title: Specify dtype option on import or set low_memory=False
body: <p>I am using the following code:</p>
  
  <pre><code>df = pd.read_csv('/Python Test/AcquirerRussell3000.csv')
  </code></pre>
  
  <p>I have the following type of data: </p>
  
  <pre><code>18.07.2000  27.1875         0 08.08.2000  25.3125       0.1 05.09.2000  \
  0   19.07.00  26.6250 -0.020690   09.08.00  25.2344 -0.003085   06.09.00   
  1   20.07.00  26.6250  0.000000   10.08.00  25.1406 -0.003717   07.09.00   
  2   21.07.00  25.6875 -0.035211   11.08.00  25.5781  0.017402   08.09.00   
  3   24.07.00  26.2500  0.021898   14.08.00  25.4375 -0.005497   11.09.00   
  4   25.07.00  26.6875  0.016667   15.08.00  25.5625  0.004914   12.09.00 
  </code></pre>
  
  <p>I am getting the following error:</p>
  
  <pre><code>Pythone Test/untitled0.py:1: DtypeWarning: Columns (long list of numbers) have mixed types. 
  Specify dtype option on import or set low_memory=False.
  </code></pre>
  
  <p>So every 3rd column is a date the rest are numbers. I guess there is no single dtype since dates are strings and the rest is a float or int? I have about 5000 columns or more and around 400 rows.</p>
  
  <p>I have seen similar questions to this but dont quite know how to apply this to my data. Furthermore I want to run the following code after to stack the data frame.</p>
  
  <pre><code>a = np.arange(len(df.columns))
  df.columns = [a % 3, a // 3]
  df = df.stack().reset_index(drop=True)
  
  df.to_csv('AcquirerRussell3000stacked.csv', sep=',')
  </code></pre>
  
  <p>What dtype should I use? Or should I just set low_memory to false?</p>