Jul 10, 2025 · Jul 22, 2025 · Dec 2, 2024 · Jul 22, 2025 · Jul 22, 2025 · Jul 22, 2025
diff --git a/doc/python/density-heatmaps.md b/doc/python/density-heatmaps.md

 The earlier examples using `px.density_map` and `go.Densitymap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24. These trace types are now the recommended way to make tile-based density heatmaps. There are also traces that use [Mapbox](https://docs.mapbox.com): `density_mapbox` and `go.Densitymapbox`.

 To use these trace types, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
 To use these trace types, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

 Here's one of the earlier examples rewritten to use `px.density_mapbox`.

diff --git a/doc/python/figure-structure.md b/doc/python/figure-structure.md

 ### Figures as Trees of Attributes

 Plotly.js supports inputs adhering to a well-defined schema, whose overall architecture is explained in this page and which is exhaustively documented in the [Figure Reference](reference/index.md) (which is itself generated from a [machine-readable JSON representation of the schema](https://raw.githubusercontent.com/plotly/plotly.js/master/dist/plot-schema.json)). Figures are represented as trees with named nodes called "attributes". The root node of the tree has three top-level attributes: `data`, `layout` and `frames` (see below).
 Plotly.js supports inputs adhering to a well-defined schema, whose overall architecture is explained in this page and which is exhaustively documented in the [Figure Reference](reference/graph_objects/index.md) (which is itself generated from a [machine-readable JSON representation of the schema](https://raw.githubusercontent.com/plotly/plotly.js/master/dist/plot-schema.json)). Figures are represented as trees with named nodes called "attributes". The root node of the tree has three top-level attributes: `data`, `layout` and `frames` (see below).

 Attributes are referred to in text and in the [Figure Reference](reference/index.md) by their full "path" i.e. the dot-delimited concatenation of their parents. For example `"layout.width"` refers to the attribute whose key is `"width"` inside a dict which is the value associated with a key `"layout"` at the root of the figure. If one of the parents is a list rather than a dict, a set of brackets is inserted in the path when referring to the attribute in the abstract, e.g. `"layout.annotations[].text"`. Finally, as explained below, the top-level "data" attribute defines a list of typed objects called "traces" with the schema dependent upon the type, and these attributes' paths are listed in the [Figure Reference](reference/index.md) as `"data[type=scatter].name"`.
 Attributes are referred to in text and in the [Figure Reference](reference/graph_objects/index.md) by their full "path" i.e. the dot-delimited concatenation of their parents. For example `"layout.width"` refers to the attribute whose key is `"width"` inside a dict which is the value associated with a key `"layout"` at the root of the figure. If one of the parents is a list rather than a dict, a set of brackets is inserted in the path when referring to the attribute in the abstract, e.g. `"layout.annotations[].text"`. Finally, as explained below, the top-level "data" attribute defines a list of typed objects called "traces" with the schema dependent upon the type, and these attributes' paths are listed in the [Figure Reference](reference/graph_objects/index.md) as `"data[type=scatter].name"`.

 The [`plotly.graph_objects` module contains an automatically-generated hierarchy of  Python classes](graph-objects.md) which represent non-leaf attributes in the figure schema and provide a Pythonic API for them. When [manipulating a `plotly.graph_objects.Figure` object](creating-and-updating-figures.md), attributes can be set either directly using Python object attributes e.g. `fig.layout.title.font.family="Open Sans"` or using [update methods and "magic underscores"](creating-and-updating-figures.md#magic-underscore-notation) e.g. `fig.update_layout(title_font_family="Open Sans")`

 When building a figure, it is *not necessary to populate every attribute* of every object. At render-time, [the JavaScript layer will compute default values](figure-introspection.md) for each required unspecified attribute, depending upon the ones that are specified, as documented in the [Figure Reference](reference/index.md). An example of this would be `layout.xaxis.range`, which may be specified explicitly, but if not will be computed based on the range of `x` values for every trace linked to that axis. The JavaScript layer will ignore unknown attributes or malformed values, although the `plotly.graph_objects` module provides Python-side validation for attribute values. Note also that if [the `layout.template` key is present (as it is by default)](templates.md) then default values will be drawn first from the contents of the template and only if missing from there will the JavaScript layer infer further defaults. The built-in template can be disabled by setting `layout.template="none"`.
 When building a figure, it is *not necessary to populate every attribute* of every object. At render-time, [the JavaScript layer will compute default values](figure-introspection.md) for each required unspecified attribute, depending upon the ones that are specified, as documented in the [Figure Reference](reference/graph_objects/index.md). An example of this would be `layout.xaxis.range`, which may be specified explicitly, but if not will be computed based on the range of `x` values for every trace linked to that axis. The JavaScript layer will ignore unknown attributes or malformed values, although the `plotly.graph_objects` module provides Python-side validation for attribute values. Note also that if [the `layout.template` key is present (as it is by default)](templates.md) then default values will be drawn first from the contents of the template and only if missing from there will the JavaScript layer infer further defaults. The built-in template can be disabled by setting `layout.template="none"`.

 ### The Top-Level `data` Attribute


 ### 3D, Polar, Ternary and Smith Trace Types and Subplots

 Beyond 2D cartesian subplots, figures can include [three-dimensional cartesian subplots](3d-charts.md), [polar subplots](polar-chart.md), [ternary subplots](ternary-plots.md) and [smith subplots](smith-charts.md). The following trace types support attributes named `scene`, `polar`, `smith` or `ternary`, whose values must refer to corresponding objects in the layout portion of the figure i.e. `ternary="ternary2"` etc. Note that attributes such as `layout.scene` and `layout.ternary2` etc do not have to be explicitly defined, in which case default values will be inferred. Multiple traces of a compatible type can be placed on the same subplot.
 Beyond 2D cartesian subplots, figures can include [three-dimensional cartesian subplots](/python/3d-charts/), [polar subplots](polar-chart.md), [ternary subplots](ternary-plots.md) and [smith subplots](smith-charts.md). The following trace types support attributes named `scene`, `polar`, `smith` or `ternary`, whose values must refer to corresponding objects in the layout portion of the figure i.e. `ternary="ternary2"` etc. Note that attributes such as `layout.scene` and `layout.ternary2` etc do not have to be explicitly defined, in which case default values will be inferred. Multiple traces of a compatible type can be placed on the same subplot.

 The following trace types are compatible with 3D subplots via the `scene` attribute, which contains special [camera controls](3d-camera-controls.md):

diff --git a/doc/python/filled-area-tile-maps.md b/doc/python/filled-area-tile-maps.md

 The earlier examples using `go.Scattermap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. This trace was introduced in Plotly.py 5.24 and is now the recommended way to draw filled areas on tile-based maps. There is also a trace that uses [Mapbox](https://docs.mapbox.com), called `go.Scattermapbox`.

 To use the `Scattermapbox` trace type, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
 To use the `Scattermapbox` trace type, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

 Here's one of the earlier examples rewritten to use `Scattermapbox`.

diff --git a/doc/python/graph-objects.md b/doc/python/graph-objects.md
 Graph objects have several benefits compared to plain Python dictionaries:

 1. Graph objects provide precise data validation. If you provide an invalid property name or an invalid property value as the key to a graph object, an exception will be raised with a helpful error message describing the problem. This is not the case if you use plain Python dictionaries and lists to build your figures.
 2. Graph objects contain descriptions of each valid property as Python docstrings, with a [full API reference available](https://plotly.com/python-api-reference/). You can use these docstrings in the development environment of your choice to learn about the available properties as an alternative to consulting the online [Full Reference](/reference/).
 2. Graph objects contain descriptions of each valid property as Python docstrings, with a [full API reference available](https://plotly.com/python-api-reference/). You can use these docstrings in the development environment of your choice to learn about the available properties as an alternative to consulting the online [Full Reference](/reference/graph_objects/index.md).
 3. Properties of graph objects can be accessed using both dictionary-style key lookup (e.g. `fig["layout"]`) or class-style property access (e.g. `fig.layout`).
 4. Graph objects support higher-level convenience functions for making updates to already constructed figures (`.update_layout()`, `.add_trace()` etc).
 5. Graph object constructors and update methods accept "magic underscores" (e.g. `go.Figure(layout_title_text="The Title")` rather than `dict(layout=dict(title=dict(text="The Title")))`) for more compact code.

 The figures produced by Plotly Express can always be built from the ground up using graph objects, but this approach typically takes **5-100 lines of code rather than 1**.

 Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without. Note that [Plotly Express functions](/python-api-reference/plotly.express.html) like [`px.bar()`](bar-charts.md) can accept a DataFrame as their first argument with column names passed to the `x` and `y` arguments, while [Graph Objects functions](/python-api-reference/plotly.graph_objects.html) like [`go.Bar()`](bar-charts.md#basic-bar-charts-with-plotlygraphobjects) require the data values to be passed directly to the `x` and `y` arguments as a tuple, list, NumPy array, or Pandas Series.
 Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without. Note that [Plotly Express functions](/reference/plotly-express.md) like [`px.bar()`](bar-charts.md) can accept a DataFrame as their first argument with column names passed to the `x` and `y` arguments, while [Graph Objects functions](/reference/graph_objects/index.md) like [`go.Bar()`](bar-charts.md#basic-bar-charts-with-plotlygraphobjects) require the data values to be passed directly to the `x` and `y` arguments as a tuple, list, NumPy array, or Pandas Series.

 The data in this example is in "long form" but [Plotly Express also accepts data in "wide form"](wide-form.md) and the line-count savings from Plotly Express over graph objects are comparable. More complex figures such as [sunbursts](sunburst-charts.md), [parallel coordinates](parallel-coordinates-plot.md), [facet plots](facet-plots.md) or [animations](animations.md) require many more lines of figure-specific graph objects code, whereas switching from one representation to another with Plotly Express usually involves changing just a few characters.

diff --git a/doc/python/graphing-multiple-chart-types.md b/doc/python/graphing-multiple-chart-types.md

 ### Chart Types versus Trace Types

 Plotly's [figure data structure](figure-structure.md) supports defining [subplots](subplots.md) of [various types](mixed-subplots.md) (e.g. [cartesian](axes.md), [polar](polar-chart.md), [3-dimensional](/3d-charts/), [maps](/maps/) etc) with attached traces of [various compatible types](figure-structure.md) (e.g. scatter, bar, choropleth, surface etc). This means that **Plotly figures are not constrained to representing a fixed set of "chart types"** such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.
 Plotly's [figure data structure](figure-structure.md) supports defining [subplots](subplots.md) of [various types](mixed-subplots.md) (e.g. [cartesian](axes.md), [polar](polar-chart.md), [3-dimensional](/python/3d-charts/), [maps](/python/maps/) etc) with attached traces of [various compatible types](figure-structure.md) (e.g. scatter, bar, choropleth, surface etc). This means that **Plotly figures are not constrained to representing a fixed set of "chart types"** such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.


 ### Multiple Trace Types with Plotly Express
diff --git a/doc/python/hexbin-mapbox.md b/doc/python/hexbin-mapbox.md

 This page details the use of a [figure factory](figure-factories.md). For more examples with Choropleth maps, see [this page](choropleth-maps.md).

 In order to use mapbox styles that require a mapbox token, set the token with `plotly.express`. You can also use styles that do not require a mapbox token. See more information on [this page](mapbox-layers.md).
 In order to use mapbox styles that require a mapbox token, set the token with `plotly.express`. You can also use styles that do not require a mapbox token. See more information on [this page](/python/mapbox-layers/).

 ```python
 import plotly.figure_factory as ff
diff --git a/doc/python/horizontal-vertical-shapes.md b/doc/python/horizontal-vertical-shapes.md

 #### Adding Text Annotations

 [Text annotations](../text-and-annotations) can optionally be added to an autoshape
 [Text annotations](text-and-annotations.md) can optionally be added to an autoshape
 using the `annotation_text` keyword argument, and positioned using the `annotation_position` argument:

 ```python
diff --git a/doc/python/lines-on-tile-maps.md b/doc/python/lines-on-tile-maps.md

 The earlier examples using `px.line_map` and `go.Scattermap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24 and are now the recommended way to draw lines on tile-based maps. There are also traces that use [Mapbox](https://docs.mapbox.com): `px.line_mapbox` and `go.Scattermapbox`

 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

 To draw a line on your map, you either can use [`px.line_mapbox`](reference/plotly-express.md#plotly.express.line_mapbox) in Plotly Express, or [`Scattermapbox`](reference/graph_objects/Scattermapbox.md) traces. Below we show you how to draw a line on Mapbox using Plotly Express.

diff --git a/doc/python/plotly-express.md b/doc/python/plotly-express.md

 * **A single entry point into `plotly`**: just `import plotly.express as px` and get access to [all the plotting functions](reference/plotly-express.md), plus [built-in demo datasets under `px.data`](reference/data.md) and [built-in color scales and sequences under `px.color`](reference/colors.md). Every PX function returns a `plotly.graph_objects.Figure` object, so you can edit it using all the same methods like [`update_layout` and `add_trace`](creating-and-updating-figures.md#updating-figures).
 * **Sensible, Overridable Defaults**: PX functions will infer sensible defaults wherever possible, and will always let you override them.
 * **Flexible Input Formats**: PX functions [accept input in a variety of formats](px-arguments.md), from `list`s and `dict`s to [long-form or wide-form `DataFrame`s](wide-form.md) to [`numpy` arrays and `xarrays`](imshow.md) to [GeoPandas `GeoDataFrames`](maps.md).
 * **Flexible Input Formats**: PX functions [accept input in a variety of formats](px-arguments.md), from `list`s and `dict`s to [long-form or wide-form `DataFrame`s](wide-form.md) to [`numpy` arrays and `xarrays`](imshow.md) to [GeoPandas `GeoDataFrames`](/python/maps/).
 * **Automatic Trace and Layout configuration**: PX functions will create one [trace](figure-structure.md) per animation frame for each unique combination of data values mapped to discrete color, symbol, line-dash, facet-row and/or facet-column. Traces' [`legendgroup` and `showlegend` attributes](legend.md) are set such that only one legend item appears per unique combination of discrete color, symbol and/or line-dash. Traces are automatically linked to a correctly-configured [subplot of the appropriate type](figure-structure.md).
 * **Automatic Figure Labelling**: PX functions [label axes, legends and colorbars](figure-labels.md) based in the input `DataFrame` or `xarray`, and provide [extra control with the `labels` argument](styling-plotly-express.md).
 * **Automatic Hover Labels**: PX functions populate the hover-label using the labels mentioned above, and provide [extra control with the `hover_name` and `hover_data` arguments](hover-text-and-formatting.md).
diff --git a/doc/python/tile-county-choropleth.md b/doc/python/tile-county-choropleth.md

 The earlier examples using `px.choropleth_map` and `go.Choroplethmap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24 and are now the recommended way to create tile-based choropleth maps. There are also choropleth traces that use [Mapbox](https://docs.mapbox.com): `px.choropleth_mapbox` and `go.Choroplethmapbox`

 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

 Here's an exmaple of using the Mapbox Light base map, which requires a free token.

diff --git a/doc/python/tile-scatter-maps.md b/doc/python/tile-scatter-maps.md

 The earlier examples using `px.scatter_map` and `go.Scattermap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24 and are now the recommended way to create scatter plots on tile-based maps. There are also traces that use [Mapbox](https://docs.mapbox.com): `px.scatter_mapbox` and `go.Scattermapbox`

 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

 Here's the first example rewritten to use `px.scatter_mapbox`.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -74,7 +74,7 @@ fig.show()

		The earlier examples using `px.density_map` and `go.Densitymap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24. These trace types are now the recommended way to make tile-based density heatmaps. There are also traces that use [Mapbox](https://docs.mapbox.com): `density_mapbox` and `go.Densitymapbox`.

		To use these trace types, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
		To use these trace types, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

		Here's one of the earlier examples rewritten to use `px.density_mapbox`.

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -68,13 +68,13 @@ IFrame(snippet_url + 'figure-structure', width='100%', height=1200)

		### Figures as Trees of Attributes

		Plotly.js supports inputs adhering to a well-defined schema, whose overall architecture is explained in this page and which is exhaustively documented in the [Figure Reference](reference/index.md) (which is itself generated from a [machine-readable JSON representation of the schema](https://raw.githubusercontent.com/plotly/plotly.js/master/dist/plot-schema.json)). Figures are represented as trees with named nodes called "attributes". The root node of the tree has three top-level attributes: `data`, `layout` and `frames` (see below).
		Plotly.js supports inputs adhering to a well-defined schema, whose overall architecture is explained in this page and which is exhaustively documented in the [Figure Reference](reference/graph_objects/index.md) (which is itself generated from a [machine-readable JSON representation of the schema](https://raw.githubusercontent.com/plotly/plotly.js/master/dist/plot-schema.json)). Figures are represented as trees with named nodes called "attributes". The root node of the tree has three top-level attributes: `data`, `layout` and `frames` (see below).

		Attributes are referred to in text and in the [Figure Reference](reference/index.md) by their full "path" i.e. the dot-delimited concatenation of their parents. For example `"layout.width"` refers to the attribute whose key is `"width"` inside a dict which is the value associated with a key `"layout"` at the root of the figure. If one of the parents is a list rather than a dict, a set of brackets is inserted in the path when referring to the attribute in the abstract, e.g. `"layout.annotations[].text"`. Finally, as explained below, the top-level "data" attribute defines a list of typed objects called "traces" with the schema dependent upon the type, and these attributes' paths are listed in the [Figure Reference](reference/index.md) as `"data[type=scatter].name"`.
		Attributes are referred to in text and in the [Figure Reference](reference/graph_objects/index.md) by their full "path" i.e. the dot-delimited concatenation of their parents. For example `"layout.width"` refers to the attribute whose key is `"width"` inside a dict which is the value associated with a key `"layout"` at the root of the figure. If one of the parents is a list rather than a dict, a set of brackets is inserted in the path when referring to the attribute in the abstract, e.g. `"layout.annotations[].text"`. Finally, as explained below, the top-level "data" attribute defines a list of typed objects called "traces" with the schema dependent upon the type, and these attributes' paths are listed in the [Figure Reference](reference/graph_objects/index.md) as `"data[type=scatter].name"`.

		The [`plotly.graph_objects` module contains an automatically-generated hierarchy of Python classes](graph-objects.md) which represent non-leaf attributes in the figure schema and provide a Pythonic API for them. When [manipulating a `plotly.graph_objects.Figure` object](creating-and-updating-figures.md), attributes can be set either directly using Python object attributes e.g. `fig.layout.title.font.family="Open Sans"` or using [update methods and "magic underscores"](creating-and-updating-figures.md#magic-underscore-notation) e.g. `fig.update_layout(title_font_family="Open Sans")`

		When building a figure, it is not necessary to populate every attribute of every object. At render-time, [the JavaScript layer will compute default values](figure-introspection.md) for each required unspecified attribute, depending upon the ones that are specified, as documented in the [Figure Reference](reference/index.md). An example of this would be `layout.xaxis.range`, which may be specified explicitly, but if not will be computed based on the range of `x` values for every trace linked to that axis. The JavaScript layer will ignore unknown attributes or malformed values, although the `plotly.graph_objects` module provides Python-side validation for attribute values. Note also that if [the `layout.template` key is present (as it is by default)](templates.md) then default values will be drawn first from the contents of the template and only if missing from there will the JavaScript layer infer further defaults. The built-in template can be disabled by setting `layout.template="none"`.
		When building a figure, it is not necessary to populate every attribute of every object. At render-time, [the JavaScript layer will compute default values](figure-introspection.md) for each required unspecified attribute, depending upon the ones that are specified, as documented in the [Figure Reference](reference/graph_objects/index.md). An example of this would be `layout.xaxis.range`, which may be specified explicitly, but if not will be computed based on the range of `x` values for every trace linked to that axis. The JavaScript layer will ignore unknown attributes or malformed values, although the `plotly.graph_objects` module provides Python-side validation for attribute values. Note also that if [the `layout.template` key is present (as it is by default)](templates.md) then default values will be drawn first from the contents of the template and only if missing from there will the JavaScript layer infer further defaults. The built-in template can be disabled by setting `layout.template="none"`.

		### The Top-Level `data` Attribute

Expand DownExpand Up		@@ -157,7 +157,7 @@ The following trace types are compatible with 2d-cartesian subplots via the `xax

		### 3D, Polar, Ternary and Smith Trace Types and Subplots

		Beyond 2D cartesian subplots, figures can include [three-dimensional cartesian subplots](3d-charts.md), [polar subplots](polar-chart.md), [ternary subplots](ternary-plots.md) and [smith subplots](smith-charts.md). The following trace types support attributes named `scene`, `polar`, `smith` or `ternary`, whose values must refer to corresponding objects in the layout portion of the figure i.e. `ternary="ternary2"` etc. Note that attributes such as `layout.scene` and `layout.ternary2` etc do not have to be explicitly defined, in which case default values will be inferred. Multiple traces of a compatible type can be placed on the same subplot.
		Beyond 2D cartesian subplots, figures can include [three-dimensional cartesian subplots](/python/3d-charts/), [polar subplots](polar-chart.md), [ternary subplots](ternary-plots.md) and [smith subplots](smith-charts.md). The following trace types support attributes named `scene`, `polar`, `smith` or `ternary`, whose values must refer to corresponding objects in the layout portion of the figure i.e. `ternary="ternary2"` etc. Note that attributes such as `layout.scene` and `layout.ternary2` etc do not have to be explicitly defined, in which case default values will be inferred. Multiple traces of a compatible type can be placed on the same subplot.

		The following trace types are compatible with 3D subplots via the `scene` attribute, which contains special [camera controls](3d-camera-controls.md):

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -141,7 +141,7 @@ fig.show()

		The earlier examples using `go.Scattermap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. This trace was introduced in Plotly.py 5.24 and is now the recommended way to draw filled areas on tile-based maps. There is also a trace that uses [Mapbox](https://docs.mapbox.com), called `go.Scattermapbox`.

		To use the `Scattermapbox` trace type, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
		To use the `Scattermapbox` trace type, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

		Here's one of the earlier examples rewritten to use `Scattermapbox`.

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -50,7 +50,7 @@ The objects contained in the list which is the [value of the attribute `data` ar
		Graph objects have several benefits compared to plain Python dictionaries:

		1. Graph objects provide precise data validation. If you provide an invalid property name or an invalid property value as the key to a graph object, an exception will be raised with a helpful error message describing the problem. This is not the case if you use plain Python dictionaries and lists to build your figures.
		2. Graph objects contain descriptions of each valid property as Python docstrings, with a [full API reference available](https://plotly.com/python-api-reference/). You can use these docstrings in the development environment of your choice to learn about the available properties as an alternative to consulting the online [Full Reference](/reference/).
		2. Graph objects contain descriptions of each valid property as Python docstrings, with a [full API reference available](https://plotly.com/python-api-reference/). You can use these docstrings in the development environment of your choice to learn about the available properties as an alternative to consulting the online [Full Reference](/reference/graph_objects/index.md).
		3. Properties of graph objects can be accessed using both dictionary-style key lookup (e.g. `fig["layout"]`) or class-style property access (e.g. `fig.layout`).
		4. Graph objects support higher-level convenience functions for making updates to already constructed figures (`.update_layout()`, `.add_trace()` etc).
		5. Graph object constructors and update methods accept "magic underscores" (e.g. `go.Figure(layout_title_text="The Title")` rather than `dict(layout=dict(title=dict(text="The Title")))`) for more compact code.
Expand All		@@ -68,7 +68,7 @@ Note that the figures produced by Plotly Express in a single function-call a

		The figures produced by Plotly Express can always be built from the ground up using graph objects, but this approach typically takes 5-100 lines of code rather than 1.

		Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without. Note that [Plotly Express functions](/python-api-reference/plotly.express.html) like [`px.bar()`](bar-charts.md) can accept a DataFrame as their first argument with column names passed to the `x` and `y` arguments, while [Graph Objects functions](/python-api-reference/plotly.graph_objects.html) like [`go.Bar()`](bar-charts.md#basic-bar-charts-with-plotlygraphobjects) require the data values to be passed directly to the `x` and `y` arguments as a tuple, list, NumPy array, or Pandas Series.
		Here is a simple example of how to produce the same figure object from the same data, once with Plotly Express and once without. Note that [Plotly Express functions](/reference/plotly-express.md) like [`px.bar()`](bar-charts.md) can accept a DataFrame as their first argument with column names passed to the `x` and `y` arguments, while [Graph Objects functions](/reference/graph_objects/index.md) like [`go.Bar()`](bar-charts.md#basic-bar-charts-with-plotlygraphobjects) require the data values to be passed directly to the `x` and `y` arguments as a tuple, list, NumPy array, or Pandas Series.

		The data in this example is in "long form" but [Plotly Express also accepts data in "wide form"](wide-form.md) and the line-count savings from Plotly Express over graph objects are comparable. More complex figures such as [sunbursts](sunburst-charts.md), [parallel coordinates](parallel-coordinates-plot.md), [facet plots](facet-plots.md) or [animations](animations.md) require many more lines of figure-specific graph objects code, whereas switching from one representation to another with Plotly Express usually involves changing just a few characters.

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Expand Up		@@ -35,7 +35,7 @@ jupyter:

		### Chart Types versus Trace Types

		Plotly's [figure data structure](figure-structure.md) supports defining [subplots](subplots.md) of [various types](mixed-subplots.md) (e.g. [cartesian](axes.md), [polar](polar-chart.md), [3-dimensional](/3d-charts/), [maps](/maps/) etc) with attached traces of [various compatible types](figure-structure.md) (e.g. scatter, bar, choropleth, surface etc). This means that Plotly figures are not constrained to representing a fixed set of "chart types" such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.
		Plotly's [figure data structure](figure-structure.md) supports defining [subplots](subplots.md) of [various types](mixed-subplots.md) (e.g. [cartesian](axes.md), [polar](polar-chart.md), [3-dimensional](/python/3d-charts/), [maps](/python/maps/) etc) with attached traces of [various compatible types](figure-structure.md) (e.g. scatter, bar, choropleth, surface etc). This means that Plotly figures are not constrained to representing a fixed set of "chart types" such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.


		### Multiple Trace Types with Plotly Express
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Expand Up		@@ -37,7 +37,7 @@ jupyter:

		This page details the use of a [figure factory](figure-factories.md). For more examples with Choropleth maps, see [this page](choropleth-maps.md).

		In order to use mapbox styles that require a mapbox token, set the token with `plotly.express`. You can also use styles that do not require a mapbox token. See more information on [this page](mapbox-layers.md).
		In order to use mapbox styles that require a mapbox token, set the token with `plotly.express`. You can also use styles that do not require a mapbox token. See more information on [this page](/python/mapbox-layers/).

		```python
		import plotly.figure_factory as ff
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Expand Up		@@ -86,7 +86,7 @@ IFrame(snippet_url + 'horizontal-vertical-shapes', width='100%', height=1200)

		#### Adding Text Annotations

		[Text annotations](../text-and-annotations) can optionally be added to an autoshape
		[Text annotations](text-and-annotations.md) can optionally be added to an autoshape
		using the `annotation_text` keyword argument, and positioned using the `annotation_position` argument:

		```python
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Expand Up		@@ -133,7 +133,7 @@ fig.show()

		The earlier examples using `px.line_map` and `go.Scattermap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24 and are now the recommended way to draw lines on tile-based maps. There are also traces that use [Mapbox](https://docs.mapbox.com): `px.line_mapbox` and `go.Scattermapbox`

		To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
		To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

		To draw a line on your map, you either can use [`px.line_mapbox`](reference/plotly-express.md#plotly.express.line_mapbox) in Plotly Express, or [`Scattermapbox`](reference/graph_objects/Scattermapbox.md) traces. Below we show you how to draw a line on Mapbox using Plotly Express.

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Expand Up		@@ -70,7 +70,7 @@ The Plotly Express API in general offers the following features:

		* A single entry point into `plotly`: just `import plotly.express as px` and get access to [all the plotting functions](reference/plotly-express.md), plus [built-in demo datasets under `px.data`](reference/data.md) and [built-in color scales and sequences under `px.color`](reference/colors.md). Every PX function returns a `plotly.graph_objects.Figure` object, so you can edit it using all the same methods like [`update_layout` and `add_trace`](creating-and-updating-figures.md#updating-figures).
		* Sensible, Overridable Defaults: PX functions will infer sensible defaults wherever possible, and will always let you override them.
		* Flexible Input Formats: PX functions [accept input in a variety of formats](px-arguments.md), from `list`s and `dict`s to [long-form or wide-form `DataFrame`s](wide-form.md) to [`numpy` arrays and `xarrays`](imshow.md) to [GeoPandas `GeoDataFrames`](maps.md).
		* Flexible Input Formats: PX functions [accept input in a variety of formats](px-arguments.md), from `list`s and `dict`s to [long-form or wide-form `DataFrame`s](wide-form.md) to [`numpy` arrays and `xarrays`](imshow.md) to [GeoPandas `GeoDataFrames`](/python/maps/).
		* Automatic Trace and Layout configuration: PX functions will create one [trace](figure-structure.md) per animation frame for each unique combination of data values mapped to discrete color, symbol, line-dash, facet-row and/or facet-column. Traces' [`legendgroup` and `showlegend` attributes](legend.md) are set such that only one legend item appears per unique combination of discrete color, symbol and/or line-dash. Traces are automatically linked to a correctly-configured [subplot of the appropriate type](figure-structure.md).
		* Automatic Figure Labelling: PX functions [label axes, legends and colorbars](figure-labels.md) based in the input `DataFrame` or `xarray`, and provide [extra control with the `labels` argument](styling-plotly-express.md).
		* Automatic Hover Labels: PX functions populate the hover-label using the labels mentioned above, and provide [extra control with the `hover_name` and `hover_data` arguments](hover-text-and-formatting.md).
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Expand Up		@@ -224,7 +224,7 @@ fig.show()

		The earlier examples using `px.choropleth_map` and `go.Choroplethmap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. These traces were introduced in Plotly.py 5.24 and are now the recommended way to create tile-based choropleth maps. There are also choropleth traces that use [Mapbox](https://docs.mapbox.com): `px.choropleth_mapbox` and `go.Choroplethmapbox`

		To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](mapbox-layers.md) documentation for more information.
		To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.

		Here's an exmaple of using the Mapbox Light base map, which requires a free token.

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