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I use following code to generate graph, but nothing display, can anyone help? Thank you!

# Visualize all the original dimensions
import plotly.express as px
df=px.data.iris()
features=["sepal_width", "sepal_length", "petal_width", "petal_length"]

fig=px.scatter_matrix(df, dimensions=features, color="species")
fig.update_traces(diagonal_visible=False)
fig.show()

import plotly.express as px
from sklearn.decomposition import PCA

df = px.data.iris()
features = ["sepal_width", "sepal_length", "petal_width", "petal_length"]

import sklearn.decomposition as PCA
pca = PCA()
components = pca.fit_transform(df[features])
labels = {
    str(i): f"PC {i+1} ({var:.1f}%)"
    for i, var in enumerate(pca.explained_variance_ratio_ * 100)
}

fig = px.scatter_matrix(
    components,
    labels=labels,
    dimensions=range(4),
    color=df["species"]
)
fig.update_traces(diagonal_visible=False)
fig.show()


# Visualize a subset of the principal components
import pandas as pd
import plotly.express as px
from sklearn.decomposition import PCA
from sklearn.datasets import load_boston

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)
n_components = 4

pca = PCA(n_components=n_components)
components = pca.fit_transform(df)

total_var = pca.explained_variance_ratio_.sum() * 100

labels = {str(i): f"PC {i+1}" for i in range(n_components)}
labels['color'] = 'Median Price'

fig = px.scatter_matrix(
    components,
    color=boston.target,
    dimensions=range(n_components),
    labels=labels,
    title=f'Total Explained Variance: {total_var:.2f}%',
)
fig.update_traces(diagonal_visible=False)
fig.show()

I use following code to generate graph, but nothing display, can anyone help? Thank you!

# Visualize all the original dimensions
import plotly.express as px
df=px.data.iris()
features=["sepal_width", "sepal_length", "petal_width", "petal_length"]

fig=px.scatter_matrix(df, dimensions=features, color="species")
fig.update_traces(diagonal_visible=False)
fig.show()

import plotly.express as px
from sklearn.decomposition import PCA

df = px.data.iris()
features = ["sepal_width", "sepal_length", "petal_width", "petal_length"]

import sklearn.decomposition as PCA
pca = PCA()
components = pca.fit_transform(df[features])
labels = {
    str(i): f"PC {i+1} ({var:.1f}%)"
    for i, var in enumerate(pca.explained_variance_ratio_ * 100)
}

fig = px.scatter_matrix(
    components,
    labels=labels,
    dimensions=range(4),
    color=df["species"]
)
fig.update_traces(diagonal_visible=False)
fig.show()


# Visualize a subset of the principal components
import pandas as pd
import plotly.express as px
from sklearn.decomposition import PCA
from sklearn.datasets import load_boston

boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston.feature_names)
n_components = 4

pca = PCA(n_components=n_components)
components = pca.fit_transform(df)

total_var = pca.explained_variance_ratio_.sum() * 100

labels = {str(i): f"PC {i+1}" for i in range(n_components)}
labels['color'] = 'Median Price'

fig = px.scatter_matrix(
    components,
    color=boston.target,
    dimensions=range(n_components),
    labels=labels,
    title=f'Total Explained Variance: {total_var:.2f}%',
)
fig.update_traces(diagonal_visible=False)
fig.show()

I have added all code, similar structure code was applied to visualize data, no error message came up, but graph didn't show up.

The code I posted is almost complete (I stopped at the beginning since no outcome obtained), it is from here: https://plotly.com/python/pca-visualization/

When I tried the first run, error message reminded "no plotly module", after installed, no error message, but the graph still didn't show up.

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