Find the 10 countries with the most projects.

Find the top 10 major project themes (using column mjtheme_namecode).

For some entries in the exercise above, the name is missing. Create a dataframe filling in the missing names.

Import Packages, Load JSON file as a Dataframe, Inspect - Get a look at the state and structure of the dataset.

Inspect the columns needed for the exercises.

Find the 10 Countries with the Most Projects -

Retrieve a value count of country names, which is sorted in descending order; save the first 10 results.

Load JSON file as a String, Flatten, Inspect - The column needed to finish the exercises contains nested data, so it needs to be normalized.

Find the Top 10 Major Project Themes - This is accomplished by a value count of project codes since names are known to be missing.
Create a Dataframe Filling in the Missing Names - Sort the dataframe, fill empty cells with NULL values, then replace those by backfilling.
Find the Top 10 Major Project Themes by Theme Name - Produce a more descriptive list of top projects by getting a value count of project names.