title: Speed up getting distance between two lat and lon
body: <p>I have two DataFrame containing Lat and Lon. I want to find distance from one <code>(Lat, Lon)</code> pair to <strong>ALL</strong> <code>(Lat, Lon)</code> from another DataFrame and get the minimum. The package that I am using <code>geopy</code>. The code is as follows:</p>
  
  <pre><code>from geopy import distance
  import numpy as np
  
  distanceMiles = []
  count = 0
  for id1, row1 in df1.iterrows():
      target = (row1["LAT"], row1["LON"])
      count = count + 1
      print(count)
      for id2, row2 in df2.iterrows():
          point = (row2["LAT"], row2["LON"])
          distanceMiles.append(distance.distance(target, point).miles)
  
      closestPoint = np.argmin(distanceMiles)
      distanceMiles = []
  </code></pre>
  
  <p>The problem is that <code>df1</code> has <code>168K</code> rows and <code>df2</code> has <code>1200</code> rows. How do I make it faster?</p>