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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).
npx skill4agent add microck/ordinary-claude-skills plotlyuv pip install plotlyimport 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()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 shapesfrom 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)fig = px.scatter(df, x='x', y='y', template='plotly_dark')
# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white# 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')fig.write_html('chart.html') # Full standalone
fig.write_html('chart.html', include_plotlyjs='cdn') # Smaller fileuv pip install kaleidofig.write_image('chart.png') # PNG
fig.write_image('chart.pdf') # PDF
fig.write_image('chart.svg') # SVGimport 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)])# 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 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']
)])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)uv pip install dashimport 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)