update choropleth for maplibre changes

Author: LiamConnors

doc/python/mapbox-county-choropleth.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:
@@ -20,36 +20,32 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.8.8
+    version: 3.10.0
   plotly:
-    description: How to make a Mapbox Choropleth Map of US Counties in Python with
-      Plotly.
+    description: How to make a choropleth map of US counties in Python with Plotly.
     display_as: maps
     language: python
     layout: base
-    name: Mapbox Choropleth Maps
+    name: Tile Choropleth Maps
     order: 1
     page_type: example_index
-    permalink: python/mapbox-county-choropleth/
+    permalink: python/tile-county-choropleth/
+    redirect_from: python/mapbox-county-choropleth/
     thumbnail: thumbnail/mapbox-choropleth.png
 ---
 
-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 **tile-map** choropleth maps, but you can also build [**outline** choropleth maps using our non-Mapbox trace types](/python/choropleth-maps).
+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 **tile-map** choropleth maps, but you can also build [**outline** choropleth maps](/python/choropleth-maps).
 
-Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth_mapbox` function or the lower-level `go.Choroplethmapbox` graph object.
-
-#### Mapbox Access Tokens and Base Map Configuration
-
-To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.
+Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth_map` function or the lower-level `go.Choroplethmap` graph object.
 
 ### Introduction: main parameters for choropleth tile maps
 
-Making choropleth Mapbox maps requires two main types of input:
+Making choropleth maps requires two main types of input:
 
 1. GeoJSON-formatted geometry information where each feature has either an `id` field or some identifying value in `properties`.
 2. A list of values indexed by feature identifier.
 
-The GeoJSON data is passed to the `geojson` argument, and the data is passed into the `color` argument of `px.choropleth_mapbox` (`z` if using `graph_objects`), in the same order as the IDs are passed into the `location` argument.
+The GeoJSON data is passed to the `geojson` argument, and the data is passed into the `color` argument of `px.choropleth_map` (`z` if using `graph_objects`), in the same order as the IDs are passed into the `location` argument.
 
 **Note** the `geojson` attribute can also be the URL to a GeoJSON file, which can speed up map rendering in certain cases.
 
@@ -77,11 +73,11 @@ df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-
 df.head()
 ```
 
-### Choropleth map using plotly.express and carto base map (no token needed)
+### Choropleth map using plotly.express and carto base map
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
-With `px.choropleth_mapbox`, each row of the DataFrame is represented as a region of the choropleth.
+With `px.choropleth_map`, each row of the DataFrame is represented as a region of the choropleth.
 
 ```python
 from urllib.request import urlopen
@@ -95,10 +91,10 @@ df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-
 
 import plotly.express as px
 
-fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp',
+fig = px.choropleth_map(df, geojson=counties, locations='fips', color='unemp',
                            color_continuous_scale="Viridis",
                            range_color=(0, 12),
-                           mapbox_style="carto-positron",
+                           map_style="carto-positron",
                            zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
                            opacity=0.5,
                            labels={'unemp':'unemployment rate'}
@@ -148,10 +144,10 @@ import plotly.express as px
 df = px.data.election()
 geojson = px.data.election_geojson()
 
-fig = px.choropleth_mapbox(df, geojson=geojson, color="Bergeron",
+fig = px.choropleth_map(df, geojson=geojson, color="Bergeron",
                            locations="district", featureidkey="properties.district",
                            center={"lat": 45.5517, "lon": -73.7073},
-                           mapbox_style="carto-positron", zoom=9)
+                           map_style="carto-positron", zoom=9)
 fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
 fig.show()
 ```
@@ -166,17 +162,17 @@ import plotly.express as px
 df = px.data.election()
 geojson = px.data.election_geojson()
 
-fig = px.choropleth_mapbox(df, geojson=geojson, color="winner",
+fig = px.choropleth_map(df, geojson=geojson, color="winner",
                            locations="district", featureidkey="properties.district",
                            center={"lat": 45.5517, "lon": -73.7073},
-                           mapbox_style="carto-positron", zoom=9)
+                           map_style="carto-positron", zoom=9)
 fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
 fig.show()
 ```
 
 ### Using GeoPandas Data Frames
 
-`px.choropleth_mapbox` accepts the `geometry` of a [GeoPandas](https://geopandas.org/) data frame as the input to `geojson` if the `geometry` contains polygons.
+`px.choropleth_map` accepts the `geometry` of a [GeoPandas](https://geopandas.org/) data frame as the input to `geojson` if the `geometry` contains polygons.
 
 ```python
 import plotly.express as px
@@ -187,19 +183,19 @@ geo_df = gpd.GeoDataFrame.from_features(
     px.data.election_geojson()["features"]
 ).merge(df, on="district").set_index("district")
 
-fig = px.choropleth_mapbox(geo_df,
+fig = px.choropleth_map(geo_df,
                            geojson=geo_df.geometry,
                            locations=geo_df.index,
                            color="Joly",
                            center={"lat": 45.5517, "lon": -73.7073},
-                           mapbox_style="open-street-map",
+                           map_style="open-street-map",
                            zoom=8.5)
 fig.show()
 ```
 
-### Choropleth map using plotly.graph_objects and carto base map (no token needed)
+### Choropleth map using plotly.graph_objects and carto base map
 
-If Plotly Express does not provide a good starting point, it is also possible to use [the more generic `go.Choroplethmapbox` class from `plotly.graph_objects`](/python/graph-objects/).
+If Plotly Express does not provide a good starting point, it is also possible to use [the more generic `go.Choroplethmap` class from `plotly.graph_objects`](/python/graph-objects/).
 
 ```python
 from urllib.request import urlopen
@@ -213,21 +209,26 @@ df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-
 
 import plotly.graph_objects as go
 
-fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
+fig = go.Figure(go.Choroplethmap(geojson=counties, locations=df.fips, z=df.unemp,
                                     colorscale="Viridis", zmin=0, zmax=12,
                                     marker_opacity=0.5, marker_line_width=0))
-fig.update_layout(mapbox_style="carto-positron",
-                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
+fig.update_layout(map_style="carto-positron",
+                  map_zoom=3, map_center = {"lat": 37.0902, "lon": -95.7129})
 fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
 fig.show()
 ```
 
-#### Mapbox Light base map: free token needed
+### Mapbox Maps
+
+The earlier examples using `px.choropleth_map` and `go.Choroplethmap` use Maplibre for rendering. These traces were introduced in Plotly.py 5.24. These trace types are now the recommended way to create tile-based choropleth maps. There are also choropleth traces that use Mapbox: `px.choropleth_mapbox` and `go.Choroplethmapbox`
+
+To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.
+
+Here's an exmaple of using the Mapbox Light base map, which requires a free token.
 
 ```python
 token = open(".mapbox_token").read() # you will need your own token
 
-
 from urllib.request import urlopen
 import json
 with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
@@ -249,5 +250,6 @@ fig.show()
 
 #### Reference
 
-See [function reference for `px.(choropleth_mapbox)`](https://plotly.com/python-api-reference/generated/plotly.express.choropleth_mapbox) or https://plotly.com/python/reference/choroplethmapbox/ for more information about mapbox and their attribute options.
+See [function reference for `px.(choropleth_map)`](https://plotly.com/python-api-reference/generated/plotly.express.choropleth_map) or https://plotly.com/python/reference/choroplethmap/ for more information about the attributes available.
 
+For Mapbox-based tile maps, see [function reference for `px.(choropleth_mapbox)`](https://plotly.com/python-api-reference/generated/plotly.express.choropleth_mapbox) or https://plotly.com/python/reference/choroplethmapbox/.