3D PCA Result


3D scatterplots can be useful to display the result of a PCA, in the case you would like to display 3 principal components. This post provides an example to show how to display PCA in your 3D plots using the sklearn library.

Here is an example showing how to display the result of a PCA in 3D scatterplots. Note that the 3 red lines highlighting the dimensions.

# libraries
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
 
# Get the iris dataset
sns.set_style("white")
df = sns.load_dataset('iris')

# create figure
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
 
# Keep the 'species' column appart + make it numeric for coloring
df['species']=pd.Categorical(df['species'])
my_color=df['species'].cat.codes
df = df.drop('species', 1)
 
# Run The PCA
pca = PCA(n_components=3)
pca.fit(df)
 
# Store results of PCA in a data frame
result=pd.DataFrame(pca.transform(df), columns=['PCA%i' % i for i in range(3)], index=df.index)
 
# Plot initialisation
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(result['PCA0'], result['PCA1'], result['PCA2'], c=my_color, cmap="Set2_r", s=60)
 
# make simple, bare axis lines through space:
xAxisLine = ((min(result['PCA0']), max(result['PCA0'])), (0, 0), (0,0))
ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')
yAxisLine = ((0, 0), (min(result['PCA1']), max(result['PCA1'])), (0,0))
ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')
zAxisLine = ((0, 0), (0,0), (min(result['PCA2']), max(result['PCA2'])))
ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')
 
# label the axes
ax.set_xlabel("PC1")
ax.set_ylabel("PC2")
ax.set_zlabel("PC3")
ax.set_title("PCA on the iris data set")
plt.show()
<Figure size 480x480 with 0 Axes>

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