3391a348f3f7cd07a96c8e6a4b05e3e9f60c8567,pandas/tests/groupby/test_categorical.py,,,#,36

Before Change

    return result.reindex(index, fill_value=fill_value).sort_index()


_results_for_groupbys_with_missing_categories = dict(
    // This maps the builtin groupby functions to their expected outputs for
    // missing categories when they are called on a categorical grouper with
    // observed=False. Some functions are expected to return NaN, some zero.
    // These expected values can be used across several tests (i.e. they are
    // the same for SeriesGroupBy and DataFrameGroupBy) but they should only be
    // hardcoded in one place.
    [
        ("all", np.NaN),
        ("any", np.NaN),
        ("count", 0),
        ("corrwith", np.NaN),
        ("first", np.NaN),
        ("idxmax", np.NaN),
        ("idxmin", np.NaN),
        ("last", np.NaN),
        ("mad", np.NaN),
        ("max", np.NaN),
        ("mean", np.NaN),
        ("median", np.NaN),
        ("min", np.NaN),
        ("nth", np.NaN),
        ("nunique", 0),
        ("prod", np.NaN),
        ("quantile", np.NaN),
        ("sem", np.NaN),
        ("size", 0),
        ("skew", np.NaN),
        ("std", np.NaN),
        ("sum", 0),
        ("var", np.NaN),
    ]
)


def test_apply_use_categorical_name(df):
    cats = qcut(df.C, 4)

After Change

    return result.reindex(index, fill_value=fill_value).sort_index()


_results_for_groupbys_with_missing_categories = {
    // This maps the builtin groupby functions to their expected outputs for
    // missing categories when they are called on a categorical grouper with
    // observed=False. Some functions are expected to return NaN, some zero.
    // These expected values can be used across several tests (i.e. they are
    // the same for SeriesGroupBy and DataFrameGroupBy) but they should only be
    // hardcoded in one place.
    "all": np.NaN,
    "any": np.NaN,
    "count": 0,
    "corrwith": np.NaN,
    "first": np.NaN,
    "idxmax": np.NaN,
    "idxmin": np.NaN,
    "last": np.NaN,
    "mad": np.NaN,
    "max": np.NaN,
    "mean": np.NaN,
    "median": np.NaN,
    "min": np.NaN,
    "nth": np.NaN,
    "nunique": 0,
    "prod": np.NaN,
    "quantile": np.NaN,
    "sem": np.NaN,
    "size": 0,
    "skew": np.NaN,
    "std": np.NaN,
    "sum": 0,
    "var": np.NaN,
}


def test_apply_use_categorical_name(df):
    cats = qcut(df.C, 4)

Instances