various updates for new map traces

Author: LiamConnors

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/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/).
@@ -453,10 +453,10 @@ fig.show()
 *New in 5.23*
 
 You can adjust tick label positions by moving them a number of pixels away from the axis using `ticklabelstandoff` or along the axis using `ticklabelshift`.
- 
+
 In this example, `ticklabelshift=25` shifts the labels 25 pixels to the right along the x-axis. By providing a negative value, we could move the labels 25 pixels to the left, (`ticklabelshift=-25`).
 
-Here, `ticklabelstandoff=15` moves the labels further 15 pixels away from the x-axis. A negative value here would move them close to the axis. 
+Here, `ticklabelstandoff=15` moves the labels further 15 pixels away from the x-axis. A negative value here would move them close to the axis.
 
 ```python
 import plotly.express as px
@@ -483,7 +483,7 @@ fig.show()
 
 On date or linear axes, use `ticklabelindex` to draw a label for a minor tick instead of a major tick.
 
-To draw the label for the minor tick before each major tick, set `ticklabelindex` -1, like in the following example. 
+To draw the label for the minor tick before each major tick, set `ticklabelindex` -1, like in the following example.
 
 ```python
 import plotly.express as px

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:
@@ -20,7 +20,7 @@ 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

doc/python/choropleth-maps.md

@@ -33,7 +33,7 @@ jupyter:
     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**](/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:
@@ -20,7 +20,7 @@ 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.
@@ -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]],
@@ -62,11 +62,11 @@ 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 = [
+# 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,
@@ -113,7 +113,3 @@ 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 [mapbox subplots for tile 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/python/filled-area-on-mapbox.md

@@ -36,7 +36,7 @@ jupyter:
 
 There are three different ways to show a filled area on a tile-based map:
 
-- Using a [Scattermap](https://plotly.com/python/reference/scattermap/) trace and set `fill` attribute to 'toself'
+- Using a [Scattermap](https://plotly.com/python/reference/scattermap/) trace and setting the `fill` attribute to 'toself'
 - Using a map layout (i.e. by minimally using an empty [Scattermap](https://plotly.com/python/reference/scattermap/) trace) and adding a GeoJSON layer
 - Using the [Choroplethmap](https://plotly.com/python/tile-county-choropleth/) trace type
 

doc/python/heatmaps.md

@@ -34,7 +34,7 @@ jupyter:
     thumbnail: thumbnail/heatmap.jpg
 ---
 
-The term "heatmap" usually refers to a cartesian plot with data visualized as colored rectangular tiles, which is the subject of this page. It is also sometimes used to refer to [actual maps with density data displayed as color intensity](/python/mapbox-density-heatmaps/).
+The term "heatmap" usually refers to a cartesian plot with data visualized as colored rectangular tiles, which is the subject of this page. It is also sometimes used to refer to [actual maps with density data displayed as color intensity](/python/tile-density-heatmaps/).
 
 Plotly supports two different types of colored-tile heatmaps:
 

doc/python/hover-text-and-formatting.md

@@ -285,7 +285,7 @@ fig.show()
 
 ### Specifying the formatting and labeling of custom fields in a Plotly Express figure using a hovertemplate
 
-This example adds custom fields to a Plotly Express figure using the custom_data parameter and then adds a hover template that applies d3 formats to each element of the customdata[n] array and uses HTML to customize the fonts and spacing. 
+This example adds custom fields to a Plotly Express figure using the custom_data parameter and then adds a hover template that applies d3 formats to each element of the customdata[n] array and uses HTML to customize the fonts and spacing.
 
 ```python
 # %%
@@ -301,20 +301,20 @@ df = df.sort_values(['continent', 'country'])
 
 df.rename(columns={"gdpPercap":'GDP per capita', "lifeExp":'Life Expectancy (years)'}, inplace=True)
 
-fig=px.scatter(df, 
+fig=px.scatter(df,
                x='GDP per capita',
-               y='Life Expectancy (years)', 
-               color='continent', 
-               size=np.sqrt(df['pop']),           
+               y='Life Expectancy (years)',
+               color='continent',
+               size=np.sqrt(df['pop']),
                # Specifying data to make available to the hovertemplate
                # The px custom_data parameter has an underscore, while the analogous graph objects customdata parameter has no underscore.
                # The px custom_data parameter is a list of column names in the data frame, while the graph objects customdata parameter expects a data frame or a numpy array.
-               custom_data=['country', 'continent', 'pop'], 
+               custom_data=['country', 'continent', 'pop'],
 )
 
 # Plotly express does not have a hovertemplate parameter in the graph creation function, so we apply the template with update_traces
 fig.update_traces(
-    hovertemplate = 
+    hovertemplate =
                 "<b>%{customdata[0]}</b><br>" +
                 "<b>%{customdata[1]}</b><br><br>" +
                 "GDP per Capita: %{x:$,.0f}<br>" +
@@ -371,7 +371,7 @@ fig.show()
 
 ### Advanced Hover Template
 
-This produces the same graphic as in "Specifying the formatting and labeling of custom fields in a Plotly Express figure using a hovertemplate" above, but does so with the `customdata` and `text` parameters of `graph_objects`.  It shows how to specify columns from a dataframe to include in the customdata array using the df[["col_i", "col_j"]] subsetting notation.  It then references those variables using e.g. %{customdata[0]} in the hovertemplate.  It includes comments about major differences between the parameters used by `graph_objects` and `plotly.express`.  
+This produces the same graphic as in "Specifying the formatting and labeling of custom fields in a Plotly Express figure using a hovertemplate" above, but does so with the `customdata` and `text` parameters of `graph_objects`.  It shows how to specify columns from a dataframe to include in the customdata array using the df[["col_i", "col_j"]] subsetting notation.  It then references those variables using e.g. %{customdata[0]} in the hovertemplate.  It includes comments about major differences between the parameters used by `graph_objects` and `plotly.express`.
 
 ```python
 import plotly.graph_objects as go
@@ -404,12 +404,12 @@ for continent_name, df in continent_data.items():
             name=continent_name,
 
             # The next three parameters specify the hover text
-            # Text supports just one customized field per trace 
-            # and is implemented here with text=df['continent'],  
-            # Custom data supports multiple fields through numeric indices in the hovertemplate 
-            # In we weren't using the text parameter in our example, 
+            # Text supports just one customized field per trace
+            # and is implemented here with text=df['continent'],
+            # Custom data supports multiple fields through numeric indices in the hovertemplate
+            # In we weren't using the text parameter in our example,
             # we could instead add continent as a third customdata field.
-            customdata=df[['country','pop']],  
+            customdata=df[['country','pop']],
             hovertemplate=
                 "<b>%{customdata[0]}</b><br>" +
                 "<b>%{text}</b><br><br>" +
@@ -462,14 +462,12 @@ fig.update_layout(title_text='Hover to see the value of z1, z2 and z3 together')
 fig.show()
 ```
 
-### Setting the Hover Template in Mapbox Maps
+### Setting the Hover Template in Tile Maps
 
 ```python
 import plotly.graph_objects as go
 
-token = open(".mapbox_token").read() # you need your own token
-
-fig = go.Figure(go.Scattermapbox(
+fig = go.Figure(go.Scattermap(
     name = "",
     mode = "markers+text+lines",
     lon = [-75, -80, -50],
@@ -481,7 +479,7 @@ fig = go.Figure(go.Scattermapbox(
     "latitude: %{lat}<br>" ))
 
 fig.update_layout(
-    mapbox = {
+    map = {
         'accesstoken': token,
         'style': "outdoors", 'zoom': 1},
     showlegend = False)