Choropleth Maps

Last updated on Sep 30, 2021

Offline Plotly Usage

Get imports and set everything up to be working offline.

import chart_studio.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

Now set up everything so that the figures show up in the notebook:

init_notebook_mode(connected=True)

More info on other options for Offline Plotly usage can be found here.

Choropleth US Maps

Plotly’s mapping can be a bit hard to get used to at first, remember to reference the cheat sheet in the data visualization folder, or find it online here.

import pandas as pd

Now we need to begin to build our data dictionary. Easiest way to do this is to use the dict() function of the general form:

  • type = ‘choropleth’,
  • locations = list of states
  • locationmode = ‘USA-states’
  • colorscale=

Either a predefined string:

'pairs' | 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' | 'Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'

or create a custom colorscale

  • text= list or array of text to display per point
  • z= array of values on z axis (color of state)
  • colorbar = {‘title’:‘Colorbar Title’})

Here is a simple example:

data = dict(type = 'choropleth',
            locations = ['AZ','CA','NY'],
            locationmode = 'USA-states',
            colorscale= 'Portland',
            text= ['text1','text2','text3'],
            z=[1.0,2.0,3.0],
            colorbar = {'title':'Colorbar Title'})

Then we create the layout nested dictionary:

layout = dict(geo = {'scope':'usa'})

Then we use:

go.Figure(data = [data],layout = layout)

to set up the object that finally gets passed into iplot()

choromap = go.Figure(data = [data],layout = layout)

iplot(choromap)

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"histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}},
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Real Data US Map Choropleth

Now let’s show an example with some real data as well as some other options we can add to the dictionaries in data and layout.

df = pd.read_csv('2011_US_AGRI_Exports')
df.head()

codestatecategorytotal exportsbeefporkpoultrydairyfruits freshfruits proctotal fruitsveggies freshveggies proctotal veggiescornwheatcottontext
0ALAlabamastate1390.6334.410.6481.04.068.017.125.115.58.914.3334.970.0317.61Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...
1AKAlaskastate13.310.20.10.00.190.00.00.000.61.01.560.00.00.00Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...
2AZArizonastate1463.1771.317.90.0105.4819.341.060.27147.5239.4386.917.348.7423.95Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...
3ARArkansasstate3586.0253.229.4562.93.532.24.76.884.47.111.4569.5114.5665.44Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...
4CACaliforniastate16472.88228.711.1225.4929.952791.85944.68736.40803.21303.52106.7934.6249.31064.95California<br>Beef 228.7 Dairy 929.95<br>Frui...

Now out data dictionary with some extra marker and colorbar arguments:

data = dict(type='choropleth',
            colorscale = 'YIOrRd',
            locations = df['code'],
            z = df['total exports'],
            locationmode = 'USA-states',
            text = df['text'],
            marker = dict(line = dict(color = 'rgb(255,255,255)',width = 2)),
            colorbar = {'title':"Millions USD"}
            )

And our layout dictionary with some more arguments:

layout = dict(title = '2011 US Agriculture Exports by State',
              geo = dict(scope='usa',
                         showlakes = True,
                         lakecolor = 'rgb(85,173,240)')
             )

choromap = go.Figure(data = [data],layout = layout)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-15-243104ae4228> in <module>
----> 1 choromap = go.Figure(data = [data],layout = layout)


~/.local/lib/python3.6/site-packages/plotly/graph_objs/_figure.py in __init__(self, data, layout, frames, skip_invalid, **kwargs)
    606             is invalid AND skip_invalid is False
    607         """
--> 608         super(Figure, self).__init__(data, layout, frames, skip_invalid, **kwargs)
    609 
    610     def add_area(


~/.local/lib/python3.6/site-packages/plotly/basedatatypes.py in __init__(self, data, layout_plotly, frames, skip_invalid, **kwargs)
    155 
    156         # ### Import traces ###
--> 157         data = self._data_validator.validate_coerce(data, skip_invalid=skip_invalid)
    158 
    159         # ### Save tuple of trace objects ###


~/.local/lib/python3.6/site-packages/_plotly_utils/basevalidators.py in validate_coerce(self, v, skip_invalid)
   2643                     else:
   2644                         trace = self.class_map[trace_type](
-> 2645                             skip_invalid=skip_invalid, **v_copy
   2646                         )
   2647                         res.append(trace)


~/.local/lib/python3.6/site-packages/plotly/graph_objs/__init__.py in __init__(self, arg, autocolorscale, coloraxis, colorbar, colorscale, customdata, customdatasrc, geo, hoverinfo, hoverinfosrc, hoverlabel, hovertemplate, hovertemplatesrc, hovertext, hovertextsrc, ids, idssrc, locationmode, locations, locationssrc, marker, meta, metasrc, name, reversescale, selected, selectedpoints, showscale, stream, text, textsrc, uid, uirevision, unselected, visible, z, zauto, zmax, zmid, zmin, zsrc, **kwargs)
  81395         self["colorbar"] = colorbar if colorbar is not None else _v
  81396         _v = arg.pop("colorscale", None)
> 81397         self["colorscale"] = colorscale if colorscale is not None else _v
  81398         _v = arg.pop("customdata", None)
  81399         self["customdata"] = customdata if customdata is not None else _v


~/.local/lib/python3.6/site-packages/plotly/basedatatypes.py in __setitem__(self, prop, value)
   3477             # ### Handle simple property ###
   3478             else:
-> 3479                 self._set_prop(prop, value)
   3480 
   3481         # Handle non-scalar case


~/.local/lib/python3.6/site-packages/plotly/basedatatypes.py in _set_prop(self, prop, val)
   3764                 return
   3765             else:
-> 3766                 raise err
   3767 
   3768         # val is None


~/.local/lib/python3.6/site-packages/plotly/basedatatypes.py in _set_prop(self, prop, val)
   3759         validator = self._validators.get(prop)
   3760         try:
-> 3761             val = validator.validate_coerce(val)
   3762         except ValueError as err:
   3763             if self._skip_invalid:


~/.local/lib/python3.6/site-packages/_plotly_utils/basevalidators.py in validate_coerce(self, v)
   1619 
   1620         if not v_valid:
-> 1621             self.raise_invalid_val(v)
   1622 
   1623         return v


~/.local/lib/python3.6/site-packages/_plotly_utils/basevalidators.py in raise_invalid_val(self, v, inds)
    281                 typ=type_str(v),
    282                 v=repr(v),
--> 283                 valid_clr_desc=self.description(),
    284             )
    285         )


ValueError: 
    Invalid value of type 'builtins.str' received for the 'colorscale' property of choropleth
        Received value: 'YIOrRd'

    The 'colorscale' property is a colorscale and may be
    specified as:
      - A list of colors that will be spaced evenly to create the colorscale.
        Many predefined colorscale lists are included in the sequential, diverging,
        and cyclical modules in the plotly.colors package.
      - A list of 2-element lists where the first element is the
        normalized color level value (starting at 0 and ending at 1), 
        and the second item is a valid color string.
        (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
      - One of the following named colorscales:
            ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
             'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
             'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
             'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
             'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
             'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
             'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
             'orrd', 'oryel', 'peach', 'phase', 'picnic', 'pinkyl', 'piyg',
             'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn', 'puor',
             'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu', 'rdgy',
             'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar', 'spectral',
             'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn', 'tealrose',
             'tempo', 'temps', 'thermal', 'tropic', 'turbid', 'twilight',
             'viridis', 'ylgn', 'ylgnbu', 'ylorbr', 'ylorrd']

iplot(choromap)

World Choropleth Map

Now let’s see an example with a World Map:

df = pd.read_csv('2014_World_GDP')
df.head()

data = dict(
        type = 'choropleth',
        locations = df['CODE'],
        z = df['GDP (BILLIONS)'],
        text = df['COUNTRY'],
        colorbar = {'title' : 'GDP Billions US'},
      ) 

layout = dict(
    title = '2014 Global GDP',
    geo = dict(
        showframe = False,
        projection = {'type':'Mercator'}
    )
)

choromap = go.Figure(data = [data],layout = layout)
iplot(choromap)
Greydon Gilmore
Greydon Gilmore
Electrophysiologist

My research interests include deep brain stimulation, machine learning and signal processing.

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