'c' argument looks like a single numeric RGB or RGBA sequence

Question

I am getting the following error in my juypter notebook. I have updated mathplotlib to the latest but still get the error

'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.

X=lab3_data
range_n_clusters = [2, 3, 4, 5, 6,7,8]

for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(18, 7)

# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([0, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])

# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = cluster.KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)

# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
      "The average silhouette_score is :", silhouette_avg)

# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)

y_lower = 10
for i in range(n_clusters):
    # Aggregate the silhouette scores for samples belonging to
    # cluster i, and sort them
    ith_cluster_silhouette_values = \
        sample_silhouette_values[cluster_labels == i]

    ith_cluster_silhouette_values.sort()

    size_cluster_i = ith_cluster_silhouette_values.shape[0]
    y_upper = y_lower + size_cluster_i

    color = cm.nipy_spectral(float(i) / n_clusters)
    ax1.fill_betweenx(np.arange(y_lower, y_upper),
                      0, ith_cluster_silhouette_values,
                      facecolor=color, edgecolor=color, alpha=0.7)

    # Label the silhouette plots with their cluster numbers at the middle
    ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

    # Compute the new y_lower for next plot
    y_lower = y_upper + 10  # 10 for the 0 samples

ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")

# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")

ax1.set_yticks([])  # Clear the yaxis labels / ticks
ax1.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])

# 2nd Plot showing the actual clusters formed
# append the cluster centers to the dataset
lab3_data_and_centers = np.r_[lab3_data,clusterer.cluster_centers_]
# project both th data and the k-Means cluster centers to a 2D space
XYcoordinates = manifold.MDS(n_components=2).fit_transform(lab3_data_and_centers)
# plot the transformed examples and the centers
# use the cluster assignment to colour the examples
# plot the transformed examples and the centers
# use the cluster assignment to colour the examples

clustering_scatterplot(points=XYcoordinates[:-n_clusters,:], 
                       labels=cluster_labels,
                       centers=XYcoordinates[-n_clusters:,:], 
                       title='MDS')

plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
              "with n_clusters = %d" % n_clusters),
             fontsize=14, fontweight='bold')


plt.show()

Answer 1

As a workaround put:

 from matplotlib.axes._axes import _log as matplotlib_axes_logger
 matplotlib_axes_logger.setLevel('ERROR')

Answer 2

You can also make your c argument 2D with:

    c=color.reshape(1,-1)

or

    c=np.array([color])

or just change your original color array to 2D:

    color = cm.nipy_spectral(float(i) / n_clusters).reshape(1,-1)

p.s.: As I need 50 reputation to comment, I just open a new answer, though this should be just a comment below D Adams' solution using built-in numpy.atleast_2D().