plotly Map in Python (3 Examples)

Hi! This tutorial will show you how to make interactive plotly map in the Python programming language.

We will look at examples of how to make a choropleth map in Python.

Here is an overview:

Let’s get into the Python code!

Install & Import plotly Library

In order to build an interactive map in plotly, we will need to install and import plotly in our Python programming environment. So, in your preferred Python coding IDE, run the lines of code below to install and import plotly:

# install plotly
pip install plotly
 
# import plotly express
import plotly.express as px

With plotly installed and imported into our Python environment, we can now access its map-building functions, including the ones we will make use of in this tutorial.
 

Example 1: Build Simple Choropleth Map

In this example, we will build a simple choropleth map using the election dataset and GeoJSON file that come pre-loaded in plotly.

To load and preview the election dataset, run the lines of code below:

# load GeoJSON file
geojson = px.data.election_geojson()
 
# load dataset
df = px.data.election()
 
# view first 10 rows of the dataset
print(df.head(10))
 
 
#               district	Coderre	Bergeron Joly	total	winner	  result   district_id
# 0  101-Bois-de-Liesse	        2481	1829	3024	7334	Joly	  plurality	101
# 1  102-Cap-Saint-Jacques	2525	1163	2675	6363	Joly	  plurality	102
# 2  11-Sault-au-Récollet	3348	2770	2532	8650	Coderre	  plurality	11
# 3  111-Mile-End	        1734	4782	2514	9030	Bergeron  majority	111
# 4  112-DeLorimier	        1770	5933	3044	10747	Bergeron  majority	112
# 5  113-Jeanne-Mance	        1455	3599	2316	7370	Bergeron  plurality	113
# 6  12-Saint-Sulpice	        3252	2521	2543	8316	Coderre	  plurality	12
# 7  121-La Pointe-aux-Prairies 5456	1760	3330	10546	Coderre	  majority	121
# 8  122-Pointe-aux-Trembles	4734	1879	2852	9465	Coderre	  majority	122
# 9  123-Rivière-des-Prairies	5737	958	1656	8351	Coderre	  majority	123

With our dataset and GeoJSON file loaded, let us now build a choropleth map:

fig = px.choropleth(data_frame = df, geojson=geojson, color="Joly",
                    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()

In the px.choropleth() function, we parsed the dataset to the data_frame = argument, which takes either a DataFrame or an array-like object.

We then set the color to “Joly”, and location to “district”, both of which are columns in the dataset. The featureidkey = argument takes a path to a field in the GeoJSON feature object with which to match the values passed in to locations = argument.

As for the projection = argument, there are so many options of the kind of projection you can parse to that argument. You can see the options here.

The update_geo() function takes the argument fitbounds =, which determines if the plot’s view settings are auto-computed to fit trace data. If set to “locations”, then only the trace’s visible locations are considered in the fitbounds computations. Since we don’t want the fitbounds to be visible, we set the visible = argument to “False”.
 

Example 2: Build Simple Choropleth Map with Discrete Colors

In this second example, we will build the same map as in the previous example, but will instead use discrete colors to color the map:

fig = px.choropleth(data_frame = 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()

The code remains practically the same as in the previous example, with the only difference being that, here, we parsed a discrete variable to the color = argument in the px.choropleth() function, which colored the map by the categories in the “winner” variable.

We also parsed an array to the hover_data = argument, which displayed the data on hover.
 

Example 3: Build Simple Choropleth Map by Defining Discrete Colors

In this last example, we will build the same map, but will define the discrete colors to color the map:

fig = px.choropleth(data_frame = df, geojson=geojson, color="winner",
                    locations="district", featureidkey="properties.district",
                    color_discrete_map={"Bergeron":"#00ffff",
                                        "Coderre":"#cc00ff",
                                        "Joly":"#ffff00"},
                    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()

The code is virtually the same as in the previous example. The only difference, this time, is that we parsed a dictionary to the color_discrete_map = argument in the px.choropleth() function, which assigned colors to each of the categories in the “winner” column of the dataset and colored the map accordingly.

Video, Further Resources & Summary

Do you need more explanations on how to build interactive plotly maps in Python? Then you should have a look at the following YouTube video of the Statistics Globe YouTube channel.

In the video, we explain in some more detail how to build interactive plotly maps in Python.

The YouTube video will be added soon.

With that, we have demonstrated how to build an interactive plotly map in the Python programming language. As you can see, it is easy to build a choropleth map and visualize geospatial data in plotly.

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This post has shown how to build interactive plotly maps in Python. In case you have further questions, you may leave a comment below.

This page was created in collaboration with Ifeanyi Idiaye. You might check out Ifeanyi’s personal author page to read more about his academic background and the other articles he has written for the Statistics Globe website.