title: Drop columns if rows contain a specific value in Pandas
body: <p>I am starting to learn Pandas. I have seen a lot of questions here in SO where people ask how to delete a row if a column matches certain value. </p>
  
  <p>In my case it is the opposite. Imagine having this dataframe:</p>
  
  <p><a href="https://i.stack.imgur.com/juzeZ.png" rel="noreferrer"><img src="https://i.stack.imgur.com/juzeZ.png" alt="Dataframe"></a></p>
  
  <p>Where you want to know is, if any column has in any of its row the value <code>salty</code>, that column should be deleted, having as a result:</p>
  
  <p><a href="https://i.stack.imgur.com/w0MWm.png" rel="noreferrer"><img src="https://i.stack.imgur.com/w0MWm.png" alt="Dataframe 2"></a></p>
  
  <p>I have tried with several similarities to this:</p>
  
  <pre><code>if df.loc[df['A'] == 'salty']:
     df.drop(df.columns[0], axis=1, inplace=True)
  </code></pre>
  
  <p>But I am quite lost at finding documentation onto how to delete columns based on a row value of that column. That code is a mix of finding a specific column and deleting always the first column (as my idea was to search the value of a row in that column, in ALL columns in a <code>for</code> loop. </p>