title: Convert pandas column of numpy arrays to numpy array of higher dimension
body: <p>I have a pandas dataframe of shape <code>(75,9)</code>.</p>
  
  <p>Only one of those columns is of numpy arrays, each of which is of shape <code>(100, 4, 3)</code></p>
  
  <p>I have a strange phenomenon:</p>
  
  <pre><code>data = self.df[self.column_name].values[0]
  </code></pre>
  
  <p>is of shape <code>(100,4,3)</code>, but</p>
  
  <pre><code>data = self.df[self.column_name].values
  </code></pre>
  
  <p>is of shape (75,), with <code>min</code> and <code>max</code> are 'not a numeric object'</p>
  
  <p>I expected <code>data = self.df[self.column_name].values</code> to be of shape (75, 100, 4, 3), with some <code>min</code> and <code>max</code>.</p>
  
  <p><strong>How can I make a column of numpy arrays behave like a numpy array of a higher dimension</strong> (with length=number of rows in the dataframe)?</p>
  
  <hr>
  
  <p>Reproducing:</p>
  
  <pre><code>    some_df = pd.DataFrame(columns=['A'])
      for i in range(10):
          some_df.loc[i] = [np.random.rand(4, 6)]
      print some_df['A'].values.shape
      print some_df['A'].values[0].shape
  </code></pre>
  
  <p>prints <code>(10L,)</code>,<code>(4L,6L)</code>  instead of desired <code>(10L, 4L, 6L)</code>,<code>(4L,6L)</code></p>