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Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
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 