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