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Plotly

Data & AnalyticsData SciencePython
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What it is

Plotly is a Python graphing library that makes interactive, publication-quality graphs online. It supports a wide variety of chart types, including line plots, scatter plots, bar charts, 3D plots, maps, and dashboards.

Plotly allows the creation of interactive visualizations with Python. You can generate figures using Plotly Express for simple charts or use the lower-level Graph Objects API for more complex and customized visualizations.

Installation

pip install plotly

Getting started

The smallest useful thing you can do with it, and what each part means.

Simple line plot with Plotly Express
import plotly.express as px
import pandas as pd
df = pd.DataFrame({'x':[1,2,3,4], 'y':[10,15,13,17]})
fig = px.line(df, x='x', y='y', title='Line Plot')
fig.show()
Creates an interactive line plot from a Pandas DataFrame and displays it in a browser or notebook.
Scatter plot with Plotly Express
import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')
fig.show()
Generates a scatter plot with color-coded species categories using Plotly Express.

Advanced usage

Where the library earns its place over a simpler alternative.

Customizing layout with Graph Objects
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=[1,2,3], y=[4,5,6], mode='lines+markers', name='Line 1'))
fig.update_layout(title='Custom Layout', xaxis_title='X Axis', yaxis_title='Y Axis')
fig.show()
Demonstrates using Graph Objects to create a scatter plot with custom layout titles and styling.
3D Surface plot
import plotly.graph_objects as go
import numpy as np
x = np.linspace(-5,5,50)
y = np.linspace(-5,5,50)
x, y = np.meshgrid(x, y)
z = np.sin(np.sqrt(x**2 + y**2))
fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])
fig.show()
Creates an interactive 3D surface plot of a mathematical function.
Subplots and multiple traces
from plotly.subplots import make_subplots
import plotly.graph_objects as go
fig = make_subplots(rows=1, cols=2)
fig.add_trace(go.Bar(x=[1,2,3], y=[2,5,3]), row=1, col=1)
fig.add_trace(go.Scatter(x=[1,2,3], y=[5,3,6]), row=1, col=2)
fig.show()
Shows how to create multiple plots in a single figure using subplots with different trace types.
Interactive animations
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter(df.query('year==2007'), x='gdpPercap', y='lifeExp', size='pop', color='continent', hover_name='country', size_max=60, animation_frame='year')
fig.show()
Creates an animated scatter plot showing changes over years using Plotly Express.

Errors and fixes

The failures you are most likely to hit, and what actually resolves them.

ValueError: Invalid value
Check that your x and y data arrays have the same length and that column names match your DataFrame.
ModuleNotFoundError: No module named 'plotly'
Ensure Plotly is installed in your environment using pip or conda.
TypeError: figure.show() missing
Verify you have created a valid figure object using Plotly Express or Graph Objects.

Best practices

  • Use Plotly Express for quick, simple visualizations; use Graph Objects for detailed, customized figures.
  • Leverage hover labels, annotations, and legends for better interactivity.
  • Combine subplots to display multiple visualizations in one figure.
  • Use color scales and consistent styling for clarity and accessibility.
  • Export figures to HTML for sharing interactive visualizations easily.

Background

Why it exists, and what it was reacting to.

Plotly was created by Chris Parmer, Jack Parmer, and Alex Johnson in 2013 to provide a framework for interactive, web-ready visualizations. It integrates seamlessly with Python, R, MATLAB, and JavaScript, and allows users to create interactive plots for analysis, reporting, and dashboards.

Related libraries

Others in Data & Analytics for Python.

← All Python librariesPython guide