title: Filter spark RDD with PySpark by column name and its numerical value
body: <p>I am translating Scala / Spark model into Python / Spark. The problem is I have RDD with about 1 million observations and about 33 columns. 
  I am splitting the RDD based on numerical threshold ('Time'). The time variable is in numerical format (double) (not posix).</p>
  
  <p>Here is Scala source code:</p>
  
  <pre><code>// get the time to split the data.
  val splitTime = data.stat.approxQuantile("Time", Array(0.7), 0.001).head
  
  val trainingData = data.filter(s"Time&lt;$splitTime").cache()
  val validData = data.filter(s"Time&gt;=$splitTime").cache()
  </code></pre>
  
  <p>and here is my PySpark failed interpretation:</p>
  
  <pre><code>splitTime = data.approxQuantile("Time", [0.7], 0.001)
  trainingData = data.filter(data["Time"] &lt; splitTime)
  validData = data.filter(data["Time"] &gt;= splitTime)
  </code></pre>
  
  <p>The first line works fine. The problem starts when I try to use the threshold on the RDD. I also couldn't decode the Scala format <code>s" &gt;=$ "</code> around the condition and its importance in the condition. Internet source on the meaning of <code>s" &gt;=$ "</code> are vague.</p>