Mapping with Power: Drawing Polygons using Plotly's Choropleth
Visualizing data on a map is a powerful way to gain insights and communicate information effectively. Plotly, a versatile data visualization library, provides an intuitive way to create interactive choropleth maps, which use color to represent data values on geographic regions. This article explores how to use Plotly's choropleth function to draw polygons on a map, representing your data in a visually compelling way.
The Scenario: Visualizing Election Results
Imagine you have data on the election results for different counties in a state. You want to create a map that visually represents the winning party in each county, highlighting the overall pattern of the election. Here's how you could achieve this using Plotly:
Original Code:
import plotly.graph_objects as go
import pandas as pd
# Sample election results data
election_data = pd.DataFrame({
'fips': ['01001', '01003', '01005', '01007'],
'county': ['Autauga', 'Baldwin', 'Barbour', 'Bibb'],
'winner': ['Democrat', 'Republican', 'Republican', 'Democrat']
})
# Create the map using Choropleth
fig = go.Figure(data=go.Choropleth(
locations=election_data['fips'],
z=election_data['winner'],
locationmode='USA-states',
colorscale='Reds',
autocolorscale=False,
marker_line_color='darkgray',
marker_line_width=0.5,
colorbar_title="Winning Party",
geo='geo2'
))
fig.update_layout(
title_text='Election Results by County',
geo_scope='usa'
)
fig.show()
Analyzing the Code:
-
Data Preparation: We start by importing the necessary libraries -
plotly.graph_objectsfor creating the map andpandasfor data manipulation. We then create a simple DataFrame to store the election results for four counties, including their unique identifiers (fips), county names, and winning party. -
Creating the Choropleth: The
go.Choroplethfunction creates the core of our map. It takes the following key parameters:locations: This holds the unique identifiers for each county. We use thefipscodes in our example.z: This represents the data we want to visualize - the winning party in this case.locationmode: This specifies how the locations are defined.USA-statesassumes we are working with county-level data within the US.colorscale: This determines the color scheme used for the choropleth. Here, we chooseRedsfor a visually appealing representation.autocolorscale: Set toFalseto customize the colorscale.marker_line_colorandmarker_line_width: Control the outline of each county polygon.colorbar_title: Adds a label to the colorbar explaining the color scale.geo: Links the map to a specific geographic projection, in this case,geo2for the US.
-
Updating the Layout: The
update_layoutmethod allows customization of the map's title and overall appearance. We add a title and specify the geographical scope as the US (geo_scope='usa').
Enhancing the Visualization:
-
Customizing Colors: Instead of the default
Redscolorscale, you can use other colorscales provided by Plotly or define your own custom color scheme using a list of colors. This can help you visually differentiate the winning parties more effectively. -
Adding Hover Information: You can use the
hovertemplateparameter ingo.Choroplethto display more detailed information about each county when hovering over it. For example, you could include the county name, the number of votes, and the margin of victory. -
Interactive Features: Plotly maps are highly interactive. Users can zoom in on specific areas, explore different perspectives, and even toggle the visibility of different data layers.
Conclusion:
Plotly's choropleth function provides a powerful and user-friendly way to create interactive maps that visually represent geographic data. By customizing colors, adding hover information, and leveraging Plotly's interactive features, you can create compelling and informative maps that enhance your data analysis and storytelling.
Resources:
