How can I efficiently plot a distance matrix using seaborn?

So I have a dataset of more ore less 11.000 records, with 4 features all them are discrete or continue. I perform clustering using K-means, then I add the column "cluster" to the dataframe using kmeans.labels_. Now I want to plot the distance matrix so I used pdist from scipy, but the matrix is not plotted.

Here is my code.

from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
import gc

# distance matrix
def distance_matrix(df_labeled, metric="euclidean"):
    df_labeled.sort_values(by=['cluster'], inplace=True)
    dist = pdist(df_labeled, metric)
    dist = squareform(dist)    
    sns.heatmap(dist, cmap="mako")
    print(dist)
    del dist
    gc.collect()

distance_matrix(finalDf)

Output:

[[ 0.          2.71373462  3.84599479 ...  7.59910903  8.10265588
   8.27195104]
 [ 2.71373462  0.          2.94410672 ...  7.90444283  8.28225031
   8.48094661]
 [ 3.84599479  2.94410672  0.         ...  9.78706347 10.42014451
  10.61261498]
 ...
 [ 7.59910903  7.90444283  9.78706347 ...  0.          1.27795469
   1.44711258]
 [ 8.10265588  8.28225031 10.42014451 ...  1.27795469  0.
   0.52333107]
 [ 8.27195104  8.48094661 10.61261498 ...  1.44711258  0.52333107
   0.        ]]

I get the following graph:

As you can see, the plot is empty. Also I have to free up some RAM because google colab crashes.

How can I solve the problem?

from How can I efficiently plot a distance matrix using seaborn?