title: Pandas DataFrame returns NaN in all cells after applying a filter on the colum names
body: <p>I am trying to apply a filter on the column names which start with 'Inc' on a pandas dataframe. This returns 9 columns and I am looking for values that contain 'Yes' in all of those 9. Once that is done I would like to return all the rows (and columns) from the df that have 'Yes' in all 9 columns. I tried the following <code>df[df.filter(like='Inc') == 'Yes']</code> but it returns all the columns with NaN except for the 9 'Inc' columns.</p>
  <p>A very naive method of implementation that actually worked for me, was as follows but is not very scalable:</p>
  <p><code>df[(df['Inc1_E1_C1'] == 'Yes') &amp; (df['Inc2_E1_C1'] == 'Yes') &amp; (df['Inc3_E1_C1'] == 'Yes')  &amp; (df['Inc5_E1_C1'] == 'Yes') &amp; (df['Inc6_E1_C1'] == 'Yes') &amp; (df['Inc7_E1_C1'] == 'Yes')  &amp; (df['Inc8_E1_C1'] == 'Yes') &amp; (df['Inc9_E1_C1'] == 'Yes') &amp; (df['Inc_4_E1_C1'] == 'Yes')]</code></p>
  <p>Does anyone have a better way of having this implemented?</p>
  <p><a href="https://i.stack.imgur.com/zk0FU.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/zk0FU.png" alt="This is the output while using the Naive implementation" /></a>
  <a href="https://i.stack.imgur.com/L68eg.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/L68eg.png" alt="This is what I get when using the filter to get all 'Inc' columns and then search for rows that satisfy the 'Yes' condition" /></a></p>