title: Pandas DataFrame Index by Month?
body: <p>Not sure if this is possible using pandas.  However I would like to make a DataFrame as follows.<br>
  Except I only want to have months and days in the index without years.</p>
  
  <pre><code>import pandas as pd
  import numpy as np 
  df2 = pd.DataFrame(np.random.randn(12, 4), index=pd.date_range('1-1', periods=12, freq='M'), columns=['2007', '2008', '2009', '2010'])
  </code></pre>
  
  <p>Just to give a little more info.  I have done the following.  </p>
  
  <pre><code>df = pd.Series(np.random.randn(72), index=pd.date_range('1/1/2000', periods=72, freq='M'))
  </code></pre>
  
  <p>Then I can use grouby as follows:</p>
  
  <pre><code>groupYear_Month = df.groupby(lambda x: (x.year, x.month)).sum()
  </code></pre>
  
  <p>Which yields:</p>
  
  <pre><code>groupYear_Month.head()
  Out[9]: 
  (2000, 1)    1.077949
  (2000, 2)   -0.563224
  (2000, 3)   -2.016833
  (2000, 4)   -0.140693
  (2000, 5)    2.113549
  dtype: float64
  </code></pre>
  
  <p>Now I can:</p>
  
  <pre><code>groupYear_Month.index = pd.MultiIndex.from_tuples(groupYear_Month.index)
  </code></pre>
  
  <p>However, this kills the date format.  For example I don't get a two month 01, 02 ... 12.<br>
  I can unstack it now and get the years at the column level.  </p>
  
  <pre><code>groupYear_Month.unstack(0)
  </code></pre>
  
  <p>This works but it is no longer a date index.  </p>
  
  <p>Thanks</p>