5 Best Ways to Plot 4D Scatter Plot with Custom Colors and Custom Area Size in Python Matplotlib

Rate this post

πŸ’‘ Problem Formulation: Visualizing 4-dimensional data can be challenging, but with Python’s Matplotlib, we can represent the fourth dimension through color or size. If we have data with four variables (e.g., x, y, z, w), we aim to plot this as a scatter plot where x, y, and z are coordinates, and ‘w’ influences the color and/or the size of the scatter points. Our desired output is to visually distinguish the different ‘w’ values within the 3D space.

Method 1: Use Color to Represent the Fourth Dimension

This method encodes the fourth dimension using color. In Python’s Matplotlib, the scatter() function’s ‘c’ parameter can be used to set the color of each point. A colormap can translate the numerical value of the fourth dimension to a specific color on the plot.

Here’s an example:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# Sample data
x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)
w = np.random.standard_normal(100)

# Create a 3D scatter plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, c=w, cmap='viridis')

# Add a colorbar
plt.colorbar(scatter)

# Show plot
plt.show()

The output is a 3D scatter plot where each point’s color corresponds to its ‘w’ value according to the ‘viridis’ colormap.

The provided code generates a figure with a 3D scatter plot. The variables x, y, and z define the coordinates in the 3D space, while ‘w’ provides the fourth dimension, depicted by varying the color. The viridis colormap translates ‘w’ values into a gradient of colors, which is then shown alongside the plot for reference.

Method 2: Use Area Size to Represent the Fourth Dimension

Alternatively, size can represent the fourth dimension. Matplotlib’s scatter() function’s ‘s’ parameter adjusts each point’s size. By mapping the fourth dimension to point sizes, we can visualize variations within the other three dimensions.

Here’s an example:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# Sample data
x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)
w = np.abs(np.random.standard_normal(100)) * 100  # Ensure positive size values

# Create a 3D scatter plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, s=w)

# Show plot
plt.show()

The output is a 3D scatter plot with variable point sizes, visually conveying the fourth dimension’s values.

In this code sample, we’ve again created a 3D scatter plot. This time, however, the ‘s’ parameter in the scatter() function determines the area size of each scatter point, thus reflecting the fourth dimension (variable ‘w’). We ensure ‘w’ is positive since the area cannot be negative, and we multiply it by 100 to better visualize the differences in size.

Method 3: Combine Color and Size for a Comprehensive 4D Representation

This method combines both color and size to represent the fourth dimension. By simultaneously varying the color and size of the scatter points in the plot, we can create a more nuanced visualization of 4D data.

Here’s an example:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# Sample data
x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)
w = np.random.standard_normal(100)

# Normalize 'w' for color and size scaling
w_normalized = (w - np.min(w)) / (np.max(w) - np.min(w))
sizes = w_normalized * 200
colors = w_normalized

# Create a 3D scatter plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
scatter = ax.scatter(x, y, z, s=sizes, c=colors, cmap='coolwarm')

# Add a colorbar
plt.colorbar(scatter)

# Show plot
plt.show()

The output is a 3D scatter plot with points varying in size and color, enhancing the visualization of the fourth dimension’s complexity.

In this script, we’ve normalized ‘w’ so that its values can be effectively used for both color and size. After normalization, ‘w’ values are used to modulate the point sizes (with scaling for visibility) and mapped to a color gradient using the cmap='coolwarm' parameter. This combination helps to intricately display the fourth dimension’s variation in the scatter plot.

Method 4: Interactive 4D Scatter Plots with Plotly

While Matplotlib is powerful, the library Plotly allows for interactive 4D scatter plots. Plotly’s ability to create hoverable and zoomable plots can offer an enhanced user experience while exploring 4D data.

Here’s an example:

import plotly.express as px
import numpy as np

# Sample data in a DataFrame
df = pd.DataFrame({'X': np.random.standard_normal(100),
                   'Y': np.random.standard_normal(100),
                   'Z': np.random.standard_normal(100),
                   'W': np.random.standard_normal(100)})

# Create an interactive 3D scatter plot
fig = px.scatter_3d(df, x='X', y='Y', z='Z', 
                    color='W', size='W', color_continuous_scale='Viridis', size_max=18)

# Show plot
fig.show()

The output is an interactive 3D scatter plot, where the fourth dimension is expressed through both color and size, and can be inspected in detail through interaction.

This example uses Plotly to create an interactive 3D scatter plot where the ‘W’ column in our DataFrame symbolizes the fourth dimension. By passing ‘W’ as both the size and color parameter, the plot allows users to hover over data points, zoom in, and pan around to explore the 4D data in a dynamic and user-friendly way. Additionally, size_max is used to control the maximum size of scatter points on the plot to maintain readability.

Bonus One-Liner Method 5: Quick 4D Plot Using Seaborn

If you’re looking for a one-liner solution and don’t need a 3D plot, Seaborn’s pairplot() can pair up dimensions and plot them against each other in 2D, while using color and size for the third and fourth dimensions.

Here’s an example:

import seaborn as sns
import pandas as pd
import numpy as np

# Sample data in a DataFrame
df = pd.DataFrame({'x': np.random.standard_normal(100),
                   'y': np.random.standard_normal(100),
                   'z': np.random.standard_normal(100),
                   'w': np.random.standard_normal(100)})

# Quick pair plot
sns.pairplot(df, vars=['x', 'y', 'z'], hue='z', size=df['w']*10)

The output is a grid of 2D scatter plots comparing each pair of dimensions, with color for the third and point size for the fourth.

This one-liner creates a series of 2D scatter plots covering all possible dimension pairings using Seaborn. It’s a straightforward way to quickly visualize high-dimensional data, although it loses the 3D spatial relationship by default. The ‘hue’ parameter colors the points based on ‘z’, while the ‘size’ scales the points according to ‘w’. This approach is quite quick for an initial review of the data relationships.

Summary/Discussion

  • Method 1: Color for Fourth Dimension. Provides clear differentiation of one additional dimension using color. May become less effective if many data points overlap or in cases of colorblindness.
  • Method 2: Size for Fourth Dimension. Utilizes size to convey additional dimensional values. Can be visually overwhelming if there is a large range of values leading to excessively large or tiny points.
  • Method 3: Combining Color and Size. Offers a rich representation of multi-dimensional data. Complexity increases, and it may be hard to interpret accurately without interactive capabilities.
  • Method 4: Interactive 4D Scatter Plots with Plotly. Interactivity greatly aids in data exploration and understanding. Requires an understanding of Plotly and potentially more computing resources than simple Matplotlib plots.
  • Method 5: Quick Seaborn Pairplot. Fast and easy to generate preliminary views of multi-dimensional data, but compromises the full 4D spatial representation for quick analysis.