title: Pandas read_csv ignoring column dtypes when I pass skip_footer arg
body: <p>When I try to import a csv file into a dataframe pandas (0.13.1) is ignoring the dtype parameter. Is there a way to stop pandas from inferring the data type on its own?</p>
  
  <p>I am merging several CSV files and sometimes the customer contains letters and pandas imports  as a string. When I try to merge the two dataframes I get an error because I'm trying to merge two different types. I need everything stored as strings.</p>
  
  <p>Data snippet:</p>
  
  <pre><code>|WAREHOUSE|ERROR|CUSTOMER|ORDER NO|
  |---------|-----|--------|--------|
  |3615     |     |03106   |253734  |
  |3615     |     |03156   |290550  |
  |3615     |     |03175   |262207  |
  |3615     |     |03175   |262207  |
  |3615     |     |03175   |262207  |
  |3615     |     |03175   |262207  |
  |3615     |     |03175   |262207  |
  |3615     |     |03175   |262207  |
  |3615     |     |03175   |262207  |
  </code></pre>
  
  <p>Import line:</p>
  
  <pre><code>df = pd.read_csv("SomeFile.csv", 
                   header=1,
                   skip_footer=1, 
                   usecols=[2, 3], 
                   dtype={'ORDER NO': str, 'CUSTOMER': str})
  </code></pre>
  
  <p><code>df.dtypes</code> outputs this:</p>
  
  <pre><code>ORDER NO    int64
  CUSTOMER    int64
  dtype: object
  </code></pre>