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  plotly:
    description: How to make choropleth maps in Python with Plotly.
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    layout: base
    name: Choropleth Maps
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    permalink: python/choropleth-maps/
    thumbnail: thumbnail/choropleth.jpg
---

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 **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).

Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.

#### Base Map Configuration

Plotly figures made with [Plotly Express](/python/plotly-express/) `px.scatter_geo`, `px.line_geo` or `px.choropleth` functions or containing `go.Choropleth` or `go.Scattergeo` [graph objects](/python/graph-objects/) have a `go.layout.Geo` object which can be used to [control the appearance of the base map](/python/map-configuration/) onto which data is plotted.

### Introduction: main parameters for choropleth outline maps

Making choropleth maps requires two main types of input:

1. Geometry information:
   1. This can either be a supplied GeoJSON file where each feature has either an `id` field or some identifying value in `properties`; or
   2. one of the built-in geometries within `plotly`: US states and world countries (see below)
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` (`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.

### Choropleth Map with plotly.express

[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/).

#### GeoJSON with `feature.id`

Here we load a GeoJSON file containing the geometry information for US counties, where `feature.id` is a [FIPS code](https://en.wikipedia.org/wiki/FIPS_county_code).

```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)

counties["features"][0]
```

#### Data indexed by `id`

Here we load unemployment data by county, also indexed by [FIPS code](https://en.wikipedia.org/wiki/FIPS_county_code).

```python
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})
df.head()
```

### Choropleth map using GeoJSON

**Note** In this example we set `layout.geo.scope` to `usa` to automatically configure the map to display USA-centric data in an appropriate projection. See the [Geo map configuration documentation](/python/map-configuration/) for more information on scopes.

```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(df, geojson=counties, locations='fips', color='unemp',
                           color_continuous_scale="Viridis",
                           range_color=(0, 12),
                           scope="usa",
                           labels={'unemp':'unemployment rate'}
                          )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
```

### Indexing by GeoJSON Properties

If the GeoJSON you are using either does not have an `id` field or you wish to use one of the keys in the `properties` field, you may use the `featureidkey` parameter to specify where to match the values of `locations`.

In the following GeoJSON object/data-file pairing, the values of `properties.district` match the values of the `district` column:

```python
import plotly.express as px

df = px.data.election()
geojson = px.data.election_geojson()

print(df["district"][2])
print(geojson["features"][0]["properties"])
```

To use them together, we set `locations` to `district` and `featureidkey` to `"properties.district"`. The `color` is set to the number of votes by the candidate named Bergeron.

**Note** In this example we set `layout.geo.visible` to `False` to hide the base map and frame, and we set `layout.geo.fitbounds` to `'locations'` to automatically zoom the map to show just the area of interest. See the [Geo map configuration documentation](/python/map-configuration/) for more information on projections and bounds.

```python
import plotly.express as px

df = px.data.election()
geojson = px.data.election_geojson()

fig = px.choropleth(df, geojson=geojson, color="Bergeron",
                    locations="district", featureidkey="properties.district",
                    projection="mercator"
                   )
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
```

### Choropleth maps in Dash

[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

Get started  with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & publish apps like this with <a class="plotly-red" href="https://plotly.com/dash/">Dash Enterprise</a> or <a class="plotly-red" href="https://plotly.com/cloud/">Plotly Cloud</a>.**

```python hide_code=true
from IPython.display import IFrame
snippet_url = 'https://python-docs-dash-snippets.herokuapp.com/python-docs-dash-snippets/'
IFrame(snippet_url + 'choropleth-maps', width='100%', height=1200)
```

<div style="font-size: 0.9em;"><div style="width: calc(100% - 30px); box-shadow: none; border: thin solid rgb(229, 229, 229);"><div style="padding: 5px;"><div><p><strong>Sign up for Dash Club</strong> → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture.
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### Discrete Colors

In addition to [continuous colors](/python/colorscales/), we can [discretely-color](/python/discrete-color/) our choropleth maps by setting `color` to a non-numerical column, like the name of the winner of an election.

**Note** In this example we set `layout.geo.visible` to `False` to hide the base map and frame, and we set `layout.geo.fitbounds` to `'locations'` to automatically zoom the map to show just the area of interest. See the [Geo map configuration documentation](/python/map-configuration/) for more information on projections and bounds.

```python
import plotly.express as px

df = px.data.election()
geojson = px.data.election_geojson()

fig = px.choropleth(df, geojson=geojson, color="winner",
                    locations="district", featureidkey="properties.district",
                    projection="mercator", hover_data=["Bergeron", "Coderre", "Joly"]
                   )
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
```

### Using GeoPandas Data Frames

`px.choropleth` 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
import geopandas as gpd

df = px.data.election()
geo_df = gpd.GeoDataFrame.from_features(
    px.data.election_geojson()["features"]
).merge(df, on="district").set_index("district")

fig = px.choropleth(geo_df,
                   geojson=geo_df.geometry,
                   locations=geo_df.index,
                   color="Joly",
                   projection="mercator")
fig.update_geos(fitbounds="locations", visible=False)
fig.show()
```


### Using Built-in Country and State Geometries

Plotly comes with two built-in geometries which do not require an external GeoJSON file:

1. USA States
2. Countries

In **Plotly.py 6.3 and later**, the built-in countries geometry is created from the following sources:
- [UN data](https://geoportal.un.org/arcgis/sharing/rest/content/items/d7caaff3ef4b4f7c82689b7c4694ad92/data) for country borders, coastlines, land, and ocean layers.
- Natural Earth data for lakes, rivers, and subunits layers.

In **earlier versions of Plotly.py**, the built-in countries geometry is based on Natural Earth data only. Plotly includes data from Natural Earth "as-is". This dataset draws boundaries of countries according to de facto status. See the [Natural Earth page for more details](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/).

To use the built-in countries geometry, provide `locations` as [three-letter ISO country codes](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3).

```python
import plotly.express as px

df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha",
                    color="lifeExp", # lifeExp is a column of gapminder
                    hover_name="country", # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)
fig.show()
```

To use the USA States geometry, set `locationmode='USA-states'` and provide `locations` as two-letter state abbreviations:

```python
import plotly.express as px

fig = px.choropleth(locations=["CA", "TX", "NY"], locationmode="USA-states", color=[1,2,3], scope="usa")
fig.show()
```

### Choropleth Maps with go.Choropleth

#### United States Choropleth Map

```python
import plotly.graph_objects as go

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')

fig = go.Figure(data=go.Choropleth(
    locations=df['code'], # Spatial coordinates
    z = df['total exports'].astype(float), # Data to be color-coded
    locationmode = 'USA-states', # set of locations match entries in `locations`
    colorscale = 'Reds',
    colorbar_title = "Millions USD",
))

fig.update_layout(
    title_text = '2011 US Agriculture Exports by State',
    geo_scope='usa', # limite map scope to USA
)

fig.show()
```

#### Customize choropleth chart

```python
import plotly.graph_objects as go

import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')

for col in df.columns:
    df[col] = df[col].astype(str)

df['text'] = df['state'] + '<br>' + \
    'Beef ' + df['beef'] + ' Dairy ' + df['dairy'] + '<br>' + \
    'Fruits ' + df['total fruits'] + ' Veggies ' + df['total veggies'] + '<br>' + \
    'Wheat ' + df['wheat'] + ' Corn ' + df['corn']

fig = go.Figure(data=go.Choropleth(
    locations=df['code'],
    z=df['total exports'].astype(float),
    locationmode='USA-states',
    colorscale='Reds',
    autocolorscale=False,
    text=df['text'], # hover text
    marker_line_color='white', # line markers between states
    colorbar=dict(
        title=dict(
            text="Millions USD"
            )
    )
))

fig.update_layout(
    title_text='2011 US Agriculture Exports by State<br>(Hover for breakdown)',
    geo = dict(
        scope='usa',
        projection=go.layout.geo.Projection(type = 'albers usa'),
        showlakes=True, # lakes
        lakecolor='rgb(255, 255, 255)'),
)

fig.show()
```

#### World Choropleth Map

```python
import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

fig = go.Figure(data=go.Choropleth(
    locations = df['CODE'],
    z = df['GDP (BILLIONS)'],
    text = df['COUNTRY'],
    colorscale = 'Blues',
    autocolorscale=False,
    reversescale=True,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_tickprefix = '$',
    colorbar_title = 'GDP<br>Billions US$',
))

fig.update_layout(
    title_text='2014 Global GDP',
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
            CIA World Factbook</a>',
        showarrow = False
    )]
)

fig.show()
```

#### County Choropleth Figure Factory

Plotly also includes a [legacy "figure factory" for creating US county-level choropleth maps](/python/county-choropleth/).

```python
import plotly.figure_factory as ff

import numpy as np
import pandas as pd

df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv')
df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(lambda x: str(x).zfill(2))
df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(lambda x: str(x).zfill(3))
df_sample['FIPS'] = df_sample['State FIPS Code'] + df_sample['County FIPS Code']

colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef", "#b3d2e9", "#9ecae1",
    "#85bcdb", "#6baed6", "#57a0ce", "#4292c6", "#3082be", "#2171b5", "#1361a9",
    "#08519c", "#0b4083", "#08306b"
]
endpts = list(np.linspace(1, 12, len(colorscale) - 1))
fips = df_sample['FIPS'].tolist()
values = df_sample['Unemployment Rate (%)'].tolist()


fig = ff.create_choropleth(
    fips=fips, values=values, scope=['usa'],
    binning_endpoints=endpts, colorscale=colorscale,
    show_state_data=False,
    show_hover=True,
    asp = 2.9,
    title_text = 'USA by Unemployment %',
    legend_title = '% unemployed'
)
fig.layout.template = None
fig.show()
```

#### Reference

See [function reference for `px.(choropleth)`](https://plotly.com/python-api-reference/generated/plotly.express.choropleth) or https://plotly.com/python/reference/choropleth/ for more information and chart attribute options!
