Welcome to Matplotlib2:05 with Ken Alger
Let's briefly discuss some of the use cases and high-level features of matplotlib.
[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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