Project Description

This project explores the evolving patterns of irregular migration through the Darién Gap — a treacherous jungle corridor connecting South and Central America, and the border between Panama and Colombia — using data collected between 2020 and 2025. The analysis focuses on the volume of migrants in transit, categorized by country of origin and aggregated monthly, offering a granular view of shifting migration trends over time.

Using Python for data wrangling and Power BI for visualization, I built a dynamic dashboard that reveals:

• 📈 Temporal trends in migrant flows across five years

• 🌐 Geographic insights by country and region

• 🧭 Regional comparisons to highlight geopolitical and humanitarian shifts

• 🔍 Data cleaning and transformation using pandas, including reshaping, mapping, and aggregation

The raw data was sourced from structured Excel files and enriched with a country-to-region mapping to enable regional analysis. I applied a VLOOKUP-style merge in Python to assign each country to its corresponding region, enabling deeper insights into migration pressure points across Latin America, Africa, and Asia.

This project demonstrates my ability to:

• Clean and transform complex datasets using Python

• Perform time-series and categorical analysis

• Design intuitive, interactive dashboards in Power BI

• Communicate data-driven stories with clarity and impact

Whether you're a policymaker, humanitarian analyst, or data enthusiast, this dashboard offers a compelling lens into one of the most urgent migration corridors in the world.

Source: Panama National Migration Service

If you’d like to dive deeper into the analysis and see the full Python workflow behind these visualizations, I’ve published my notebooks on Kaggle. You can explore the code, experiment with the data, and even fork the project to try your own variations. Visit my Kaggle page here: Link