Python Bokeh - making a pie chart

Bokeh is a Python library used for creating interactive visualizations in a web browser. It provides powerful tools that offer flexibility, interactivity, and scalability for exploring various data insights.

What is a pie chart?

Pie charts are used to summarise nominal datasets into a circular graph to provide information at a glance. Different colors represent each sector to create an evident visual difference, and a key is provided against each color to identify the category.

math: To access the pi for creating the chart.
pandas: To manipulate data and create a series to hold data.
bokeh.io: To control the output and display of the plots. We specifically import output_file and save methods.
bokeh.palettes: To assign a color palette to the chart to improve its visual appearance. There are various available themes we use Colorblind to improve interface accessibility.
bokeh.plotting: To create and customize plots without working directly with the lower-level Bokeh models. We specifically import figure and show methods from it.
bokeh.transform: To transform the data by adding visual properties such as colors, sizes, and positions. We specifically import cumsum to calculate the cumulative sum of the data field.

Example code

In this example, we present different movie genres corresponding to their daily audience in a pie chart illustration to show which genre is most liked.

from math import pi, degrees
import pandas as pd
from bokeh.palettes import Colorblind
from bokeh.plotting import figure, show
from bokeh.transform import cumsum
from bokeh.io import output_file, save

#sample dataset
genre = {
    'Horror': 106,
    'SciFi': 126,
    'Comedy': 84,
    'Action': 130,
    'Fantasy': 74,
    'Romance': 96,
    'History': 40,
}

#creating data
data = pd.Series(genre).reset_index(name='value').rename(columns={'index': 'movie'})
data['angle'] = data['value']/data['value'].sum() * 2*pi
data['color'] = Colorblind[len(genre)]

#creating chart
myChart = figure(height=400, 
                 width=650, 
                 title="Movie genre - liking based on the audience per day", 
                 toolbar_location=None,
                 tools="hover", 
                 tooltips="@movie: @value,  Angle: @angle{0.0}", 
                 x_range=(-0.5, 1.0))

#creating sectors
myChart.wedge(x=0, y=1, 
              radius=0.4,
              start_angle=cumsum('angle', include_zero=True), 
              end_angle=cumsum('angle'),
              line_color="white", 
              fill_color='color',
              legend_field='movie', 
              source=data)

#clearing interface
myChart.axis.axis_label = None
myChart.axis.visible = False
myChart.grid.grid_line_color = None

#displaying output
output_file("output.html")
show(myChart)

Creating a pie chart through python bokeh.

Code explanation

Lines 1–6: Import all the necessary libraries and modules.
Lines 9–16: Create a dictionary named genre with the movie genres as a key and their audience per day as the value.
Line 20: Create a series data using pandas and assign each genre key as an index and the corresponding audience count as value to it. We rename() the data columns to movie and value.
Line 21: Calculate the angles for each genre key using the formula.

Note: The formula divides each genre's audience count by the sum of total audience count and then multiply it with the double of pi to normalise the data values.

Line 22: Set a color palette to the chart and pass the length of the genre dictionary as a parameter. Assign it to color in data.
Lines 25–31: Create a chart figure using figure() and define its dimensions, title, tools and tooltip properties, and range.

Note: Hover over the slices to see the genre , its count and its angle.

Lines 34–41: Create the pie slices using wedge(), define the properties, and set the source.
- x and y are the center coordinates of the chart.
- radius sets the radius of the chart.
- start-angle and end-angle are specified using cumsum to calculate when each slice starts and ends.
- line_color specifies the border color of the pie chart.
- fill_color specifies the color assigned to each category from the color column.
- legend_field specifies the column we use for each slice label in the legend.
Lines 44–46: Clear the interface by setting the axis gridlines to none and its visibility to false.
Lines 46–47: Set the output to output.html to specify the endpoint where the plot will appear and use show() to display the created plot.

Code output

The pie chart is displayed at the output.html endpoint with the Colorblind color palette to represent each wedge of the dataset.

Python Bokeh - making a pie chart

What is a pie chart?

Real-life application

Required imports

Example code

Code explanation

Code output

Common query