def QUANTILE(src_column, *args):
    """
    Builtin approximate quantile aggregator for groupby.
    Accepts as an argument, one or more of a list of quantiles to query.
    For instance:

    To extract the median

    >>> sf.groupby("user",
    ...   {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.5)})

    To extract a few quantiles

    >>> sf.groupby("user",
    ...   {'rating_quantiles':tc.aggregate.QUANTILE('rating', [0.25,0.5,0.75])})

    Or equivalently

    >>> sf.groupby("user",
    ...     {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.25,0.5,0.75)})

    The returned quantiles are guaranteed to have 0.5% accuracy. That is to say,
    if the requested quantile is 0.50, the resultant quantile value may be
    between 0.495 and 0.505 of the true quantile.
    """
    if len(args) == 1:
        quantiles = args[0]
    else:
        quantiles = list(args)

    if not _is_non_string_iterable(quantiles):
        quantiles = [quantiles]
    query = ",".join([str(i) for i in quantiles])
    return ("__builtin__quantile__[" + query + "]", [src_column])
Builtin approximate quantile aggregator for groupby.
    Accepts as an argument, one or more of a list of quantiles to query.
    For instance:

    To extract the median

    >>> sf.groupby("user",
    ...   {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.5)})

    To extract a few quantiles

    >>> sf.groupby("user",
    ...   {'rating_quantiles':tc.aggregate.QUANTILE('rating', [0.25,0.5,0.75])})

    Or equivalently

    >>> sf.groupby("user",
    ...     {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.25,0.5,0.75)})

    The returned quantiles are guaranteed to have 0.5% accuracy. That is to say,
    if the requested quantile is 0.50, the resultant quantile value may be
    between 0.495 and 0.505 of the true quantile.