The 10th cohort of my Python, Data, and AI bootcamp (PythonDAB) starts on November 5th. If you want to get fluent with these technologies in a small, collaborative cohort that emphasizes discussion, interaction, and hands-on practice, then this might be the program for you. Get more information at https://lernerpython.com/bootcamp/ or join the (second) free info session I'm running on Tuesday, registering here: https://us02web.zoom.us/webinar/register/WN_-DTwqi_uRx20iHgaH25jFg
Twice each year, the International Monetary Fund, which lends money to countries in order to improve their infrastructure, publishes its World Economic Outlook. Their latest WEO came out earlier this month, and it says that the world economy is currently experiencing contradictary forces:
- On the one hand, geopolitics (including the wars in Ukraine and the Middle East) are making the world economy more fragile.
- On the other hand, unemployment remains relatively low, and economies are doing well enough that interest rates have remained high, to tamp down any potential inflation. They also point to the huge investment in artificial intelligence as helping economies.
This week, I thought it would be interesting to dive into the IMF's most recent report, and see how various regions and countries in the world are doing.
Data and five questions
The data comes from the IMF's World Economic Outlook, whose main page is at https://data.imf.org/datasets/WEO. You can download a PDF report, but we'll be looking at an Excel file with the full data. You can download it from
Paid subscribers, both to Bamboo Weekly and to my LernerPython+data membership program (https://LernerPython.com) get all of the questions and answers, as well as downloadable data files, downloadable versions of my notebooks, one-click access to my notebooks, and invitations to monthly office hours.
Learning goals for this week include: Working with Excel files, plotting with Plotly, Marimo widgets, window functions, and sorting.
Here are my five questions and tasks for this week. I'll be back tomorrow with my solutions and explanations.
- Read the "Countries" sheet from the Excel file into a Pandas data frame. Then, looking at GDP per capita in US dollars with current prices in 2026 (
NGDPD): What are the mean and median values across all countries, and what does that tell us? Create a boxplot with all of the per-capita GDP levels; what does that show? What 10 countries have the highest/lowest per-capita GDP? Which have grown the most in the last 25 years (for countries with values from 25 years ago)? - Create a choropleth map, showing the GDP per capita in US dollars. In Marimo, let the user move a slider to change which year's data will be displayed.