Mastering Plotly in Python: Interactive Data Visualization Made Easy

Chapter 3: Advanced Plotting Techniques in Plotly

🔹 1. Introduction

In this chapter, we will explore advanced plotting techniques in Plotly, enabling you to create more complex, dynamic, and insightful visualizations. Plotly’s capabilities extend beyond basic charts to more advanced options, such as multi-trace plots, 3D plots, subplots, and customized layouts. These techniques allow you to display data in a way that is not only visually appealing but also highly interactive.

Whether you’re comparing multiple datasets, visualizing three-dimensional data, or fine-tuning your plot layout, this chapter will help you expand your Plotly skills. You'll learn how to:

• Combine multiple plots into a single figure
• Create 3D plots and visualize multidimensional data
• Use subplots to show multiple related charts in one figure
• Customize the layout to optimize your visualizations

🔹 2. Creating Multi-Trace Plots

One of the most powerful features of Plotly is the ability to combine multiple traces (or data series) into a single plot. This allows you to compare different datasets or variables on the same graph, which is especially useful in data analysis.

✅ Example: Multiple Line Charts

You can overlay multiple line charts on a single figure to compare different trends. Here’s how to plot two lines representing sales and profit over time:

import plotly.graph_objects as go

import pandas as pd

# Sample data

data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],

        'Sales': [100, 120, 150, 170, 200],

        'Profit': [50, 60, 80, 90, 120]}

df = pd.DataFrame(data)

# Create a figure with two line traces

fig = go.Figure()

# Add the Sales line trace

fig.add_trace(go.Scatter(x=df['Month'], y=df['Sales'], mode='lines', name='Sales'))

# Add the Profit line trace

fig.add_trace(go.Scatter(x=df['Month'], y=df['Profit'], mode='lines', name='Profit'))

# Update layout

fig.update_layout(title='Sales and Profit Over Time', xaxis_title='Month', yaxis_title='Value')

# Show the plot

fig.show()

✅ Example: Multiple Bar Charts

Similarly, you can combine multiple bar charts:

fig = go.Figure()

# Add the Sales bar trace

fig.add_trace(go.Bar(x=df['Month'], y=df['Sales'], name='Sales'))

# Add the Profit bar trace

fig.add_trace(go.Bar(x=df['Month'], y=df['Profit'], name='Profit'))

# Update layout

fig.update_layout(title='Sales and Profit Comparison', barmode='group')

# Show the plot

fig.show()

This code creates a grouped bar chart with Sales and Profit represented side-by-side for each month.

🔹 3. Creating 3D Plots

Plotly excels in handling 3D plots, which are essential for visualizing data with three variables. You can create 3D scatter plots, surface plots, and mesh plots.

✅ Example: 3D Scatter Plot

In a 3D scatter plot, each point is represented in three-dimensional space.

import plotly.graph_objects as go

# Sample 3D data

data = {

    'X': [1, 2, 3, 4, 5],

    'Y': [10, 11, 12, 13, 14],

    'Z': [100, 200, 300, 400, 500]

}

fig = go.Figure(data=[go.Scatter3d(

    x=data['X'], y=data['Y'], z=data['Z'],

    mode='markers', marker=dict(size=12, color='blue', opacity=0.8)

)])

# Update layout

fig.update_layout(title='3D Scatter Plot', scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'))

# Show the plot

fig.show()

This creates a 3D scatter plot where each point is positioned based on its X, Y, and Z coordinates.

✅ Example: 3D Surface Plot

You can also create a 3D surface plot for visualizing continuous data across a grid:

import numpy as np

# Create meshgrid for X and Y

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

# Calculate Z values based on X, Y grid

z = np.sin(np.sqrt(x**2 + y**2))

fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])

# Update layout

fig.update_layout(title='3D Surface Plot', scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z'))

# Show the plot

fig.show()

This generates a 3D surface plot that shows the values of Z based on the X and Y grid.

🔹 4. Using Subplots

Plotly allows you to create subplots—multiple charts in one figure that share a common axis or are independent. This is useful when comparing different datasets in a structured way.

✅ Example: Creating Subplots

You can create subplots with subplot titles, different chart types, and shared axes.

from plotly.subplots import make_subplots

import plotly.graph_objects as go

# Create a subplot grid

fig = make_subplots(rows=1, cols=2)

# Add a line plot to the first subplot

fig.add_trace(go.Scatter(x=df['Month'], y=df['Sales'], mode='lines', name='Sales'), row=1, col=1)

# Add a bar plot to the second subplot

fig.add_trace(go.Bar(x=df['Month'], y=df['Profit'], name='Profit'), row=1, col=2)

# Update layout

fig.update_layout(title='Sales and Profit Comparison', showlegend=False)

# Show the plot

fig.show()

This creates a single row of two subplots: one with a line chart and another with a bar chart.

🔹 5. Customizing Layouts

The layout of a plot controls its visual appearance, including axis labels, legends, and titles. Plotly provides extensive options for customizing these elements.

✅ Example: Custom Layout with Multiple Elements

fig = go.Figure()

# Add a line plot

fig.add_trace(go.Scatter(x=df['Month'], y=df['Sales'], mode='lines', name='Sales'))

# Update layout with customizations

fig.update_layout(

    title='Sales Trend Over Time',

    xaxis=dict(title='Month', showgrid=True),

    yaxis=dict(title='Sales', showgrid=True),

    plot_bgcolor='lightgray',

    font=dict(family='Arial, sans-serif', size=14, color='black')

)

# Show the plot

fig.show()

In this example, we customize the background color, axis labels, and font properties of the plot.

🔹 6. Summary Table