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fix(histogram): add NULL handling for histogram #35693

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fix(histogram): add NULL handling for histogram #35693

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2f23078
fix(histogram): add NULL handling for histogram (#33738)
panrach Oct 4, 2025
ccef6c9
fix(histogram): remove unnecessary check
panrach Oct 5, 2025
50cfe80
move dropna so the non numeric check is not redundant
panrach Oct 5, 2025
49d7165
fix(histogram): add NULL handling for histogram (apache#33738)
panrach Oct 5, 2025
8e93aca
fix(histogram): add handling for empty df
panrach Oct 6, 2025
7e37e67
test(backend): add histogram tests
janani-gurram Oct 16, 2025
55b453f
fix(histogram): prevent creating new df
panrach Oct 17, 2025
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5 changes: 5 additions & 0 deletions superset/utils/pandas_postprocessing/histogram.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,11 @@ def histogram(
if groupby is None:
groupby = []

# drop empty values from the target column
df.dropna(subset=[column], inplace=True)
if df.empty:
return df
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Input DataFrame modified in-place category Design

Tell me more
What is the issue?

The function modifies the input DataFrame in-place by dropping rows with NULLs, which violates the principle of not mutating input parameters and can cause unexpected side effects for the caller.

Why this matters

Callers may not expect their original DataFrame to be modified, leading to data loss in the calling code and potential bugs in downstream processing that depends on the original DataFrame structure.

Suggested change ∙ Feature Preview

Create a copy of the DataFrame before dropping NULLs:

# drop empty values from the target column
df = df.dropna(subset=[column])
if df.empty:
    return df
Provide feedback to improve future suggestions

Nice Catch Incorrect Not in Scope Not in coding standard Other

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# convert to numeric, coercing errors to NaN
df[column] = to_numeric(df[column], errors="coerce")

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67 changes: 67 additions & 0 deletions tests/unit_tests/pandas_postprocessing/test_histogram.py
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Expand Up @@ -140,3 +140,70 @@ def test_histogram_with_some_non_numeric_values():
histogram(data_with_non_numeric, "a", ["group"], bins)
except ValueError as e:
assert str(e) == "Column 'group' contains non-numeric values" # noqa: PT017


def test_histogram_with_groupby_and_some_null_values():
data_with_groupby_and_some_nulls = DataFrame(
{
"group": ["A", "A", "B", "B", "A", "A", "B", "B", "A", "A"],
"a": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
"b": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
}
)

result = histogram(data_with_groupby_and_some_nulls, "a", ["group"], bins)
assert result.shape == (2, bins + 1)
assert result.columns.tolist() == [
"group",
"1.0 - 2.8",
"2.8 - 4.6",
"4.6 - 6.4",
"6.4 - 8.2",
"8.2 - 10.0",
]
assert result.values.tolist() == [["A", 2, 0, 1, 0, 2], ["B", 0, 2, 0, 2, 0]]


def test_histogram_with_no_groupby_and_some_null_values():
data_with_no_groupby_and_some_nulls = DataFrame(
{
"a": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
"b": [1, 2, 3, 4, 5, None, 7, 8, 9, 10],
}
)

result = histogram(data_with_no_groupby_and_some_nulls, "a", [], bins)
assert result.shape == (1, bins)
assert result.columns.tolist() == [
"1.0 - 2.8",
"2.8 - 4.6",
"4.6 - 6.4",
"6.4 - 8.2",
"8.2 - 10.0",
]
assert result.values.tolist() == [[2, 2, 1, 2, 2]]


def test_histogram_with_groupby_and_all_null_values():
data_with_groupby_and_all_nulls = DataFrame(
{
"group": ["A", "A", "B", "B", "A", "A", "B", "B", "A", "A"],
"a": [None, None, None, None, None, None, None, None, None, None],
"b": [None, None, None, None, None, None, None, None, None, None],
}
)

result = histogram(data_with_groupby_and_all_nulls, "a", ["group"], bins)
assert result.empty


def test_histogram_with_no_groupby_and_all_null_values():
data_with_no_groupby_and_all_nulls = DataFrame(
{
"a": [None, None, None, None, None, None, None, None, None, None],
"b": [None, None, None, None, None, None, None, None, None, None],
}
)

result = histogram(data_with_no_groupby_and_all_nulls, "a", [], bins)
assert result.empty
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