def METHOD_NAME():
    uniqueness_score = UniquenessDataCheck.uniqueness_score
    # Test uniqueness for a simple series.
    # [0,1,2,0,1,2,0,1,2,0]
    data = pd.Series([x % 3 for x in range(10)])
    scores = uniqueness_score(data)
    ans = 0.66
    assert scores == ans
    # Test uniqueness for the same series, repeated.  Should be the score.
    # [0,1,2,0,1,2,0,1,2,0,0,1,2,0,1,2,0,1,2,0]
    data = pd.Series([x % 3 for x in range(10)] * 2)
    scores = uniqueness_score(data)
    ans = 0.66
    assert scores == ans
    # Test uniqueness for a simple series with NaN.
    # [0,1,2,0,1,2,0,1,2,0]
    data = pd.Series([x % 3 for x in range(10)] + [np.nan])
    scores = uniqueness_score(data)
    ans = 0.66
    assert scores == ans
    # Test uniqueness in each column of a DataFrame
    data = pd.DataFrame(
        {
            "most_unique": [float(x) for x in range(10)],  # [0,1,2,3,4,5,6,7,8,9]
            "more_unique": [x % 5 for x in range(10)],  # [0,1,2,3,4,0,1,2,3,4]
            "unique": [x % 3 for x in range(10)],  # [0,1,2,0,1,2,0,1,2,0]
            "less_unique": [x % 2 for x in range(10)],  # [0,1,0,1,0,1,0,1,0,1]
            "not_unique": [float(1) for x in range(10)],
        },
    )  # [1,1,1,1,1,1,1,1,1,1]
    scores = data.apply(uniqueness_score)
    ans = pd.Series(
        {
            "most_unique": 0.90,
            "more_unique": 0.80,
            "unique": 0.66,
            "less_unique": 0.50,
            "not_unique": 0.00,
        },
    )
    assert scores.round(7).equals(ans)