title: Using dask.bag.from_sequence to calculate kernel-density estimate
body: <p>I am trying to calculate the kernel-density estimate of a fairly large two-dimensional dataset to <a href="https://stackoverflow.com/a/20107592/1534504">colour the points in a scatter plot</a>. The function <code>scipy.stats.gaussian_kde</code> takes a long time so I figured I could use dask (v0.15.2) to get the result faster. However, I am unsure if my approach actually got any speed-up. Here is an example:</p>
  
  <pre><code>import numpy as np
  from scipy.stats import gaussian_kde
  import dask.bag as db
  
  xy = np.random.rand(2, 1000000)
  kde = gaussian_kde(xy)
  
  chunker = (xy[:, i:i+10000] for i in range(100))
  
  compute_job = db.from_sequence(chunker).map(kde)
  
  results = compute_job.compute()
  z = np.hstack(results)
  </code></pre>
  
  <p>This takes over 60hrs to complete on a quad-core Xeon E5-2609 @ 2.4Hz with a dataset of 2,677,920 coordinate pairs. Am I using dask correctly?</p>