How to Make a 4D Plot with Matplotlib Using Arbitrary Data

How to make a 4d plot with matplotlib using arbitrary data

Great question Tengis, all the math folks love to show off the flashy surface plots with functions given, while leaving out dealing with real world data. The sample code you provided uses gradients since the relationships of a variables are modeled using functions. For this example I will generate random data using a standard normal distribution.

Anyways here is how you can quickly plot 4D random (arbitrary) data with first three variables are on the axis and the fourth being color:

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)
c = np.random.standard_normal(100)

img = ax.scatter(x, y, z, c=c, cmap=plt.hot())
fig.colorbar(img)
plt.show()

Note: A heatmap with the hot color scheme (yellow to red) was used for the 4th dimension

Result:

]1

Making a 4-D scatter plot using Matplotlib in Python

Here is a slightly more general solution where you can explicitly specify the ranges you want regardless of the spacing. I did not have the complete data so I modified your limits from 100, 200, 300 to 140, 180, 200 based on the provided data.

A couple of things:

• You probably want to use scatter3d as you mentioned it in your question instead of plot.
• I am using NumPy to read in the data because this way you will have the data as NumPy arrays which make the masking and slicing easy.
• Here I am creating 3 conditional masks depending on the magnitude of dims.
• Next, you store these masks in a list and then iterate over it to use one mask at a time.

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

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

X, Y, Z, dims = np.loadtxt('sample.txt', unpack=True, skiprows=1)

mask1 = (dims<140)
mask2 = ((dims>=140) & (dims<180))
mask3 = ((dims>=180) & (dims<200))

masks = [mask1, mask2, mask3]
colors = ['r', 'b', 'g'] # color order as you specified in the question

for mask, color in zip(masks, colors): 
    ax.scatter3D(X[mask], Y[mask], Z[mask], c=color)

ax.set_xlabel('Center X (mm)')
ax.set_ylabel('Center Y (mm)')
ax.set_zlabel('Center Z (mm)')
plt.show()

How to make a Scatter Plot for a Dataset with 4 Attribtues and 5th attribute being the Cluster

I would recommend something like seaborn's pairplot:

import seaborn as sns

sns.pairplot(df, hue="cluster")

See the images in the link, of what it looks like.
This creates several pairwise scatterplots instead of trying to make a 3D plot and arbitrarily flatten one of the dimensions.

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