Welcome to the course! In this course, we will learn the `matplotlib` library, probably, one of the most popular visualization tools for Python. Within the course, we will focus on `pyplot` - state-based interface to `matplotlib`.

>Note
>
>Common practice is using the `plt` alias for `matplotlib.pyplot`. This is not compulsory, but widely used by Python community, so I recommend you to follow this rule.

The basic concept of building plots in `matplotlib` is using two objects: **Figure** as a space for building your graph and **Axes** - an area where points can be specified (usually in terms of x-y coordinates, but also in a 3D plot, for example, and so on).

To create both **Figure** and **Axes** objects, you can use the `.subplots()` function from `matplotlib.pyplot` using multiple assignment (we need to create two variables: for `Figure` and `Axes` objects). That's why using an alias is a good practice. Finally, after assigning `Figure` and `Axes` objects to certain variables, we can initialize an empty plot by applying the `.plot()` function to the `Axes` object. Finally, we use `plt.show()` to display the plot.

# Import the library
import matplotlib.pyplot as plt

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

# Initialize an empty plot
ax.plot()

# 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

Creating an Empty Plot