def aggregate_spark(data, columns, args):
    """Aggregate a column in a PySpark context.
​
    This function is required.
​
    Args:
        data: A dataframe including all of the raw columns.
​
        columns: A dict with the same structure as the aggregator's input
            columns specifying the names of the dataframe's columns that
            contain the input columns.
​
        args: A dict with the same structure as the aggregator's input args
            containing the values of the args.
​
    Returns:
        Any json-serializable object that matches the data type of the aggregator.
    """
    pass

def aggregate_spark(data, columns, args):
    from pyspark.ml.feature import QuantileDiscretizer
​
    discretizer = QuantileDiscretizer(
        numBuckets=args["num_buckets"], inputCol=columns["col"], outputCol="_"
    ).fit(data)
​
    return discretizer.getSplits()

pyspark==2.4.0
boto3==1.9.78
msgpack==0.6.1
numpy>=1.13.3,<2
requirements-parser==0.2.0
packaging==19.0.0