title: scipy.stats.norm for array of values
body: <p>I'd like to calculate the probability values for an array of values, using Gaussians with different means and variances. For e.g., I can do this</p>
  
  <pre><code>scipy.stats.norm.pdf(9, [10, 12, 14], [2, 4, 5])
  </code></pre>
  
  <p>and it gives me the pdf values for the value 9, for the three different Gaussians - N(10, 2), N(12, 4) and N(14, 5). I'd like to do the same for multiple values, like so</p>
  
  <pre><code>scipy.stats.norm.pdf([8,9], [10, 12, 14], [2, 4, 5])
  </code></pre>
  
  <p>where the pdf values for 8 and 9 are calculated for the three Gaussians and I'd get a 2D array as a return. </p>
  
  <p>Questions</p>
  
  <ol>
  <li>Is there anything in numpy or scipy that'll allow me to do this?</li>
  <li>If the answer to #1 is no, how can I accomplish this without using a for loop (please note, I looked at vectorize in scipy, but it uses a for loop under the hood). I need to avoid a for loop since I'll be doing this on millions of values, for several Gaussians.</li>
  </ol>