title: Removing NULL , NAN, empty space from PySpark DataFrame
body: <p>I have a dataframe in PySpark which contains empty space, Null, and Nan. 
  I want to remove rows which have any of those. I tried below commands, but, nothing seems to work.</p>
  
  <pre><code>myDF.na.drop().show()
  myDF.na.drop(how='any').show()
  </code></pre>
  
  <p>Below is the dataframe:</p>
  
  <pre><code>+---+----------+----------+-----+-----+
  |age|  category|      date|empId| name|
  +---+----------+----------+-----+-----+
  | 25|electronic|17-01-2018|  101|  abc|
  | 24|    sports|16-01-2018|  102|  def|
  | 23|electronic|17-01-2018|  103|  hhh|
  | 23|electronic|16-01-2018|  104|  yyy|
  | 29|       men|12-01-2018|  105| ajay|
  | 31|      kids|17-01-2018|  106|vijay|
  |   |       Men|       nan|  107|Sumit|
  +---+----------+----------+-----+-----+
  </code></pre>
  
  <p>What am I missing? What is the best way to tackle NULL, Nan or empty spaces so that there is no problem in the actual calculation?</p>