Plotly Choropleths

This example reviews plotting a choropleth from a GeoDataFrame using plotly.

import mapped
import geopandas as gpd
import plotly
mapped.__version__, gpd.__version__, plotly.__version__

('20.4.2', '0.7.0', '4.6.0')

First load some example polygon data.

gdf = gpd.read_file(gpd.datasets.get_path('nybb'))

gdf['Population'] = [
    479_458,
    2_358_582,
    2_648_771,
    1_664_727,
    1_471_160,
]

gdf.set_index('BoroName', inplace=True)

Importing mapped monkeypatches geopandas to add several tools to the geopandas.GeoDataFrame class, including plotly_choropleth.

gdf.plotly_choropleth('Population')

For another example, let’s plot the same election results data as found in the plotly express mapbox choropleth example. We can load this example data as a GeoDataFrame from the mapped.example_data module.

from mapped.example_data import election

election_data = election()
election_data.head()

	district	Coderre	Bergeron	Joly	total	winner	result	district_id	geometry
0	101-Bois-de-Liesse	2481	1829	3024	7334	Joly	plurality	101	MULTIPOLYGON (((-73.81877 45.51408, -73.81824 ...
1	102-Cap-Saint-Jacques	2525	1163	2675	6363	Joly	plurality	102	MULTIPOLYGON (((-73.84617 45.48899, -73.85053 ...
2	11-Sault-au-Récollet	3348	2770	2532	8650	Coderre	plurality	11	MULTIPOLYGON (((-73.63632 45.57592, -73.63628 ...
3	111-Mile-End	1734	4782	2514	9030	Bergeron	majority	111	POLYGON ((-73.57920 45.52801, -73.58151 45.525...
4	112-DeLorimier	1770	5933	3044	10747	Bergeron	majority	112	POLYGON ((-73.56556 45.52136, -73.57406 45.525...

The plotly express choropleth_mapbox function is great, but it requires a lot of arguments to be set to work properly.

import plotly.express as px

fig = px.choropleth_mapbox(
    election_data,
    geojson=election_data.__geo_interface__,
    color="Bergeron",
    locations="district",
    featureidkey="properties.district",
    center={"lat": 45.5517, "lon": -73.7073},
    zoom=9,
)
plotly.graph_objects.FigureWidget(fig)

We can use the mapped interface to create substantially the same plot with just a single argument: the column to use to colorize the choropleth.

election_data.plotly_choropleth(color="Bergeron")

GeoDataFrame.plotly_choropleth(color=None, *, zoom='auto', mapbox_style=None, margins=0, figuretype=None, fig=None, center=None, opacity=1.0, text=None, **kwargs)

Make a choropleth point map in a plotly FigureWidget.

Parameters:

• gdf (geopandas.GeoDataFrame) – The areas to plot.
• zoom (‘auto’ or int or float) – Sets the initial zoom level for the map, up to 20.
• mapbox_style (str, optional) – Sets the style for the basemap tiles. Totally free options include “carto-positron”, “stamen-terrain”, “stamen-toner”, “open-street-map”, possibly others. Options from the set {‘basic’,’streets’,’outdoors’,’light’,’dark’,’satellite’, ‘satellite-streets’} will load vector tiles from MapBox, which requires a token to be set. This defaults to “carto-positron” if no mapbox token is set, or ‘basic’ if a token is available.
• margins (int, optional) – Set margins on the figure.
• figuretype (class, optional) – Which plotly figure class to use, defaults to plotly.go.FigureWidget.
• fig (plotly.go.Figure or plotly.go.FigureWidget) – An existing figure, to which the new trace(s) will be appended.
• color (str or int or Series or array-like) – Either a name of a column in gdf, or a pandas Series or array_like object. Values are used to assign color to markers.
• hover_name (str or int or Series or array-like) – Either a name of a column in gdf, or a pandas Series or array_like object. Values appear in bold in the hover tooltip.
• hover_data (list of str or int, or Series or array-like) – Either names of columns in gdf, or pandas Series, or array_like objects Values appear as extra data in the hover tooltip.
• opacity (float, default 0.5) – Value between 0 and 1. Sets the opacity for markers.
• color_discrete_sequence (list of str) – Strings should define valid CSS-colors. When color is set and the values in the corresponding column are not numeric, values in that column are assigned colors by cycling through color_discrete_sequence in the order described in category_orders, unless the value of color is a key in color_discrete_map. Various useful color sequences are available in the plotly.express.colors submodules, specifically plotly.express.colors.qualitative.
• color_discrete_map (dict with str keys and str values (default {})) – String values should define valid CSS-colors Used to override color_discrete_sequence to assign a specific colors to marks corresponding with specific values. Keys in color_discrete_map should be values in the column denoted by color.
• color_continuous_scale (list of str) – Strings should define valid CSS-colors This list is used to build a continuous color scale when the column denoted by color contains numeric data. Various useful color scales are available in the plotly.express.colors submodules, specifically plotly.express.colors.sequential, plotly.express.colors.diverging and plotly.express.colors.cyclical.
• range_color (list of two numbers) – If provided, overrides auto-scaling on the continuous color scale.
• color_continuous_midpoint (number (default None)) – If set, computes the bounds of the continuous color scale to have the desired midpoint. Setting this value is recommended when using plotly.express.colors.diverging color scales as the inputs to color_continuous_scale.
• width (int (default None)) – The figure width in pixels.
• height (int (default 600)) – The figure height in pixels.
• **kwargs – Other keyword arguments are passed through to the plotly.express.choropleth_mapbox constructor, allowing substantial further customization of the resulting figure.

Returns:

Return type:

plotly.go.FigureWidget