tests/unit_tests/pandas_postprocessing/test_histogram.py

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from pandas import DataFrame

from superset.utils.pandas_postprocessing import histogram

data = DataFrame(
    {
        "group": ["A", "A", "B", "B", "A", "A", "B", "B", "A", "A"],
        "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
        "b": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    }
)

bins = 5


def test_histogram_no_groupby():
    data_with_no_groupings = DataFrame(
        {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "b": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}
    )
    result = histogram(data_with_no_groupings, "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, 2, 2, 2]]


def test_histogram_with_groupby():
    result = histogram(data, "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, 2, 0, 2], ["B", 0, 2, 0, 2, 0]]


def test_histogram_with_groupby_and_normalize():
    result = histogram(data, "a", ["group"], bins, normalize=True)
    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", 0.2, 0.0, 0.2, 0.0, 0.2],
        ["B", 0.0, 0.2, 0.0, 0.2, 0.0],
    ]


def test_histogram_with_groupby_and_cumulative():
    result = histogram(data, "a", ["group"], bins, cumulative=True)
    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, 2, 4, 4, 6], ["B", 0, 2, 2, 4, 4]]


def test_histogram_with_groupby_and_cumulative_and_normalize():
    result = histogram(data, "a", ["group"], bins, cumulative=True, normalize=True)
    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",
            0.06666666666666667,
            0.06666666666666667,
            0.13333333333333333,
            0.13333333333333333,
            0.2,
        ],
        [
            "B",
            0.0,
            0.06666666666666667,
            0.06666666666666667,
            0.13333333333333333,
            0.13333333333333333,
        ],
    ]


def test_histogram_with_non_numeric_column():
    try:
        histogram(data, "group", None, bins)
    except ValueError as e:
        assert str(e) == "Column 'group' contains non-numeric values"  # noqa: PT017


def test_histogram_with_some_non_numeric_values():
    data_with_non_numeric = DataFrame(
        {
            "group": ["A", "A", "B", "B", "A", "A", "B", "B", "A", "A"],
            "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, "10"],
            "b": [1, 2, 3, 4, 5, 6, 7, 8, 9, "10"],
        }
    )
    try:
        histogram(data_with_non_numeric, "a", ["group"], bins)
    except ValueError as e:
        assert str(e) == "Column 'group' contains non-numeric values"  # noqa: PT017