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[MUSIC]

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Hi, I'm Ken.

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I'm excited to introduce you to
the basics of data visualization,

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or data viz, and
the Python plotting library, matplotlib.

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In this course, we'll look at some of
the more common charts in matplotlib,

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such as line charts,
scatter plots, histograms, and

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box plots, and
I'll briefly talk about a few others.

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We'll use a public data set,
walk through some chart options, and

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see the strengths of each
representing specific data patterns.

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Before we get to the charts though,

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let's talk a little bit about
the library we'll be using, matplotlib.

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Matplotlib is widely used in
industry by data analysts,

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business analysts,
scientists, and researchers.

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It's especially well suited for
publication quality images.

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Matplotlib can output the images to
the screen and save images in a wide

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variety of file formats, including PDF,
PNG, JPEG, SVG, and many more.

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While it can be used to generate
interactive, web-based charts,

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libraries such as Bokeh or
Seaborn are better suited to that task.

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I've included links in the teacher's
notes for those resources.

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If we take a look at matplotlib.org,

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we see that we can use
it in a variety of ways.

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It works in Python scripts,
jupyter notebooks, and the Python shell.

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We can integrate it with our
web application servers, or

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add additional toolkits to extend
the graphing capabilities.

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Those go beyond the scope of this course.

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But I'd encourage you to take a look
at those options on their site.

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One of the cool things that
the site has is an example gallery

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of different charts that
matplotlib can generate.

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Many of these are more
industry-specific or

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more advanced than we'll
be tackling in this course.

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But the gallery shows
the power that matplotlib

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brings to the world of data viz.

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Let's kick things off with matplotlib and
go through some of the syntax and

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plotting options it provides.