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Fix Frame.abs to support bool type and disallow non-numeric types. #1980

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Dec 23, 2020
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Fix Frame.abs to support bool type and disallow non-numeric types. #1980

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Fix Frame.abs to support bool type and disallow non-numeric types.
ueshin Dec 22, 2020
87d0a2c
Fix.
ueshin Dec 22, 2020
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13 changes: 12 additions & 1 deletion databricks/koalas/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1757,7 +1757,18 @@ def abs(self) -> Union["DataFrame", "Series"]:
2 6 30 30
3 7 40 50
"""
return self._apply_series_op(lambda kser: kser._with_new_scol(F.abs(kser.spark.column)))

def abs(kser):
if isinstance(kser.spark.data_type, BooleanType):
return kser
elif isinstance(kser.spark.data_type, NumericType):
return kser.spark.transform(F.abs)
else:
raise TypeError(
"bad operand type for abs(): {}".format(kser.spark.data_type.simpleString())
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@HyukjinKwon HyukjinKwon Dec 23, 2020 •
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Hm, should we show pandas' dtype instead of Spark data type?

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@ueshin ueshin Dec 23, 2020 •
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In that case, shall we address it separately. The same error message will appear in the other places.

)

return self._apply_series_op(abs)

# TODO: by argument only support the grouping name and as_index only for now. Documentation
# should be updated when it's supported.
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18 changes: 14 additions & 4 deletions databricks/koalas/tests/test_stats.py
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Expand Up @@ -94,16 +94,26 @@ def test_sum(self):
def test_abs(self):
pdf = pd.DataFrame(
{
"A": [1, -2, 3, -4, 5],
"B": [1.0, -2, 3, -4, 5],
"C": [-6.0, -7, -8, -9, 10],
"D": ["a", "b", "c", "d", "e"],
"A": [1, -2, np.nan, -4, 5],
"B": [1.0, -2, np.nan, -4, 5],
"C": [-6.0, -7, -8, np.nan, 10],
"D": ["a", "b", "c", "d", np.nan],
"E": [True, np.nan, False, True, True],
}
)
kdf = ks.from_pandas(pdf)
self.assert_eq(kdf.A.abs(), pdf.A.abs())
self.assert_eq(kdf.B.abs(), pdf.B.abs())
self.assert_eq(kdf.E.abs(), pdf.E.abs())
# pandas' bug?
# self.assert_eq(kdf[["B", "C", "E"]].abs(), pdf[["B", "C", "E"]].abs())
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@ueshin ueshin Dec 22, 2020 •
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The pandas result is:

>>> pdf = pd.DataFrame(
...     {
...         "A": [1, -2, 3, -4, 5],
...         "B": [1.0, -2, 3, -4, 5],
...         "C": [-6.0, -7, -8, -9, 10],
...         "D": ["a", "b", "c", "d", "e"],
...         "E": [True, False, False, True, True],
...     }
... )
>>> pdf[["B", "C", "E"]].abs()
   B   C  E
0  1   6  1
1  2   7  0
2  3   8  0
3  4   9  1
4  5  10  1

whereas calculating separately:

>>> pdf[["B", "C"]].abs()
     B     C
0  1.0   6.0
1  2.0   7.0
2  3.0   8.0
3  4.0   9.0
4  5.0  10.0
>>> pdf[["E"]].abs()
       E
0   True
1  False
2  False
3   True
4   True

I believe this is a pandas bug.

The current Koalas behavior:

>>> ks.from_pandas(pdf)[["B", "C", "E"]].abs()
     B     C      E
0  1.0   6.0   True
1  2.0   7.0  False
2  3.0   8.0  False
3  4.0   9.0   True
4  5.0  10.0   True

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Seems pandas bug to me, too. Let me report this to pandas dev.

self.assert_eq(kdf[["B", "C"]].abs(), pdf[["B", "C"]].abs())
self.assert_eq(kdf[["E"]].abs(), pdf[["E"]].abs())

with self.assertRaisesRegex(TypeError, "bad operand type for abs\\(\\): string"):
kdf.abs()
with self.assertRaisesRegex(TypeError, "bad operand type for abs\\(\\): string"):
kdf.D.abs()

def test_axis_on_dataframe(self):
# The number of each count is intentionally big
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