def range(self, start, end=None, step=1, numPartitions=None):
        """
        Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
        ``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
        step value ``step``.

        :param start: the start value
        :param end: the end value (exclusive)
        :param step: the incremental step (default: 1)
        :param numPartitions: the number of partitions of the DataFrame
        :return: :class:`DataFrame`

        >>> spark.range(1, 7, 2).collect()
        [Row(id=1), Row(id=3), Row(id=5)]

        If only one argument is specified, it will be used as the end value.

        >>> spark.range(3).collect()
        [Row(id=0), Row(id=1), Row(id=2)]
        """
        if numPartitions is None:
            numPartitions = self._sc.defaultParallelism

        if end is None:
            jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions))
        else:
            jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions))

        return DataFrame(jdf, self._wrapped)
def range ( self , start , end = None , step = 1 , numPartitions = None ) : if numPartitions is None : numPartitions = self . _sc . defaultParallelism if end is None : jdf = self . _jsparkSession . range ( 0 , int ( start ) , int ( step ) , int ( numPartitions ) ) else : jdf = self . _jsparkSession . range ( int ( start ) , int ( end ) , int ( step ) , int ( numPartitions ) ) return DataFrame ( jdf , self . _wrapped )
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
        ``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
        step value ``step``.

        :param start: the start value
        :param end: the end value (exclusive)
        :param step: the incremental step (default: 1)
        :param numPartitions: the number of partitions of the DataFrame
        :return: :class:`DataFrame`

        >>> spark.range(1, 7, 2).collect()
        [Row(id=1), Row(id=3), Row(id=5)]

        If only one argument is specified, it will be used as the end value.

        >>> spark.range(3).collect()
        [Row(id=0), Row(id=1), Row(id=2)]