Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Australia Australia 99.5 -0.0333% 9
Austria Austria 100 0% 1
Azerbaijan Azerbaijan 99.9 +0.111% 2
Burkina Faso Burkina Faso 18.8 -24.5% 49
Bahrain Bahrain 89.9 -0.144% 25
Bahamas Bahamas 99.1 +1.59% 11
Bosnia & Herzegovina Bosnia & Herzegovina 91.4 -4.16% 23
Belarus Belarus 99.9 -0.0138% 3
Bolivia Bolivia 76.1 +0.821% 40
Brazil Brazil 85.1 -0.785% 34
Botswana Botswana 90 +0.551% 24
Canada Canada 96.3 +0.201% 15
Switzerland Switzerland 99.7 +0.0091% 5
Chile Chile 92.2 +1.79% 21
Colombia Colombia 82.3 +0.768% 38
Costa Rica Costa Rica 86.1 +2.67% 31
Dominican Republic Dominican Republic 67.6 -9.08% 42
Spain Spain 94.2 -0.176% 18
France France 100 +4.12% 1
United Kingdom United Kingdom 99.5 +0.0615% 8
Georgia Georgia 99.5 +0.125% 10
Gambia Gambia 53.9 +32.3% 46
Guatemala Guatemala 52.6 -0.122% 47
Hong Kong SAR China Hong Kong SAR China 100 +4.06% 1
Honduras Honduras 61.4 -4.07% 45
Indonesia Indonesia 83.8 +0.727% 35
India India 65.6 +4.39% 43
Jordan Jordan 87.9 -1.48% 27
South Korea South Korea 97.7 +0.296% 13
Latvia Latvia 100 +1.37% 1
Moldova Moldova 99.5 +0.0205% 7
Mexico Mexico 85.7 +2.88% 33
North Macedonia North Macedonia 94.9 +0.119% 17
Mongolia Mongolia 97.6 +1.39% 14
Panama Panama 88.6 +0.119% 26
Peru Peru 85.7 +14.4% 32
Poland Poland 99.7 +1.23% 4
Portugal Portugal 95.8 +0.122% 16
Russia Russia 100 0% 1
Rwanda Rwanda 83.4 +90.1% 36
Saudi Arabia Saudi Arabia 92.5 +3.61% 20
Singapore Singapore 78.8 -12.2% 39
El Salvador El Salvador 64.5 +1.14% 44
Serbia Serbia 98.7 -0.0256% 12
Eswatini Eswatini 91.8 +29.7% 22
Thailand Thailand 74.8 +2.21% 41
Tunisia Tunisia 83.2 +46.9% 37
Turkey Turkey 86.2 +1% 30
Uruguay Uruguay 92.8 -0.086% 19
United States United States 99.7 +0.627% 6
Vietnam Vietnam 87.3 +0.0818% 29
Yemen Yemen 45.5 +38.6% 48
South Africa South Africa 87.6 +0.385% 28 

                    
# 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.CUAT.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.CUAT.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))