Country

Indicator

Value type

Values present

Children out of school, female (% of female primary school age)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	11.1	+24.1%	24
Andorra	8.07	-10.2%	38
Armenia	10.2	+11.4%	27
Australia	0.189	-20.4%	97
Austria	1.77	+63.4%	76
Azerbaijan	4.8	-43.1%	52
Belgium	1.25	-15.7%	84
Benin	8.43	-23.7%	35
Burkina Faso	31.9	+10.2%	5
Bulgaria	5.24	+4.07%	50
Bahrain	7.76	+297%	40
Bosnia & Herzegovina	15.2	+7.75%	15
Belarus	8.6	+14.6%	34
Belize	9.05	+71.1%	31
Bolivia	3.14	-20.5%	64
Brazil	3.7	-34.5%	60
Barbados	7.28	+46.1%	42
Brunei	2.98	-12.8%	65
Bhutan	0.909	+400%	90
Botswana	9.04	-37.8%	32
Canada	4.23	+3.53%	55
Switzerland	0.229	+34.7%	96
Chile	1.99	-27.5%	73
Côte d’Ivoire	13.1	-0.288%	19
Colombia	6.79	+3.18%	46
Costa Rica	4.73	+3.87%	53
Cuba	3.97	+98.3%	58
Cayman Islands	22.3	-1.93%	10
Germany	1.52	+30.6%	81
Dominica	12	-2.72%	21
Denmark	0.543	+29.4%	94
Dominican Republic	7	-27.1%	44
Eritrea	47.8	-1.24%	1
Spain	1.7	-3.86%	78
Estonia	1.79	-13.4%	75
Ethiopia	26.6	-4.21%	8
Finland	2.03	+46.2%	72
Micronesia (Federated States of)	14.8	+3.11%	16
Georgia	0.991	-93.6%	88
Guatemala	7.76	-3.17%	41
Hong Kong SAR China	1.51	+2,149%	82
Honduras	19.2	-7.44%	12
Hungary	3.61	-7.78%	62
Iceland	1.58	+17.3%	80
Israel	3.47	-0.32%	63
Italy	2.59	+18.8%	69
Jamaica	10.4	-38.5%	26
Jordan	2.2	-24.1%	71
Kazakhstan	8.28	-4.3%	37
Kyrgyzstan	6.72	+16.8%	48
Cambodia	11.3	+55.8%	23
Kiribati	7.11	+38.9%	43
South Korea	1.18	-23.3%	86
Laos	8.39	+27.8%	36
Liberia	36.7	+5.07%	3
Lesotho	25.2	-2.92%	9
Luxembourg	0.172	-90.4%	98
Latvia	1.07	+17.2%	87
Macao SAR China	14.1	+21.2%	17
Maldives	4.88	+10,866%	51
Marshall Islands	0.546	-35.6%	93
North Macedonia	2.66	-61.9%	68
Malta	6.79	+30.1%	45
Mongolia	4.51	-5.09%	54
Malaysia	4	+23.1%	57
Niger	41.6	-0.982%	2
Netherlands	0.275	+306%	95
Norway	0.96	+10.9%	89
Nauru	9.13	-1.73%	30
New Zealand	2.41	+90.5%	70
Oman	10.8	+3.62%	25
Pakistan	30.3	-4.35%	6
Panama	3.63	-15.3%	61
Philippines	8.78	-7.68%	33
Palau	18.3	+3,071%	13
Paraguay	11.9	-2.24%	22
Palestinian Territories	9.46	+12.2%	29
Qatar	6.77	-44.5%	47
Romania	15.7	+61.7%	14
Russia	1.96	+32.3%	74
Senegal	20.7	+1.4%	11
Singapore	0.799	+10.5%	91
El Salvador	14	-0.446%	18
San Marino	5.35	-6.02%	49
Serbia	1.64	-52.5%	79
Slovakia	2.78	-35.4%	67
Slovenia	1.19	-18.2%	85
Sweden	0.574	+10.2%	92
Seychelles	1.73	+0.319%	77
Syria	28.7	-12.9%	7
Chad	32.6	-1.55%	4
Tunisia	1.27	+187%	83
Tuvalu	2.8	-50.1%	66
Tanzania	13	-1.22%	20
United States	3.79	-9.1%	59
Uzbekistan	4.2	+129%	56
Vanuatu	8.01	+65.8%	39
South Africa	10.1	+277%	28

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.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.FE.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))

Compare

Children out of school, female (% of female primary school age)

Year: 2022

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