feat: fixing some dangling references in doc/python example files

Author: gvwilson

doc/python/2D-Histogram.md

@@ -38,11 +38,11 @@ jupyter:
 
 ## 2D Histograms or Density Heatmaps
 
-A 2D histogram, also known as a density heatmap, is the 2-dimensional generalization of a [histogram](../histograms/) which resembles a [heatmap](../heatmaps/) 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](https://plotly.com/python/2d-histogram-contour/)) is often used to manage over-plotting, or situations where showing large data sets as [scatter plots](../line-and-scatter/) 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/) to precompute the aggregations before displaying the data with Plotly.
+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](https://plotly.com/python/2d-histogram-contour/)) 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.
 
 ## Density Heatmaps with Plotly Express
 
-[Plotly Express](../plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](../px-arguments/) and produces [easy-to-style figures](../styling-plotly-express/). The Plotly Express function `density_heatmap()` can be used to produce density heatmaps.
+[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). The Plotly Express function `density_heatmap()` can be used to produce density heatmaps.
 
 ```python
 import plotly.express as px
@@ -52,7 +52,7 @@ fig = px.density_heatmap(df, x="total_bill", y="tip")
 fig.show()
 ```
 
-The number of bins can be controlled with `nbinsx` and `nbinsy` and the [color scale](../colorscales/) with `color_continuous_scale`.
+The number of bins can be controlled with `nbinsx` and `nbinsy` and the [color scale](colorscales.md) with `color_continuous_scale`.
 
 ```python
 import plotly.express as px
@@ -62,7 +62,7 @@ fig = px.density_heatmap(df, x="total_bill", y="tip", nbinsx=20, nbinsy=20, colo
 fig.show()
 ```
 
-Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal [`histogram`](../histograms/). Other allowable values are `violin`, `box` and `rug`.
+Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal [`histogram`](histograms.md). Other allowable values are `violin`, `box` and `rug`.
 
 ```python
 import plotly.express as px
@@ -72,7 +72,7 @@ fig = px.density_heatmap(df, x="total_bill", y="tip", marginal_x="histogram", ma
 fig.show()
 ```
 
-Density heatmaps can also be [faceted](../facet-plots/):
+Density heatmaps can also be [faceted](facet-plots.md):
 
 ```python
 import plotly.express as px
@@ -110,7 +110,7 @@ fig.show()
 
 ### 2D Histograms with Graph Objects
 
-To build this kind of figure using [graph objects](../graph-objects/) without using Plotly Express, we can use the `go.Histogram2d` class.
+To build this kind of figure using [graph objects](graph-objects.md) without using Plotly Express, we can use the `go.Histogram2d` class.
 
 
 ### 2D Histogram of a Bivariate Normal Distribution ###

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

@@ -36,11 +36,11 @@ jupyter:
 
 ## 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/) which resembles a [contour plot](../contour-plots/) 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/)) is often used to manage over-plotting, or situations where showing large data sets as [scatter plots](../line-and-scatter/) would result in points overlapping each other and hiding patterns.
+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.
 
 ## Density Contours with Plotly Express
 
-[Plotly Express](../plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](../px-arguments/) and produces [easy-to-style figures](../styling-plotly-express/). The Plotly Express function `density_contour()` can be used to produce density contours.
+[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). The Plotly Express function `density_contour()` can be used to produce density contours.
 
 ```python
 import plotly.express as px
@@ -50,7 +50,7 @@ fig = px.density_contour(df, x="total_bill", y="tip")
 fig.show()
 ```
 
-Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal [`histogram`](../histograms/). Other allowable values are `violin`, `box` and `rug`.
+Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal [`histogram`](histograms.md). Other allowable values are `violin`, `box` and `rug`.
 
 ```python
 import plotly.express as px
@@ -60,7 +60,7 @@ fig = px.density_contour(df, x="total_bill", y="tip", marginal_x="histogram", ma
 fig.show()
 ```
 
-Density contours can also be [faceted](../facet-plots/) and [discretely colored](../discrete-color/):
+Density contours can also be [faceted](facet-plots.md) and [discretely colored](discrete-color.md):
 
 ```python
 import plotly.express as px
@@ -70,7 +70,7 @@ fig = px.density_contour(df, x="total_bill", y="tip", facet_col="sex", color="sm
 fig.show()
 ```
 
-Plotly Express density contours can be [continuously-colored](../colorscales/) and labeled:
+Plotly Express density contours can be [continuously-colored](colorscales.md) and labeled:
 
 ```python
 import plotly.express as px
@@ -95,7 +95,7 @@ fig.show()
 
 ### 2D Histograms with Graph Objects
 
-To build this kind of figure with [graph objects](../graph-objects/) without using Plotly Express, we can use the `go.Histogram2d` class.
+To build this kind of figure with [graph objects](graph-objects.md) without using Plotly Express, we can use the `go.Histogram2d` class.
 
 #### Basic 2D Histogram Contour
 

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

@@ -35,7 +35,7 @@ jupyter:
 
 ## 3D scatter plot with Plotly Express
 
-[Plotly Express](../plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](../px-arguments/) and produces [easy-to-style figures](../styling-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).
 
 Like the [2D scatter plot](https://plotly.com/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.
 
@@ -94,7 +94,7 @@ IFrame(snippet_url + '3d-scatter-plots', width='100%', height=1200)
 
 #### Basic 3D Scatter Plot
 
-If Plotly Express does not provide a good starting point, it is also possible to use [the more generic `go.Scatter3D` class from `plotly.graph_objects`](../graph-objects/).
+If Plotly Express does not provide a good starting point, it is also possible to use [the more generic `go.Scatter3D` class from `plotly.graph_objects`](graph-objects.md).
 Like the [2D scatter plot](https://plotly.com/python/line-and-scatter/) `go.Scatter`, `go.Scatter3d` plots individual data in three-dimensional space.
 
 ```python

doc/python/3d-volume.md

@@ -33,7 +33,7 @@ jupyter:
     thumbnail: thumbnail/3d-volume-plots.jpg
 ---
 
-A volume plot with `go.Volume` shows several partially transparent isosurfaces for volume rendering. The API of `go.Volume` is close to the one of `go.Isosurface`. However, whereas [isosurface plots](../3d-isosurface-plots/) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `go.Volume` results in a depth effect and better volume rendering.
+A volume plot with `go.Volume` shows several partially transparent isosurfaces for volume rendering. The API of `go.Volume` is close to the one of `go.Isosurface`. However, whereas [isosurface plots](3d-isosurface-plots.md) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `go.Volume` results in a depth effect and better volume rendering.
 
 ## Simple volume plot with go.Volume
 
@@ -257,4 +257,4 @@ fig.show()
 See https://plotly.com/python/reference/volume/ for more information and chart attribute options!
 
 #### See also
-[3D isosurface documentation](../3d-isosurface-plots/)
+[3D isosurface documentation](3d-isosurface-plots.md)

doc/python/animations.md

@@ -36,7 +36,7 @@ jupyter:
 
 #### Animated figures with Plotly Express
 
-Several [Plotly Express](../plotly-express/)  functions support the creation of animated figures through the `animation_frame` and `animation_group` arguments.
+Several [Plotly Express](plotly-express.md)  functions support the creation of animated figures through the `animation_frame` and `animation_group` arguments.
 
 Here is an example of an animated scatter plot created with Plotly Express. Note that you should always fix the `x_range` and `y_range` to ensure that your data remains visible throughout the animation.
 
@@ -83,7 +83,7 @@ fig.show()
 
 #### Animated figures with Graph Objects
 
-The remainder of this section describes the low-level [graph objects](../graph-objects/) API for constructing animated figures manually.
+The remainder of this section describes the low-level [graph objects](graph-objects.md) API for constructing animated figures manually.
 
 #### Frames
 

doc/python/annotated-heatmap.md

@@ -38,7 +38,7 @@ jupyter:
 
 *New in v5.5*
 
-As of version 5.5.0 of `plotly`, the **recommended way to [display annotated heatmaps is to use `px.imshow()`](../heatmaps/)** rather than the now-deprecated `create_annotated_heatmap` figure factory documented below for historical reasons.
+As of version 5.5.0 of `plotly`, the **recommended way to [display annotated heatmaps is to use `px.imshow()`](heatmaps.md)** rather than the now-deprecated `create_annotated_heatmap` figure factory documented below for historical reasons.
 
 
 #### Basic Annotated Heatmap for z-annotations
@@ -62,7 +62,7 @@ fig.show()
 
 ### Deprecated Figure Factory
 
-The remaining examples show how to create Annotated Heatmaps with the deprecated `create_annotated_heatmap` [figure factory](../figure-factories/).
+The remaining examples show how to create Annotated Heatmaps with the deprecated `create_annotated_heatmap` [figure factory](figure-factories.md).
 
 
 #### Simple Annotated Heatmap

doc/python/axes.md

@@ -33,30 +33,30 @@ jupyter:
     thumbnail: thumbnail/axes.png
 ---
 
-This tutorial explain how to set the properties of [2-dimensional Cartesian axes](../figure-structure/#2d-cartesian-trace-types-and-subplots), namely [`go.layout.XAxis`](../reference/layout/xaxis/) and [`go.layout.YAxis`](../reference/layout/xaxis/).
+This tutorial explain how to set the properties of [2-dimensional Cartesian axes](figure-structure.md#2d-cartesian-trace-types-and-subplots), namely [`go.layout.XAxis`](/reference/layout/xaxis.md) and [`go.layout.YAxis`](/reference/layout/xaxis.md).
 
 Other kinds of subplots and axes are described in other tutorials:
 
-- [3D axes](../3d-axes) The axis object is [`go.layout.Scene`](../reference/layout/scene/)
-- [Polar axes](../polar-chart/). The axis object is [`go.layout.Polar`](../reference/layout/polar/)
-- [Ternary axes](../ternary-plots). The axis object is [`go.layout.Ternary`](../reference/layout/ternary/)
-- [Geo axes](../map-configuration/). The axis object is [`go.layout.Geo`](../reference/layout/geo/)
-- [Map axes](../tile-map-layers/). The axis object is [`go.layout.Map`](../reference/layout/map/)
-- [Color axes](../colorscales/). The axis object is [`go.layout.Coloraxis`](../reference/layout/coloraxis/).
+- [3D axes](3d-axes.md) The axis object is [`go.layout.Scene`](/reference/layout/scene.md)
+- [Polar axes](polar-chart.md). The axis object is [`go.layout.Polar`](/reference/layout/polar.md)
+- [Ternary axes](ternary-plots.md). The axis object is [`go.layout.Ternary`](/reference/layout/ternary.md)
+- [Geo axes](map-configuration.md). The axis object is [`go.layout.Geo`](/reference/layout/geo.md)
+- [Map axes](tile-map-layers.md). The axis object is [`go.layout.Map`](/reference/layout/map.md)
+- [Color axes](colorscales.md). The axis object is [`go.layout.Coloraxis`](/reference/layout/coloraxis.md).
 
-**See also** the tutorials on [facet plots](../facet-plots/), [subplots](../subplots) and [multiple axes](../multiple-axes/).
+**See also** the tutorials on [facet plots](facet-plots.md), [subplots](subplots.md) and [multiple axes](multiple-axes.md).
 
 ### 2-D Cartesian Axis Types and Auto-Detection
 
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
 - `'linear'` as described in this page
-- `'log'` (see the [log plot tutorial](../log-plot/))
-- `'date'` (see the [tutorial on timeseries](../time-series/))
-- `'category'` (see the [categorical axes tutorial](../categorical-axes/))
-- `'multicategory'` (see the [categorical axes tutorial](../categorical-axes/))
+- `'log'` (see the [log plot tutorial](log-plot.md))
+- `'date'` (see the [tutorial on timeseries](time-series.md))
+- `'category'` (see the [categorical axes tutorial](categorical-axes.md))
+- `'multicategory'` (see the [categorical axes tutorial](categorical-axes.md))
 
-The axis type is auto-detected by looking at data from the first [trace](../figure-structure/) linked to this axis:
+The axis type is auto-detected by looking at data from the first [trace](figure-structure.md) linked to this axis:
 
 * First check for `multicategory`, then `date`, then `category`, else default to `linear` (`log` is never automatically selected)
 * `multicategory` is just a shape test: is the array nested?
@@ -86,14 +86,14 @@ The different groups of Cartesian axes properties are
 - range of the axis
 - domain of the axis
 
-The examples on this page apply to axes of any type, but extra attributes are available for [axes of type `category`](../categorical-axes/) and [axes of type `date`](../time-series/).
+The examples on this page apply to axes of any type, but extra attributes are available for [axes of type `category`](categorical-axes.md) and [axes of type `date`](time-series.md).
 
 
 #### Set and Style Axes Title Labels
 
 ##### Set axis title text with Plotly Express
 
-Axis titles are automatically set to the column names when [using Plotly Express with a data frame as input](../px-arguments/).
+Axis titles are automatically set to the column names when [using Plotly Express with a data frame as input](px-arguments.md).
 
 ```python
 import plotly.express as px
@@ -102,7 +102,7 @@ fig = px.scatter(df, x="total_bill", y="tip", color="sex")
 fig.show()
 ```
 
-Axis titles (and [legend titles](../legend/)) can also be overridden using the `labels` argument of Plotly Express functions:
+Axis titles (and [legend titles](legend.md)) can also be overridden using the `labels` argument of Plotly Express functions:
 
 ```python
 import plotly.express as px