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The indexes of DataFrame.describe(percentiles=[0.29, 0.57, 0.58]) are incorrect #48298

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Merged
mroeschke merged 12 commits into from
Sep 6, 2022
Merged

The indexes of DataFrame.describe(percentiles=[0.29, 0.57, 0.58]) are incorrect #48298

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e20d833
add test
Aug 29, 2022
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add test
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Merge branch 'main' into dataframe_percentiles_46362
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Aug 31, 2022
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Merge branch 'main' into dataframe_percentiles_46362
Aug 31, 2022
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Merge branch 'main' into dataframe_percentiles_46362
Aug 31, 2022
62fd28e
Update doc/source/whatsnew/v1.6.0.rst
Aug 31, 2022
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Merge branch 'main' into dataframe_percentiles_46362
Aug 31, 2022
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Merge branch 'main' into dataframe_percentiles_46362
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Merge branch 'main' into dataframe_percentiles_46362
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Merge branch 'main' into dataframe_percentiles_46362
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.6.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ Indexing
^^^^^^^^
- Bug in :meth:`DataFrame.reindex` filling with wrong values when indexing columns and index for ``uint`` dtypes (:issue:`48184`)
- Bug in :meth:`DataFrame.reindex` casting dtype to ``object`` when :class:`DataFrame` has single extension array column when re-indexing ``columns`` and ``index`` (:issue:`48190`)
-
- Bug in :func:`~DataFrame.describe` when formatting percentiles in the resulting index showed more decimals than needed (:issue:`46362`)

Missing
^^^^^^^
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7 changes: 4 additions & 3 deletions pandas/io/formats/format.py
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Expand Up @@ -1720,11 +1720,12 @@ def format_percentiles(
raise ValueError("percentiles should all be in the interval [0,1]")

percentiles = 100 * percentiles
percentiles_round_type = percentiles.round().astype(int)

int_idx = np.isclose(percentiles.astype(int), percentiles)
int_idx = np.isclose(percentiles_round_type, percentiles)

if np.all(int_idx):
out = percentiles.astype(int).astype(str)
out = percentiles_round_type.astype(str)
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@mroeschke mroeschke Sep 1, 2022

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Does the example in the original issue hit this path or the one below? Just want to make sure we have 2 tests that didn't work before and hit this change and the one below to work correctly now

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@weikhor weikhor Sep 2, 2022 •
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Does the example in the original issue hit this path or the one below? Just want to make sure we have 2 tests that didn't work before and hit this change and the one below to work correctly now

a) The example from issue will hit this path.

pd.DataFrame(data=[1, 2, 3], columns=['value']).describe(percentiles=[0.29, 0.57, 0.58])

if np.all(int_idx):
        out = percentiles_round_type.astype(str)
        return [i + "%" for i in out]

b) The example below I own create will hit below path.

pd.DataFrame(data=[1, 2, 3], columns=['value']).describe(percentiles=[0.291, 0.57, 0.58])

out[int_idx] = percentiles[int_idx].round().astype(int).astype(str)

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Could you add the pd.DataFrame(data=[1, 2, 3], columns=['value']).describe(percentiles=[0.29, 0.57, 0.58]) case as a unit test?

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@weikhor weikhor Sep 3, 2022

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Have add test

return [i + "%" for i in out]

unique_pcts = np.unique(percentiles)
Expand All @@ -1737,7 +1738,7 @@ def format_percentiles(
).astype(int)
prec = max(1, prec)
out = np.empty_like(percentiles, dtype=object)
out[int_idx] = percentiles[int_idx].astype(int).astype(str)
out[int_idx] = percentiles[int_idx].round().astype(int).astype(str)

out[~int_idx] = percentiles[~int_idx].round(prec).astype(str)
return [i + "%" for i in out]
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36 changes: 23 additions & 13 deletions pandas/tests/io/formats/test_format.py
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Expand Up @@ -3306,24 +3306,34 @@ def test_nat_representations(self):
assert f(NaT) == "NaT"


def test_format_percentiles():
result = fmt.format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
expected = ["1.999%", "2.001%", "50%", "66.667%", "99.99%"]
@pytest.mark.parametrize(
"percentiles, expected",
[
(
[0.01999, 0.02001, 0.5, 0.666666, 0.9999],
["1.999%", "2.001%", "50%", "66.667%", "99.99%"],
),
(
[0, 0.5, 0.02001, 0.5, 0.666666, 0.9999],
["0%", "50%", "2.0%", "50%", "66.67%", "99.99%"],
),
([0.281, 0.29, 0.57, 0.58], ["28.1%", "29%", "57%", "58%"]),
([0.28, 0.29, 0.57, 0.58], ["28%", "29%", "57%", "58%"]),
],
)
def test_format_percentiles(percentiles, expected):
result = fmt.format_percentiles(percentiles)
assert result == expected

result = fmt.format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
expected = ["0%", "50%", "2.0%", "50%", "66.67%", "99.99%"]
assert result == expected

@pytest.mark.parametrize(
"percentiles",
[([0.1, np.nan, 0.5]), ([-0.001, 0.1, 0.5]), ([2, 0.1, 0.5]), ([0.1, 0.5, "a"])],
)
def test_error_format_percentiles(percentiles):
msg = r"percentiles should all be in the interval \[0,1\]"
with pytest.raises(ValueError, match=msg):
fmt.format_percentiles([0.1, np.nan, 0.5])
with pytest.raises(ValueError, match=msg):
fmt.format_percentiles([-0.001, 0.1, 0.5])
with pytest.raises(ValueError, match=msg):
fmt.format_percentiles([2, 0.1, 0.5])
with pytest.raises(ValueError, match=msg):
fmt.format_percentiles([0.1, 0.5, "a"])
fmt.format_percentiles(percentiles)


def test_format_percentiles_integer_idx():
Expand Down