  Course Content

# Ultimate Visualization with Python

1. Matplotlib Introduction

2. Creating Commonly Used Plots

4. More Statistical Plots

5. Plotting with Seaborn

Ultimate Visualization with Python

##   Scatter Plot

You did a great job learning about line plots, so now it will be much easier for you to dive into scatter plots.

A scatter plot is simply a plot which represents a relationship between two variables (x and y) using dots or other markers. Creating a scatter plot is perhaps the simplest way to check if two variables are correlated (not the most precise, but still can give some insight).

It is similar to a line plot, except for the fact that it has no lines, only markers. In order to create a scatter plot, all you have to do is to use a `scatter()` function of the `pyplot`, passing first values for x-axis, then values for y-axis. Let’s have a look at an example:  The `scatter()` function's syntax resembles that of `plot()`. However, unlike `plot()`, you must always provide values for both `x` and `y` parameters.

In our case, the y values are determined linearly by the formula `y = 2x + 5`. Our scatter plot visually illustrates the positive linear relationship between these two variables: `y` increases with increasing `x` and decreases with decreasing `x`.

It is also possible to set other markers instead of dots and set their size using `marker` and `s` parameters respectively:  Here we used `'x'` as markers instead of `'o'` (dots) by default and set their size to 100. Feel free to experiment with the `s` (size) parameter. We will focus more on plot customization in the next section, but, as for now, you can use `scatter()` function documentation to explore more.

Plotting multiple scatter plots can be accomplished simply by calling the `scatter()` function twice with different `x` and `y` arguments (similarly to line plots).

1. Replace the underscores, so that `y` array contains squared elements of the `x` array.
3. Pass `x` and `y` in this function in the correct order. 