School enrollment, secondary, female (% gross)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 92.7 -0.6% 32
Andorra Andorra 99.9 +2.89% 16
United Arab Emirates United Arab Emirates 102 +20.3% 10
Armenia Armenia 95.4 -1.56% 25
Azerbaijan Azerbaijan 91 +2.44% 41
Burkina Faso Burkina Faso 33.7 -6.94% 78
Bangladesh Bangladesh 76.6 -0.952% 58
Bahrain Bahrain 98.2 -3.18% 19
Bahamas Bahamas 84.9 +34.9% 52
Bosnia & Herzegovina Bosnia & Herzegovina 85 -0.682% 51
Belarus Belarus 92.5 -0.0298% 35
Belize Belize 84.3 -4.91% 53
Bermuda Bermuda 76 -4.83% 60
Bolivia Bolivia 92.4 +0.937% 36
Barbados Barbados 107 -1.03% 8
Brunei Brunei 89.5 +0.36% 43
Côte d’Ivoire Côte d’Ivoire 64 +22.6% 65
Cameroon Cameroon 42.1 -1.64% 75
Comoros Comoros 55.5 -14.6% 70
Cuba Cuba 96.3 -7.78% 22
Curaçao Curaçao 129 -7.94% 1
Cayman Islands Cayman Islands 113 -3.6% 5
Dominica Dominica 83.4 -1.81% 55
Dominican Republic Dominican Republic 76.3 -4.81% 59
Algeria Algeria 105 -2.67% 9
Ecuador Ecuador 94.7 -1.51% 29
Fiji Fiji 97.7 +2.77% 20
Georgia Georgia 102 +0.411% 11
Gibraltar Gibraltar 93 +9.62% 31
Guatemala Guatemala 48.5 +3.07% 74
Guyana Guyana 85.3 -3.74% 50
Honduras Honduras 60.1 +0.935% 68
Indonesia Indonesia 98.6 -0.767% 18
India India 78.8 -2.11% 57
Jamaica Jamaica 86.6 +3.22% 47
Jordan Jordan 93.2 -0.329% 30
Kazakhstan Kazakhstan 97.1 +1.57% 21
Kyrgyzstan Kyrgyzstan 92.6 -1.33% 33
Cambodia Cambodia 64 +0.352% 66
Kiribati Kiribati 100 +6.21% 15
Laos Laos 53.4 -4.68% 71
Lebanon Lebanon 68.9 +4.27% 63
St. Lucia St. Lucia 92 +0.331% 37
Macao SAR China Macao SAR China 91.8 +2.49% 38
Morocco Morocco 89 +5.07% 45
Maldives Maldives 83.7 +9.86% 54
Mali Mali 38 +8.23% 77
Montenegro Montenegro 95.1 +1.81% 27
Mongolia Mongolia 101 +1.11% 14
Malaysia Malaysia 87.7 +0.984% 46
Niger Niger 21.9 +1.36% 79
Nicaragua Nicaragua 70.1 -0.898% 62
Nepal Nepal 89.7 +6.38% 42
Nauru Nauru 91.4 -5.96% 40
Oman Oman 95.9 +5.13% 23
Peru Peru 108 +8.48% 7
Philippines Philippines 95.6 -1.86% 24
Palau Palau 95.2 +10.6% 26
Puerto Rico Puerto Rico 115 +22.3% 4
Paraguay Paraguay 81.6 -4.26% 56
Palestinian Territories Palestinian Territories 92.5 -0.486% 34
Russia Russia 91.6 -1.27% 39
Rwanda Rwanda 48.8 +2.69% 73
Senegal Senegal 50.6 -2.23% 72
El Salvador El Salvador 68.7 +0.242% 64
San Marino San Marino 59.8 +0.685% 69
Somalia Somalia 2.71 -30.2% 81
Seychelles Seychelles 75.9 -2.88% 61
Syria Syria 41.1 +0.708% 76
Turks & Caicos Islands Turks & Caicos Islands 101 +6.42% 13
Chad Chad 20.1 +11.8% 80
Togo Togo 60.8 +4.66% 67
Thailand Thailand 110 +3.53% 6
Tajikistan Tajikistan 85.8 -1.58% 49
Tonga Tonga 102 +0.765% 12
Trinidad & Tobago Trinidad & Tobago 89.1 +2.02% 44
Tuvalu Tuvalu 95.1 -2.6% 28
Uzbekistan Uzbekistan 86.2 -0.295% 48
St. Vincent & Grenadines St. Vincent & Grenadines 125 -2.85% 2
Venezuela Venezuela 99.7 +13.2% 17
Vanuatu Vanuatu 117 +53.4% 3 

                    
# 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.SEC.ENRR.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.SEC.ENRR.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))