Country

Indicator

Value type

Values present

Children out of school, primary, female

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	8,802	+22.5%	51
Andorra	182	-12.5%	92
Armenia	8,110	+12.8%	54
Australia	2,081	-20.5%	68
Austria	2,964	+65.4%	64
Azerbaijan	14,382	-44.8%	46
Belgium	4,873	-16.2%	59
Benin	88,224	-21.4%	20
Burkina Faso	604,459	+12.5%	5
Bulgaria	6,211	+1.95%	57
Bahrain	4,740	+319%	60
Bosnia & Herzegovina	12,162	+5.52%	48
Belarus	20,062	+16.3%	39
Belize	2,017	+73.3%	71
Bolivia	21,648	-20.5%	37
Brazil	265,585	-35%	13
Barbados	666	+44.2%	83
Brunei	593	-12.3%	87
Bhutan	329	+384%	90
Botswana	17,576	-28.9%	42
Canada	51,359	+3.75%	26
Switzerland	591	+36.2%	88
Chile	15,069	-27.8%	44
Côte d’Ivoire	293,992	+1.27%	11
Colombia	131,157	+3.56%	15
Costa Rica	10,331	+3.89%	50
Cuba	14,402	+97.4%	45
Cayman Islands	643	+3.71%	85
Germany	22,689	+33.8%	36
Dominica	383	-4.96%	89
Denmark	1,150	+27.1%	76
Dominican Republic	39,475	-27.3%	30
Eritrea	113,518	-1.82%	17
Spain	23,033	-5.23%	35
Estonia	772	-14.9%	81
Ethiopia	2,371,739	-3.25%	2
Finland	3,689	+45.6%	62
Micronesia (Federated States of)	993	+1.22%	78
Georgia	1,584	-93.8%	73
Guatemala	88,928	-2.56%	19
Hong Kong SAR China	2,561	+2,052%	66
Honduras	116,411	-7.69%	16
Hungary	6,403	-7.07%	56
Iceland	257	+16.3%	91
Israel	17,822	+1.36%	41
Italy	32,597	+16.4%	32
Jamaica	12,046	-39.6%	49
Jordan	12,195	-24.4%	47
Kazakhstan	62,099	-2.7%	23
Kyrgyzstan	20,476	+19.9%	38
Cambodia	108,346	+56.4%	18
Kiribati	654	+40.6%	84
South Korea	15,583	-23.7%	43
Laos	31,254	+28.8%	33
Liberia	153,645	+5.84%	14
Lesotho	45,042	-1.91%	28
Luxembourg	35	-90.3%	97
Latvia	620	+16.8%	86
Macao SAR China	2,850	+29.5%	65
Maldives	1,088	+9,791%	77
Marshall Islands	15	-40%	99
North Macedonia	1,401	-64%	75
Malta	941	+32.2%	79
Mongolia	8,552	-1.62%	52
Malaysia	59,902	+24.7%	24
Niger	870,277	+2.63%	3
Netherlands	1,471	+302%	74
Norway	2,077	+9.89%	69
Nauru	88	+3.53%	94
New Zealand	4,572	+88.8%	61
Oman	19,469	+9.43%	40
Pakistan	4,270,165	-4.07%	1
Panama	8,262	-14.6%	53
Philippines	586,366	-7.48%	6
Palau	125	+3,025%	93
Paraguay	44,541	-1.01%	29
Palestinian Territories	25,457	+14.4%	34
Qatar	5,786	-46.4%	58
Romania	77,792	+70.6%	21
Russia	72,428	+35.6%	22
Saudi Arabia	34,898	+19.9%	31
Senegal	285,564	+3.28%	12
Singapore	920	+11.2%	80
El Salvador	45,409	-0.648%	27
San Marino	42	-8.7%	96
Serbia	2,051	-53.6%	70
Slovakia	3,148	-35.5%	63
Slovenia	763	-18.8%	82
Sweden	2,102	+10.7%	67
Seychelles	83	+7.79%	95
Syria	408,010	-18.5%	9
Chad	504,084	+1.51%	7
Tunisia	7,858	+194%	55
Tuvalu	19	-50%	98
Tanzania	769,035	+1.33%	4
United States	448,218	-9.52%	8
Uzbekistan	53,023	+135%	25
Vanuatu	1,978	+68.9%	72
South Africa	395,363	+281%	10

Python
R

                    
# 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.FE'

# 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.FE'

# 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))

Compare

Children out of school, primary, female

Year: 2022

Acquire full dataset with Python or R from source