In this post, we will look at how to manipulate the different tools and tooltips that you can use to interact with data visualizations that are made using Bokeh in Python. Tool are the icons that are displayed by default to the right of a visual when looking at a Bokeh output. To the right is what default tools look like. Tooltips provide interactive data based on the position of the mouse.

We will now go through the process of changing these tools and tooltips for various reasons and purposes.

Load Libraries

First, we need to load the libraries we need to make our tools. Below is the code followed by an explanation.

from pydataset import data
from bokeh.plotting import figure
from bokeh.io import output_file, show

We start by loading “data” from “pydataset”. This library contains the actual data we are going to use. The other libraries are all related to Bokeh’s “figure” which will create details for our visualization. In addition, we will need the “output_file” to make our HTML document and the “show” function to display our visualization.

Data Preparation

Data preparation is straightforward. All we have to do is load our data into an object. We will use the “Duncan” dataset, which contains data on various jobs’ income, education, and prestige. Below is the code followed by a snippet of the actual data.

df=data('Duncan')
df.head()

Default Settings for Tools

We will now look at a basic plot with the basic tools. Below is the code.

# Create a new figure
fig = figure(x_axis_label="income", y_axis_label="prestige")

# Add circle glyphs
fig.circle(x=df["income"], y=df["prestige"])

# Call function to produce html file and display plot
output_file(filename="my_first_plot.html")
show(fig)

There is nothing new here. We create the figure for our axes first. Then we add the points in the next line of code. Lastly, we write some code to create an output. The default tools has 7 options. Below they are explained from top to bottom.

1. At the top, is the logo that takes you to the Bokeh website
2. Pan tool
3. Box zoom
4. Wheel zoom
5. Save figure
6. Reset figure
7. Takes you to Bokeh documentation

We will now show how to customize the available tools.

Custom Settings for Tooltips

In order to make a set of custom tools, we need to make some small modifications to the previous code as shown below.

# Create a list of tools
tools = ["lasso_select", "wheel_zoom", "reset","save"]

# Create figure and set tools
# Create a new figure
fig = figure(x_axis_label="income", y_axis_label="prestige",tools=tools)

# Add circle glyphs
fig.circle(x=df["income"], y=df["prestige"])

# Call function to produce html file and display plot
output_file(filename="my_first_plot.html")
show(fig)

What is new in the code is the object called “tools”. This object contains a list of the tooltips we want to be available in our plot. The names of the tools is available in the Bokeh documentation. We then add this object “tools” to the argument called “tools” in the line of code where we create the “fig” object. If you compare the second plot to the first plot you can see we have fewer tools in the second one as determine by our code.

Hover Tooltip

The hover tooltip allows you to place your mouse over the plot and have information displayed about what your mouse is resting upon. Being able to do this can be useful for gaining insights about your data. Below is the code and the output followed by an explanation.

# Import ColumnDataSource
from bokeh.models import ColumnDataSource

# Create source
source = ColumnDataSource(data=df)

# Create TOOLTIPS and add to figure
TOOLTIPS = [("Education", "@education"), ("Position", "@type"), ("Income", "@income")]
fig = figure(x_axis_label="education", y_axis_label="income", tooltips=TOOLTIPS)

# Add circle glyphs
fig.circle(x="education", y="income", source=source)
output_file(filename="first_tooltips.html")
show(fig)

Here is what happened.

1. We loaded a new library called ColumnDataSource. This function allows us to create a data structure that is unique to Bokeh. This is not required but will appear in the future.
2. We then save are dataset using the new function and called it “source”
3. Next, we create a list called “TOOLTIPS” this list contains tuples which are in parentheses. The first string in the parentheses will be the name that appears in the hover. The second string in the parentheses accesses the value in the dataset. For example, if you look at the hover in the plot above the first line says “Education” and the number 72. The string “Education” is just the first string in the tuple and the value 72 is the value of education from the dataset for that particular data point
4. The rest of the code is a review of what has been done previously. The only difference is that we use the argument “tooltip” instead of “tool”

Conclusion

With tooltips and tools you can make some rather professional looking visualization with a minimum amount of code. That is what makes the Bokeh library so powerful.

Bokeh is another data visualization library available in Python. One of Bokeh’s unique features is that it allows for interaction. In this post, we will learn how to make a basic scatterplot in Bokeh while also exploring some of the basic interactions that are provided by default.

Data Preparation

We are going to make a scatterplot using the “Duncan” data set that is available in the “pydataset” library. Below is the initial code.

from pydataset import data
from bokeh.plotting import figure
from bokeh.io import output_file, show

The code above is just the needed libraries. We loaded “pydataset” because this is where our data will come from. All of the other libraries are related to “bokeh.” “Figure” allows us to set up our axes for the scatterplot. “Output_file” allows us to create the file of our plot. Lastly, “show” allows us to show the plot of our visualization. In the code below we will load our dataset, give it a name, and print the first few rows.

df=data('Duncan')
df.head()

In the code above we store the “Duncan” dataset in an object called “df” using the data() function. We then display a snippet of the data using the .head() function. The “Duncan” data shares information on jobs as defined by several variables. We will now proceed

Making the Scatterplot

We will now make our scatterplot. We have to do this in three steps.

1. Make the axis
2. Add the data to the plot
3. Create the output file and show the results

Below is the code with the output

# Create a new figure
fig = figure(x_axis_label="education", y_axis_label="income") #labels axises

# Add circle glyphs
fig.circle(x=df["education"], y=df["income"]) #adds the dots

# Call function to produce html file and display plot
output_file(filename="my_first_plot.html")
show(fig)

At the top of the code, we create our axis information using the “figure” function. Here we are plotting education vs income and storing all of this in an object called “fig”. Next, we insert the data into our plot using the “circle” function. To insert the data we also have to subset the “df” dataframe for the variables that we want. Note that the data added to a plot are called “glyphs” in Bokeh. Lastly, we create an output file using a function with the same name and show the results.

To the right of your plot, there are also some interaction buttons as shown below

Here is what they do from top to bottom.

1. Takes you to bokeh.org
2. Pan the image
3. Box zoom
4. Wheel zoom
5. Download image
6. Resets image
7. It takes you to information about the bokeh function

There are other interactions possible but these are the default ones when you make a plot.

Conclusion

Bokeh is one of many tools used in Python for data visualization. It is a powerful tool that can be used in certain contexts. The interactive tools can also enhance the user experience.