How to use bokeh for data visual

Bokeh is a popular Python library used for interactive data visualization. It allows you to create visually appealing and interactive plots, charts, and graphs. Here are the steps to use Bokeh for data visualization:

1. Install Bokeh: Start by installing Bokeh library using pip or conda command in your Python environment.

pip install bokeh

2. Import the necessary modules: Import the required modules from the Bokeh library, such as figure for creating plots and output_file for saving the visualization.

from bokeh.plotting import figure
from bokeh.io import output_notebook, show
output_notebook()

3. Prepare the data: Organize and preprocess your data in a suitable format for visualization. Ensure that the data is in a structure that can be easily plotted.

x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

4. Create a figure: Use the figure function to create a blank figure canvas where you will add your visual elements.

p = figure(title = "Bokeh Line Graph") 

5. Add glyphs: Add glyphs (visual elements) to the figure using methods like line, scatter, bar, etc. These glyphs represent your data points on the plot.

p.line(x, y) 

6. Customize the plot: Customize various aspects of the plot, such as title, axis labels, colors, legends, and tooltips, to make it more informative and visually appealing.

p.background_fill_color = "#ebebeb"
p.title.text_color = "#878787"
p.title.text_font_size = "16px"

7. Add interactivity: Bokeh allows you to create interactive plots. You can add interactive features like hover tooltips, zooming, panning, and selection tools to enhance the user experience.

8. Save or show the plot: Finally, you can either save the plot to an HTML file using the output_file function or display it directly in a Jupyter Notebook or web application using the show function.

show(p)

9. Further customization: Bokeh offers many additional features and customization options. You can explore its documentation and examples to learn more about advanced functionalities like layouts, widgets, and server-based applications.

Remember to consult the Bokeh documentation and examples for detailed usage instructions and to explore the full range of capabilities it offers.

Complete code and the result:

# importing the modules
from bokeh.plotting import figure
from bokeh.io import output_notebook, show
output_notebook()

# the points to be plotted
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

# instantiating the figure object
p = figure(title = "Bokeh Line Graph") 

# plotting the line graph
p.line(x, y)

# customize the plot
p.background_fill_color = "#ebebeb"
p.title.text_color = "#878787"
p.title.text_font_size = "16px"

# displaying the model
show(graph)