Key Features
Interactive Charts
Zoom, pan, hover, and export visuals with built-in interactivity.
Wide Chart Support
Create line, bar, scatter, pie, box, violin, heatmaps, and 3D plots.
Dash Integration
Build full-fledged dashboards and web apps using Plotly + Dash.
Geospatial & 3D
Render maps, choropleths, and 3D surface plots for spatial analytics.
How It Works
Install & Import
Use `pip install plotly` and `import plotly.express as px` or `plotly.graph_objects`.
Prepare Data
Use Pandas or NumPy to structure your data for plotting.
Create Chart
Use `px.bar()`, `px.line()`, or `go.Figure()` to build interactive visuals.
Customize Layout
Add titles, legends, annotations, and themes using layout options.
Render or Embed
Display in Jupyter, export as HTML, or embed in Dash apps.
Code Example
# Plotly Express Bar Chart Example
import plotly.express as px
import pandas as pd
df = pd.read_csv("sales.csv")
fig = px.bar(df, x="Region", y="Revenue", color="Region", title="Revenue by Region")
fig.show()Use Cases
Interactive Dashboards
Build responsive dashboards with filters, tabs, and live charts using Dash.
Exploratory Data Analysis
Visualize distributions, trends, and correlations with hover insights.
Geospatial Analytics
Plot maps, choropleths, and location-based data for spatial insights.
ML Model Monitoring
Track predictions, residuals, and performance metrics with dynamic charts.
Integrations & Resources
Explore Plotly’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- Python, R, JavaScript
- Pandas, NumPy, Scikit-learn
- Dash, Flask, Streamlit
- Jupyter, Colab, VS Code
- HTML, SVG, JSON export
Helpful Resources
FAQ
Common questions about Plotly’s capabilities, usage, and ecosystem.