Plotly is an amazing data visualization library that allows you to create beautiful and interactive data visualizations with just a few lines of code. Plotly is available for Python, R, MATLAB, Perl, and Julia also. It is therefore available for almost all the common programming languages among data science professionals. If you are interested in learning how to use plotly for data visualization, this article is for you. In this article, I will present a tutorial on Plotly for data visualization using Python.

Plotly Tutorial using Python

If you have never used this data visualization library before, you can easily install it on your system using the pip command in your terminal or the command prompt:

  • pip install plotly

It's up to you to decide when to use plotly or any other data visualization library. As a data science newbie, our first data visualization library is matplotlib which is very simple and easy to understand. Plotly is as easy to learn as matplotlib, so if you know how to use matplotlib for data visualization, you should start by creating some of the very basic visualizations using Plotly. Now let's take a look at a tutorial on some of the most basic data visualizations with Plotly using Python.

Bar Plot:

A bar chart is used to compare the distribution of data points based on their values in different types of categories. So here is how to easily visualize a bar chart using the plotly library in Python:

bar plot with plotly

Line Plot:

A line plot is used to measure changes in values over different periods. Here's how to easily visualize a line plot using the plotly library in Python:

line plot

Scatter Plot:

A scatter plot is used to visualize the relationship of two or more variables. It is best to use when you have matched numeric data points. Here is how to visualize a scatter plot using the plotly library in Python:

scatter plot with plotly

These were some of the very basic data visualizations with Plotly. You can find a dedicated tutorial on all other advanced data visualizations where it is better to use the plotly library from below:

  1. Violin Plot
  2. Sunburst Plot
  3. t-SNE
  4. Treemap
  5. Choropleth Map
  6. Radar Plot
  7. Candlestick chart

You can learn more about visualizing data using Plotly from this library's official documentation from here.

Summary

So this is how you can use the Plotly library in Python for interactive data visualization. It is an amazing data visualization library that allows you to create beautiful and interactive data visualizations with just a few lines of code. I hope you liked this article on a tutorial on the Plotly library by using the Python programming language. Feel free to ask your valuable questions in the comments section below.