by Mohamed A. Warsame, Educator, Data Scientist, Applied Machine Learning, MSc certified Economist, Writer, Poet

Picture this scenario. Out of nowhere, you are appointed as Senior Advisor to the Ministry of Humanitarian and Disaster Management in Somalia, the government agency responsible for handling nationwide emergencies. Struck by sheer joy, you’d love to update your LinkedIn profile, your Twitter timeline, and all your other platforms to celebrate. Even though you have no clue how you were shortlisted or selected.

So, where do we start? As every so often, Google comes to our rescue. For chasing after rainfall data on Somalia – though not a joy – isn’t that hard, fortunately. Following some hiccups, I have found that TAMSAT seems to be the only consistent, easily accessible, and freely available data source on rainfall for Somalia. It actually provides satellite-based rainfall estimates for the whole of Africa — configurable up to daily granularity and a 4km radius resolution. Right after specifying the geo location, the desired granularity and time period of analysis, you’ll receive a download link either containing a CSV or NETCDF file. Let’s read that in using the Pandas library.

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