Plotly Essentials

Master Plotly for Interactive Data Visualization

Open-source graphing library for Python, R, and JavaScript — ideal for building interactive, publication-quality charts and dashboards.

Models Deployed

12,430+

Active Developers

58,900+

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 Model Training

# 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

Official Docs GitHub Repo Tutorials