Dashboards are collections of bars, charts, and graphs that help us visualize different attributes of a dataset. A dashboard works as a graphical user interface which helps us identify the key performance indicators relevant to the dataset or the particular business model. Python provides different open-source libraries that can help you create your own dashboard with your dataset. Today we will be talking about Bokeh which is an open-source python library for interactive visualizations for the modern web browsers.
Bokeh provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. It can be used for different purposes like creating interactive plots, dashboards, and even data-driven applications.
In this article we will discuss:
- Creating Bokeh Visualization and Analyzing it.
- Creating a Sales Dashboard using Bokeh
Implementation of Bokeh:
Like any other library, we need to install Bokeh for exploring it by pip install bokeh
- Importing required libraries
We will import pandas for loading the dataset and will import different functions of bokeh as and when required.
import pandas as pd
from bokeh.plotting import figure, output_file, show
- Loading the dataset
We will create a sales dashboard for which we need sales data of a company, here I will use a dataset which contains Sales of a company and different attributes on which it depends.
df = pd.read_csv(‘Advertising.csv’)
df
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