plotly

name plotly description Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG). Plotly Python graphing library for creating interactive, publication-quality visualizations with 40+ chart types. Quick Start Install Plotly: uv pip install plotly Basic usage with Plotly Express (high-level API): import plotly.express as px import pandas as pd df = pd.DataFrame({ 'x' : [ 1 , 2 , 3 , 4 ], 'y' : [ 10 , 11 , 12 , 13 ] }) fig = px.scatter(df, x= 'x' , y= 'y' , title= 'My First Plot' ) fig.show() Choosing Between APIs Use Plotly Express (px) For quick, standard visualizations with sensible defaults: Working with pandas DataFrames Creating common chart types (scatter, line, bar, histogram, etc.) Need automatic color encoding and legends Want minimal code (1-5 lines) See reference/plotly-express.md for complete guide. Use Graph Objects (go) For fine-grained control and custom visualizations: Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts) Building complex multi-trace figures from scratch Need precise control over individual components Creating specialized visualizations with custom shapes and annotations See reference/graph-objects.md for complete guide. Note: Plotly Express returns graph objects Figure, so you can combine approaches: fig = px.scatter(df, x= 'x' , y= 'y' ) fig.update_layout(title= 'Custom Title' ) # Use go methods on px figure fig.add_hline(y= 10 ) # Add shapes Core Capabilities 1. Chart Types Plotly supports 40+ chart types organized into categories: Basic Charts: scatter, line, bar, pie, area, bubble Statistical Charts: histogram, box plot, violin, distribution, error bars Scientific Charts: heatmap, contour, ternary, image display Financial Charts: candlestick, OHLC, waterfall, funnel, time series Maps: scatter maps, choropleth, density maps (geographic visualization) 3D Charts: scatter3d, surface, mesh, cone, volume Specialized: sunburst, treemap, sankey, parallel coordinates, gauge For detailed examples and usage of all chart types, see reference/chart-types.md . 2. Layouts and Styling Subplots: Create multi-plot figures with shared axes: from plotly.subplots import make_subplots import plotly.graph_objects as go fig = make_subplots(rows= 2 , cols= 2 , subplot_titles=( 'A' , 'B' , 'C' , 'D' )) fig.add_trace(go.Scatter(x=[ 1 , 2 ], y=[ 3 , 4 ]), row= 1 , col= 1 ) Templates: Apply coordinated styling: fig = px.scatter(df, x= 'x' , y= 'y' , template= 'plotly_dark' ) # Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white Customization: Control every aspect of appearance: Colors (discrete sequences, continuous scales) Fonts and text Axes (ranges, ticks, grids) Legends Margins and sizing Annotations and shapes For complete layout and styling options, see reference/layouts-styling.md . 3. Interactivity Built-in interactive features: Hover tooltips with customizable data Pan and zoom Legend toggling Box/lasso selection Rangesliders for time series Buttons and dropdowns Animations # Custom hover template fig.update_traces( hovertemplate= '<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>' ) # Add rangeslider fig.update_xaxes(rangeslider_visible= True ) # Animations fig = px.scatter(df, x= 'x' , y= 'y' , animation_frame= 'year' ) For complete interactivity guide, see reference/export-interactivity.md . 4. Export Options Interactive HTML: fig.write_html( 'chart.html' ) # Full standalone fig.write_html( 'chart.html' , include_plotlyjs= 'cdn' ) # Smaller file Static Images (requires kaleido): uv pip install kaleido fig.write_image( 'chart.png' ) # PNG fig.write_image( 'chart.pdf' ) # PDF fig.write_image( 'chart.svg' ) # SVG For complete export options, see reference/export-interactivity.md . Common Workflows Scientific Data Visualization import plotly.express as px # Scatter plot with trendline fig = px.scatter(df, x= 'temperature' , y= 'yield' , trendline= 'ols' ) # Heatmap from matrix fig = px.imshow(correlation_matrix, text_auto= True , color_continuous_scale= 'RdBu' ) # 3D surface plot import plotly.graph_objects as go fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)]) Statistical Analysis # Distribution comparison fig = px.histogram(df, x= 'values' , color= 'group' , marginal= 'box' , nbins= 30 ) # Box plot with all points fig = px.box(df, x= 'category' , y= 'value' , points= 'all' ) # Violin plot fig = px.violin(df, x= 'group' , y= 'measurement' , box= True ) Time Series and Financial # Time series with rangeslider fig = px.line(df, x= 'date' , y= 'price' ) fig.update_xaxes(rangeslider_visible= True ) # Candlestick chart import plotly.graph_objects as go fig = go.Figure(data=[go.Candlestick( x=df[ 'date' ], open =df[ 'open' ], high=df[ 'high' ], low=df[ 'low' ], close=df[ 'close' ] )]) Multi-Plot Dashboards from plotly.subplots import make_subplots import plotly.graph_objects as go fig = make_subplots( rows= 2 , cols= 2 , subplot_titles=( 'Scatter' , 'Bar' , 'Histogram' , 'Box' ), specs=[[{ 'type' : 'scatter' }, { 'type' : 'bar' }], [{ 'type' : 'histogram' }, { 'type' : 'box' }]] ) fig.add_trace(go.Scatter(x=[ 1 , 2 , 3 ], y=[ 4 , 5 , 6 ]), row= 1 , col= 1 ) fig.add_trace(go.Bar(x=[ 'A' , 'B' ], y=[ 1 , 2 ]), row= 1 , col= 2 ) fig.add_trace(go.Histogram(x=data), row= 2 , col= 1 ) fig.add_trace(go.Box(y=data), row= 2 , col= 2 ) fig.update_layout(height= 800 , showlegend= False ) Integration with Dash For interactive web applications, use Dash (Plotly's web app framework): uv pip install dash import dash from dash import dcc, html import plotly.express as px app = dash.Dash(__name__) fig = px.scatter(df, x= 'x' , y= 'y' ) app.layout = html.Div([ html.H1( 'Dashboard' ), dcc.Graph(figure=fig) ]) app.run_server(debug= True ) Reference Files plotly-express.md - High-level API for quick visualizations graph-objects.md - Low-level API for fine-grained control chart-types.md - Complete catalog of 40+ chart types with examples layouts-styling.md - Subplots, templates, colors, customization export-interactivity.md - Export options and interactive features Additional Resources Official documentation: https://plotly.com/python/ API reference: https://plotly.com/python-api-reference/ Community forum: https://community.plotly.com/