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Cover image for Scatter Plots on Mapbox with Plotly Express in Python & How to Embed Them

Scatter Plots on Mapbox with Plotly Express in Python & How to Embed Them

Isabella
Learning #rstats, python, & #datascience πŸ€Έβ€β™€οΈ
Originally published at isabella-b.com ・3 min read

Last week I was exploring the Los Angeles Times' database of police killings in L.A. County, trying to learn more about the Black and Latino communities that have been disproportionately affected by police violence based on data since 2000. I made a simple graphic about it that you can find in my blog post.

I also used that data to practice some EDA and data visualization in python. The data includes the latitude and longitude of where each person killed by police died, so one of the things I tried was mapping them with Mapbox and plotly Express. This map shows the places of death of the people killed who were Black.

Live map

Alt Text

To make this Mapbox map with Plotly you'll need a Mapbox account and a public Mapbox access token. This is easy to get, and the code to create the map is fairly simple. I will go through the whole process.

First, import plotly Express:

import plotly.express as px

Next, you'll have to set your Mapbox access token and call it from a file in your directory called .mapbox_token that contains your Mapbox access token.

If you don't have one yet, to get a token you have to create a Mapbox account, go to > Account > + Create a token, name your token, then Create token. Copy the token and paste it to your .mapbox_token file in your directory.

Now we'll set it:

px.set_mapbox_access_token(open(".mapbox_token").read())

To create the plot, use px.scatter_mapbox(), and input your data frame and latitude & longitude fields to be used:

fig = px.scatter_mapbox(data_frame=black_killings, lat='y', lon='x', 
  opacity=0.5, 
  hover_name="full_name", 
  hover_data=["year","neighborhood","cause"], 
  zoom=10)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0},  # remove the white gutter between the frame and map
        # hover appearance
        hoverlabel=dict( 
        bgcolor="white",     # white background
        font_size=16,        # label font size
        font_family="Inter") # label font
)
fig.show()

Optional settings:

opacity - the opacity of the dots

hover_name - controls which column is displayed in bold as the tooltip title

hover_data - list of columns whose values will be displayed in the body of the tooltip
zoom - set the map's initial zoom level

The details of update_layout are commented above.

Embed the plot

To embed a plotly plot on a website, the easiest way if your data source is small, is by hosting it in plotly's Chart Studio then embedding its <iframe>. Alternatively, you can generate an HTML file of the visualization, host it somewhere like GitHub pages (free) or your personal website, then call that page in the <iframe> to embed it. In this post I'll use the Chart Studio route, and it's applicable to any plotly visualization you create.

If you don't have the Chart Studio python package yet, you can install it using the package manager pip in your terminal with pip install chart_studio. You will need a plotly Chart Studio account and your API key.

To get your API key: Click your username in the top right > Profile > API Keys > Regenerate Key

Now import chart_studio and set your credentials:

import chart_studio
username = 'your-username' 
api_key = '' 
chart_studio.tools.set_credentials_file(username=username, api_key=api_key)

Save your plot to your Chart Studio cloud account with py.plot(). It creates a unique URL for your plot that plotly uses in the <iframe> it generates that you will use to embed your visualization on a website.

import chart_studio.plotly as py
py.plot(fig, filename = 'file-name', auto_open=True)

Running that should open the plot in Chart Studio in your browser. In the bottom right there is an icon where plotly provides the code to the <iframe> you can use.

Now you can use the <iframe> to embed your interactive plotly visualization on any website!

I hope you found this helpful. If you have any questions, feel free to comment below or tweet/DM me.

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