Plotly Customization Tips You Can’t Miss for Better Data Storytelling
Unleash the Full Potential of Plotly with These Simple and Effective Customization Techniques
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 = go.Figure()
fig.add_trace(go.Scatter(x=df['sepal_width'], y=df['petal_length'], mode='markers'))
# Customize layout
title='Iris Dataset Scatter Plot',
# Show plot
In this example, we first load the Iris dataset from the
plotly.express library. Then, we create a scatter plot using
go.Scatter, and add it to a
go.Figure object. We customize the layout of the plot using
fig.update_layout, specifying the title, axis labels, and tick values. Finally, we show the plot using