title: How to call pandas read_csv() without it parsing date string
body: <p>I am working with some data that I download from the web in csv format.  The original data is shown as following.</p>
  
  <pre><code>Test Data
  "Date","T1","T2","T3","T4","T5","T6","T7","T8"
  "105/11/01","123,855","1,150,909","9.30","9.36","9.27","9.28","-0.06","60",
  "105/11/02","114,385","1,062,118","9.26","9.42","9.23","9.31","+0.03","78",
  "105/11/03","71,350","659,848","9.30","9.30","9.20","9.28","-0.03","42",
  </code></pre>
  
  <p>I use following code to read it</p>
  
  <pre><code>import pandas as pd
  df = pd.read_csv("test.csv", skiprows=[0], usecols=[0,3,4,5])
  </code></pre>
  
  <p>I have also tried to use</p>
  
  <pre><code>import pandas as pd
  df = pd.read_csv("test.csv", skiprows=[0], usecols=[0,3,4,5], keep_date_col=True)
  </code></pre>
  
  <p>I always get the following results</p>
  
  <pre><code>           Date    T3    T4   T5
  105/11/01  9.30  9.36  9.27  NaN
  105/11/02  9.26  9.42  9.23  NaN
  105/11/03  9.30  9.30  9.20  NaN
  </code></pre>
  
  <p>This is what I want to get</p>
  
  <pre><code>     Date    T3    T4    T5
  105/11/01  9.30  9.36  9.27
  105/11/02  9.26  9.42  9.23
  105/11/03  9.30  9.30  9.20
  </code></pre>
  
  <p>As you can see that pandas treat the date string not a part of the data and shift the index to one column left which cause the last column to be <code>NaN</code>.</p>
  
  <p>I have read the pandas document on <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer">read_csv()</a> and found it can parse date with <code>parse_dates</code>, <code>keep_date_col</code> parameters, but is there any way to NOT parse date as it is doing now?</p>