interpolate. Why not TypeError: interpolate() got an unexpected keyword argument 'fill_value'?


def test_interp_limit_forward(self):
s = Series([1, 3, np.nan, np.nan, np.nan, 11])

Expand Down