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Saving Your Work3:12 with Ken Alger
Let's see how easy matplotlib makes it to save our charts for publication to the web and other publication formats.
So far we've just been displaying our charts to the screen. 0:00 As I mentioned earlier in the course, 0:03 one of the Matplotlib's strengths is producing the publication ready images. 0:05 Let's see how we can save some visualizations of our Iris data, so 0:09 we can share our results with others. 0:13 Let's use our box plot code here as an example to generate our files. 0:15 To save files, we need to utilize map plot libs figure object and save fig method. 0:19 Like you mentioned, we signed a variable, fig, to plt.figure. 0:26 We come in here, plt.figure. 0:31 And then down here at the bottom. 0:39 Savefig, and we pass in a file name for our chart. 0:45 We'll call it petal_length_boxplot.png. 0:50 We can run this out. 0:58 This is pretty cool. 1:08 We can see if we go over here into our folder, And there it is. 1:10 Our petal_length_boxplot.png file was created, now, 1:15 in this example, we generated the PNG file which is great for websites. 1:19 Let's jump back over to our notebook and see what other file types are supported. 1:23 Let's do a new cell. 1:30 So we import matplotlib.pyplot as plt. 1:33 Send our figure object, and 1:46 then if we print out canvas.get_supported_filetypes. 1:50 And run our cell. 2:00 As you can see, there's support for a lot of different file types or 2:04 in matplotlib language hard copy or non-interactive back ends. 2:08 These handle most printed or static display needs. 2:13 While it is beyond the scope of this course, there are also user interface or 2:17 interactive back end options too. 2:22 I'll put a link in the teacher's notes for resources on those. 2:25 This approach for 2:28 finding supported file types works outside of jupyter notebooks too. 2:29 In case you want to use matplotlib's power in a different environment. 2:32 We've covered a bunch of stuff in this stage. 2:37 We explored our Iris Dataset in several ways, 2:39 used several different visualization tools and techniques, and 2:42 learned how to leverage matplotlib to find patterns in data. 2:46 Data visualization is a fundamental aspect of many job roles today. 2:49 For both data experts and non-experts alike. 2:54 You're well on your way to a communicating information and 2:57 generating useful visualizations. 2:59 When we get back together, let's move on to a different data set and 3:03 answer some specific business decision questions with our new data viz skills. 3:06
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