School enrollment, primary and secondary (gross), gender parity index (GPI)

Source: worldbank.org, 01.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 1.03 +0.168% 12
Armenia Armenia 1.02 -1.03% 17
Azerbaijan Azerbaijan 0.994 -1.12% 36
Benin Benin 0.897 +0.818% 54
Burkina Faso Burkina Faso 1.04 +1.87% 8
Bangladesh Bangladesh 1.14 -0.91% 3
Belarus Belarus 0.993 +0.168% 38
Belize Belize 0.989 -0.194% 42
Barbados Barbados 0.998 -0.159% 32
Botswana Botswana 1.02 +0.663% 15
China China 1.01 -0.124% 24
Côte d’Ivoire Côte d’Ivoire 0.915 +1.9% 52
Cuba Cuba 1 +1.39% 28
Djibouti Djibouti 0.973 -2.98% 45
Dominica Dominica 0.97 -0.207% 47
Dominican Republic Dominican Republic 1.02 +1.35% 16
Fiji Fiji 1.02 -0.0196% 19
Georgia Georgia 1.01 -0.133% 27
Gibraltar Gibraltar 0.999 -3.98% 31
Gambia Gambia 1.15 +15.2% 2
Guatemala Guatemala 0.994 +1.39% 34
Hong Kong SAR China Hong Kong SAR China 1.02 +0.384% 18
India India 0.992 -1.56% 39
Jordan Jordan 1 -0.322% 29
Kyrgyzstan Kyrgyzstan 1 +0.252% 30
Cambodia Cambodia 1.04 +3.74% 10
St. Kitts & Nevis St. Kitts & Nevis 0.966 -3.17% 48
Laos Laos 0.963 +0.658% 49
Macao SAR China Macao SAR China 0.986 -0.0182% 43
Morocco Morocco 0.971 +0.938% 46
Moldova Moldova 0.99 +0.0293% 40
Marshall Islands Marshall Islands 1.01 -1.39% 23
Montenegro Montenegro 1.01 +0.172% 20
Mongolia Mongolia 0.998 -0.157% 33
Mauritius Mauritius 1.04 +1.31% 9
Nepal Nepal 0.993 -1.87% 37
Oman Oman 0.96 -1.49% 51
Panama Panama 1.01 -0.0803% 25
Peru Peru 0.96 +1.11% 50
Philippines Philippines 1.03 +0.22% 11
Palestinian Territories Palestinian Territories 1.05 -0.38% 6
Rwanda Rwanda 1.02 +1.29% 14
Saudi Arabia Saudi Arabia 0.989 +1.51% 41
Senegal Senegal 1.18 +1.91% 1
San Marino San Marino 0.973 +0.143% 44
Serbia Serbia 1.01 +0.322% 22
Suriname Suriname 1.09 -4.29% 4
Seychelles Seychelles 1.06 -0.429% 5
Turks & Caicos Islands Turks & Caicos Islands 1.02 -0.236% 13
Chad Chad 0.752 +3.31% 55
Togo Togo 0.91 +4.08% 53
Thailand Thailand 1.01 +1.77% 21
Tuvalu Tuvalu 1.01 -0.422% 26
Tanzania Tanzania 1.04 +0.332% 7
Uzbekistan Uzbekistan 0.994 +0.14% 35 

                    
# 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.ENR.PRSC.FM.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.ENR.PRSC.FM.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))