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Copy file name to clipboardExpand all lines: CHANGELOG.md
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@@ -5,13 +5,14 @@ This project adheres to [Semantic Versioning](http://semver.org/).
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## [UNRELEASED]
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### Updated
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- Updated Plotly.js from version 2.27.0 to version 2.28.0. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#2280----2024-01-05) for more information. These changes are reflected in the auto-generated `plotly.graph_objects` module. Notable changes include:
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- Add `align` option to sankey nodes to control horizontal alignment [[#6800](https://github.com/plotly/plotly.js/pull/6800)],
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with thanks to @adamreeve for the contribution!
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- Add the possibility of loading "virtual-webgl" script for WebGL 1 to help display several WebGL contexts on a page [[#6784](https://github.com/plotly/plotly.js/pull/6784)], with thanks to @greggman for the contribution!
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- Add options to use base64 encoding (`bdata`) and `shape` (for 2 dimensional arrays) to declare various typed arrays i.e. `dtype=(float64|float32|int32|int16|int8|uint32|uint16|uint8)`[[#5230](https://github.com/plotly/plotly.js/pull/5230)]
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- Adjust stamen styles to point to `stadiamaps.com`, the users may also need to provide their own API_KEY via `config.mapboxAccessToken`[[#6776](https://github.com/plotly/plotly.js/pull/6776), [#6778](https://github.com/plotly/plotly.js/pull/6778)]
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- Removed Python 3.6 and Python 3.7 support [[#4492](https://github.com/plotly/plotly.py/pull/4492)]
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### Fixed
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- Ensure scatter `mode` is deterministic from `px`[[#4429](https://github.com/plotly/plotly.py/pull/4429)]
Copy file name to clipboardExpand all lines: doc/python/county-choropleth.md
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@@ -47,9 +47,9 @@ Run the following commands to install the correct versions of the following modu
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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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@@ -6,9 +6,9 @@ jupyter:
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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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