old_contents: from __future__ import print_function
  import os, sys
  import logging
  
  logging.basicConfig(level=logging.INFO)
  logger = logging.getLogger(__name__)
  
  from . import sparkjob
  
  try : 
      def start_spark(master = 'local[*]', 
                      spark_conf='./spark_conf', 
                      executor_memory=None,
                      profiling=False, 
                      graphframes_package='graphframes:graphframes:0.2.0-spark2.0-s_2.11', 
                      extra_conf = None):
          """Launch a SparkContext 
          
          Inputs
          ------
  
          master : URL to spark master in the form 'spark://<master>:<port>'
          
          spark_conf : path to a spark configuration directory
  
          executor_memory : executor memory in java memory string format, e.g. '4G'
  
          profiling: whether to turn on python profiling or not
  
          graphframes_package : which graphframes to load
          """
  
          os.environ['PYSPARK_SUBMIT_ARGS'] = "--packages {graphframes_package} pyspark-shell"\
                                              .format(graphframes_package=graphframes_package)
          
          os.environ['SPARK_CONF_DIR'] = os.path.realpath(spark_conf)
  
          os.environ['PYSPARK_PYTHON'] = sys.executable
  
          from pyspark import SparkContext, SparkConf
  
          conf = SparkConf()
  
          conf.set('spark.driver.maxResultSize', '0')
  
          if executor_memory is not None: 
              conf.set('spark.executor.memory', executor_memory)
          if profiling: 
              conf.set('spark.python.profile', 'true')
          else:
              conf.set('spark.python.profile', 'false')
          
          if extra_conf is not None: 
              for k,v in extra_conf.iteritems(): 
                  conf.set(k,v)
  
          sc = SparkContext(master=master, conf=conf)
  
          return sc
  
      def get_sqc(sc):
          from pyspark.sql import SQLContext
  
          return SQLContext(sc)
  
  except ImportError: 
      logger.warning('Could not import pyspark -- make sure SPARK_HOME is set')
new_contents: from __future__ import print_function
  import os, sys
  import logging
  
  logging.basicConfig(level=logging.INFO)
  logger = logging.getLogger(__name__)
  
  from . import sparkjob
  
  try : 
      def start_spark(master = 'local[*]', 
                      spark_conf='./spark_conf', 
                      executor_memory=None,
                      profiling=False, 
                      graphframes_package='graphframes:graphframes:0.3.0-spark2.0-s_2.11', 
                      extra_conf = None):
          """Launch a SparkContext 
          
          Inputs
          ------
  
          master : URL to spark master in the form 'spark://<master>:<port>'
          
          spark_conf : path to a spark configuration directory
  
          executor_memory : executor memory in java memory string format, e.g. '4G'
  
          profiling: whether to turn on python profiling or not
  
          graphframes_package : which graphframes to load
          """
  
          os.environ['PYSPARK_SUBMIT_ARGS'] = "--packages {graphframes_package} pyspark-shell"\
                                              .format(graphframes_package=graphframes_package)
          
          os.environ['SPARK_CONF_DIR'] = os.path.realpath(spark_conf)
  
          os.environ['PYSPARK_PYTHON'] = sys.executable
  
          from pyspark import SparkContext, SparkConf
  
          conf = SparkConf()
  
          conf.set('spark.driver.maxResultSize', '0')
  
          if executor_memory is not None: 
              conf.set('spark.executor.memory', executor_memory)
          if profiling: 
              conf.set('spark.python.profile', 'true')
          else:
              conf.set('spark.python.profile', 'false')
          
          if extra_conf is not None: 
              for k,v in extra_conf.iteritems(): 
                  conf.set(k,v)
  
          sc = SparkContext(master=master, conf=conf)
  
          return sc
  
      def get_sqc(sc):
          from pyspark.sql import SQLContext
  
          return SQLContext(sc)
  
  except ImportError: 
      logger.warning('Could not import pyspark -- make sure SPARK_HOME is set')
subject: change to graphframes 3.0