title: Boolean mask from pandas datetime index using .loc accessor
body: <p>Asumme the following toy code:</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  rng = pd.date_range('1/1/2011', periods=72, freq='H')
  avec = np.random.rand(len(rng))
  bvec = np.random.rand(len(rng))
  df = pd.DataFrame({"A":avec,"B":bvec}, index=rng)
  </code></pre>
  
  <p>I can now select a part of the time interval with</p>
  
  <pre><code>df.loc["2011-01-02",:]
  </code></pre>
  
  <p>Is there a way to efficiently access the boolean mask that corresponds to the resulting slice, i.e:</p>
  
  <pre><code>array([False, False, False, False, False, False, False, False, False,
         False, False, False, False, False, False, False, False, False,
         False, False, False, False, False, False,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True, False, False, False, False, False, False,
         False, False, False, False, False, False, False, False, False,
         False, False, False, False, False, False, False, False, False], dtype=bool)
  </code></pre>
  
  <p>I have tried the suggestions in <a href="https://stackoverflow.com/questions/44162585/filtering-and-comparing-dates-with-pandas">an earlier stackoverflow answer</a> but <code>df.index.date</code> takes a very long time to run on my dataset...</p>