Additionally, we can customize more line parameters, such as **color**, **line style**, **points style**. These are the simplest, but the `.plot()` function has many more parameters available to set (you can read about this in the documentation). Let's consider all of the mentioned parameters:




For example, let's modify our example plot even more!

# 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', color = 'green', marker = '1', linestyle = '--') 
ax.plot(swe.index.astype(int), swe.values, label = 'Sweden', color = 'blue', marker = 'v', linestyle = ':')

# Add axis labels
ax.set_xlabel('Year')
ax.set_ylabel('CO2 emission level (tonnes per person)')

# Display the legend, title, and plot
plt.legend()
plt.title('CO2 Emission Levels in Italy and Sweden')
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

Changing Line Style