title: Data type conversion issue in Pandas Dataframe
body: <p>I have a csv file that looks like this from a !cat</p>
  
  <pre><code>,City,region,Res_Comm,mkt_type,Quradate,National_exp,Alabama_exp,Sales_exp,Inventory_exp,Price_exp,Credit_exp
  0,Dothan,South_Central-Montgomery-Auburn-Wiregrass-Dothan,Residential,Rural,2010-01-15,2,2,3,2,3,3
  1,Dothan,South_Central-Montgomery-Auburn-Wiregrass-Dothan,Residential,Suburban_Urban,2010-07-15,2,2,3,2,2,2
  2,Dothan,South_Central-Montgomery-Auburn-Wiregrass-Dothan,Residential,Suburban_Urban,2011-01-15,2,2,2,2,2,2
  </code></pre>
  
  <p>When I read it in via a read_csv I get a dataframe all of the ..._exp fields are single digit numbers that I need to do basic math with (It was working great when I was using read-table with another variant of the file)</p>
  
  <pre><code>df = pd.io.parsers.read_csv('/home/tom/Dropbox/Projects/annonallanswerswithmaster1012013.csv',index_col=0,parse_dates=['Quradate'])
  </code></pre>
  
  <p>But when I go to do any math I get a type error indicating the column is string eg:</p>
  
  <pre><code>df['Credit_exp'] = df['Credit_exp']/2
  TypeError: unsupported operand type(s) for /: 'str' and 'int'
  </code></pre>
  
  <p>I don't see how to convert or get it as a int?
  I tried specifying field types like ,dtype={'Credit_exp': np.int32, ... in the file read options,, it did not like that 
  and I tried to do a type conversion like
  df['Credit_exp'] = int(df['Credit_exp'])
  Which just gave me:</p>
  
  <pre><code>TypeError: only length-1 arrays can be converted to Python scalars
  </code></pre>
  
  <p>So there is something obvious I'm missing...</p>