title: skipping unknown number of lines to read the header python pandas
body: <p>i have an excel data that i read in with python pandas:</p>
  
  <pre><code>import pandas as pd
  data = pd.read_csv('..../file.txt', sep='\t' )
  </code></pre>
  
  <p>the mock data looks like this:</p>
  
  <pre><code>unwantedjunkline1
  unwantedjunkline2
  unwantedjunkline3
   ID     ColumnA     ColumnB     ColumnC
   1         A          B            C
   2         A          B            C
   3         A          B            C
  ...
  </code></pre>
  
  <p>the data in this case contains 3 junk lines(lines i don't want to read in) before hitting the header and sometimes it contains 4 or more suck junk lines. so in this case i read in the data :</p>
  
  <pre><code>data = pd.read_csv('..../file.txt', sep='\t', skiprows = 3 )
  </code></pre>
  
  <p>data looks like:</p>
  
  <pre><code> ID     ColumnA     ColumnB     ColumnC
   1         A          B            C
   2         A          B            C
   3         A          B            C
  ...
  </code></pre>
  
  <p><strong>But each time the number of unwanted lines is different</strong>, is there a way to read in  a table file using pandas without using 'skiprows=' but instead using some command that matches the header so it knows to start reading from the header? so I don't have to click open the file to count how many unwanted lines the file contains each time and then manually change the 'skiprows=' option.</p>