Spark Convert Data Frame Column to dense Vector for StandardScaler() "Column must be of type org.apache.spark.ml.linalg.VectorUDT"
<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>