title: Python - Pandas: AttributeError: 'numpy.ndarray' object has no attribute 'start'
body: <p>Python - Pandas: AttributeError: 'numpy.ndarray' object has no attribute 'start'</p>
  
  <p>Code that generates the error:</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  from datetime import time
  
  data = pd.read_csv('/temp/zondata/pvlog.csv', delimiter=';', parse_dates=True, index_col=1)
  Gewenst = data['T_Sanyo_Open']
  </code></pre>
  
  <p>Gives no errors, and gives a result like the table at the bottom of this question: </p>
  
  <pre><code>Gewenst['2010']
  </code></pre>
  
  <p>or</p>
  
  <pre><code>Gewenst['2010-09']
  </code></pre>
  
  <p>Gives me previously described error when I want a specific bounded range of data:</p>
  
  <pre><code>Gewenst['2010-9':'2010-10']
  </code></pre>
  
  <p>I found a solution when I add this rule of code: </p>
  
  <pre><code>Gewenst = Gewenst.resample('1Min', fill_method='ffill') 
  </code></pre>
  
  <p>But I don't want to resample the data.</p>
  
  <p>table:</p>
  
  <pre><code>2010-08-31 12:36:53    30.37
  2010-08-31 12:45:08    28.03
  2010-08-31 12:55:09    25.16
  2010-08-31 13:00:09    23.28
  2010-08-31 13:05:09    22.37
  2010-08-31 13:10:09    21.84
  2010-08-31 13:15:08    22.19
  2010-08-31 13:20:09    22.41
  2010-08-31 13:25:09    23.16
  2010-08-31 13:35:09    23.59
  2010-08-31 13:40:09    26.75
  2010-08-31 13:45:09    29.47
  2010-08-31 13:50:10    33.13
  2010-08-31 13:55:08    35.91
  2010-08-31 14:00:08    37.78
  ...
  2013-06-07 01:35:10    40.00
  2013-06-07 01:40:10    40.00
  2013-06-07 01:45:10    39.50
  2013-06-07 01:50:12    39.75
  2013-06-07 01:55:10    39.25
  2013-06-07 02:00:10    39.50
  2013-06-07 02:05:11    39.25
  2013-06-07 02:10:11    39.25
  2013-06-07 02:15:10    38.75
  2013-06-07 02:20:11    38.75
  2013-06-07 02:25:11    38.75
  2013-06-07 02:30:10    39.25
  2013-06-07 02:40:10    39.25
  2013-06-07 02:45:10    39.00
  2013-06-07 02:50:10    39.25
  </code></pre>
  
  <p>Does anyone have solution, or is this a bug in Pandas?</p>