Data Visualization in Python using Bokeh Library
The bokeh library provides two visualization interfaces to the users:
In this tutorial, we will learn how to create different types of data visualization graphs and charts using the bokeh library in Python.
To install the bokeh library, we can use the following command:
Code 1: To Create Scatter Circle Markers
We can create Scatter Circle Markers on the Plot by using the bokeh library. For this, we will use the circle() function.
Code 2: To Create a Single Line
We can create a single line by using the bokeh library in Python. For this, we will use the line() function.
Code 3: To Create a Bar chart
Bar charts are used for representing the categorical data with rectangular bars. The length of the bar is proportional to the values they are representing.
Example: Vertical Bar Chart
Example 2: Horizontal Bar Chart
Code 4: To Create Box Plot
Box plot is used for representing the statistical data on the plots, and it is helpful for summarizing the statistical properties of several groups present in the dataset.
Code 5: To Create Scatter Plot
A Scatter plot is used for plotting values of two variables in a dataset. It is helpful in finding the correlation between the two variables which are selected.
In this tutorial, we have discussed various types of data visualization, namely Bar chart, Box plot, Scatter Markers, Scatter Plots, and Single lines, which can be created using the bokeh library in Python.
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