Introduction to Bokeh

Python 3: Automating Your Job Tasks Superhero Level: Data Visualization with Bokeh and Python 3
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Hello, and welcome to this section of the course on data visualization. After discussing data analysis with pandas in the previous section, I thought it would be a great idea to also focus on how to properly translate data sets. And I mean, Python lists up to pandas data frames into a nicer format, meaning beautiful interactive charts. But first things first. For now, let's see what's the tool that we are going to use to achieve our data visualization goals. Although there are quite a few very good and famous Python libraries for practically every data visualization need, and I should mention here libraries such as matplotlib, seaboard and plotly, among many others, I chose my favorite library, the one I used the most, which is bouquet.

So to install this library on your computer, just open up the windows command line and type in Install bulky, and then wait for the download and installation process to finish. Of course, make sure you are connected to the internet before installing any new module, I have already installed bulky so I won't do it again. However, just to be on the same page, you should now pause the video and go ahead and install it on your own system. Please keep in mind that bouquet is a very comprehensive library with tons of classes, methods and parameters and other features that would require 10s of hours of video to go through, and even then we may perhaps just scratch the surface. In this section of the course we are going to build and test six different types of interactive plots starting with the next lecture. However, with bouquet you are able to create many more types of plots for various areas of expertise, from weather statistics to financial analysis, each of them with its own set of features and interactive options.

You can find them all in bouquets official documentation, which is absolutely amazing, containing every bit of information about its classes and methods, and also optional features, along with lots of very useful real life examples of plots and their underlying code. That's why I strongly recommend keeping the bulky documentation at hand anytime you work with this library. And by the way, I will provide you with three of the most useful links in my opinion to get you started. They are the bulky quickstart guide, as you can see right here, the gallery of examples, this one right here, and finally, the styling visual attributes guide. You can find these three links attached to this lecture and also in an upcoming text lecture. Go ahead and bookmark each of these links and also browse the rest of the documentation to understand the true power of bulky.

Another thing that I want to mention here is that each time you need to clarify some concepts, or get your terminology straight regarding balki. You can head over to the quickstart guide right here, and scroll down to the concepts section right here, where you can find the definitions of the core concepts in bulky with examples and links to other parts of the documentation in order to have a complete understanding of bouquets main concepts and tools. Okay, enough theory for now I let you read the documentation and the quickstart guide. And it's now time to move on to the next video and build a basic interactive line plot with bouquets. So I'll see you in the next lecture.

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