Children out of school (% of primary school age)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 10.9 +28.2% 31
Andorra Andorra 0.403 -94.3% 81
United Arab Emirates United Arab Emirates 0.176 -99% 84
Armenia Armenia 7.72 -17% 40
Azerbaijan Azerbaijan 1.54 -64.1% 71
Burkina Faso Burkina Faso 41.9 +30.3% 2
Bangladesh Bangladesh 5.26 +1,025% 45
Bahrain Bahrain 7 -13.3% 42
Bahamas Bahamas 29.3 +54.5% 6
Bosnia & Herzegovina Bosnia & Herzegovina 15.6 +5% 20
Belarus Belarus 4.15 -31% 54
Belize Belize 12.4 +29.9% 28
Bolivia Bolivia 4.16 +23% 53
Barbados Barbados 9.2 +34.6% 36
Brunei Brunei 3.55 -3.67% 60
Côte d’Ivoire Côte d’Ivoire 3.85 -60.6% 56
Cameroon Cameroon 3.71 -5.69% 58
Congo - Brazzaville Congo - Brazzaville 16 -23.9% 18
Comoros Comoros 22.2 -17.4% 14
Cuba Cuba 4.64 +5.66% 50
Curaçao Curaçao 3.42 +724% 61
Cayman Islands Cayman Islands 14.5 +7.06% 21
Dominica Dominica 12.9 +7.92% 26
Dominican Republic Dominican Republic 12.9 +70.8% 25
Algeria Algeria 0.976 +55.2% 74
Ecuador Ecuador 4.72 +181% 49
Ethiopia Ethiopia 23.2 -2.89% 13
Fiji Fiji 1.88 +6.02% 67
Georgia Georgia 2.5 +88.8% 65
Guatemala Guatemala 7.46 -2.99% 41
Guyana Guyana 14.1 -18.3% 24
Honduras Honduras 20.7 +0.775% 15
Indonesia Indonesia 0.71 -41.1% 77
India India 1.47 +862% 72
Jamaica Jamaica 15.7 +63.7% 19
Jordan Jordan 2.73 +44.8% 64
Kazakhstan Kazakhstan 4.4 -50.3% 51
Cambodia Cambodia 4.04 -68.6% 55
Kiribati Kiribati 12.2 +56.7% 30
Laos Laos 7.83 -1.61% 39
Lebanon Lebanon 24.2 -21.1% 10
St. Lucia St. Lucia 6.62 +216% 44
Lesotho Lesotho 26.7 +5.96% 8
Macao SAR China Macao SAR China 14.2 -13% 23
Morocco Morocco 0.715 +35.5% 76
Maldives Maldives 9.53 +58.4% 35
Mali Mali 32.5 -25.6% 5
Montenegro Montenegro 0.218 +26.1% 83
Mongolia Mongolia 5.06 -3.01% 46
Mauritius Mauritius 1.74 -71.4% 69
Malaysia Malaysia 1.85 -60.5% 68
Niger Niger 37.9 -3.24% 3
Nicaragua Nicaragua 9.75 +20.6% 33
Nepal Nepal 3.6 +255% 59
Nauru Nauru 3.75 -65.8% 57
Oman Oman 4.31 -61% 52
Panama Panama 9.75 +150% 32
Peru Peru 0.258 -0.124% 82
Philippines Philippines 9.56 -1.05% 34
Palau Palau 12.6 -28.8% 27
Puerto Rico Puerto Rico 17.9 +275% 16
Paraguay Paraguay 12.3 +2.72% 29
Palestinian Territories Palestinian Territories 8.92 -3% 37
Russia Russia 4.94 +87.3% 47
Rwanda Rwanda 0.489 +380% 80
Senegal Senegal 26.6 -0.0793% 9
Solomon Islands Solomon Islands 28.6 +332% 7
El Salvador El Salvador 14.3 -7.95% 22
San Marino San Marino 6.77 +7.19% 43
Suriname Suriname 48.9 +151% 1
Seychelles Seychelles 2.83 +47.1% 63
Syria Syria 23.6 -18.5% 12
Chad Chad 23.9 -6.94% 11
Togo Togo 0.772 -18.7% 75
Thailand Thailand 1.6 +359% 70
Tonga Tonga 0.513 -77.5% 79
Trinidad & Tobago Trinidad & Tobago 33.9 +269% 4
Tunisia Tunisia 1.94 +13.3% 66
Tuvalu Tuvalu 0.976 -63.8% 74
Tanzania Tanzania 16.4 +7.33% 17
Uzbekistan Uzbekistan 4.81 +17% 48
St. Vincent & Grenadines St. Vincent & Grenadines 1.39 -17.2% 73
Venezuela Venezuela 0.548 -95.6% 78
Vanuatu Vanuatu 8.58 +5.25% 38
Samoa Samoa 2.98 +206% 62 

                    
# 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.UNER.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.UNER.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))