title: Boolean indexing with Numpy Array, tuples and Ellipsis
body: <p>I understand Numpy array boolean indexing concept with integers</p>
  
  <pre><code>In [95]: a=np.array([1, 2, 3, 2, 1, 2])
  
  In [96]: a==1
  Out[96]: array([ True, False, False, False,  True, False], dtype=bool
  
  In [97]: a[a==1]
  Out[97]: array([1, 1])
  </code></pre>
  
  <p>But I don't understand how comparison works with Numpy Array and tuple
  and so why</p>
  
  <pre><code>In [106]: a = np.array([(2011, 3), (2011, 3), (2011, 3), ..., (2015, 6), (2015, 6),
     (2015, 6), (2011, 3), (2011, 3)], dtype=object)
  </code></pre>
  
  <p>(...) being an Ellipsis</p>
  
  <p>returns</p>
  
  <pre><code>In [107]: a==(2011,3)
  Out[107]: False
  </code></pre>
  
  <p>I was expecting an array like</p>
  
  <pre><code>array([ True,  True,  True, False, False, False, False,  True,  True], dtype=bool)
  </code></pre>
  
  <p>How can I compare a Numpy Array with tuples to a tuple to get an array of boolean ?</p>
  
  <p>With Pandas that's quite simple</p>
  
  <pre><code>In [113]: pd.Series(a)==(2011, 3)
  Out[113]:
  0     True
  1     True
  2     True
  3    False
  4    False
  5    False
  6    False
  7     True
  8     True
  dtype: bool
  </code></pre>
  
  <p>But I'm looking for a pure Numpy solution.</p>