Primary completion rate, male (% of relevant age group)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 98.6 -5.97% 23
Andorra Andorra 70.4 +9.83% 72
United Arab Emirates United Arab Emirates 97.4 +17.3% 25
Armenia Armenia 92.6 -0.515% 42
Azerbaijan Azerbaijan 96.5 -3.81% 27
Burkina Faso Burkina Faso 46.4 -12.8% 86
Bangladesh Bangladesh 101 +0.172% 16
Bahrain Bahrain 89.1 -3.69% 54
Bosnia & Herzegovina Bosnia & Herzegovina 86.4 -1.29% 58
Belarus Belarus 93.6 -1.84% 39
Belize Belize 95.9 -10.8% 30
Bolivia Bolivia 96.1 -0.0908% 28
Barbados Barbados 95.6 +2.14% 31
Brunei Brunei 94.8 -5.52% 33
Côte d’Ivoire Côte d’Ivoire 80.3 +16.4% 64
Cameroon Cameroon 74 +1.84% 68
Congo - Kinshasa Congo - Kinshasa 79.8 -7.36% 65
Congo - Brazzaville Congo - Brazzaville 71.9 +11.8% 71
Cuba Cuba 92 +0.549% 45
Cayman Islands Cayman Islands 103 +0.0684% 13
Dominica Dominica 92.5 +10.8% 43
Dominican Republic Dominican Republic 84.2 -5.28% 60
Algeria Algeria 98.7 +5.66% 22
Ecuador Ecuador 99 +0.77% 20
Ethiopia Ethiopia 56.4 -11.1% 81
Fiji Fiji 114 -1.45% 3
Georgia Georgia 108 -3.5% 5
Gibraltar Gibraltar 135 +17% 1
Gambia Gambia 68.6 -0.127% 73
Guatemala Guatemala 85.4 -1.54% 59
Honduras Honduras 73.8 +8.8% 69
Indonesia Indonesia 103 -1.29% 14
India India 93.3 -5.31% 40
Jamaica Jamaica 78.8 -6.76% 67
Jordan Jordan 96.7 +0.705% 26
Kazakhstan Kazakhstan 106 +2.91% 7
Kyrgyzstan Kyrgyzstan 93.7 -1.5% 37
Cambodia Cambodia 87.3 -0.662% 56
Kiribati Kiribati 101 -1.07% 17
Laos Laos 89.5 +0.512% 52
Lebanon Lebanon 66.9 -1.87% 75
St. Lucia St. Lucia 89.6 -9.69% 51
Lesotho Lesotho 66.5 -4.74% 76
Macao SAR China Macao SAR China 87.1 -2.11% 57
Morocco Morocco 104 +3.94% 12
Madagascar Madagascar 57.6 +1.54% 80
Maldives Maldives 98.7 +5.93% 21
Mali Mali 51 +3.47% 84
Montenegro Montenegro 105 -5.81% 9
Mongolia Mongolia 94.5 -1.53% 36
Mozambique Mozambique 59 -23.7% 79
Mauritius Mauritius 90.5 -3.15% 48
Malawi Malawi 63.9 +6.99% 77
Malaysia Malaysia 97.6 +3.84% 24
Niger Niger 49 -11.9% 85
Nepal Nepal 116 +9.16% 2
Nauru Nauru 109 +23.2% 4
Oman Oman 94.6 +9.52% 35
Panama Panama 92.8 +0.153% 41
Peru Peru 104 +1.55% 10
Philippines Philippines 80.5 -7.32% 63
Palau Palau 99.2 +5.8% 19
Puerto Rico Puerto Rico 73.6 -11.9% 70
Paraguay Paraguay 90.7 +4.78% 47
Palestinian Territories Palestinian Territories 92.1 -2.17% 44
Rwanda Rwanda 53.5 -40.2% 82
Senegal Senegal 53.4 -2.69% 83
Solomon Islands Solomon Islands 68.4 -14.9% 74
Sierra Leone Sierra Leone 95.1 -1.04% 32
El Salvador El Salvador 79.3 +2.47% 66
San Marino San Marino 93.6 +1.43% 38
Eswatini Eswatini 89.2 -6.42% 53
Seychelles Seychelles 94.6 -4.24% 34
Syria Syria 60.7 +6.49% 78
Togo Togo 91.1 +0.396% 46
Thailand Thailand 104 +1.28% 11
Timor-Leste Timor-Leste 90 +23.6% 50
Tonga Tonga 88.9 -3.96% 55
Trinidad & Tobago Trinidad & Tobago 84 -3.2% 61
Tuvalu Tuvalu 90.3 -6.59% 49
Tanzania Tanzania 80.7 +15.6% 62
Uzbekistan Uzbekistan 95.9 -2.39% 29
St. Vincent & Grenadines St. Vincent & Grenadines 107 -6.5% 6
Venezuela Venezuela 103 +16.4% 15
Vanuatu Vanuatu 105 -2.05% 8
Samoa Samoa 101 -2.43% 18 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SE.PRM.CMPT.MA.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SE.PRM.CMPT.MA.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))