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REGR: .describe on unsigned dtypes results in object #48473

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Merged
merged 7 commits into from
Sep 10, 2022
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REGR: .describe on unsigned dtypes results in object #48473

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d907794
REGR: .describe on unsigned dtypes results in object
rhshadrach Sep 8, 2022
97e6cdd
Improve implementation
rhshadrach Sep 9, 2022
ff4a95f
Set via dtype
rhshadrach Sep 10, 2022
60a9b6c
Merge branch 'main' of https://github.com/pandas-dev/pandas into desc…
rhshadrach Sep 10, 2022
359cfd7
fixup
rhshadrach Sep 10, 2022
21b839d
More tests, fix complex
rhshadrach Sep 10, 2022
3e3aa9f
Fix comment
rhshadrach Sep 10, 2022
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7 changes: 6 additions & 1 deletion pandas/core/describe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -240,7 +240,12 @@ def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
+ series.quantile(percentiles).tolist()
+ [series.max()]
)
return Series(d, index=stat_index, name=series.name)

result = Series(d, index=stat_index, name=series.name)
if isinstance(d[1], float):
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Can we determine the target dtype based on the input dtype? Like when we have numeric cast to float, else keep?

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Thanks - I was worried about doing this because of object dtype with numeric data, but I see now object dtype takes a different path.

# GH#48340 - don't rely on inference, always return float on numeric data
result = result.astype(float)
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Nit: Can we just set dtype=... when creating the Series?

return result


def describe_categorical_1d(
Expand Down
23 changes: 23 additions & 0 deletions pandas/tests/series/methods/test_describe.py
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@@ -1,4 +1,5 @@
import numpy as np
import pytest

from pandas import (
Period,
Expand Down Expand Up @@ -149,3 +150,25 @@ def test_datetime_is_numeric_includes_datetime(self):
index=["count", "mean", "min", "25%", "50%", "75%", "max"],
)
tm.assert_series_equal(result, expected)

@pytest.mark.parametrize(
"dtype", ["int32", "int64", "uint32", "uint64", "float32", "float64"]
)
def test_numeric_result_is_float(self, dtype):
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Maybe any_real_numpy_dtype? Not sure if this fits the requirement here of what you want to test

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Thanks - I also tried complex and found this was buggy in that case. Fixed and added all numeric dtypes.

# GH#48340 - describe should always return dtype float on numeric input
ser = Series([0, 1], dtype=dtype)
result = ser.describe()
expected = Series(
[
2.0,
0.5,
ser.std(),
0,
0.25,
0.5,
0.75,
1.0,
],
index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
)
tm.assert_series_equal(result, expected)