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Seaborn is a rich data visualization library that is built on top of the plotting library, Matplotlib.
Advantages of Seaborn vs Matplotlib
- Integrates well with Pandas Dataframe objects
- Simplified interface, easier to read and write
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[MUSIC]
0:00
Howdy data analysts, my name is AJ.
0:09
And I am an instructor at Treehouse.
0:12
My pronouns include he and they.
0:15
I'm excited to introduce you to a powerful
data visualization library called
0:18
Seaborn.
0:23
Before we get started, make sure you have
completed the prerequisite material for
0:25
this course.
0:29
You can find them in
the teacher's notes below.
0:31
Seaborn is a rich data visualization
library that is built on
0:34
top of the plotting library Matplotlib.
0:38
It offers a set of high level tools for
creating statistical charts and plots.
0:41
It is more convenient than Matplotlib for
quickly visualizing data,
0:48
because it integrates well with
Pandas DataFrame objects.
0:52
Recall that Pandas is a popular
data analysis library.
0:57
And that a DataFrame represents tabular
data, like what you would find in a table,
1:01
spreadsheet, or
a comma separated values CSV file.
1:09
Seaborn will work with
Pandas DataFrames and
1:13
convert them under the hood into
code that Matplotlib can understand.
1:16
And Seaborn boasts a simplified interface
compared to Matplotlib, making it easier
1:22
to create a wide variety of high
quality plots with fewer lines of code.
1:28
In this introductory course, we will go
over three different families of plots
1:34
built into Seaborn: relational,
distribution, and categorical plots.
1:40
Relational plots are used for highlighting
the relationship between two variables.
1:48
In Seaborn,
these are scatter plots and line plots.
1:53
Distribution plots make it easy to
see the shape of a data set, and
1:58
help understand how its
values are distributed.
2:02
These include histograms and
kernel density estimates.
2:06
Seaborn has three sub families of
categorical plots which help explore
2:10
categorical data.
2:15
Categorical data means variables
that are words instead of numbers.
2:18
Seaborn has categorical scatter plots,
including strip and
2:23
swarm plots, distribution plots,
which include box and violin plots,
2:27
and estimation plots,
which include bar and count plots.
2:32
Note that Seaborn includes more plots
than we will go over during this course.
2:37
The Seaborn API documentation and the
tutorial are excellent starting points for
2:43
getting to know all the different
kinds of available plots.
2:50
You can also take a look at
the many additionally customized
2:55
charts in the Seaborn gallery.
3:00
In this course,
3:02
I will guide you through an overview of
different plots available in Seaborn.
3:03
Then I will be using data
from two Japanese games,
3:08
Pokemon and Yugioh to perform
exploratory data analysis.
3:12
I will have examples and challenges for
you based on these imported data sets.
3:18
Are you ready?
3:23
In the next series of instructions,
3:24
we will begin with
an overview of Seaborn plots.
3:27
I'll catch you on the other side!
3:31
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