Fix failing docs build #5460

Original file line number	Diff line number	Diff line change
Expand Up		@@ -79,7 +79,7 @@ fig.show()

		#### Plot a Dendrogram with a Heatmap

	See also the [Dash Bio demo](https://dash-bio.plotly.host/dash-clustergram/).
	This example uses randomly generated sample data to demonstrate how to plot a dendrogram with a heatmap.

		```python
		import plotly.graph_objects as go
Expand All		@@ -89,12 +89,11 @@ import numpy as np
		from scipy.spatial.distance import pdist, squareform


	# get data
	data = np.genfromtxt("http://files.figshare.com/2133304/ExpRawData_E_TABM_84_A_AFFY_44.tab",
	names=True,usecols=tuple(range(1,30)),dtype=float, delimiter="\t")
	data_array = data.view((float, len(data.dtype.names)))
	data_array = data_array.transpose()
	labels = data.dtype.names
	# Generate sample data
	np.random.seed(1)
	X = np.random.rand(15, 15)
	labels = [f'Sample_{i}' for i in range(15)]
	data_array = X

		# Initialize figure by creating upper dendrogram
		fig = ff.create_dendrogram(data_array, orientation='bottom', labels=labels)
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12 changes: 6 additions & 6 deletions doc/python/ml-pca.md

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Expand Up		@@ -105,16 +105,16 @@ fig.show()

		When you will have too many features to visualize, you might be interested in only visualizing the most relevant components. Those components often capture a majority of the [explained variance](https://en.wikipedia.org/wiki/Explained_variation), which is a good way to tell if those components are sufficient for modelling this dataset.

	In the example below, our dataset contains 8 features, but we only select the first 2 components.
	In the example below, our dataset contains 10 features, but we only select the first 2 components.

		```python
		import pandas as pd
		import plotly.express as px
		from sklearn.decomposition import PCA
	from sklearn.datasets import fetch_california_housing
	from sklearn.datasets import load_diabetes

	housing = fetch_california_housing(as_frame=True)
	df = housing.data
	diabetes = load_diabetes()
	df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
		n_components = 2

		pca = PCA(n_components=n_components)
Expand All		@@ -123,11 +123,11 @@ 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'
	labels['color'] = 'Disease Progression'

		fig = px.scatter_matrix(
		components,
	color=housing.target,
	color=diabetes.target,
		dimensions=range(n_components),
		labels=labels,
		title=f'Total Explained Variance: {total_var:.2f}%',
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