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Commit 9ecae0a

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emmanuellenicolaskruchten
emmanuelle
and
nicolaskruchten
authored
mapbox examples with px (plotly#1998)
* mapbox examples with px * Update doc/python/mapbox-density-heatmaps.md Co-Authored-By: Nicolas Kruchten <nicolas@plot.ly>
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‎doc/python/mapbox-county-choropleth.md

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version: 3.6.8
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version: 3.7.3
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plotly:
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description: How to make a Mapbox Choropleth Map of US Counties in Python with
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Plotly.
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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.
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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.
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### Introduction: main parameters for choropleth mapbox charts
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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.
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#### GeoJSON with `feature.id`
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df.head()
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```
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#### Carto base map: no token needed
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### Choropleth map using plotly.express and carto base map (no token needed)
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
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With `px.choropleth_mapbox`, each row of the DataFrame is represented as a region of the choropleth.
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```python
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from urllib.request import urlopen
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import json
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with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
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counties = json.load(response)
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import pandas as pd
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df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
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dtype={"fips": str})
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import plotly.express as px
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fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp',
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color_continuous_scale="Viridis",
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range_color=(0, 12),
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mapbox_style="carto-positron",
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zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
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opacity=0.5,
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labels={'unemp':'unemployment rate'}
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)
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fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
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fig.show()
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```
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### Choropleth map using plotly.graph_objects and carto base map (no token needed)
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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`.
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```python
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from urllib.request import urlopen

‎doc/python/mapbox-density-heatmaps.md

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plotly:
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description: How to make a Mapbox Density Heatmap in Python
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with Plotly.
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description: How to make a Mapbox Density Heatmap in Python with Plotly.
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display_as: maps
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language: python
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layout: base
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#### Stamen Terrain base map: no token needed
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### Stamen Terrain base map (no token needed): density mapbox with `plotly.express`
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
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With `px.density_mapbox`, each row of the DataFrame is represented as a point smoothed with a given radius of influence.
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```python
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import pandas as pd
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df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
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import plotly.express as px
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fig = px.density_mapbox(df, lat='Latitude', lon='Longitude', z='Magnitude', radius=10,
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center=dict(lat=0, lon=180), zoom=0,
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mapbox_style="stamen-terrain")
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fig.show()
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```
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### Stamen Terrain base map (no token needed): density mapbox with `plotly.graph_objects`
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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`.
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```python
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import pandas as pd
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quakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')
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import plotly.graph_objects as go
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fig = go.Figure(go.Densitymapbox(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude, radius=10))
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fig = go.Figure(go.Densitymapbox(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude,
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radius=10))
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fig.update_layout(mapbox_style="stamen-terrain", mapbox_center_lon=180)
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fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
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fig.show()

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