Reverse the colormap.

README.md

@@ -156,7 +156,7 @@ have them you can run:
 
 ![alt-text][large-geoplot]
 
-[large-geoplot]: https://i.imgur.com/glZonpD.png
+[large-geoplot]: https://i.imgur.com/BJROapk.png
 
 If no geographical context can be provided, `geoplot` will compute a
 [quadtree](https://en.wikipedia.org/wiki/Quadtree) nullity distribution, as above, which splits the dataset into
@@ -177,15 +177,15 @@ If you can specify a geographic grouping within the dataset, you can plot your d
 
 ![alt-text][hull-geoplot]
 
-[hull-geoplot]: https://i.imgur.com/RALL9d9.png
+[hull-geoplot]: https://i.imgur.com/osnPwEE.png
 
 Convex hulls are usually more interpretable than the quadtree, especially when the underlying dataset is relatively 
 small (as this one is). We again see a data nullity distribution that's seemingly at random, giving us confidence 
 that the nullity of collision records is not geographically variable.
 
 The `msno.geoplot` chart type extends the `aggplot` function in the `geoplot` package, and accepts keyword arguments 
 to the latter as parameters. [The `geoplot` documentation provides further details](https://residentmario.github.io/geoplot/index.html) 
-(including how to pick [a better map projection](https://i.imgur.com/KSryo6o.png)). For more advanced configuration 
+(including how to pick [a better map projection](https://i.imgur.com/0aaNa9Q.png)). For more advanced configuration 
 details for the rest of the plot types, refer to the `CONFIGURATION.md` file in this repository.
 
 That concludes our tour of `missingno`!

missingno/missingno.py

@@ -427,7 +427,7 @@ def dendrogram(df, method='average',
 def geoplot(df,
             filter=None, n=0, p=0, sort=None,
             x=None, y=None, figsize=(25, 10), inline=False,
-            by=None, cmap='YlGn_r', **kwargs):
+            by=None, cmap='YlGn', **kwargs):
     """
     Generates a geographical data nullity heatmap, which shows the distribution of missing data across geographic
     regions. The precise output depends on the inputs provided. If no geographical context is provided, a quadtree
@@ -445,7 +445,7 @@ def geoplot(df,
     :param y: The variable in the dataset containing the y-coordinates of the dataset.
     :param by: If specified, plot in convex hull mode, using the given column to cluster points in the same area. If
     not specified, plot in quadtree mode.
-    :param cmap: The colormap to display the data with. Defaults to `YlGn_r`.
+    :param cmap: The colormap to display the data with. Defaults to `YlGn`.
     :param inline: Whether or not the figure is inline. If it's not then instead of getting plotted, this method will
     return its figure.
     :param kwargs: Additional keyword arguments are passed to the underlying `geoplot` function.
@@ -458,7 +458,7 @@ def geoplot(df,
     df = nullity_filter(df, filter=filter, n=n, p=p)
     df = nullity_sort(df, sort=sort)
 
-    nullity = df.isnull().sum(axis='columns') / df.shape[1]
+    nullity = df.notnull().sum(axis='columns') / df.shape[1]
     if x and y:
         gdf = gpd.GeoDataFrame(nullity, columns=['nullity'],
                                geometry=df.apply(lambda srs: Point(srs[x], srs[y]), axis='columns'))