Using data in file 'data/world_bank_projects.json'

• Find the 10 countries with most projects
• Find the top 10 major project themes (using column 'mjtheme_namecode')
• In 2. above you will notice that some entries have only the code and the name is missing. Create a dataframe with the missing names filled in.

• Load JSON file into pandas dataframe
• Find out column(s) for country names
• Each dataframe row represents a project
• Group by countryname and get the number of projects for each country by counting the number of rows

People's Republic of China         19
Republic of Indonesia              19
Socialist Republic of Vietnam      17
Republic of India                  16
Republic of Yemen                  13
Nepal                              12
People's Republic of Bangladesh    12
Kingdom of Morocco                 12
Africa                             11
Republic of Mozambique             11

• Load JSON file into JSON object
• Each project can belong to one or more project theme as can be seen from the mjtheme_namecode column
• Flatten mjtheme_namecode column into code and name
• Some names are empty
• Deduce empty names from other rows with equal code
• Group by names and count the number of projects for each name (project theme) by counting the number of rows

Environment and natural resources management    250
Rural development                               216
Human development                               210
Public sector governance                        199
Social protection and risk management           168
Financial and private sector development        146
Social dev/gender/inclusion                     130
Trade and integration                           77
Urban development                               50
Economic management                             38

• Already done above