title: fastest way to use numpy.interp on a 2-D array
body: <p>I have the following problem. I am trying to find the fastest way to use the interpolation method of numpy on a 2-D array of x-coordinates.</p>
  
  <pre><code>import numpy as np
  
  xp = [0.0, 0.25, 0.5, 0.75, 1.0]
  
  np.random.seed(100)
  x = np.random.rand(10)
  fp = np.random.rand(10, 5)
  </code></pre>
  
  <p>So basically, <code>xp</code> would be the x-coordinates of the data points, <code>x</code> would be an array containing the x-coordinates of the values I want to interpolate, and <code>fp</code> would be a 2-D array containing y-coordinates of the datapoints.</p>
  
  <pre><code>xp
  [0.0, 0.25, 0.5, 0.75, 1.0]
  
  x
  array([ 0.54340494,  0.27836939,  0.42451759,  0.84477613,  0.00471886,
          0.12156912,  0.67074908,  0.82585276,  0.13670659,  0.57509333])
  
  fp
  array([[ 0.89132195,  0.20920212,  0.18532822,  0.10837689,  0.21969749],
         [ 0.97862378,  0.81168315,  0.17194101,  0.81622475,  0.27407375],
         [ 0.43170418,  0.94002982,  0.81764938,  0.33611195,  0.17541045],
         [ 0.37283205,  0.00568851,  0.25242635,  0.79566251,  0.01525497],
         [ 0.59884338,  0.60380454,  0.10514769,  0.38194344,  0.03647606],
         [ 0.89041156,  0.98092086,  0.05994199,  0.89054594,  0.5769015 ],
         [ 0.74247969,  0.63018394,  0.58184219,  0.02043913,  0.21002658],
         [ 0.54468488,  0.76911517,  0.25069523,  0.28589569,  0.85239509],
         [ 0.97500649,  0.88485329,  0.35950784,  0.59885895,  0.35479561],
         [ 0.34019022,  0.17808099,  0.23769421,  0.04486228,  0.50543143]])
  </code></pre>
  
  <p>The desired outcome should look like this:</p>
  
  <pre><code>array([ 0.17196795,  0.73908678,  0.85459966,  0.49980648,  0.59893702,
          0.9344241 ,  0.19840596,  0.45777785,  0.92570835,  0.17977264])
  </code></pre>
  
  <p>Again, looking for the fastest way to do cause this is a simplified version of my problem, which has a length of about 1 million versus 10.</p>
  
  <p>Thanks</p>