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You have completed Introduction to Data Visualization with Matplotlib!
You have completed Introduction to Data Visualization with Matplotlib!
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Let's briefly discuss some of the use cases and high-level features of matplotlib.
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[MUSIC]
0:00
Hi, I'm Ken.
0:09
I'm excited to introduce you to
the basics of data visualization,
0:10
or data viz, and
the Python plotting library, matplotlib.
0:12
In this course, we'll look at some of
the more common charts in matplotlib,
0:14
such as line charts,
scatter plots, histograms, and
0:20
box plots, and
I'll briefly talk about a few others.
0:24
We'll use a public data set,
walk through some chart options, and
0:30
see the strengths of each
representing specific data patterns.
0:33
Before we get to the charts though,
0:38
let's talk a little bit about
the library we'll be using, matplotlib.
0:39
Matplotlib is widely used in
industry by data analysts,
0:43
business analysts,
scientists, and researchers.
0:48
It's especially well suited for
publication quality images.
0:51
Matplotlib can output the images to
the screen and save images in a wide
0:55
variety of file formats, including PDF,
PNG, JPEG, SVG, and many more.
1:00
While it can be used to generate
interactive, web-based charts,
1:06
libraries such as Bokeh or
Seaborn are better suited to that task.
1:11
I've included links in the teacher's
notes for those resources.
1:16
If we take a look at matplotlib.org,
1:19
we see that we can use
it in a variety of ways.
1:22
It works in Python scripts,
jupyter notebooks, and the Python shell.
1:25
We can integrate it with our
web application servers, or
1:29
add additional toolkits to extend
the graphing capabilities.
1:32
Those go beyond the scope of this course.
1:35
But I'd encourage you to take a look
at those options on their site.
1:37
One of the cool things that
the site has is an example gallery
1:41
of different charts that
matplotlib can generate.
1:44
Many of these are more
industry-specific or
1:47
more advanced than we'll
be tackling in this course.
1:49
But the gallery shows
the power that matplotlib
1:51
brings to the world of data viz.
1:54
Let's kick things off with matplotlib and
go through some of the syntax and
1:59
plotting options it provides.
2:03
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