title: Pandas: cannot safely convert passed user dtype of int32 for float64
body: <p>I am stumped by a problem with loading my data into a Pandas dataframe using <code>read_table()</code>. The error says <code>TypeError: Cannot cast array from dtype('float64') to dtype('int32') according to the rule 'safe'</code> and <code>ValueError: cannot safely convert passed user dtype of int32 for float64 dtyped data in column 2</code></p>
  
  <p>test.py:</p>
  
  <pre><code>import numpy as np
  import os
  import pandas as pd
  
  # put test.csv in same folder as script
  mydir = os.path.dirname(os.path.abspath(__file__))
  csv_path = os.path.join(mydir, "test.csv")
  
  df = pd.read_table(csv_path, sep=' ',
                     comment='#',
                     header=None,
                     skip_blank_lines=True,
                     names=["A", "B", "C", "D", "E", "F", "G"],
                     dtype={"A": np.int32,
                         "B": np.int32,
                         "C": np.float64,
                         "D": np.float64,
                         "E": np.float64,
                         "F": np.float64,
                         "G": np.int32})
  </code></pre>
  
  <p>test.csv:</p>
  
  <p><code>2270433 3 21322.889 11924.667 5228.753 1.0 -1 
  2270432 3 21322.297 11924.667 5228.605 1.0 2270433
  </code></p>