您可以使姓名信息仅在鼠标悬停在某个数据点上时显示。当前，您正尝试将每个乘客的姓名绘制在数据点旁边。由于有许多数据点彼此靠近，直接将姓名包含在图中会导致每个乘客的姓名放在彼此的顶部。您可以通过将绘图代码更改为以下内容来修复此问题：

fig = px.scatter(titanic, x='x0', y='x1', color='kmeans', hover_name='Name')
fig.update_layout(title_text="KMeans Clustering of Titanic Passengers",
                  title_font_size=30)
fig.show()

基本上，我们对上面代码所做的唯一更改是使用哪个参数来包含'Name'信息。以下是更改后的外观：

现在，只有当您将鼠标悬停在数据点上时，才会显示名称。

完整代码

下面是考虑到上述更改的完整代码：

# Import required module
import pandas as pd
import plotly.express as px
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer

# Where our data is located in our machine
train_data_filepath = '/Users/erikingwersen/Downloads/train.csv'
test_data_filepath = '/Users/erikingwersen/Downloads/test.csv'

# Read the train data from downloaded file
titanic = pd.read_csv(train_data_filepath)

documents = titanic['Name']

X = TfidfVectorizer(stop_words='english').fit_transform(documents)

# Initialize kmeans with 20 centroids
kmeans = KMeans(n_clusters=20, random_state=42)

# Fit the model
kmeans.fit(X)

# Store cluster labels in a variable
clusters = kmeans.labels_
titanic['kmeans'] = clusters
documents = titanic['Name']

X = TfidfVectorizer(stop_words='english').fit_transform(documents)

# Initialize PCA with 2 components
pca = PCA(n_components=2, random_state=42)

# Pass our X to the pca and store the reduced vectors into pca_vecs
pca_vecs = pca.fit_transform(X.toarray())

# Save our two dimensions into x0 and x1
x0, x1 = pca_vecs[:, 0], pca_vecs[:, 1]

# Assign clusters and pca vectors to our dataframe 
titanic[['cluster', 'x0', 'x1']] = [
    [x, y, z] for x, y, z in zip(clusters, x0, x1)
]

titanic.head()

fig = px.scatter(titanic, x='x0', y='x1', color='kmeans', hover_name='Name')
fig.update_layout(title_text="KMeans Clustering of Titanic Passengers",
                  title_font_size=30)
fig.show()