1 00:00:00,000 --> 00:00:09,619 [MUSIC] 2 00:00:09,619 --> 00:00:10,768 Hi, I'm Ken. 3 00:00:10,768 --> 00:00:12,575 I'm excited to introduce you to the basics of data visualization, 4 00:00:12,575 --> 00:00:14,236 or data viz, and the Python plotting library, matplotlib. 5 00:00:14,236 --> 00:00:20,427 In this course, we'll look at some of the more common charts in matplotlib, 6 00:00:20,427 --> 00:00:24,903 such as line charts, scatter plots, histograms, and 7 00:00:24,903 --> 00:00:30,220 box plots, and I'll briefly talk about a few others. 8 00:00:30,220 --> 00:00:33,620 We'll use a public data set, walk through some chart options, and 9 00:00:33,620 --> 00:00:36,960 see the strengths of each representing specific data patterns. 10 00:00:38,150 --> 00:00:39,465 Before we get to the charts though, 11 00:00:39,465 --> 00:00:43,900 let's talk a little bit about the library we'll be using, matplotlib. 12 00:00:43,900 --> 00:00:48,030 Matplotlib is widely used in industry by data analysts, 13 00:00:48,030 --> 00:00:51,590 business analysts, scientists, and researchers. 14 00:00:51,590 --> 00:00:55,610 It's especially well suited for publication quality images. 15 00:00:55,610 --> 00:01:00,980 Matplotlib can output the images to the screen and save images in a wide 16 00:01:00,980 --> 00:01:06,910 variety of file formats, including PDF, PNG, JPEG, SVG, and many more. 17 00:01:06,910 --> 00:01:11,373 While it can be used to generate interactive, web-based charts, 18 00:01:11,373 --> 00:01:16,220 libraries such as Bokeh or Seaborn are better suited to that task. 19 00:01:16,220 --> 00:01:18,740 I've included links in the teacher's notes for those resources. 20 00:01:19,970 --> 00:01:22,240 If we take a look at matplotlib.org, 21 00:01:22,240 --> 00:01:25,070 we see that we can use it in a variety of ways. 22 00:01:25,070 --> 00:01:29,650 It works in Python scripts, jupyter notebooks, and the Python shell. 23 00:01:29,650 --> 00:01:32,650 We can integrate it with our web application servers, or 24 00:01:32,650 --> 00:01:35,915 add additional toolkits to extend the graphing capabilities. 25 00:01:35,915 --> 00:01:37,960 Those go beyond the scope of this course. 26 00:01:37,960 --> 00:01:41,345 But I'd encourage you to take a look at those options on their site. 27 00:01:41,345 --> 00:01:44,735 One of the cool things that the site has is an example gallery 28 00:01:44,735 --> 00:01:47,195 of different charts that matplotlib can generate. 29 00:01:47,195 --> 00:01:49,285 Many of these are more industry-specific or 30 00:01:49,285 --> 00:01:51,955 more advanced than we'll be tackling in this course. 31 00:01:51,955 --> 00:01:54,885 But the gallery shows the power that matplotlib 32 00:01:54,885 --> 00:01:56,577 brings to the world of data viz. 33 00:01:59,700 --> 00:02:03,446 Let's kick things off with matplotlib and go through some of the syntax and 34 00:02:03,446 --> 00:02:05,060 plotting options it provides.