geopandas: how to "normalize" geoseries
<p>I have a GeoDataFrame with some polygons (from some County shape files). </p>

<pre><code>0       POLYGON ((-118.71455 46.822508, -118.67662 46....
1       POLYGON ((-118.663169 47.043673, -118.66334 47...
2       POLYGON ((-118.484262 46.73667, -118.495995 46...
3       POLYGON ((-118.483051 46.839378, -118.482689 4...
4       POLYGON ((-118.82874 46.77891, -118.822177 46....
5       POLYGON ((-118.606476 46.96391999999999, -118....
</code></pre>

<p>I'm trying to do take some intersections of these polygons with some polygons in another GeoDataFrame (same state). The coordinates of the polygons, however, are completely different.</p>

<pre><code>0       POLYGON ((552346.2856999999 380222.8998000007,...
1       POLYGON ((529754.7249999996 409135.9135999996,...
2       POLYGON ((552333.0957000004 371726.9315000009,...
3       POLYGON ((542734.0181999998 376530.5831000004,...
4       POLYGON ((552291.6204000004 362052.4251000006,...
</code></pre>

<p>They're the same state, and the plots look the same. I'm new to shapefiles, but think if I could "normalize" these polygons (divide all the points in the x dimension by their smallest value, same for the y dimension), then the polygons in the two DataFrames should be comparable.</p>

<p>I've tried:</p>

<pre><code>df.scale(origin=(1,1))
</code></pre>

<p>and also</p>

<pre><code>df.scale(xfact=1.0/df.total_bounds[0], yfact=1.0/df.total_bounds[1])
</code></pre>

<p>without any luck. They just return their original values (or close to, values should be between 1 and 2). I could loop through these polygons and unpack their points and divide them, but I am hoping there's a more elegant solution way.</p>

<p>Running the latest dev version of geopandas 0.1.0, python 2.7 (I know...time to move on), OSX. </p>