title: Multiple incrementing conditional clauses in pandas
body: <p>I have a dataframe called raw_df. 
  I want the rows in which the first column have <code>E01</code>, <code>E02</code>, ... <code>E14</code>.
  How can I write a list comprehension (or another way) that can produce this, without typing it out manually.</p>
  
  <pre><code>row_data = raw_df.loc[
                (raw_df.col0=='E01') | (raw_df.col0=='E02') | 
                (raw_df.col0=='E03') | (raw_df.col0=='E04') | 
                (raw_df.col0=='E05') | (raw_df.col0=='E06') | 
                (raw_df.col0=='E07') | (raw_df.col0=='E08') | 
                (raw_df.col0=='E09') | (raw_df.col0=='E10') |
                (raw_df.col0=='E11') | (raw_df.col0=='E12') | 
                (raw_df.col0=='E13') | (raw_df.col0=='E14') 
                ]
  </code></pre>
  
  <p>I know it can be done with <code>eval</code> making a string list comprehension, but I understand that it's dangerous &amp; should not be used.</p>
  
  <p>Similarly, <code>setattr</code> only works for <code>xx.yy=z</code>, but not <code>xx.yy</code><strong>==</strong><code>z</code></p>