old_contents: import logging
  
  from .pandas_context import PandasCSVDataContext
  
  logger = logging.getLogger(__name__)
  
  try:
      from .sqlalchemy_context import SqlAlchemyDataContext
  except ImportError:
      logger.info("Unable to load SqlAlchemy context; install optional sqlalchemy dependency for support")
  
  try:
      from .spark_context import SparkCSVDataContext
      from .spark_parquet_context import SparkParquetContext
      from .databricks_context import DatabricksTableContext
  except ImportError:
      logger.info("Unable to load Spark contexts; install optional spark dependency for support")
  
  
  def get_data_context(context_type, options, *args, **kwargs):
      """Return a data_context object which exposes options to list datasets and get a dataset from
      that context. This is a new API in Great Expectations 0.4, and is subject to rapid change.
  
      :param context_type: (string) one of "SqlAlchemy", "PandasCSV", "SparkCSV", or "DatabricksTable"
      :param options: options to be passed to the data context's connect method.
      :return: a new DataContext object
      """
      if context_type == "SqlAlchemy":
          return SqlAlchemyDataContext(options, *args, **kwargs)
      elif context_type == "PandasCSV":
          return PandasCSVDataContext(options, *args, **kwargs)
      elif context_type == "SparkCSV":
          return SparkCSVDataContext(options, *args, **kwargs)
      elif context_type == "SparkParquet":
          return SparkParquetDataContext(options, *args, **kwargs)
      elif context_type == "DatabricksTable":
          return DatabricksTableContext(options, *args, **kwargs)
      else:
          raise ValueError("Unknown data context.")
  
new_contents: import logging
  
  from .pandas_context import PandasCSVDataContext
  
  logger = logging.getLogger(__name__)
  
  try:
      from .sqlalchemy_context import SqlAlchemyDataContext
  except ImportError:
      logger.info("Unable to load SqlAlchemy context; install optional sqlalchemy dependency for support")
  
  try:
      from .spark_context import SparkCSVDataContext
      from .spark_parquet_context import SparkParquetDataContext
      from .databricks_context import DatabricksTableContext
  except ImportError:
      logger.info("Unable to load Spark contexts; install optional spark dependency for support")
  
  
  def get_data_context(context_type, options, *args, **kwargs):
      """Return a data_context object which exposes options to list datasets and get a dataset from
      that context. This is a new API in Great Expectations 0.4, and is subject to rapid change.
  
      :param context_type: (string) one of "SqlAlchemy", "PandasCSV", "SparkCSV", or "DatabricksTable"
      :param options: options to be passed to the data context's connect method.
      :return: a new DataContext object
      """
      if context_type == "SqlAlchemy":
          return SqlAlchemyDataContext(options, *args, **kwargs)
      elif context_type == "PandasCSV":
          return PandasCSVDataContext(options, *args, **kwargs)
      elif context_type == "SparkCSV":
          return SparkCSVDataContext(options, *args, **kwargs)
      elif context_type == "SparkParquet":
          return SparkParquetDataContext(options, *args, **kwargs)
      elif context_type == "DatabricksTable":
          return DatabricksTableContext(options, *args, **kwargs)
      else:
          raise ValueError("Unknown data context.")
  
subject: Fix typo with spark parquet data context