Visualizing 3D clustering using matplotlib

Question

I have clustered 3 features Feature1, Feature2 and Feature3 and came up with 2 clusters. I am trying to visualize a 3D cluster using matplotlib.

In the below table, there are three features upon which the clustering is executed. Number of clusters is 2.

    Feature1        Feature2    Feature3    ClusterIndex
  0 1.349656e-09    1.000000    1.090542e-09    0
  1 1.029752e-07    1.000000    6.040669e-08    0
  2 2.311729e-07    1.000000    1.568289e-11    0
  3 1.455860e-08    6.05e-08    1.000000        1
  4 3.095807e-07    2.07e-07    1.000000        1

Tried this code:

   fig = plt.figure()
   ax = fig.add_subplot(111, projection='3d')
   x = np.array(df['Feature1'])
   y = np.array(df['Feature2'])
   z = np.array(df['Feature3'])
   ax.scatter(x,y,z, marker=colormap[kmeans.labels_], s=40)

However, I get the error "ValueError: could not convert string to float: red". The marker part is thus where I get the error.

2D visualization of clusters is pretty simple by plotting the points in a scatter plot and distinguishing it with cluster labels.

Just wondering is there a way to do 3D visualization of clusters.

Any suggestions would be highly appreciated !!

I get the error "ValueError: could not convert string to float: red". The marker part is where I get the error. It is not able to convert strings to floats. Typecasting does not help. In 2D plotting, it works but not sure why it does not work for 3D plotting. — user3447653
– user3447653, Commented Apr 18, 2017 at 15:39
@ ImportanceOfBeingErnest : kmeans.labels is the cluster indices like 0 and 1 (As i have 2 clusters). Colormap converts labels to colors. — user3447653
– user3447653, Commented Apr 18, 2017 at 15:43

ImportanceOfBeingErnest · Accepted Answer · 2017-04-18 16:01:11Z

In principle, the code from the question should work. However it is unclear what marker=colormap[kmeans.labels_] would do and why it is needed.

The 3D scatter plot works exactly as the 2D version of it.

The marker argument would expect a marker string, like "s" or "o" to determine the marker shape.
The color can be set using the c argument. You can provide a single color or an array/a list of colors. In the example below we simply provide the cluster index to c and use a colormap.

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

v = np.random.rand(10,4)
v[:,3] = np.random.randint(0,2,size=10)
df = pd.DataFrame(v, columns=['Feature1', 'Feature2','Feature3',"Cluster"])
print (df)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array(df['Feature1'])
y = np.array(df['Feature2'])
z = np.array(df['Feature3'])

ax.scatter(x,y,z, marker="s", c=df["Cluster"], s=40, cmap="RdBu")

plt.show()

Collectives™ on Stack Overflow

Visualizing 3D clustering using matplotlib

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related