How to Plot City Wise Data on a World Map using Python's Matplotlib and Plotly

Discover how to visualize city-specific events on a world map by converting city names into country-wise data with Python. Learn the step-by-step solution today!
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Plotting City Wise Data on a World Map

Visualizing data can often reveal insights that are not immediately apparent. In this post, we will explore how to plot the number of events occurring in various cities around the world on a map. We’ll focus on a real-world problem — converting city names into country-level data — so we can use it for geographic representation.

The Challenge: City Data to Map Plotting

Imagine you have a dictionary containing various cities and the number of events that occur in them. For example, you might have data like this:

[[See Video to Reveal this Text or Code Snippet]]

The challenge arises when we want to plot this data on a world map. The standard method only allows for plotting based on country names and not city names. Thus, we need to translate our city-oriented data into a format suitable for world mapping.

The Solution: Using Python Libraries

Step 1: Getting Started with Necessary Libraries

For this task, we will utilize the following Python libraries:

Geopy for geocoding city names to their respective countries.

Plotly Express for creating visual maps.

Make sure you have these libraries installed:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Write the Code to Map City to Country

First, we will create a Python script that will transform our city data into a more usable format. Below is the code that accomplishes this task.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Preparing Data for Plotting

Once we have the country_counts dictionary set up, we can use it alongside Plotly to visualize this data. First, convert the country-level data into a DataFrame:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Plotting the Data on a World Map

Now that we have our DataFrame prepared, we can use Plotly Express to create a choropleth map:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In this guide, we addressed the issue of plotting city-wise event data on a world map by first determining the country each city belongs to. We utilized Python libraries like Geopy for geocoding and Plotly for visualization. With this method, you can effectively visualize data points that are originally city-based in a manner that’s more conducive to geographical presentation.

Now you are ready to apply this knowledge to your datasets and create spectacular visualizations! Happy plotting!