Plotting k-means output - python Can anyone help with plotting k-means results from GraphLab k-means tool. 

sf = gl.SFrame(df_only_tags)
K = int(sqrt(sf.num_rows() / 2.0))

kmeans_model = gl.kmeans.create(sf, num_clusters=6)
kmeans_model.summary()

print(kmeans_model['cluster_info'][['cluster_id', 'size', 'sum_squared_distance']])
print(kmeans_model['cluster_id'].head())
kmeans_model.show()


I am able to print my results just fine, but looking for the best way to represent my results visually:

+------------+-------+----------------------+
| cluster_id |  size | sum_squared_distance |
+------------+-------+----------------------+
|     0      | 10594 |     1034.9124542     |
|     1      |  312  |    42.9715532884     |
|     2      |   2   |    84.9999923706     |
|     3      |   1   |         0.0          |
|     4      |   98  |    213.489804491     |
|     5      |   1   |         0.0          |
+------------+-------+----------------------+
[6 rows x 3 columns]

+--------+------------+-----------------+
| row_id | cluster_id |     distance    |
+--------+------------+-----------------+
|   0    |     0      | 0.0553383231163 |
|   1    |     0      | 0.0553383231163 |
|   2    |     0      | 0.0553383231163 |
|   3    |     0      | 0.0553383231163 |
|   4    |     0      | 0.0553383231163 |
|   5    |     0      | 0.0553383231163 |
|   6    |     0      | 0.0553383231163 |
|   7    |     0      | 0.0553383231163 |
|   8    |     0      | 0.0553383231163 |
|   9    |     0      | 0.0553383231163 |
+--------+------------+-----------------+
[10 rows x 3 columns]


as always, much appreciated
If the dim of your data is more than 2, you may want to transform your data with PCA, t-SNE or Custom linear projections.

t-SNE is recommended for clustering result.

For example:

from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, random_state=0)
data = tsne.fit_transform(data)


This may lose some information, but useful for visualization.