Plotly Customization Tips You Can’t Miss for Better Data Storytelling

Unleash the Full Potential of Plotly with These Simple and Effective Customization Techniques

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Introduction

Plotly is one of the most popular data visualization libraries in Python. It is a powerful tool that enables users to create interactive and visually appealing charts and graphs to analyze and communicate complex data.

Whether you’re a data scientist, business analyst, or student, understanding how to use Plotly can help you gain valuable insights and tell compelling stories with your data.

In this blog, I will give 5 customization tips that will take your visualization skills to the next level.

1. Using plotly.graph_objects to create custom layouts and axes

plotly.graph_objects is a low-level interface to creating Plotly visualizations that allows for greater customization than plotly.express. It allows users to specify custom layouts, axes, and other elements of a plot.

Here’s an example of how to use plotly.graph_objects to customize the layout of a scatter plot:

import plotly.graph_objects as go
import plotly.express as px

# Load sample data
df = px.data.iris()

# Create scatter plot
fig =…