Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Well done!
You have completed Data Visualization with Bokeh!
You have completed Data Visualization with Bokeh!
Preview
Adding colors and legends to graphs and charts helps to make data easier to understand. Bokeh makes the process easy.
Code for color_mapper:
color_mapper = CategoricalColorMapper(factors=['Asia', 'Africa', 'Antarctica', 'Australia', 'Central America', 'Europe', 'North America', 'Oceania', 'South America'], palette=['#00FF00', '#FFD343', 'darkgray', 'brown', 'cyan', 'crimson', 'red', '#0000FF', 'purple'])
Related Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign upRelated Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign up
When we were last looking at the data,
0:00
we were using a scatter plot to see
if there were any correlation between
0:02
a country's population and life expectancy
from our world population dataset.
0:05
We used Bokeh column data source to
map our data into usable columns and
0:09
were able to generate the scatter plot
to get a better idea of any relationship
0:13
between life expectancy and population.
0:17
There are couple of issues
with our last plot though.
0:19
Everything was cluttered and
same color, and nothing was labeled.
0:22
Obviously, this makes it a challenge
to come with any conclusions about our
0:26
dataset.
0:30
Bokeh includes some great tools for
this which allow us to map colors to
0:31
specific items or categories of items to
quickly add legends to our visualizations.
0:36
If we remember back when we looked at the
headers in our data set, one of the pieces
0:43
of data we get about each country is the
continent on which the country resides.
0:47
It might be nice,
0:51
when we're trying to figure out
a correlation between life expectancy and
0:52
population, if region of the world
leads to any insightful information.
0:55
One of the functions Bokeh provides
is categorical color mapper.
1:01
Which as the name would indicate
maps colors to different
1:05
categorical data buckets.
1:09
This allows to utilize
different categories or
1:11
in Bokeh terms factors which
in our case are countries and
1:13
assigned certain color values,
a palette to each category.
1:17
One of the functions Bokeh provides
is categorical color mapper, which,
1:22
as the name would indicate, maps colors
to different categorical data buckets.
1:28
This allows us to utilize
different categories, or
1:33
in Bokeh's terms, factors,
which in our case are countries, and
1:36
assigned certain color values,
palette, to each category.
1:39
The colors we pass into the categorical
color mapper function can be any valid CSS
1:44
or hex color,
meaning we can use either green, or
1:49
your favorite #00FF00 for our value.
1:53
Let's jump back into our project and add
some colors to our continent buckets and
1:57
then show how we can add a legend to our
plot to add context to the information.
2:01
Here's where we left off at
the end of our last video.
2:06
To get started adding some color, we need
to add a color mapper to our code so
2:08
that we can assign specific
colors to our continents.
2:12
We'll import categorical color mapper from
bokeh.models, then create our color mapper
2:16
with our continent buckets or factors,
and then assign each factor a color.
2:22
From bokeh.models import
2:26
CategoricalColorMapper.
2:31
Now, we look at all of the continent
options in our entire data set,
2:36
we see that we have nine different buckets,
Asia, Africa, Antarctica,
2:40
Australia, Central America, Europe,
North America, Oceana, and South America.
2:45
We pass those in as a list
into the factors argument
2:52
of CategoricalColorMapper.
2:54
Then in a matching linked list, pass
in our colors to the palette argument.
2:56
I've already typed this out and
I'll paste it in here.
3:00
I've included that in the teacher's notes
to save you from having to type it
3:10
in as well.
3:13
Then we can use our color
mapper in our plot.
3:14
We do that by adding another
argument one we call plot.circle.
3:17
Or whatever glyph we're using in
our plot with the color parameter.
3:20
We'll pass into the color
parameter a dictionary and
3:24
which field we are basing our color mapper
on and the associated color mapper.
3:28
At this point we should remove our
five country limitation though so
3:33
we can see all of
the countries represented.
3:37
Let's also, just for fun, change our
glyph from a circle to a diamond.
3:40
Just to show some other available glyphs.
3:45
We'll also adjust the size
of our glyph down slightly.
3:47
Then when we show our plot and
3:50
run our script,
we should see our data separated by color.
3:52
So we'll change circle to diamond.
3:55
Change our size down to 10.
4:02
And then add in our color
parameter which is dict.
4:06
Based on the continent field and
4:12
a transform parameter is our color_mapper.
4:18
Now, we should be able to run our script.
4:29
Nice, we have our data
split out into buckets and
4:32
associated with each other by color based
on which continent a country resides.
4:35
We are still missing a way to know which
color represents which continent though.
4:40
We need a legend for our plot.
4:43
Fortunately that is easily
done with Bokeh by passing in
4:45
a legend parameter with the column name
that we want to use in the legend.
4:49
In this case continent.
4:53
So we can add our legend.
4:54
And base it on our continent field.
5:01
And run our script again.
5:05
Well that certainly helps but
5:09
it is sitting right on top
of some of our data points.
5:11
Let’s adjust the location
of the legend and
5:14
move it to the bottom right of our
plot and add a background color to it
5:16
as well to help it stand out visually
before we show the plot again.
5:20
We can do that by adjusting
the plot.legend and
5:23
assigning it a location, bottom_right.
5:28
And the background fill_color.
5:39
I will do lightgrey.
5:45
And show our plot again.
5:53
This is looking more better as we
can get a general idea of at least
5:56
which continents the countries are on and
start to put some context to our data.
6:00
This still isn't super
helpful though as our glyphs
6:04
aren't currently tagged in any way
as to which country they represent.
6:07
Let's tackle that in our next video.
6:11
You need to sign up for Treehouse in order to download course files.
Sign upYou need to sign up for Treehouse in order to set up Workspace
Sign up