'visualize the data points clustering output using python

I have dataframe df1, df1 containing the topics, with their cluster

Here is a sample input dataframe:

df1 = pd.DataFrame({'topics':['algebra', 'calculus', 'number theory', 'atom', 'chemical reaction', 'geometry',
                             'Linear Algebra-Advanced', 'evolution', 'botany',  'electricity', 'quantum',
                             'zoology', 'mechanics','Differential Equations', 'Electric Charges and Fields'],    
                    'cluster':[0, 0, 0, 1, 1, 0,0,  2, 2, 3, 3, 2, 3, 0, 3]
                   })

I Visualize clusters with elements as we can see in the following output:

#set up colors per clusters using a dict
cluster_colors = {0: '#1b9e77', 1: '#d95f02', 2: '#7570b3', 3: '#EEC900'}

#set up cluster names using a dict
cluster_names = {0: 'Math courses', 
                 1: 'Chemistry courses', 
                 2: 'Biology courses', 
                 3: 'Physics courses'}
xs, ys = df1['topics'], df1['cluster']

df = pd.DataFrame(dict(x=xs, y=ys)) 

#group by cluster
groups = df.groupby('y')

# set up plot
fig, ax = plt.subplots(figsize=(22, 14)) # set size
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling

#iterate through groups to layer the plot
#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label
for name, group in groups:
    ax.plot(group.x, group.y, marker='o', linestyle='', ms=14, 
            label=cluster_names[name], color=cluster_colors[name], 
            mec='none')
    ax.set_aspect('auto')
    ax.tick_params(\
        axis= 'x',          # changes apply to the x-axis
        which='both',      # both major and minor ticks are affected
        bottom='off',      # ticks along the bottom edge are off
        top='off',         # ticks along the top edge are off
        labelbottom='off')
    ax.tick_params(\
        axis= 'y',         # changes apply to the y-axis
        which='both',      # both major and minor ticks are affected
        left='off',      # ticks along the bottom edge are off
        top='off',         # ticks along the top edge are off
        labelleft='off')
    
ax.legend(numpoints=1)  #show legend with only 1 point

    
plt.show() #show the plot