vamseeachanta Plotly Interactive Visualization Skill
Create professional, interactive charts with 40+ chart types including 3D plots, statistical graphs, and scientific visualizations.
When to Use This Skill
Use Plotly when you need:
- Scientific visualizations - 3D plots, contours, heatmaps
- Statistical charts - Box plots, violin plots, histograms
- Large datasets - Efficient rendering of 100k+ points with WebGL
- Python/R integration - Seamless integration with data science workflows
- Quick interactive plots - High-level API with sensible defaults
- Dashboards - Interactive dashboards with Plotly Dash
Avoid when:
- Maximum customization needed (use D3.js)
- Extremely simple charts (use Chart.js)
- File size is critical concern
Core Capabilities
1. JavaScript Implementation
Basic Line Chart
<!DOCTYPE html>
<html>
<head>
<script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>
</head>
<body>
<div id="chart"></div>
<script>
const trace = {
x: [1, 2, 3, 4, 5],
y: [10, 15, 13, 17, 20],
type: 'scatter',
mode: 'lines+markers',
marker: { color: 'rgb(219, 64, 82)', size: 12 },
line: { color: 'rgb(55, 128, 191)', width: 3 }
};
const layout = {
title: 'Interactive Line Chart',
xaxis: { title: 'X Axis' },
yaxis: { title: 'Y Axis' },
hovermode: 'closest'
};
Plotly.newPlot('chart', [trace], layout, {
responsive: true,
displayModeBar: true
});
</script>
</body>
</html>
Multiple Traces
const trace1 = {
x: [1, 2, 3, 4, 5],
y: [1, 6, 3, 6, 8],
type: 'scatter',
mode: 'lines',
name: 'Series 1'
};
const trace2 = {
x: [1, 2, 3, 4, 5],
y: [5, 1, 6, 9, 2],
type: 'scatter',
mode: 'lines+markers',
name: 'Series 2'
};
const layout = {
title: 'Multi-Series Chart',
showlegend: true,
legend: { x: 1, y: 1 }
};
Plotly.newPlot('chart', [trace1, trace2], layout);
2. Python Implementation
Quick Start with Plotly Express
import plotly.express as px
import pandas as pd
# Load data from CSV
df = pd.read_csv('../data/processed/results.csv')
# Create interactive scatter plot
fig = px.scatter(
df,
x='time',
y='value',
color='category',
size='magnitude',
hover_data=['additional_info'],
title='Interactive Analysis Results'
)
# Customize layout
fig.update_layout(
template='plotly_white',
hovermode='x unified',
height=600,
font=dict(size=12)
)
# Save as HTML
fig.write_html('../reports/analysis_plot.html')
# Or display in Jupyter
fig.show()
Plotly Graph Objects (More Control)
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('../data/processed/timeseries.csv')
fig = go.Figure()
# Add trace
fig.add_trace(go.Scatter(
x=df['date'],
y=df['value'],
mode='lines+markers',
name='Value',
line=dict(color='rgb(55, 128, 191)', width=2),
marker=dict(size=8, color='rgb(219, 64, 82)'),
hovertemplate='<b>Date</b>: %{x}<br>' +
'<b>Value</b>: %{y:.2f}<br>' +
'<extra></extra>'
))
# Update layout
fig.update_layout(
title='Time Series Analysis',
xaxis_title='Date',
yaxis_title='Value',
template='plotly_dark',
hovermode='x unified'
)
fig.write_html('../reports/timeseries.html')
Complete Examples
Example 1: 3D Scatter Plot
import plotly.express as px
import numpy as np
# Generate 3D data
n = 500
x = np.random.randn(n)
y = np.random.randn(n)
z = np.random.randn(n)
colors = np.random.rand(n)
fig = px.scatter_3d(
x=x, y=y, z=z,
color=colors,
size=np.abs(z) * 10,
title='Interactive 3D Scatter Plot',
labels={'x': 'X Axis', 'y': 'Y Axis', 'z': 'Z Axis'}
)
fig.update_layout(
scene=dict(
xaxis_title='X',
yaxis_title='Y',
zaxis_title='Z'
)
)
fig.write_html('../reports/3d_scatter.html')
Example 2: Statistical Box Plot with Violin
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
df = pd.read_csv('../data/processed/measurements.csv')
# Create subplots
fig = make_subplots(
rows=1, cols=2,
subplot_titles=('Box Plot', 'Violin Plot')
)
# Box plot
fig.add_trace(
go.Box(y=df['value'], name='Box', boxmean='sd'),
row=1, col=1
)
# Violin plot
fig.add_trace(
go.Violin(y=df['value'], name='Violin', box_visible=True, meanline_visible=True),
row=1, col=2
)
fig.update_layout(
title_text='Statistical Distribution Analysis',
showlegend=False,
height=500
)
fig.write_html('../reports/statistical_analysis.html')
Example 3: Animated Time Series
import plotly.express as px
import pandas as pd
df = pd.read_csv('../data/processed/timeseries_multi.csv')
fig = px.line(
df,
x='date',
y='value',
color='category',
animation_frame='year',
animation_group='category',
title='Animated Time Series by Category',
range_y=[0, df['value'].max() * 1.1]
)
fig.update_layout(
xaxis_title='Date',
yaxis_title='Value',
hovermode='x unified'
)
fig.write_html('../reports/animated_timeseries.html')
Example 4: Interactive Heatmap with Annotations
import plotly.graph_objects as go
import numpy as np
# Generate correlation matrix
data = np.random.rand(10, 10)
labels = [f'Var {i+1}' for i in range(10)]
fig = go.Figure(data=go.Heatmap(
z=data,
x=labels,
y=labels,
colorscale='Viridis',
text=np.round(data, 2),
texttemplate='%{text}',
textfont={"size": 10},
hovertemplate='%{y} vs %{x}<br>Value: %{z:.3f}<extra></extra>'
))
fig.update_layout(
title='Correlation Heatmap',
xaxis_title='Variables',
yaxis_title='Variables',
width=700,
height=700
)
fig.write_html('../reports/heatmap.html')
Example 5: Multi-Axis Chart
import plotly.graph_objects as go
from plotly.subplots import make_subplots
fig = make_subplots(specs=[[{"secondary_y": True}]])
# Temperature (primary y-axis)
fig.add_trace(
go.Scatter(x=[1, 2, 3, 4, 5], y=[20, 22, 25, 23, 24],
name="Temperature (°C)", mode='lines+markers'),
secondary_y=False,
)
# Humidity (secondary y-axis)
fig.add_trace(
go.Scatter(x=[1, 2, 3, 4, 5], y=[65, 70, 60, 75, 68],
name="Humidity (%)", mode='lines+markers'),
secondary_y=True,
)
fig.update_xaxes(title_text="Time")
fig.update_yaxes(title_text="Temperature (°C)", secondary_y=False)
fig.update_yaxes(title_text="Humidity (%)", secondary_y=True)
fig.update_layout(title_text="Multi-Axis Chart", hovermode='x unified')
fig.write_html('../reports/multi_axis.html')
Example 6: Large Dataset with WebGL
import plotly.graph_objects as go
import numpy as np
# Generate large dataset (100,000 points)
n = 100000
x = np.random.randn(n)
y = np.random.randn(n)
# Use Scattergl for performance
fig = go.Figure(data=go.Scattergl(
x=x,
y=y,
mode='markers',
marker=dict(
size=2,
color=np.random.randn(n),
colorscale='Viridis',
showscale=True
)
))
fig.update_layout(
title='Large Dataset (100k points) with WebGL',
xaxis_title='X',
yaxis_title='Y'
)
fig.write_html('../reports/large_dataset.html')
Dashboard with Plotly Dash
import dash
from dash import dcc, html, Input, Output
import plotly.express as px
import pandas as pd
# Load data
df = pd.read_csv('../data/processed/dashboard_data.csv')
# Initialize app
app = dash.Dash(__name__)
# Layout
app.layout = html.Div([
html.H1('Interactive Dashboard'),
html.Div([
html.Label('Select Category:'),
dcc.Dropdown(
id='category-dropdown',
options=[{'label': cat, 'value': cat} for cat in df['category'].unique()],
value=df['category'].unique()[0]
)
], style={'width': '50%'}),
dcc.Graph(id='main-chart'),
dcc.Graph(id='histogram')
])
# Callbacks
@app.callback(
[Output('main-chart', 'figure'),
Output('histogram', 'figure')],
[Input('category-dropdown', 'value')]
)
def update_charts(selected_category):
filtered_df = df[df['category'] == selected_category]
# Line chart
line_fig = px.line(
filtered_df,
x='date',
y='value',
title=f'Trend for {selected_category}'
)
# Histogram
hist_fig = px.histogram(
filtered_df,
x='value',
nbins=30,
title=f'Distribution for {selected_category}'
)
return line_fig, hist_fig
if __name__ == '__main__':
app.run_server(debug=True)
Best Practices
1. Use Plotly Express for Quick Plots
# Simple and readable
fig = px.scatter(df, x='x', y='y', color='category', title='Quick Scatter')
2. Graph Objects for Complex Customization
# More control
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode='markers'))
fig.update_layout(title='Custom Chart')
3. Optimize for Large Datasets
# Use Scattergl for >10k points
fig = go.Figure(data=go.Scattergl(x=x, y=y, mode='markers'))
4. Responsive Design
fig.update_layout(
autosize=True,
margin=dict(l=20, r=20, t=40, b=20)
)
5. Custom Hover Templates
fig.update_traces(
hovertemplate='<b>%{x}</b><br>Value: %{y:.2f}<extra></extra>'
)
Chart Types Available
Basic Charts
- Line, Scatter, Bar, Histogram, Box, Violin
Scientific Charts
- Contour, Heatmap, 3D Surface, 3D Scatter, Streamline
Financial Charts
- Candlestick, OHLC, Waterfall, Funnel
Statistical Charts
- Box, Violin, Histogram, Density Heatmap, 2D Histogram
Maps
- Scatter Mapbox, Choropleth, Density Mapbox
3D Charts
- 3D Scatter, 3D Line, 3D Surface, 3D Mesh
Installation
JavaScript (CDN)
<script src="https://cdn.plot.ly/plotly-2.27.0.min.js"></script>
Python
pip install plotly
# or
uv pip install plotly
# For Dash
pip install dash
R
install.packages("plotly")
Performance Tips
- Use WebGL for large datasets - Scattergl, Scattermapbox
- Limit animation frames - Keep under 50 frames
- Simplify traces - Reduce number of data points for complex charts
- Disable hover - For static exports:
hovermode=False - Optimize layout updates - Batch updates when possible
Export Options
HTML (Recommended for Reports)
fig.write_html('report.html', include_plotlyjs='cdn')
Static Images
# Requires kaleido
fig.write_image('chart.png', width=1200, height=800)
fig.write_image('chart.pdf')
fig.write_image('chart.svg')
JSON
import json
fig.write_json('chart.json')
Integration Examples
With Pandas
import plotly.express as px
df = pd.read_csv('data.csv')
fig = px.line(df, x='date', y='value')
With NumPy
import numpy as np
import plotly.graph_objects as go
x = np.linspace(0, 10, 100)
y = np.sin(x)
fig = go.Figure(data=go.Scatter(x=x, y=y))
With Jupyter Notebooks
# Automatically displays in cell output
fig.show()
# Or use widget mode
import plotly.graph_objects as go
fig = go.FigureWidget()
Resources
- Official Docs: https://plotly.com/python/
- JavaScript Docs: https://plotly.com/javascript/
- Dash Docs: https://dash.plotly.com/
- Gallery: https://plotly.com/python/plotly-express/
- GitHub: https://github.com/plotly/plotly.py
Theming
Built-in Templates
# Available templates: plotly, plotly_white, plotly_dark, ggplot2, seaborn, simple_white
fig.update_layout(template='plotly_dark')
Custom Theme
custom_template = go.layout.Template(
layout=dict(
font=dict(family='Arial', size=14),
plot_bgcolor='#f0f0f0',
paper_bgcolor='white'
)
)
fig.update_layout(template=custom_template)
Use this skill for professional, interactive scientific and analytical visualizations with minimal code!