From 9669c41b23fa78c91b9a28482a28b2fd52dd2473 Mon Sep 17 00:00:00 2001 From: Liam Connors Date: Tue, 11 Mar 2025 16:18:58 -0400 Subject: [PATCH 1/3] Remove app that uses Boston housing prices dataset ImportError: `load_boston` has been removed from scikit-learn since version 1.2. --- doc/python/ml-pca.md | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/doc/python/ml-pca.md b/doc/python/ml-pca.md index f146e8384df..45fbe8f8a99 100644 --- a/doc/python/ml-pca.md +++ b/doc/python/ml-pca.md @@ -136,23 +136,6 @@ fig.update_traces(diagonal_visible=False) fig.show() ``` -## PCA analysis in Dash - -[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`. - -Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with Dash Enterprise.** - - -```python hide_code=true -from IPython.display import IFrame -snippet_url = 'https://python-docs-dash-snippets.herokuapp.com/python-docs-dash-snippets/' -IFrame(snippet_url + 'pca-visualization', width='100%', height=1200) -``` - -

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- - ## 2D PCA Scatter Plot In the previous examples, you saw how to visualize high-dimensional PCs. In this example, we show you how to simply visualize the first two principal components of a PCA, by reducing a dataset of 4 dimensions to 2D. From c2f15fa54ecc5c4e59559a56c53fbcd92f9d98d0 Mon Sep 17 00:00:00 2001 From: Liam Connors Date: Tue, 11 Mar 2025 16:27:08 -0400 Subject: [PATCH 2/3] Update doc/python/ml-pca.md --- doc/python/ml-pca.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/python/ml-pca.md b/doc/python/ml-pca.md index 45fbe8f8a99..248618a0f09 100644 --- a/doc/python/ml-pca.md +++ b/doc/python/ml-pca.md @@ -136,6 +136,10 @@ fig.update_traces(diagonal_visible=False) fig.show() ``` + +

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+ ## 2D PCA Scatter Plot In the previous examples, you saw how to visualize high-dimensional PCs. In this example, we show you how to simply visualize the first two principal components of a PCA, by reducing a dataset of 4 dimensions to 2D. From fe6681c744abd7bb706b444e67cf2f6158f46e27 Mon Sep 17 00:00:00 2001 From: Liam Connors Date: Tue, 11 Mar 2025 16:27:23 -0400 Subject: [PATCH 3/3] Update doc/python/ml-pca.md --- doc/python/ml-pca.md | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/python/ml-pca.md b/doc/python/ml-pca.md index 248618a0f09..1776d3be393 100644 --- a/doc/python/ml-pca.md +++ b/doc/python/ml-pca.md @@ -140,6 +140,7 @@ fig.show()

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+ ## 2D PCA Scatter Plot In the previous examples, you saw how to visualize high-dimensional PCs. In this example, we show you how to simply visualize the first two principal components of a PCA, by reducing a dataset of 4 dimensions to 2D.