School enrollment, secondary (% gross)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 96.1 -1.22% 20
Andorra Andorra 99.8 +2.33% 13
United Arab Emirates United Arab Emirates 102 +22.1% 11
Armenia Armenia 95.1 -1.8% 24
Azerbaijan Azerbaijan 92.8 +1.96% 32
Burkina Faso Burkina Faso 30.9 -8.29% 80
Bangladesh Bangladesh 71.5 -0.456% 62
Bahrain Bahrain 99.8 -3.58% 12
Bahamas Bahamas 78.8 +25.8% 57
Bosnia & Herzegovina Bosnia & Herzegovina 84.3 -1.17% 52
Belarus Belarus 93.6 -0.991% 27
Belize Belize 83.4 -4.15% 53
Bermuda Bermuda 71.8 -5.29% 61
Bolivia Bolivia 92.4 +1.02% 36
Barbados Barbados 106 -0.693% 8
Brunei Brunei 88.8 +0.507% 42
Côte d’Ivoire Côte d’Ivoire 66 +19.9% 64
Cameroon Cameroon 44.4 -1.67% 77
Congo - Kinshasa Congo - Kinshasa 56.8 -0.0957% 70
Comoros Comoros 55.2 -12.2% 71
Cuba Cuba 95.4 -7.94% 22
Curaçao Curaçao 123 -4.67% 2
Cayman Islands Cayman Islands 99.3 -2.22% 14
Dominica Dominica 86.2 -0.939% 48
Dominican Republic Dominican Republic 72.3 -5.55% 60
Algeria Algeria 103 -3.05% 10
Ecuador Ecuador 93.4 -1.42% 29
Fiji Fiji 95.9 +3.04% 21
Georgia Georgia 104 +0.544% 9
Gibraltar Gibraltar 92.4 +4.84% 35
Guatemala Guatemala 47.6 +3.29% 74
Guyana Guyana 85.4 -0.169% 50
Honduras Honduras 55.1 +1.74% 72
Indonesia Indonesia 97.2 -1.83% 18
India India 78.9 -2.85% 56
Jamaica Jamaica 86.4 +3.02% 47
Jordan Jordan 92 +1.08% 37
Kazakhstan Kazakhstan 97.2 +1.49% 17
Kyrgyzstan Kyrgyzstan 93.5 -1.54% 28
Cambodia Cambodia 60.1 +2.25% 69
Kiribati Kiribati 93.2 +6.54% 30
Laos Laos 54.5 -4.19% 73
Lebanon Lebanon 64.7 +3.54% 67
St. Lucia St. Lucia 90.4 +2.62% 38
Macao SAR China Macao SAR China 92.6 +2.62% 33
Morocco Morocco 89.8 +4.13% 39
Maldives Maldives 80.3 +10.6% 54
Mali Mali 39.9 +2.4% 78
Montenegro Montenegro 93.9 +1.86% 26
Mongolia Mongolia 99.1 +1.53% 15
Malaysia Malaysia 85.5 +1.14% 49
Niger Niger 22.9 -9.66% 82
Nicaragua Nicaragua 69.7 +2.55% 63
Nepal Nepal 89.6 +6.71% 40
Nauru Nauru 88.5 -1.59% 43
Oman Oman 96.7 +5.15% 19
Peru Peru 106 +6.84% 7
Philippines Philippines 92.9 -0.854% 31
Palau Palau 93.9 +9.57% 25
Puerto Rico Puerto Rico 114 +22% 4
Paraguay Paraguay 79.5 -3.04% 55
Palestinian Territories Palestinian Territories 88.9 -0.862% 41
Russia Russia 92.5 -1.55% 34
Rwanda Rwanda 45.1 +2.2% 76
Senegal Senegal 45.5 -3.07% 75
El Salvador El Salvador 65.7 +0.632% 65
San Marino San Marino 60.8 -1.43% 68
Somalia Somalia 3.29 -39.7% 83
Suriname Suriname 76.2 +16.2% 58
Seychelles Seychelles 74.3 -3.42% 59
Syria Syria 38.6 +0.356% 79
Turks & Caicos Islands Turks & Caicos Islands 109 +3.61% 6
Chad Chad 25.2 +4.77% 81
Togo Togo 65.3 +1.5% 66
Thailand Thailand 110 +7.83% 5
Tajikistan Tajikistan 87.4 -2.06% 44
Tonga Tonga 98.6 +1.61% 16
Trinidad & Tobago Trinidad & Tobago 87 +3.25% 45
Tuvalu Tuvalu 85.2 -6.73% 51
Uzbekistan Uzbekistan 86.5 -0.446% 46
St. Vincent & Grenadines St. Vincent & Grenadines 124 -2.63% 1
Venezuela Venezuela 95.4 +13.4% 23
Vanuatu Vanuatu 117 +55.2% 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'

# 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'

# 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))