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You have completed Data Visualization with Bokeh!
You have completed Data Visualization with Bokeh!
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Bokeh is a popular Python data visualization library. Let's talk a little about Bokeh and what's going to be covered in this course.
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[MUSIC]
0:00
Hello, I'm Ken.
0:04
In this course, I'm excited to introduce
you to the Python library, Bokeh.
0:07
Bokeh is a Python interactive
visualization library
0:12
that targets modern web browsers for
presentation.
0:16
Without using a dedicated Python
library power visualizations,
0:19
we might rely on a non-Python library,
such as D3.js for our work.
0:24
Not that there is anything wrong
with D3.js, it is a great and
0:29
powerful JavaScript library.
0:32
However, since we are working in
Python already for data processing,
0:34
it seems to make sense to stick with
our one language if we're able to.
0:39
Behind the scenes, Bokeh generates
the JavaScript, HTML, and CSS for us.
0:43
Therefore, instead of having to manage
multiple languages and libraries,
0:49
Bokeh consolidates it into one,
relatively easy step.
0:54
Bokeh allows for
interactive data visualization,
1:00
so that users can explore data themselves.
1:03
This is one reason why I like it
better than libraries like Matplotlib,
1:06
which has been used for a long time for
more basic plotting and charts.
1:10
Data visualization is an important part
of a variety of developer roles today.
1:15
Whether you're doing pie charts in Excel,
or
1:20
graphing time series data
from a scientific experiment,
1:22
how you present your data can make a large
impact on how people understand the data.
1:26
What's important about that,
you might ask.
1:31
Well, think about what happens when
you add a bar chart to a spreadsheet
1:34
to quickly determine and
visualize your data results.
1:37
Spreadsheets are great at
displaying rows of data and
1:41
their associated column topics.
1:44
But they are not super great themselves
for seeing if there are patterns in data.
1:46
This is where data visualization can help
with a variety of charts, bar, line,
1:50
pie, for example, or plots,
like box, scatter, or candlestick.
1:55
Say for example, that we have a
spreadsheet table of data that shows miles
2:00
per gallon, or MPG of a car, based on
the number of cylinders in the engine.
2:03
Looking at the table itself
can be a daunting task to
2:09
come up with any exacting information.
2:12
If however, we apply some visualization
to our data, we could use a bar graph
2:14
to showcase general buckets to see
a comparison of MPG to cylinders.
2:18
Or a scatter plot, to show each
specific entry in our data, and
2:23
where it falls in comparison to others.
2:27
Or we could visualize the data
in a box chart format to
2:29
see a more clear comparison of
the average, mean, minimum, and
2:33
maximum MPG for the various engine sizes.
2:37
Bokeh's strength is that it allows for
2:40
a wide variety of interactive plots
that are quickly and easily generated.
2:42
Further, adding interactivity to your
visualizations is often a simple process,
2:47
and allows for users to examine and
explore data further and deeper
2:52
without a lot of extra time being spent
on the development side of the project.
2:56
If we take a quick look at the Bokeh
website, we see that there is some good
3:01
information in their user guide,
especially on how to get started,
3:04
as well as some interesting visualization
projects available in the gallery section.
3:09
I put this link in the teachers notes.
3:14
So be sure to check it out to
learn more about what Bokeh offers
3:16
that we won't be covering in this course.
3:20
We'll be relying on some Python libraries.
3:22
Like NumPy and
pandas to work with our data as well,
3:25
showing how to use them with
Bokeh's column data source.
3:29
But we won’t be going into pandas
data frames or numPy arrays.
3:32
I'll assume that you have a basic
understanding of what they are and
3:36
how to generally work with them.
3:39
Again, I put links in the teacher's
note for a refresher if needed.
3:41
There are a lot of public data
sets available for exploration.
3:45
We're going to be exploring and
visualizing world population numbers
3:48
from worldwide data.info and
their world country population data sets.
3:52
The data set we'll be using also includes
three-letter country codes added to it,
3:57
which will be convenient for use later on.
4:01
Now that we have some of the basic
project requirements covered,
4:05
let's take a quick break before
we start looking at some code.
4:07
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