def transform_spark(data, columns, args, transformed_column):
    """Transform a column in a PySpark context.
​
    This function is optional (recommended for large-scale data processing).
​
    Args:
        data: A dataframe including all of the raw columns.
​
        columns: A dict with the same structure as the transformer'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 transformer's input args
            containing the runtime values of the args.
​
        transformed_column: The name of the column containing the transformed
            data that is to be appended to the dataframe.
​
    Returns:
        The original 'data' dataframe with an added column with the name of the
        transformed_column arg containing the transformed data.
    """
    pass
​
​
def transform_python(sample, args):
    """Transform a single data sample outside of a PySpark context.
​
    This function is required.
​
    Args:
        sample: A dict with the same structure as the transformer's input
            columns containing a data sample to transform.
​
        args: A dict with the same structure as the transformer's input args
            containing the runtime values of the args.
​
    Returns:
        The transformed value.
    """
    pass
​
​
def reverse_transform_python(transformed_value, args):
    """Reverse transform a single data sample outside of a PySpark context.
​
    This function is optional, and only relevant for certain one-to-one
    transformers.
​
    Args:
        transformed_value: The transformed data value.
​
        args: A dict with the same structure as the transformer's input args
            containing the runtime values of the args.
​
    Returns:
        The raw data value that corresponds to the transformed value.
    """
    pass

def transform_spark(data, columns, args, transformed_column):
    return data.withColumn(
        transformed_column, ((data[columns["num"]] - args["mean"]) / args["stddev"])
    )
​
def transform_python(sample, args):
    return (sample["num"] - args["mean"]) / args["stddev"]
​
def reverse_transform_python(transformed_value, args):
    return args["mean"] + (transformed_value * args["stddev"])

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