So far so good! You may remember that blue line refers to the first call of `.plot()` function, and the oragne line refers to the second call. But if someone other than you look on the chart, he will understand nothing. In this chapter, we will consider how to add a **legend** to a plot.

The easiest way to create a legend is to 'set' names while initializing plot. This can be done by setting the `label` parameter of `.plot()` function. After the labels are set, we can call `plt.legend()` before `plt.show()` to display the legend on the plot. For example, the code from the previous example can be modified in the following way:

# Import the libraries
import matplotlib.pyplot as plt
import pandas as pd

# Load the data
data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/ed80401e-2684-4bc4-a077-99d13a386ac7/co2.csv', index_col = 0)

# Filter the data
ita = data.loc['Italy'] 
swe = data.loc['Sweden']

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

# Initialize lines
ax.plot(ita.index.astype(int), ita.values, label = 'Italy') 
ax.plot(swe.index.astype(int), swe.values, label = 'Sweden')

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

Visualization is one of the most common ways of representing data. By using different kinds of plots (like scatter-plot, histogram, bar charts, and so on) you can find some insights in your data, or approve/reject some assumption/hypothesis. In this course, you will be introduced to the matplotlib library, and learn how to build different charts.

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.

Adding Legend to a Plot

Visualization in Python with matplotlib

Adding Legend to a Plot