Understanding Plotly Choropleth Maps in Pandas

Introduction to Plotly and Pandas

Plotly is a popular Python library for creating interactive, web-based visualizations. It offers a wide range of visualization tools, including choropleth maps, which are perfect for displaying data related to geographical locations. On the other hand, pandas is a powerful library used for data manipulation and analysis in Python. In this article, we will explore how to create a Plotly choropleth map using pandas.

Installing Required Libraries

Before we begin, make sure you have the necessary libraries installed. You can install them using pip:

pip install plotly pandas

Also, ensure that you have the usa-states.json file downloaded and saved in your working directory. This file contains the geometry for the USA states.

Creating a Sample DataFrame

Let’s create a sample DataFrame with some data to work with. We’ll include columns for jobLocation, jobType, and salary.

import pandas as pd

# Create a sample DataFrame
data = {
    'jobLocation': ['New York, NY', 'Los Angeles, CA', 'Chicago, IL', 'Houston, TX'],
    'jobType': ['Software Engineer', 'Data Scientist', 'Marketing Manager', 'Product Designer'],
    'salary': [120000, 150000, 90000, 100000]
}

df = pd.DataFrame(data)

print(df)

Output:

Understanding the Issue

The original poster’s code seems correct, but it doesn’t produce any data. This is because the locations parameter in Plotly’s choropleth() function requires two-letter state abbreviations. In our sample DataFrame, we have full city names instead of abbreviations.

Splitting Out Two-Letter State Abbreviations

To fix this issue, we need to split out the two-letter state abbreviation from each string in the jobLocation column. We can do this using the str.split() method.

import pandas as pd

# Create a sample DataFrame
data = {
    'jobLocation': ['New York, NY', 'Los Angeles, CA', 'Chicago, IL', 'Houston, TX'],
    'jobType': ['Software Engineer', 'Data Scientist', 'Marketing Manager', 'Product Designer'],
    'salary': [120000, 150000, 90000, 100000]
}

df = pd.DataFrame(data)

# Split out two-letter state abbreviations
df['state'] = df['jobLocation'].str.split(', ').str[1]

print(df)

Output:

Plotting the Choropleth Map

Now that we have split out the two-letter state abbreviations, we can proceed with plotting the choropleth map. We’ll drop rows with missing location values and modify our plotting code to use locations='state'.

import plotly.express as px

# Plot the choropleth map
fig = px.choropleth(df.dropna(subset=['state']), 
                    locations='state', 
                    locationmode='USA-states', 
                    color='salary',
                    scope="usa",
                    labels={'salary':'Salary'})

fig.show()

Output:

This code will generate a choropleth map of the USA, with each state colored according to its average salary.

Note that we’ve also changed the color parameter from jobLocation to salary. This is because Plotly requires a single column value for the color parameter. In our case, we’re using the average salary for each state as the color.

Conclusion

In this article, we’ve explored how to create a Plotly choropleth map using pandas. We’ve covered the basics of working with pandas and Plotly, including data manipulation and visualization techniques. By splitting out two-letter state abbreviations from full city names, we were able to plot a choropleth map that accurately represents the distribution of salaries across the USA.

We hope this article has been informative and helpful in your own work with Plotly and pandas. If you have any questions or need further assistance, feel free to ask!

Last modified on 2024-07-07