plotly Next Steps Video Lecture Transcript
This transcript was automatically generated by Zoom, so there may be discrepancies between the video and the text.

16:45:13 Hi! Everybody! Welcome back in this video, we wrap up our Plotley and Python section of the notes by talking about what next steps you can take.
16:45:22 If you'd like to learn more about Plotley.
16:45:25 So let's talk about what we've done so far.
16:45:28 So in this section, we introduced the Plotley Python package.
16:45:32 Along the way we saw. How do you? How you can create figures, objects within plantly, either by using graph objects directly or Ly express.
16:45:42 And then we talked about how implantly you add traces directly to the figure.
16:45:46 This again can be used, done using the graph objects directly with, add trace and graph object scatter, or by using a plot.
16:45:55 Lee express function, like Px line or Px scatter we at the after we've made our initial plots, we talked about how we can customize our figures a little bit more by changing the display of the graph.
16:46:09 Objects like the scatter point markers and the lines themselves, or about how you can change the display and aesthetics of the background of the figure, and the other non graphical elements like legends, text annotations, titles labels, and then we also talked about how you can change the appearance of your
16:46:26 Hover Effects Within Plotley along the way. We also are near the end we also made our very first dashboard by combining plant me with the dash package to look at various ways that we could interact and create a dashboard out of the auto Mpg data set that we saw in the
16:46:46 Boka package. So this is a very strong foundation for graphing.
16:46:49 With Plotley, but there's still a lot that you can learn.
16:46:52 So Potley is also capable of making things like animations, threed plots.
16:46:58 A wide variety of chart types on which we only touched on a few direct and Plotley, making more advanced and more in-depth dash applications and more so.
16:47:08 How could you learn more if you'd like to? I strongly recommend you.
16:47:13 Go through the documentation for Plotley and Dash.
16:47:17 So the Api's the both the Api reference, which has something like, you know, Px, scatter and goes through all the different inputs and outputs of that.
16:47:26 Or you can go to the nice tutorials that Plotley provides for its python version of the library again.
16:47:32 There's also the dash documentation. What gives documentation pages which give a nice series of tutorials on how to create dash applications more in depth and maybe slightly more advanced than the ones that we created in the previous notebook if you're looking for inspiration, you can
16:47:50 Also browse the galleries for Plotley, and Dash.
16:47:54 Here they give examples of various figures and dashed applications that have been created with these packages for the Dash Gallery.
16:48:05 Be mindful that the examples here are using the enterprise version of dash which is not free.
16:48:12 So it's possible that it would be difficult for you to create some of those applications using the open source version of dash, which is what we used.
16:48:20 If you're working on projects, and you're stuck on either an error or a bug, or you're not sure how to do something.
16:48:28 You can always perform a web search. So again plot Lee, and well, things like stack overflow and stack, exchange and stack forums are usually pretty good, but plot lead and dash also feature.
16:48:40 Their own community forums on their documentation pages which might be worthwhile checking out as well, and then, finally, you know the best thing to do, as always, is just to go out there and try things.
16:48:54 So if you have a visualization or a dashboard in mind that you'd like to try and make just go for it.
16:48:58 Try and make it with dash and Plotley, and then when you get stuck, either to check out the documentation or the web searches, when you run into errors, use those as learning opportunities to see what went wrong, why did this go wrong?
16:49:10 And how can I prevent something like this from going wrong in the future?
16:49:14 So that's gonna wrap up both our plotley and our Python section.
16:49:19 In the next section. We're going to dive into web-based visualization.