Updates for maplibre maps

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/apidoc/plotly.express.rst

@@ -18,12 +18,14 @@ plotly's high-level API for rapid figure generation. ::
  scatter_3d
  scatter_polar
  scatter_ternary
+ scatter_map
  scatter_mapbox
  scatter_geo
  line
  line_3d
  line_polar
  line_ternary
+ line_map
  line_mapbox
  line_geo
  area
@@ -45,9 +47,11 @@ plotly's high-level API for rapid figure generation. ::
  parallel_coordinates
  parallel_categories
  choropleth
+ choropleth_map
  choropleth_mapbox
  density_contour
  density_heatmap
+ density_map
  density_mapbox
  imshow
  set_mapbox_access_token

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/tile-map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.1'
- jupytext_version: 1.1.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,14 +20,14 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.7
+ version: 3.10.0
  plotly:
  description: How to make bubble maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Bubble Maps
- order: 4
+ order: 5
  page_type: example_index
  permalink: python/bubble-maps/
  thumbnail: thumbnail/bubble-map.jpg

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
  extension: .md
  format_name: markdown
  format_version: '1.3'
- jupytext_version: 1.14.1
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,20 +20,20 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.8.8
+ version: 3.10.0
  plotly:
  description: How to make choropleth maps in Python with Plotly.
  display_as: maps
  language: python
  layout: base
  name: Choropleth Maps
- order: 7
+ order: 8
  page_type: u-guide
  permalink: python/choropleth-maps/
  thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.1
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,15 +20,15 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.6.8
+ version: 3.10.0
  plotly:
  description: How to create colormaped representations of USA counties by FIPS
  values in Python.
  display_as: maps
  language: python
  layout: base
  name: USA County Choropleth Maps
- order: 10
+ order: 11
  page_type: u-guide
  permalink: python/county-choropleth/
  thumbnail: thumbnail/county-choropleth-usa-greybkgd.jpg
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
  text_representation:
  extension: .md
  format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.3.0
+ format_version: '1.3'
+ jupytext_version: 1.16.3
  kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
  language_info:
@@ -20,7 +20,7 @@ jupyter:
  name: python
  nbconvert_exporter: python
  pygments_lexer: ipython3
- version: 3.7.3
+ version: 3.10.0
  plotly:
  description: How to use datashader to rasterize large datasets, and visualize
  the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
  [coords_lon[-1], coords_lat[0]],
  [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
- mapbox_layers = [
- {
- "sourcetype": "image",
- "source": img,
- "coordinates": coordinates
- }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+ map_style="carto-darkmatter",
+ map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
  * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
  * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
  * `scene`, `scene2`, `scene3` etc: 3d scene subplots
- * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+ * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
  * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
  * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
  * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.mapbox` 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.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc. Note that attributes such as `layout.geo2` and `layout.map` 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 geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots