Great! Let's continue improving our plot with some customizing stuff.

Couple more things we can add to our charts are custom **labels** on the axis and **title** for the entire plot. To add labels, use `.set_xlabel('text')` and `.set_ylabel('text')` functions applied to `Axes` object to set custom (`'text'`) labels on the x-axes or y-axes, respectively.

To set the plot title use the `.title('text')` method applied to `matplotlib.pyplot` (usually imported as `plt`). For example, let's add axes labels and title to the example plot we've built.

# 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')

# 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()

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.