title: Coalesce columns in pyspark and replace values?
body: <p>I have 2 dataframes each with 1 column of boolean data. I am more interested if a row is true than if it's false, so I am trying to coalesce the 2 columns together, replacing false values in one dataframe if the same row is true in the other dataframe.</p>
  <p>For example my 2 dataframes are like this:</p>
  <pre><code>label1
  
  false
  false
  false
  true
  true
  </code></pre>
  <pre><code>label2
  
  true
  false
  false
  false
  true
  </code></pre>
  <p>Expected output coalescing label1 and label2 and prioritising true values:</p>
  <pre><code>label
  
  true
  false
  false
  true
  true
  </code></pre>
  <p>Is there a way to do this in pyspark? I have looked into using <code>coalesce(df['label1'],df['label2'])</code> but I don't know how to specify that I want true values to always be kept</p>