Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Preview
Start a free Courses trial
to watch this video
ColumnDataSource is one way in which the data of a Bokeh graph is stored. Let's take a look at some examples of how to utilize this great feature.
Deprecation Warning
This course was produced with version 0.12.5 of Bokeh. The bar chart produced in this video around the 4:50 mark utilizes the bokeh.charts module which has been deprecated in newer versions. The rest of this course relies on the bokeh.plotting module.
Additional Resources
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
Welcome back.
0:00
We've seen that we can
pass in series data,
0:01
such as lists, into our figure object,
to ultimately generate our plot.
0:04
We can pass in other sources of data
as well, such as pics, numpy arrays or
0:08
Panda's data frames.
0:12
However, Bokeh provides
its own implementation
0:14
of series data that we can use,
the column data source.
0:17
Column data source is a table like
data object which maps column names
0:21
to sequences or arrays of data.
0:26
It takes in a data series such
as a Python dictionary and
0:28
maps the key to the sequence of values.
0:32
In fact, behind the scenes,
when we pass in x equals list one,
0:35
two and three, bokeh is making
a column data source with a name of x
0:38
with a mapping of values as one,
two, and three.
0:43
It also allows for access in the values.
0:47
Inside the data, much like one
would with a Python dictionary and
0:49
easily accepts pandas
data frames as values.
0:53
In upcoming videos we'll also look at some
additional features of having our data
0:56
in a column data source,
then make using it in our Bokeh projects
1:00
more useful than sticking with
pandas data frames or numpy arrays.
1:03
We can add in data to the data source for
hover tool tips and
1:07
even use the column data source for
linked visualizations.
1:11
Again, we'll be taking
a look at this feature soon.
1:14
There's something to note
about column data source.
1:17
Like in the previous video,
the columns must all be the same length.
1:21
Missing or ragged data is not
permitted within a single data source.
1:24
Let's have a look at how we can utilize
column data source to handle some data,
1:29
and start to visualize
some actual figures.
1:32
To get started,
you want to download the course files and
1:35
get them set up in your favorite editor.
1:38
I'll head back into PyCharm.
1:41
The starter files include the dataset
we'll be using, country-pops.csv,
1:43
along with the requirements.txt file which
we already installed in a previous video.
1:47
Let's go ahead and
create a new file called stage1-3.py.
1:52
Here, we'll want to get
our CSV file imported and
1:58
take a look at the format of our data.
2:01
Since this file contains information for
all the countries of the world,
2:03
let's remind ourselves why we're
exploring data to just five rows and
2:07
display the header information as well.
2:12
We'll need to import numpy as np,
2:15
import pandas as pd, again, that's a pretty
standard naming convention for those.
2:20
We're gonna be bringing in
our country-pops.csv file.
2:30
And we'll use the pandas.read_csv method.
2:41
Bring this in.
2:47
And then for data exploration,
we'll create a np.array,
2:51
With a header, and
3:01
we need to limit our rows here, nrows=5.
3:04
And print(countries_array).
3:14
And when we run this,
we can see then that in our data,
3:20
we have quite a bit of useful information.
3:25
The English and German names of
the country, code code, population, etc.
3:28
As is often the case with datasets,
3:34
there's information included that you
might not necessarily be interested in for
3:36
the current project,
such as birth and death rates.
3:41
But, it's great to have that information
available for our future explorations.
3:44
Now that we have a table of data,
we should put it to use.
3:49
Imagine that you have this information in
your favorite spreadsheet application.
3:52
Let start with the chart that people
will often generate in Excel.
3:56
A bar chart for example.
3:59
We'll need to bring in our imports and
4:01
in this case import bokeh.charts
to plot a bar chart.
4:03
And from bokeh.io, we'll import
our output file and show methods.
4:19
Then we define our output file.
4:25
Let's call it population.html.
4:27
Next, we need to build out our bar chart
by passing in our countries pandas
4:36
data frame.
4:40
We can do that here at the bottom and
tell it which information to use.
4:42
We'll just call it bar_chart.
4:47
Next, we need to pass in our bar
chart object into the show method.
5:21
show(bar_chart) and then run our script.
5:28
Since our n rows is still set to five, we
see the five countries displayed on a bar
5:38
chart showing their
representative populations.
5:42
Pretty cool.
5:46
You'll notice that we are setting
our legend to false so
5:47
that it doesn't display.
5:50
In a later video, we will take a look
at how to customize our chart legends.
5:51
There are a variety of other charts
that can be generated as well, and
5:55
I've included links to them
in the teachers notes.
5:59
While, pandas data frames work great for
many applications.
6:01
Bokeh provides another option that
allows for some cleaner code.
6:04
And as we'll find out in a later video,
some great additional features.
6:08
To take advantage of this,
we utilize Bokeh's column data source.
6:12
This will map the column names
to the sequences of data.
6:17
Let's go a bit beyond bar charts,
or pie graphs with their data.
6:20
Since we're attempting to provide some
data analysis in our reporting, and
6:24
see what, if any, correlation there is
between the country's population, and
6:28
life expectancy.
6:32
Before we can use it, we need to import
column data source from bokeh.plotting and
6:33
pass in our data.
6:38
We'll need to do our imports from
Bokeh along with the column data
6:39
source import from bokeh.plotting,
we want to import ColumnDataSource.
6:44
And figure.
6:54
Since countries' a pandas data frame,
6:56
we can pass that into
ColumnDataSource as our data source.
6:58
And since we don't need the country
array or print function any longer,
7:01
we can clean up our code and
delete those lines.
7:05
And, while we're at it, let's rename our
output file to something more meaningful.
7:09
Since we're examining population
versus life expectancy,
7:14
let's use pop-life.html as our file name.
7:21
And we can get rid of
the numpy import as well.
7:26
And we won't be needing bar any longer.
7:29
Great.
7:38
We wanna set up our country data.
7:40
Just taking our ColumnDataSource,
and we want our countries.
7:45
Now, we just need to build
out our figure plot,
7:53
similar to what we have done in the past.
7:56
But, we'll use circle ellipse here and
7:58
not pass in any tool parameters
to accept the default.
8:01
Our plot.
8:06
And we'll label our x_axis as Population.
8:13
Our y_axis is Life Expectancy.
8:21
Great, we'll do a plot.circles,
8:31
pop in population for our x value.
8:35
Life_expectancy.
8:45
For y value.
8:48
Our source data is our country data.
8:52
And we'll make our glyphs 15 points.
8:58
That should look roughly familiar, but
what is going on there with our x and
9:01
y values.
9:05
And what does that source
equals country_data bit?
9:06
Well, we are telling our plot
to use our source variable,
9:09
our ColumnDataSource information,
as the source of our data.
9:13
Our x value is a population
value inside that data set, and
9:18
our y values are the values of the
matching Life Expectancy column of data.
9:22
Notice that capitalization matters here,
and that we need to match our x and
9:27
y values to the correct names
of our data set column headers.
9:32
Now, we just need to tell Bokeh to
show us our plot and we'll be all set.
9:36
Show plot, and run our script.
9:42
Awesome we have a plot showing us
life expectancy versus population for
9:51
our five countries.
9:55
Great work,
we have seen several things here.
9:56
How to utilize pandas data frames as
a source of data and get it to work with
10:00
Bokeh column data source, and plot that
data based on specific column names.
10:04
It's not very helpful right now though in
terms of data exploration because we can't
10:09
tell which glyph is which.
10:12
When we come back, we'll look at adding
specific colors to our glyphs and
10:15
add legends to our graph to help
better understand our data.
10:19
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