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[MUSIC]

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Howdy data analysts, my name is AJ.

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And I am an instructor at Treehouse.

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My pronouns include he and they.

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I'm excited to introduce you to a powerful
data visualization library called

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Seaborn.

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Before we get started, make sure you have
completed the prerequisite material for

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this course.

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You can find them in
the teacher's notes below.

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Seaborn is a rich data visualization
library that is built on

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top of the plotting library Matplotlib.

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It offers a set of high level tools for
creating statistical charts and plots.

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It is more convenient than Matplotlib for
quickly visualizing data,

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because it integrates well with
Pandas DataFrame objects.

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Recall that Pandas is a popular
data analysis library.

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And that a DataFrame represents tabular
data, like what you would find in a table,

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spreadsheet, or
a comma separated values CSV file.

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Seaborn will work with
Pandas DataFrames and

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convert them under the hood into
code that Matplotlib can understand.

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And Seaborn boasts a simplified interface
compared to Matplotlib, making it easier

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to create a wide variety of high
quality plots with fewer lines of code.

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In this introductory course, we will go
over three different families of plots

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built into Seaborn: relational,
distribution, and categorical plots.

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Relational plots are used for highlighting
the relationship between two variables.

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In Seaborn,
these are scatter plots and line plots.

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Distribution plots make it easy to
see the shape of a data set, and

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help understand how its
values are distributed.

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These include histograms and
kernel density estimates.

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Seaborn has three sub families of
categorical plots which help explore

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categorical data.

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Categorical data means variables
that are words instead of numbers.

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Seaborn has categorical scatter plots,
including strip and

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swarm plots, distribution plots,
which include box and violin plots,

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and estimation plots,
which include bar and count plots.

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Note that Seaborn includes more plots
than we will go over during this course.

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The Seaborn API documentation and the
tutorial are excellent starting points for

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getting to know all the different
kinds of available plots.

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You can also take a look at
the many additionally customized

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charts in the Seaborn gallery.

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In this course,

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I will guide you through an overview of
different plots available in Seaborn.

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Then I will be using data
from two Japanese games,

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Pokemon and Yugioh to perform
exploratory data analysis.

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I will have examples and challenges for
you based on these imported data sets.

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Are you ready?

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In the next series of instructions,

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we will begin with
an overview of Seaborn plots.

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I'll catch you on the other side!