title: Spark Convert Data Frame Column to dense Vector for StandardScaler() "Column must be of type org.apache.spark.ml.linalg.VectorUDT"
body: <p>I am very new to Spark and I am trying to apply StandardScaler() to a column in a DataFrame. </p>
  
  <pre><code>+---------------+
  |      DF_column|
  +---------------+
  | 0.114285714286|
  | 0.115702479339|
  | 0.267893660532|
  |0.0730337078652|
  | 0.124309392265|
  | 0.365714285714|
  | 0.111747851003|
  | 0.279538904899|
  | 0.134670487106|
  | 0.523287671233|
  | 0.404011461318|
  |          0.375|
  | 0.125517241379|
  |0.0143266475645|
  | 0.313684210526|
  | 0.381088825215|
  | 0.411428571429|
  | 0.327683615819|
  | 0.153409090909|
  | 0.344827586207|
  +---------------+
  </code></pre>
  
  <p>The problem is that applying it like this, gives me an error:</p>
  
  <blockquote>
    <p><code>requirement failed: Input column DF_column must be a vector column.</code> </p>
  </blockquote>
  
  <p>I tried using UDF but still doesn't work.</p>
  
  <pre><code>scaler = StandardScaler(inputCol='DF_column', 
      outputCol="scaledFeatures",withStd=True, withMean=False)
  </code></pre>
  
  <p>I did the example of the LIBSVM but that is easy a the TXT file is loading features as Vectors.</p>