How to annotate certain data points on a python scatterplot based on column value

Question

I am almost done with my first real deal python data science project. However, there is one last thing I can't seem to figure out. I have the following code to create a plot for my PCA and K Means clustering algorithm:

y_axis = passers_pca_kmeans['Component 1']
x_axis = passers_pca_kmeans['Component 2']

plt.figure(figsize=(10,8))
sns.scatterplot(x_axis, y_axis, hue=passers_pca_kmeans['Segment'], palette=['g','r','c','m'])
plt.title('Clusters by PCA Components')
plt.grid(zorder=0,alpha=.4)

texts = [plt.text(x0,y0,name,ha='right',va='bottom') for x0,y0,name in zip(
    passers_pca_kmeans['Component 2'], passers_pca_kmeans['Component 1'], passers_pca_kmeans.name)]

adjust_text(texts)

plt.show

• I finally got the correct code to annotate the points using adjustText, but my plot has too many points to label them all; it looks like a mess with text everywhere.
• I would like to annotate the scatterplot based on the value in the column 'Segment'.
  • The values in this column are the names of my four clusters 'first', 'second', 'third', 'fourth'.
• How do I alter my adjustText code to only annotate points where 'Segment'='first'?
  • Would this be an np.where situation?

Answer 1

You could boolean slice your input into the text call, something like:

mask = (passers_kca_means["Subject"] == "first")
x = passers_kca_means["Component 2"][mask]
y = passers_kca_means["Component 1"][mask]
names = passers_kca_means.name[mask]

texts = [plt.text(x0,y0,name,ha='right',va='bottom') for x0,y0,name in zip(x,y,names)]

You could also make an unruly list comprehension by adding an if condition:

x = passers_kca_means["Component 2"]
y = passers_kca_means["Component 1"]
names = passers_kca_means.name
subjects = passers_kca_means["Subject"]

texts = [plt.text(x0,y0,name,ha='right',va='bottom') for x0,y0,name,subject in zip(x,y,names,subjects) if subject == "first"]

I bet there is an answer with np.where as well.