Literacy rate, youth female (% of females ages 15-24)

Source: worldbank.org, 01.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 42 +64.8% 53
United Arab Emirates United Arab Emirates 100 +1.01% 1
Benin Benin 59.2 +16.1% 49
Burkina Faso Burkina Faso 58.8 +33.5% 50
Bangladesh Bangladesh 96 0% 39
Bulgaria Bulgaria 97.8 -0.173% 34
Bolivia Bolivia 99.5 +0.01% 16
Brazil Brazil 99.5 -0.0201% 18
Brunei Brunei 99.8 -0.17% 9
Côte d’Ivoire Côte d’Ivoire 58.3 -23.3% 51
Congo - Brazzaville Congo - Brazzaville 79.5 +3.32% 45
Colombia Colombia 99.4 +0.0906% 19
Costa Rica Costa Rica 99.6 -0.0238% 14
Cuba Cuba 99.9 +0.188% 7
Cyprus Cyprus 99.9 -0.09% 5
Dominican Republic Dominican Republic 99 -0.141% 28
Ecuador Ecuador 96.5 +0.26% 38
Estonia Estonia 100 -0.01% 2
Fiji Fiji 99.4 20
Gabon Gabon 93 +4.04% 42
Guinea Guinea 49.1 +14.2% 52
Gambia Gambia 62.6 -6.65% 48
Croatia Croatia 99.8 -0.22% 10
Hungary Hungary 98.9 -0.302% 30
Cambodia Cambodia 93.5 +0.544% 40
Sri Lanka Sri Lanka 99 0% 27
Lithuania Lithuania 99.9 -0.05% 4
Latvia Latvia 99.9 -0.14% 8
Macao SAR China Macao SAR China 100 -0.04% 3
Moldova Moldova 99.7 -0.3% 12
Madagascar Madagascar 82.2 +4.1% 44
Maldives Maldives 99.3 +0.456% 22
Malta Malta 99.6 +0.636% 13
Montenegro Montenegro 98.9 -0.0451% 29
Mauritania Mauritania 75 +12% 46
Mauritius Mauritius 99.6 +0.586% 15
Namibia Namibia 96.6 +0.552% 37
Nigeria Nigeria 69 +14.3% 47
Nepal Nepal 93.3 +1.28% 41
Panama Panama 99.2 +0.384% 23
Peru Peru 99.2 -0.261% 25
Poland Poland 99.9 +0.1% 6
Puerto Rico Puerto Rico 92.4 +0.435% 43
Portugal Portugal 99.8 +0.758% 11
Paraguay Paraguay 99.2 +0.588% 23
Romania Romania 100 +1.01% 1
Russia Russia 100 0% 1
Singapore Singapore 100 0% 1
El Salvador El Salvador 98.3 -0.415% 32
Suriname Suriname 98.7 +0.758% 31
Syria Syria 98 +2.94% 33
Thailand Thailand 99.1 +0.53% 26
Tonga Tonga 99.5 -0.374% 17
Ukraine Ukraine 100 +0.0173% 1
Uruguay Uruguay 99.2 -0.101% 24
Uzbekistan Uzbekistan 100 0% 1
Vanuatu Vanuatu 97.2 +1.67% 35
Samoa Samoa 99.4 -0.158% 21
South Africa South Africa 97 -2.02% 36 

                    
# 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.ADT.1524.LT.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.ADT.1524.LT.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))