Quite good! That's a good visualization! Let's expand our chart with new data.

We may want to compare the emission levels by displaying several lines on a single chart. It can be easily done by calling the `.plot()` method of `Axes` object multiple times. For instance, if you call `ax.plot()` two times, you will get two lines on one chart. By default, the first line will be blue, the second one will be orange.

# 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) 
ax.plot(swe.index.astype(int), swe.values)

# Display the plot
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.

Visualization in Python with matplotlib

Adding One More Line

Visualization in Python with matplotlib

Adding One More Line