title: pandas filter by multiple columns NULL
body: <p>I have a pandas dataframe like:</p>
  
  <pre><code>df = pd.DataFrame({'Last_Name': ['Smith', None, 'Brown'], 
                     'First_Name': ['John', None, 'Bill'],
                     'Age': [35, 45, None]})
  </code></pre>
  
  <p>And could manually filter it using:</p>
  
  <pre><code>df[df.Last_Name.isnull() &amp; df.First_Name.isnull()]
  </code></pre>
  
  <p>but this is annoying as I need to w<strong>rite a lot of duplicate code for each column/condition</strong>. It is not maintainable if there is a large number of columns. Is it possible to write a function which generates this python code for me?</p>
  
  <p>Some Background:
  My pandas dataframe is based on an initial SQL-based multi-dimensional Aggregation (grouping-sets) <a href="https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-multi-dimensional-aggregation.html" rel="noreferrer">https://jaceklaskowski.gitbooks.io/mastering-spark-sql/spark-sql-multi-dimensional-aggregation.html</a> so always some different columns are NULL. Now, I want to efficiently select these different groups and analyze them separately in pandas.</p>