**Stacked bar charts** are popular in cases when we want to expand our vision to multiple categories. For example, in the previous task, we may compare two Indian cities by placing the second bar right above the first one. In this case, the bar height will be the sum of two bars values.

To build a stacked bar chart we need to call the `.bar()` function as many times as many categories we consider. Each next `.bar()` function should have a `bottom` parameter with the y-axis values of the lower bar assigned. For example, let's represent countries' GDP by sector composition.

# Import library
import matplotlib.pyplot as plt

# Create data for chart
countries = ['United States', 'India', 'Brazil']
agricultural = [333600, 1458996, 214368]
industrial = [3722590, 2179020, 672336]
services = [15592000, 5826510, 2361296]

# Create Axes and Figure objects
fig, ax = plt.subplots()

# Initialize bar chart
ax.bar(countries, agricultural, label = 'Agricultural')
ax.bar(countries, industrial, label = 'Industrial', bottom = agricultural)
ax.bar(countries, services, label = 'Services', bottom = industrial)

# Display the plot
plt.legend()
plt.show()

In this section, we will be introduced to the very basics of matplotlib - creating empty Figure and Axes, how to build a line plot, display multiple lines on one plot, add a legend, customize line type, and so on...

In this section, we will learn about other chart types, such as bar plots, histograms, box plots, how to set necessary parameters and customize them.

In this section, we will learn the scatter plots - one of the easiest ways to discover the relationships in your data.