title: PySpark randomSplit vs SkLearn Train Test Split - Random Seed Question
body: <p>Let's say I have a <code>pandas</code> dataframe and apply <code>sklearn.model_selection.train_test_split</code> with the <code>random_seed</code> parameter set to 1. </p>
  
  <p>Let's say I then take the exact same <code>pandas</code> dataframe and create a Spark Dataframe with an instance of <code>SQLContext</code>. If I apply the PySpark <code>randomSplit</code> function with the <code>seed</code> parameter set to 1, will I always be guaranteed to obtain the same exact split?</p>