I believe that area charts are especially useful when used through faceting. It allows to quickly find out the different patterns existing in the data. This example relies on a pandas data frame. We have 2 numerical variables (year and value of something), and a categorical variable (the country). The area charts are created using the fill_between function of matplotlib. The faceting is made using the awesome FacetGrid utility of seaborn.
# libraries import numpy as np import seaborn as sns import pandas as pd import matplotlib.pyplot as plt # Create a dataset my_count=["France","Australia","Japan","USA","Germany","Congo","China","England","Spain","Greece","Marocco","South Africa","Indonesia","Peru","Chili","Brazil"] df = pd.DataFrame({ "country":np.repeat(my_count, 10), "years":range(2000, 2010) * 16, "value":np.random.rand(160) }) # Create a grid : initialize it g = sns.FacetGrid(df, col='country', hue='country', col_wrap=4, ) # Add the line over the area with the plot function g = g.map(plt.plot, 'years', 'value') # Fill the area with fill_between g = g.map(plt.fill_between, 'years', 'value', alpha=0.2).set_titles("{col_name} country") # Control the title of each facet g = g.set_titles("{col_name}") # Add a title for the whole plo plt.subplots_adjust(top=0.92) g = g.fig.suptitle('Evolution of the value of stuff in 16 countries') plot.show()