15. Seaborn Regression Plots | Data Visualization with Python | Tech2Teach
In this video, we will learn about a new visualization library in Python, which is
Seaborn. Although Seaborn is another data
visualization library, it is actually based on Matplotlib.
It was built primarily to provide a high-level interface for drawing
attractive statistical graphics, such as regression plots, box plots, and so on.
Seaborn makes creating plots very efficient. Therefore with Seaborn you
can generate plots with code that is 5 times less than with Matplotlib. Let's
see how we can use Seaborn to create a statistical graphic. Let's look into
regression plots. Let's say we have a dataframe called df_total of
total immigration to Canada from 1980 to 2013 with the year in one column and the
corresponding total immigration in another column, and say we're interested
in creating a scatter plot along with a regression line to highlight any trends
in the data. With Seaborn, you can do all this with literally one line of code. The
way to do this, we first import Seaborn and let's import it as sns. Then, we call
the Seaborn regplot function. We basically tell it to use the dataframe
df_total and to plot the column year on the horizontal axis and
the column total on the vertical axis. And the output of this one line of code
is a scatter plot with a regression line and not just that, but also 95%
confidence interval. Isn't that really amazing? Seaborn's regplot function
also accepts additional parameters for any personal customization. So you can
change the color for example using the color parameter. Let's go ahead and
change the color to green. Also, you can change the marker shape as well using
the marker parameter. Let's go ahead and change the shape of our markers to a +
marker instead of the default circular marker. In the lab session, we
explore regression plots with Seaborn in more details, so
make sure to complete this module's lab session. And with this we conclude our
short introduction to Seaborn and regression plots. I'll see you in the
next video.
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