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

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 95.1 -5.97% 32
Andorra Andorra 69.3 +11.7% 75
United Arab Emirates United Arab Emirates 99.2 +21.6% 20
Armenia Armenia 92.5 +0.39% 43
Azerbaijan Azerbaijan 96.2 -3.48% 26
Burkina Faso Burkina Faso 51.7 -11.4% 85
Bangladesh Bangladesh 108 +0.476% 5
Bahrain Bahrain 89.4 -3.85% 56
Bosnia & Herzegovina Bosnia & Herzegovina 86.4 -0.752% 59
Belarus Belarus 92.6 -0.641% 41
Belize Belize 94.3 -10.8% 35
Bolivia Bolivia 96.2 -0.354% 27
Barbados Barbados 96.7 +5.46% 25
Brunei Brunei 96.2 -4.15% 28
Côte d’Ivoire Côte d’Ivoire 80.2 +17% 66
Cameroon Cameroon 71.1 +2.37% 74
Congo - Kinshasa Congo - Kinshasa 77 -6.88% 68
Congo - Brazzaville Congo - Brazzaville 71.8 +10.2% 71
Cuba Cuba 92.6 +0.21% 42
Cayman Islands Cayman Islands 94.2 -3.37% 36
Dominica Dominica 89.2 +5.69% 57
Dominican Republic Dominican Republic 85 -4.97% 62
Algeria Algeria 99.7 +5.24% 19
Ecuador Ecuador 100 +0.737% 18
Ethiopia Ethiopia 55.9 -10% 84
Fiji Fiji 112 +0.43% 3
Georgia Georgia 107 -3.86% 6
Gibraltar Gibraltar 137 +5.25% 1
Gambia Gambia 75.6 +0.446% 70
Guatemala Guatemala 86.4 -0.666% 58
Hong Kong SAR China Hong Kong SAR China 86.3 -13.6% 60
Honduras Honduras 75.8 +8.38% 69
Indonesia Indonesia 102 -1.03% 15
India India 93.5 -5.65% 38
Jamaica Jamaica 79.5 -0.37% 67
Jordan Jordan 97.2 +1.45% 23
Kazakhstan Kazakhstan 106 +2.38% 10
Kyrgyzstan Kyrgyzstan 93 -1.48% 39
Cambodia Cambodia 89.9 -0.904% 53
Kiribati Kiribati 103 +0.897% 13
Laos Laos 90 +1.02% 52
Lebanon Lebanon 68.3 -1.11% 78
St. Lucia St. Lucia 89.6 -13.1% 54
Lesotho Lesotho 71.1 -4.64% 73
Macao SAR China Macao SAR China 89.5 -1.85% 55
Morocco Morocco 105 +3.63% 11
Madagascar Madagascar 60.4 +1.81% 81
Maldives Maldives 98.5 +1.4% 22
Mali Mali 49.7 +5.92% 86
Montenegro Montenegro 107 -3.46% 8
Mongolia Mongolia 95.3 -0.56% 31
Mozambique Mozambique 57.6 -21.6% 83
Mauritius Mauritius 90.2 -4.23% 50
Malawi Malawi 68.5 -7.34% 77
Malaysia Malaysia 98.8 +3.84% 21
Niger Niger 47.9 -9.61% 87
Nepal Nepal 115 +14.1% 2
Nauru Nauru 111 +19.8% 4
Oman Oman 94.5 +9.1% 34
Panama Panama 92.8 -0.0132% 40
Peru Peru 104 +1.49% 12
Philippines Philippines 80.7 -8.27% 65
Palau Palau 91.2 +3.47% 47
Puerto Rico Puerto Rico 69.1 -19.4% 76
Paraguay Paraguay 90.3 +4.74% 49
Palestinian Territories Palestinian Territories 91.5 -2.8% 46
Rwanda Rwanda 59.3 -37.8% 82
Senegal Senegal 61.3 +7.07% 80
Solomon Islands Solomon Islands 71.6 -12.5% 72
Sierra Leone Sierra Leone 96.8 -2.7% 24
El Salvador El Salvador 81 +1.43% 64
San Marino San Marino 96.1 +2.88% 29
Eswatini Eswatini 90.2 -8.04% 51
Seychelles Seychelles 94 -5.83% 37
Syria Syria 61.9 +6.16% 79
Togo Togo 90.7 +1.75% 48
Thailand Thailand 103 +0.628% 14
Timor-Leste Timor-Leste 91.5 +7.67% 45
Tonga Tonga 94.9 -6.43% 33
Trinidad & Tobago Trinidad & Tobago 84.9 -2.72% 63
Tuvalu Tuvalu 92.1 -5.02% 44
Tanzania Tanzania 86.2 +18.8% 61
Uzbekistan Uzbekistan 96.1 -2.18% 30
St. Vincent & Grenadines St. Vincent & Grenadines 107 -7.55% 7
Venezuela Venezuela 101 +13.1% 17
Vanuatu Vanuatu 107 +0.531% 9
Samoa Samoa 101 -2.22% 16 

                    
# 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.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.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))