How to Make an Area Plot in Python using Bokeh – Syntax and Examples

Importing the necessary libraries

In order to create an area plot in Python using Bokeh, we first need to import the necessary libraries. Bokeh is a powerful data visualization library that provides a wide range of tools for creating interactive plots. To get started, we need to import the following libraries:

import numpy as np
from bokeh.plotting import figure, show
from bokeh.io import output_notebook

The numpy library is used to generate the data for our area plot. The figure class from the bokeh.plotting module is used to create the plot, and the show function is used to display the plot. Finally, the output_notebook function from the bokeh.io module is used to display the plot inline in a Jupyter notebook.

Creating the data

Next, we need to create the data for our area plot. In this example, we will create a simple area plot with three categories and five data points for each category. We can use the numpy library to generate random data for each category:

# Create the data
categories = ['Category 1', 'Category 2', 'Category 3']
data = np.random.rand(3, 5)

In this example, we have three categories (‘Category 1’, ‘Category 2’, and ‘Category 3’) and five data points for each category. The np.random.rand function generates random numbers between 0 and 1.

Creating the figure

Once we have our data, we can create the figure for our area plot. We can use the figure class from the bokeh.plotting module to create the figure:

# Create the figure
p = figure(x_range=categories)

In this example, we pass the x_range parameter to the figure class to specify the categories as the x-axis range for our plot.

Adding the area plot

Next, we can add the area plot to our figure. We can use the p.patch method to add the area plot:

# Add the area plot
p.patch(categories + categories[::-1], list(data[0]) + list(data[1])[::-1], alpha=0.5)

In this example, we pass the categories and the reverse of the categories as the x-coordinates for the area plot. We also pass the data for the first category and the reverse of the data for the second category as the y-coordinates for the area plot. The alpha parameter is used to specify the transparency of the area plot.

Customizing the plot

We can customize our area plot by adding additional features such as grid lines, axis labels, and legends. We can use various methods and properties of the figure class to customize our plot:

# Customize the plot
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = '0pt'
p.legend.location = "top_left"

In this example, we set the grid_line_color property to None to remove the grid lines, the axis_line_color property to None to remove the axis lines, the major_tick_line_color property to None to remove the tick lines, and the major_label_text_font_size property to ‘0pt’ to remove the tick labels. We also set the location property of the legend to «top_left» to position the legend at the top left corner of the plot.

Adding labels and titles

We can add labels and titles to our area plot to provide additional information. We can use the p.title, p.xaxis, and p.yaxis properties to add labels and titles:

# Add labels and titles
p.title.text = "Area Plot"
p.xaxis.axis_label = "Categories"
p.yaxis.axis_label = "Values"

In this example, we set the text property of the title property to «Area Plot» to set the title of the plot. We also set the axis_label property of the xaxis and yaxis properties to «Categories» and «Values» respectively to set the labels for the x-axis and y-axis.

Displaying the plot

Finally, we can display our area plot using the show function:

# Display the plot
show(p)

This will open a new browser tab or window and display the area plot.