Computes specified statistics for numeric and string columns. Available statistics are:
        - count
        - mean
        - stddev
        - min
        - max
        - arbitrary approximate percentiles specified as a percentage (eg, 75%)

        If no statistics are given, this function computes count, mean, stddev, min,
        approximate quartiles (percentiles at 25%, 50%, and 75%), and max.

        .. note:: This function is meant for exploratory data analysis, as we make no
            guarantee about the backward compatibility of the schema of the resulting DataFrame.

        >>> df.summary().show()
        +-------+------------------+-----+
        |summary|               age| name|
        +-------+------------------+-----+
        |  count|                 2|    2|
        |   mean|               3.5| null|
        | stddev|2.1213203435596424| null|
        |    min|                 2|Alice|
        |    25%|                 2| null|
        |    50%|                 2| null|
        |    75%|                 5| null|
        |    max|                 5|  Bob|
        +-------+------------------+-----+

        >>> df.summary("count", "min", "25%", "75%", "max").show()
        +-------+---+-----+
        |summary|age| name|
        +-------+---+-----+
        |  count|  2|    2|
        |    min|  2|Alice|
        |    25%|  2| null|
        |    75%|  5| null|
        |    max|  5|  Bob|
        +-------+---+-----+

        To do a summary for specific columns first select them:

        >>> df.select("age", "name").summary("count").show()
        +-------+---+----+
        |summary|age|name|
        +-------+---+----+
        |  count|  2|   2|
        +-------+---+----+

        See also describe for basic statistics.

def summary(self, *statistics):
        """Computes specified statistics for numeric and string columns. Available statistics are:
        - count
        - mean
        - stddev
        - min
        - max
        - arbitrary approximate percentiles specified as a percentage (eg, 75%)

        If no statistics are given, this function computes count, mean, stddev, min,
        approximate quartiles (percentiles at 25%, 50%, and 75%), and max.

        .. note:: This function is meant for exploratory data analysis, as we make no
            guarantee about the backward compatibility of the schema of the resulting DataFrame.

        >>> df.summary().show()
        +-------+------------------+-----+
        |summary|               age| name|
        +-------+------------------+-----+
        |  count|                 2|    2|
        |   mean|               3.5| null|
        | stddev|2.1213203435596424| null|
        |    min|                 2|Alice|
        |    25%|                 2| null|
        |    50%|                 2| null|
        |    75%|                 5| null|
        |    max|                 5|  Bob|
        +-------+------------------+-----+

        >>> df.summary("count", "min", "25%", "75%", "max").show()
        +-------+---+-----+
        |summary|age| name|
        +-------+---+-----+
        |  count|  2|    2|
        |    min|  2|Alice|
        |    25%|  2| null|
        |    75%|  5| null|
        |    max|  5|  Bob|
        +-------+---+-----+

        To do a summary for specific columns first select them:

        >>> df.select("age", "name").summary("count").show()
        +-------+---+----+
        |summary|age|name|
        +-------+---+----+
        |  count|  2|   2|
        +-------+---+----+

        See also describe for basic statistics.
        """
        if len(statistics) == 1 and isinstance(statistics[0], list):
            statistics = statistics[0]
        jdf = self._jdf.summary(self._jseq(statistics))
        return DataFrame(jdf, self.sql_ctx)