Skip to main content Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/synthetic-sciences/openscience --skill plotlyLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Télécharger Zip Téléchargement... Plus depuis ce dépôt Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
Fast inference and fine-tuning platform with serverless and on-demand GPU deployments. OpenAI-compatible API for chat completions, embeddings, function calling, vision, and structured output. Supports SFT, DPO, and RL fine-tuning. SOC2 + HIPAA compliant.
Serverless inference, fine-tuning, embeddings, image generation, and batch processing on 200+ open-source models via an OpenAI-compatible API. Use when you need fast, cost-effective access to open-source LLMs without managing infrastructure.
Métiers associés SOC
Basé sur la classification professionnelle SOC
Explorateur de fichiers
6 fichiers name plotly description Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization. category visualization license MIT license metadata {"skill-author":"Synthetic Sciences"} version 1.0.0 author Synthetic Sciences tags ["Visualization","Interactive","Dashboards","Charts"] dependencies ["plotly>=5.22.0"]
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' )
fig.add_hline(y=10 )
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
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' )
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
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
fig.update_traces(
hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'
)
fig.update_xaxes(rangeslider_visible=True )
fig = px.scatter(df, x='x' , y='y' , animation_frame='year' )
4. Export Options fig.write_html('chart.html' )
fig.write_html('chart.html' , include_plotlyjs='cdn' )
Static Images (requires kaleido):
fig.write_image('chart.png' )
fig.write_image('chart.pdf' )
fig.write_image('chart.svg' )
Common Workflows
Scientific Data Visualization import plotly.express as px
fig = px.scatter(df, x='temperature' , y='yield' , trendline='ols' )
fig = px.imshow(correlation_matrix, text_auto=True , color_continuous_scale='RdBu' )
import plotly.graph_objects as go
fig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])
Statistical Analysis
fig = px.histogram(df, x='values' , color='group' , marginal='box' , nbins=30 )
fig = px.box(df, x='category' , y='value' , points='all' )
fig = px.violin(df, x='group' , y='measurement' , box=True )
Time Series and Financial
fig = px.line(df, x='date' , y='price' )
fig.update_xaxes(rangeslider_visible=True )
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):
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
Additional Resources 