Data Analysis and Visualization with Python: Using Seaborn and Plotly

INTRODUCTION:

One of the most used programming languages in the data science community is Python. Due to its strong libraries, such Seaborn and Plotly, it is frequently used for data analysis and visualisation jobs. In this tutorial, we’ll go through how to use Python’s Seaborn and Plotly for data analysis and visualisation.

Seaborn:

The Matplotlib package serves as the foundation for the Python module Seaborn. It offers a sophisticated user interface for producing interesting and useful statistical visuals. For visualising large, complicated data sets, particularly ones with categorical data, Seaborn is frequently employed. It offers a variety of plot types, including scatter plots, line plots, and bar plots.

Open-source:

The open-source visualization package Plotly, on the other hand, offers interactive and dynamic visualisations in Python. There are many different chart kinds available, including scatter plots, line charts, heat maps, and more. You may make interactive dashboards and data applications with Plotly as well.

To start using Seaborn and Plotly, you first need to import them into your Python environment. You can use the following commands to do this:

import seaborn as sns
import plotly.express as px
Once you have imported the libraries, you can start creating your visualizations. For example, you can create a scatter plot using Seaborn by using the following code:


sns.scatterplot(x='age', y='income', data=data)

This will create a scatter plot of the age and income columns in the data DataFrame.

Similarly, you can create a scatter plot using Plotly by using the following code:

fig = px.scatter(data, x='age', y='income')
fig.show()

This will create an interactive scatter plot of the age and income columns in the data DataFrame.

CONCLUSION:

Seaborn and Plotly are effective Python tools for data analysis and visualization, to sum up. They offer a sophisticated user interface for producing interesting and useful statistical graphics as well as interactive visualisations. With the use of these libraries, you can quickly produce visualizations that will aid in both your understanding of the data and the dissemination of your conclusions.