mapbox examples with px (#1998)

* mapbox examples with px

* Update doc/python/mapbox-density-heatmaps.md

Co-Authored-By: Nicolas Kruchten <[email protected]>

Author: emmanuelle

Diff for: doc/python/mapbox-county-choropleth.md

@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      jupytext_version: 1.3.0
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     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.7.3
   plotly:
     description: How to make a Mapbox Choropleth Map of US Counties in Python with
       Plotly.
@@ -40,7 +40,9 @@ jupyter:
 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.
 
 
-Making choropleth maps with `go.Choroplethmapbox` requires two main types of input: GeoJSON-formatted geometry information *where each `feature` has an `id`* and a list of values indexed by feature id. The GeoJSON data is passed to the `geojson` attribute, and the data is passed into the `z` attribute, in the same order as the IDs are passed into the `location` attribute.
+### Introduction: main parameters for choropleth mapbox charts
+
+Making choropleth maps requires two main types of input: GeoJSON-formatted geometry information *where each `feature` has an `id`* and a list of values indexed by feature id. The GeoJSON data is passed to the `geojson` attribute, and the data is passed into the `z` (`color` for `px.choropleth_mapbox`) attribute, in the same order as the IDs are passed into the `location` attribute.
 
 
 #### GeoJSON with `feature.id`
@@ -67,7 +69,39 @@ df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-
 df.head()
 ```
 
-#### Carto base map: no token needed
+### Choropleth map using plotly.express and carto base map (no token needed)
+
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+
+With `px.choropleth_mapbox`, each row of the DataFrame is represented as a region of the choropleth.
+
+```python
+from urllib.request import urlopen
+import json
+with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
+    counties = json.load(response)
+
+import pandas as pd
+df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
+                   dtype={"fips": str})
+
+import plotly.express as px
+
+fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp',
+                           color_continuous_scale="Viridis", 
+                           range_color=(0, 12),
+                           mapbox_style="carto-positron",
+                           zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
+                           opacity=0.5,
+                           labels={'unemp':'unemployment rate'}
+                          )
+fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
+fig.show()
+```
+
+### Choropleth map using plotly.graph_objects and carto base map (no token needed)
+
+If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Choroplethmapbox` function from `plotly.graph_objects`.
 
 ```python
 from urllib.request import urlopen

Diff for: doc/python/mapbox-density-heatmaps.md

@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      jupytext_version: 1.3.0
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,10 +20,9 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.3
   plotly:
-    description: How to make a Mapbox Density Heatmap in Python
-      with Plotly.
+    description: How to make a Mapbox Density Heatmap in Python with Plotly.
     display_as: maps
     language: python
     layout: base
@@ -42,14 +41,34 @@ To plot on Mapbox maps with Plotly you *may* need a Mapbox account and a public
 
 
 
-#### Stamen Terrain base map: no token needed
+### Stamen Terrain base map (no token needed): density mapbox with `plotly.express`
+
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). 
+
+With `px.density_mapbox`, each row of the DataFrame is represented as a point smoothed with a given radius of influence.
+
+```python
+import pandas as pd
+df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
+
+import plotly.express as px
+fig = px.density_mapbox(df, lat='Latitude', lon='Longitude', z='Magnitude', radius=10,
+                        center=dict(lat=0, lon=180), zoom=0,
+                        mapbox_style="stamen-terrain")
+fig.show()
+```
+
+### Stamen Terrain base map (no token needed): density mapbox with `plotly.graph_objects`
+
+If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Densitymapbox` function from `plotly.graph_objects`.
 
 ```python
 import pandas as pd
 quakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
 
 import plotly.graph_objects as go
-fig = go.Figure(go.Densitymapbox(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude, radius=10))
+fig = go.Figure(go.Densitymapbox(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude, 
+                                 radius=10))
 fig.update_layout(mapbox_style="stamen-terrain", mapbox_center_lon=180)
 fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
 fig.show()