[MUSIC]

Hello, I'm Ken.

In this course, I'm excited to introduce you to the Python library, Bokeh.

Bokeh is a Python interactive visualization library

that targets modern web browsers for presentation.

Without using a dedicated Python library power visualizations,

we might rely on a non-Python library, such as D3.js for our work.

Not that there is anything wrong with D3.js, it is a great and

powerful JavaScript library.

However, since we are working in Python already for data processing,

it seems to make sense to stick with our one language if we're able to.

Behind the scenes, Bokeh generates the JavaScript, HTML, and CSS for us.

Therefore, instead of having to manage multiple languages and libraries,

Bokeh consolidates it into one, relatively easy step.

Bokeh allows for interactive data visualization,

so that users can explore data themselves.

This is one reason why I like it better than libraries like Matplotlib,

which has been used for a long time for more basic plotting and charts.

Data visualization is an important part of a variety of developer roles today.

Whether you're doing pie charts in Excel, or

graphing time series data from a scientific experiment,

how you present your data can make a large impact on how people understand the data.

What's important about that, you might ask.

Well, think about what happens when you add a bar chart to a spreadsheet

to quickly determine and visualize your data results.

Spreadsheets are great at displaying rows of data and

their associated column topics.

But they are not super great themselves for seeing if there are patterns in data.

This is where data visualization can help with a variety of charts, bar, line,

pie, for example, or plots, like box, scatter, or candlestick.

Say for example, that we have a spreadsheet table of data that shows miles

per gallon, or MPG of a car, based on the number of cylinders in the engine.

Looking at the table itself can be a daunting task to

come up with any exacting information.

If however, we apply some visualization to our data, we could use a bar graph

to showcase general buckets to see a comparison of MPG to cylinders.

Or a scatter plot, to show each specific entry in our data, and

where it falls in comparison to others.

Or we could visualize the data in a box chart format to

see a more clear comparison of the average, mean, minimum, and

maximum MPG for the various engine sizes.

Bokeh's strength is that it allows for

a wide variety of interactive plots that are quickly and easily generated.

Further, adding interactivity to your visualizations is often a simple process,

and allows for users to examine and explore data further and deeper

without a lot of extra time being spent on the development side of the project.

If we take a quick look at the Bokeh website, we see that there is some good

information in their user guide, especially on how to get started,

as well as some interesting visualization projects available in the gallery section.

I put this link in the teachers notes.

So be sure to check it out to learn more about what Bokeh offers

that we won't be covering in this course.

We'll be relying on some Python libraries.

Like NumPy and pandas to work with our data as well,

showing how to use them with Bokeh's column data source.

But we won’t be going into pandas data frames or numPy arrays.

I'll assume that you have a basic understanding of what they are and

how to generally work with them.

Again, I put links in the teacher's note for a refresher if needed.

There are a lot of public data sets available for exploration.

We're going to be exploring and visualizing world population numbers

from worldwide data.info and their world country population data sets.

The data set we'll be using also includes three-letter country codes added to it,

which will be convenient for use later on.

Now that we have some of the basic project requirements covered,

let's take a quick break before we start looking at some code.