Python – Data visualization using Bokeh

Last Updated : 22 May, 2024

Bokeh is a data visualization library in Python that provides high-performance interactive charts and plots. Bokeh output can be obtained in various mediums like notebook, html and server. It is possible to embed bokeh plots in Django and flask apps.

Bokeh provides two visualization interfaces to users:

bokeh.models : A low level interface that provides high flexibility to application developers.
bokeh.plotting : A high level interface for creating visual glyphs.

To install bokeh package, run the following command in the terminal:

pip install bokeh

The dataset used for generating bokeh graphs is collected from Kaggle.

Code #1: Scatter Markers
To create scatter circle markers, circle() method is used.

# import modules 
from bokeh.plotting import figure, output_notebook, show 
  
# output to notebook 
output_notebook() 
  
# create figure 
p = figure(plot_width = 400, plot_height = 400) 
  
# add a circle renderer with 
# size, color and alpha 
p.circle([1, 2, 3, 4, 5], [4, 7, 1, 6, 3],  
         size = 10, color = "navy", alpha = 0.5) 
  
# show the results 
show(p)  

Output :

Code #2: Single line
To create a single line, line() method is used.

# import modules 
from bokeh.plotting import figure, output_notebook, show 
  
# output to notebook 
output_notebook() 
  
# create figure 
p = figure(plot_width = 400, plot_height = 400) 
   
# add a line renderer 
p.line([1, 2, 3, 4, 5], [3, 1, 2, 6, 5],  
        line_width = 2, color = "green") 
  
# show the results 
show(p) 

Output :

Code #3: Bar Chart
Bar chart presents categorical data with rectangular bars. The length of the bar is proportional to the values that are represented.

# import necessary modules 
import pandas as pd 
from bokeh.charts import Bar, output_notebook, show 
  
# output to notebook 
output_notebook() 
  
# read data in dataframe 
df = pd.read_csv(r"D:/kaggle/mcdonald/menu.csv") 
  
# create bar 
p = Bar(df, "Category", values = "Calories", 
        title = "Total Calories by Category",  
                        legend = "top_right") 
  
# show the results 
show(p) 

Output :

Code #4: Box Plot
Box plot is used to represent statistical data on a plot. It helps to summarize statistical properties of various data groups present in the data.

# import necessary modules 
from bokeh.charts import BoxPlot, output_notebook, show 
import pandas as pd 
  
# output to notebook 
output_notebook() 
  
# read data in dataframe 
df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") 
  
# create bar 
p = BoxPlot(df, values = "Protein", label = "Category",  
            color = "yellow", title = "Protein Summary (grouped by category)", 
             legend = "top_right") 
  
# show the results 
show(p) 

Output :

Code #5: Histogram
Histogram is used to represent distribution of numerical data. The height of a rectangle in a histogram is proportional to the frequency of values in a class interval.

# import necessary modules 
from bokeh.charts import Histogram, output_notebook, show 
import pandas as pd 
  
# output to notebook 
output_notebook() 
  
# read data in dataframe 
df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") 
  
# create histogram 
p = Histogram(df, values = "Total Fat", 
               title = "Total Fat Distribution",  
               color = "navy") 
  
# show the results 
show(p)  

Output :

Code #6: Scatter plot
Scatter plot is used to plot values of two variables in a dataset. It helps to find correlation among the two variables that are selected.

# import necessary modules 
from bokeh.charts import Scatter, output_notebook, show 
import pandas as pd 
  
# output to notebook 
output_notebook() 
  
# read data in dataframe 
df = pd.read_csv(r"D:/kaggle / mcdonald / menu.csv") 
  
# create scatter plot 
p = Scatter(df, x = "Carbohydrates", y = "Saturated Fat", 
            title = "Saturated Fat vs Carbohydrates", 
            xlabel = "Carbohydrates", ylabel = "Saturated Fat", 
            color = "orange") 
   
# show the results 
show(p)  

Output :

Pygal Introduction

SumedhKadam

Improve

Article Tags :

Python – Data visualization using Bokeh

Similar Reads

Thank You!

What kind of Experience do you want to share?