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Copy file name to clipboardExpand all lines: doc/python/county-choropleth.md
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```python
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!pip install plotly-geo==1.0.0
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!pip install geopandas==0.3.0
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!pip install pyshp==1.2.10
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!pip install shapely==1.6.3
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!pip install geopandas==0.8.1
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!pip install pyshp==2.1.2
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!pip install shapely==1.7.1
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```
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If you are using Windows, follow this post to properly install geopandas and dependencies: http://geoffboeing.com/2014/09/using-geopandas-windows/. If you are using Anaconda, do not use PIP to install the packages above. Instead use conda to install them:
Copy file name to clipboardExpand all lines: doc/python/ml-pca.md
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extension: .md
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format_name: markdown
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format_version: '1.3'
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jupytext_version: 1.14.1
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jupytext_version: 1.16.1
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kernelspec:
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display_name: Python 3
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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language_info:
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name: python
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nbconvert_exporter: python
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pygments_lexer: ipython3
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version: 3.8.8
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version: 3.10.11
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plotly:
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description: Visualize Principle Component Analysis (PCA) of your high-dimensional
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data in Python with Plotly.
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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.
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In the example below, our dataset contains 10 features, but we only select the first 4 components, since they explain over 99% of the total variance.
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In the example below, our dataset contains 8 features, but we only select the first 2 components.
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```python
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import pandas as pd
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import plotly.express as px
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from sklearn.decomposition importPCA
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from sklearn.datasets importload_boston
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from sklearn.datasets importfetch_california_housing
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