title: Replace row of a pandas dataframe with numpy array
body: <p>I have a pandas dataframe, for example:</p>
  
  <pre><code>import sys
  if sys.version_info[0] &lt; 3: 
      from StringIO import StringIO
  else:
      from io import StringIO
  
  import pandas as pd
  
  TESTDATA = StringIO("""col1;col2;col3
      (1,2);(3,4);(5,6)
      (7,8);(9,10);(11,12)
      (13,14);(15,16);(17,18)
      (19,20);(21,22);(23,24)
      """)
  
  df = pd.read_csv(TESTDATA, sep=";")
  </code></pre>
  
  <p>dataframe looks like this:</p>
  
  <pre><code>          col1     col2     col3
  0        (1,2)    (3,4)    (5,6)
  1        (7,8)   (9,10)  (11,12)
  2      (13,14)  (15,16)  (17,18)
  3      (19,20)  (21,22)  (23,24)
  </code></pre>
  
  <p>Now, let's say there's 2 rows 0 and 2 that I wish to replace with a numpy array of zeros such as:</p>
  
  <pre><code>np.zeros((1,3,2))
  """array([[[ 0.,  0.],
          [ 0.,  0.],
          [ 0.,  0.]]])"""
  </code></pre>
  
  <p>So the final result is that for the rows 0 and 2, I have tuples (0,0), (0,0), and (0,0). Could someone propose an approach?</p>