Getting Started with Plotly

The Plotly Python library is an interactive open-source graphing library. It is a very helpful tool for data visualization and understanding the data simply and easily. Plotly graph objects are a high-level interface to plotly which are easy to use. It can plot various types of graphs and charts like scatter plots, line charts, bar charts, box plots, histograms, pie charts, etc. 

In this article, we will explore the basics of plotly, how to get started with it, and it's key features that make it peferred choice for creating interactive visualization.

What is Plotly?

Plotly is a Python library built on top of the Plotly JavaScript library (plotly.js). It supports over 40 unique chart types, including statistical, financial, geographic, scientific, and 3-dimensional visualizations. Plotly is known for its ability to create beautiful, interactive web-based visualizations that can be displayed in Jupyter notebooks, saved as standalone HTML files, or integrated into web applications using Dash.

History and Evolution of Plotly

Plotly was initially released in 2013 and has evolved significantly since then. It was designed to be an open-source library for creating interactive, publication-quality graphs. Over the years, Plotly has added numerous features and chart types, expanded its integrations with other libraries, and improved its performance and ease of use.

Understanding the evolution of Plotly helps appreciate its current capabilities and its role in the data visualization ecosystem.

Why Use Plotly?

Plotly offers several advantages that make it an attractive choice for data visualization:

• Interactivity: Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.
• Ease-of-Use: It is visually attractive that can be accepted by a wide range of audiences.
• Customization: It allows us for the endless customization of our graphs that makes our plot more meaningful and understandable for others.
• Export Options: Plotly supports exporting visualizations to various formats, including static images and HTML files.

Installation and Setup with Plotly

Installation:

To install this module type the below command in the terminal.

pip install plotly

This may take some time as it will install the dependencies as well. For upgrading the version dependency refer to : How To Upgrade Plotly To Latest Version?

Plotly's Architecture in Python

Plotly is designed with a modular architecture that provides flexibility in creating visualizations. At the heart of Plotly's architecture are two primary interfaces: Plotly Express and Plotly Graph Objects. Each serves a distinct purpose and caters to different user needs, offering varying levels of complexity and customization.

1. Plotly Express

Plotly Express is a high-level, user-friendly interface for creating plots. It is designed to simplify the process of generating common types of visualizations with minimal code. Plotly Express is built on top of Plotly Graph Objects and provides a more streamlined and intuitive API for users who need to quickly produce interactive plots without delving into the intricacies of Plotly’s lower-level functionalities.

Features:

• Simplicity and Ease of Use: Plotly Express allows users to create plots using a concise and straightforward syntax. This simplicity is achieved by abstracting many of the underlying complexities of Plotly’s core functionalities, making it accessible even for users who are new to data visualization.
• Quick Visualization: Plotly Express is ideal for exploratory data analysis and quick visualizations. Users can generate a wide range of common plot types—such as scatter plots, bar charts, and line plots—using just a few lines of code.
• Automatic Handling: Plotly Express automatically manages many aspects of the plot, such as axis labels, titles, and legends. This automation helps users focus on data analysis rather than the technical details of plot configuration.

Example:

Here’s a simple example of creating a scatter plot using Plotly Express:

import plotly.express as px
import pandas as pd

# Sample data
df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [10, 11, 12, 13, 14],
    'category': ['A', 'B', 'A', 'B', 'A']
})

# Create a scatter plot
fig = px.scatter(df, x='x', y='y', color='category', title='Simple Scatter Plot')
fig.show()

Output: