feat: remove unused metadata in Markdown files

doc/python/2D-Histogram.md

@@ -1,41 +1,9 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.13.4
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.8.11
-  plotly:
-    description: How to make 2D Histograms in Python with Plotly.
-    display_as: statistical
-    language: python
-    layout: base
-    name: 2D Histograms
-    order: 5
-    page_type: u-guide
-    permalink: python/2D-Histogram/
-    redirect_from:
-    - python/2d-histogram/
-    - python/2d-histograms/
-    thumbnail: thumbnail/histogram2d.jpg
+description: How to make 2D Histograms in Python with Plotly.
+redirect_from:
+- python/2d-histogram/
+- python/2d-histograms/
 ---
-
 ## 2D Histograms or Density Heatmaps
 
 A 2D histogram, also known as a density heatmap, is the 2-dimensional generalization of a [histogram](histograms.md) which resembles a [heatmap](heatmaps.md) but is computed by grouping a set of points specified by their `x` and `y` coordinates into bins, and applying an aggregation function such as `count` or `sum` (if `z` is provided) to compute the color of the tile representing the bin. This kind of visualization (and the related [2D histogram contour, or density contour](2d-histogram-contour.md)) is often used to manage over-plotting, or situations where showing large data sets as [scatter plots](line-and-scatter.md) would result in points overlapping each other and hiding patterns. For data sets of more than a few thousand points, a better approach than the ones listed here would be to [use Plotly with Datashader](datashader.md) to precompute the aggregations before displaying the data with Plotly.

doc/python/2d-histogram-contour.md

@@ -1,39 +1,7 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.3.1
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.6.8
-  plotly:
-    description: How to make 2D Histogram Contour plots in Python with Plotly.
-    display_as: statistical
-    language: python
-    layout: base
-    name: 2D Histogram Contour
-    order: 11
-    page_type: u-guide
-    permalink: python/2d-histogram-contour/
-    redirect_from: python/2d-density-plots/
-    thumbnail: thumbnail/hist2dcontour.png
+description: How to make 2D Histogram Contour plots in Python with Plotly.
+redirect_from: python/2d-density-plots/
 ---
-
 ## 2D Histogram Contours or Density Contours
 
 A 2D histogram contour plot, also known as a density contour plot, is a 2-dimensional generalization of a [histogram](histograms.md) which resembles a [contour plot](contour-plots.md) but is computed by grouping a set of points specified by their `x` and `y` coordinates into bins, and applying an aggregation function such as `count` or `sum` (if `z` is provided) to compute the value to be used to compute contours. This kind of visualization (and the related [2D histogram, or density heatmap](2D-Histogram.md)) is often used to manage over-plotting, or situations where showing large data sets as [scatter plots](line-and-scatter.md) would result in points overlapping each other and hiding patterns.

doc/python/3d-axes.md

@@ -1,38 +1,6 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.16.4
-  kernelspec:
-    display_name: Python 3 (ipykernel)
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.10.4
-  plotly:
-    description: How to format axes of 3d plots in Python with Plotly.
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Axes
-    order: 1
-    page_type: example_index
-    permalink: python/3d-axes/
-    thumbnail: thumbnail/3d-axes.png
+description: How to format axes of 3d plots in Python with Plotly.
 ---
-
 ### Range of axes
 
 3D figures have an attribute in `layout` called `scene`, which contains

doc/python/3d-bubble-charts.md

@@ -1,39 +1,7 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.16.4
-  kernelspec:
-    display_name: Python 3 (ipykernel)
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.11.10
-  plotly:
-    description: How to make 3D Bubble Charts in Python with Plotly. Three examples
-      of 3D Bubble Charts.
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Bubble Charts
-    order: 6
-    page_type: u-guide
-    permalink: python/3d-bubble-charts/
-    thumbnail: thumbnail/3dbubble.jpg
+description: How to make 3D Bubble Charts in Python with Plotly. Three examples of
+  3D Bubble Charts.
 ---
-
 ### 3d Bubble chart with Plotly Express
 
 ```python

doc/python/3d-camera-controls.md

@@ -1,37 +1,6 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.2.3
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.7.3
-  plotly:
-    description: How to Control the Camera in your 3D Charts in Python with Plotly.
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Camera Controls
-    order: 5
-    permalink: python/3d-camera-controls/
-    thumbnail: thumbnail/3d-camera-controls.jpg
+description: How to Control the Camera in your 3D Charts in Python with Plotly.
 ---
-
 ### How camera controls work
 
 The camera position and direction is determined by three vectors: *up*, *center*, *eye*. Their coordinates refer to the 3-d domain, i.e., `(0, 0, 0)` is always the center of the domain, no matter data values.
@@ -290,4 +259,4 @@ fig.show()
 #### Reference
 
 
-See [https://plotly.com/python/reference/layout/scene/#layout-scene-camera](reference/graph_objects/layout-package/scene-package/Camera.md) for more information and chart attribute options!
+See [https://plotly.com/python/reference/layout/scene/#layout-scene-camera](reference/graph_objects/layout-package/scene-package/Camera.md) for more information and chart attribute options!

doc/python/3d-isosurface-plots.md

@@ -1,39 +1,7 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.7.3
-  plotly:
-    description: How to make 3D Isosurface Plots in Python with Plotly.
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Isosurface Plots
-    order: 10
-    page_type: u-guide
-    permalink: python/3d-isosurface-plots/
-    redirect_from: python/isosurfaces-with-marching-cubes/
-    thumbnail: thumbnail/isosurface.jpg
+description: How to make 3D Isosurface Plots in Python with Plotly.
+redirect_from: python/isosurfaces-with-marching-cubes/
 ---
-
 With ``go.Isosurface``, you can plot [isosurface contours](https://en.wikipedia.org/wiki/Isosurface) of a scalar field ``value``, which is defined on ``x``, ``y`` and ``z`` coordinates.
 
 #### Basic Isosurface

doc/python/3d-line-plots.md

@@ -1,38 +1,6 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.2.3
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.7.3
-  plotly:
-    description: How to make 3D Line Plots
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Line Plots
-    order: 7
-    page_type: u-guide
-    permalink: python/3d-line-plots/
-    thumbnail: thumbnail/3d-line.jpg
+description: How to make 3D Line Plots
 ---
-
 ### 3D Line plot with Plotly Express
 
 ```python

doc/python/3d-mesh.md

@@ -1,38 +1,6 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.14.1
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.8.8
-  plotly:
-    description: How to make 3D Mesh Plots
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Mesh Plots
-    order: 9
-    page_type: u-guide
-    permalink: python/3d-mesh/
-    thumbnail: thumbnail/3d-mesh.jpg
+description: How to make 3D Mesh Plots
 ---
-
 ### Simple 3D Mesh example ###
 
 `go.Mesh3d` draws a 3D set of triangles with vertices given by `x`, `y` and `z`. If only coordinates are given, an algorithm such as [Delaunay triangulation](https://en.wikipedia.org/wiki/Delaunay_triangulation) is used to draw the triangles. Otherwise the triangles can be given using the `i`, `j` and `k` parameters (see examples below).

doc/python/3d-scatter-plots.md

@@ -1,38 +1,6 @@
 ---
-jupyter:
-  jupytext:
-    notebook_metadata_filter: all
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.14.1
-  kernelspec:
-    display_name: Python 3
-    language: python
-    name: python3
-  language_info:
-    codemirror_mode:
-      name: ipython
-      version: 3
-    file_extension: .py
-    mimetype: text/x-python
-    name: python
-    nbconvert_exporter: python
-    pygments_lexer: ipython3
-    version: 3.8.8
-  plotly:
-    description: How to make 3D scatter plots in Python with Plotly.
-    display_as: 3d_charts
-    language: python
-    layout: base
-    name: 3D Scatter Plots
-    order: 2
-    page_type: example_index
-    permalink: python/3d-scatter-plots/
-    thumbnail: thumbnail/3d-scatter.jpg
+description: How to make 3D scatter plots in Python with Plotly.
 ---
-
 ## 3D scatter plot with Plotly Express
 
 [Plotly Express](plotly-express.md) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](px-arguments.md) and produces [easy-to-style figures](styling-plotly-express.md).