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

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merged 7 commits into from
Sep 10, 2022
Merged

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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4 changes: 3 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 +240,9 @@ 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)
# GH#48340 - don't rely on inference, always return float on numeric data
dtype = float if is_numeric_dtype(series) else None
return Series(d, index=stat_index, name=series.name, dtype=dtype)


def describe_categorical_1d(
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23 changes: 23 additions & 0 deletions pandas/tests/series/methods/test_describe.py
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Original file line number Diff line number Diff line change
@@ -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)