Sometimes, we may want to shade the area under the line plot with color for a greater visual impact. This can be achieved via the fill_between class:

fill_between(x, y1, y2=0, where=None, interpolate=False, step=None)

By default, fill_between shades the region between y=0 and the line when y2 is not specified. More complex shading behavior can be specified using the where, interpolate, and step keyword arguments. Readers can refer to the following link for more information: https://matplotlib.org/examples/pylab_examples/fill_between_demo.html.

Let's try to plot a more detailed chart by separating the two genders. We are going to explore the relative contribution of males and females towards the population growth. To do that, we can prepare a stacked area chart using the stackplot class:

# Select the aggregated population data from the world for each gender,
# during 1950 to 2017.
male_data = data[(data.Location == 'WORLD') & (data.Sex == 'Male') & (data.Time <= 2017) ]
female_data = data[(data.Location == 'WORLD') & (data.Sex == 'Female') & (data.Time <= 2017) ]

# Calculate aggregated population data across all age groups for each year 
# Set as_index=False to avoid the Time variable to be used as index
grouped_male_data = male_data.groupby('Time', as_index=False).sum()
grouped_female_data = female_data.groupby('Time', as_index=False).sum()

# Create two subplots with shared y-axis (sharey=True)
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,4), sharey=True)

# Generate a simple line plot of population vs time,
# then shade the area under the line in sky blue.
ax1.plot(grouped_data.Time, grouped_data.Value)
ax1.fill_between(grouped_data.Time, grouped_data.Value, color='skyblue')

# Use set_xlabel() or set_ylabel() instead to set the axis label of an
# axes object
ax1.set_xlabel('Year')
ax1.set_ylabel('Population (thousands)')

# Generate a stacked area plot of population vs time
ax2.stackplot(grouped_male_data.Time, grouped_male_data.Value, grouped_female_data.Value)

# Add a figure legend
ax2.legend(['Male', 'Female'], loc='upper left')

# Set the x-axis label only this time
ax2.set_xlabel('Year')
plt.show()