I am planning to publish an article about 'how many votes are counted in each region' every one hour after the June election in Korea is over at 6 p.m. So at 7 p.m., at 8 p.m., at 9 p.m., and so on, I am going to publish an article with this infogram below. Plus, the stronger the color, the bigger percentage of total poll is counted.
So in my code below, the column 'ESRI_PK' will change every one hour, to be more precise will go up, since people will count more votes. (here, ESRI_PK plays the role of the 'vote counting rate', even though in reality it isn't.)
What I would like to do is to show how many votes are counted cumulatively as time passes by. However, the scenario on the left hand side('As-is') illustrates what my current code is doing. Even though the cumulative vote counting rate goes up, the region A has always the lightest color because out of the 3 regions, the A region has the lowest counting rate.
I wish my code would follow the scenario on the right hand side('To-be')! Unfortunately though, the current plotting method will only do the left hand side.
import geopandas as gpd import matplotlib.pyplot as plt plt.xkcd() seoul=gpd.read_file('../json/seoul_municipalities_geo.json') # You can download the same file from the Github below # https://github.com/southkorea/seoul-maps/tree/master/juso/2015/json final_pic=data_result.plot(figsize=(14,10),linewidth=0.25, edgecolor='black', column='ESRI_PK',cmap='Blues',scheme='quantiles',legend=True) for index,row in seoul.iterrows(): xy=row['geometry'].centroid.coords[:] xytext=row['geometry'].centroid.coords[:] plt.annotate(row['SIG_ENG_NM'],xy=xy, xytext=xytext, horizontalalignment='center',verticalalignment='center') plt.axis('off')