anchor.diff:  
   Now that we understand how a StringIndexer works, we can create one for each
   of our problematic columns (Sex and Embarked) by running the code in listing
  -2.9.
  +2.10.
   
   .Creating two StringIndexers: one for the Sex column and one for the Embarked column.
   [source,scala]
  
positive.code: <1> We're interested in knowing the summary statistics about the Fare column.
  <2> We want Spark to show us the results.
  
  The query should return a Dataset containing the information in table 2.4.
  
  .Results of calling the describe method on the Fare column.
  [options="header"]
  
negative.code: |35.0| 8.05|[35.0, 8.05]
  |===
  
  Let's define the VectorAssembler we need in listing 2.11. As a reminder, we
  previously selected the following features: Sex, Embarked, Age, SibSp, Parch,
  Fare, Pclass.
  
  