title: PySpark Dataframes: how to filter on multiple conditions with compact code?
body: <p>If I have a list of column names and I want to filter on rows if the value of those columns are greater than zero, is there something similar to this which I can do?</p>
  
  <pre><code>columns = ['colA','colB','colC','colD','colE','colF']
  new_df = df.filter(any([df[c]&gt;0 for c in columns]))
  </code></pre>
  
  <p>This returns:</p>
  
  <blockquote>
    <p>ValueError: Cannot convert column into bool: please use '&amp;' for 'and',
    '|' for 'or', '~' for 'not' when building DataFrame boolean
    expressions</p>
  </blockquote>
  
  <p>I guess I can sum those columns and the filter on only one column (since I don't have negative numbers. But if I had the sum-trick wouldn't work. And anyway if I had to filter those columns on another condition different than the sum, how could I do what I want to do?
  Any idea?</p>