School enrollment, preprimary (% gross)

Source: worldbank.org, 01.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 69.2 -7.86% 27
Armenia Armenia 28.8 -38.2% 54
Azerbaijan Azerbaijan 46.3 +3.59% 40
Burundi Burundi 16.5 0% 64
Benin Benin 23.1 +4.11% 60
Burkina Faso Burkina Faso 6.5 +2.05% 68
Bangladesh Bangladesh 36.4 -20.2% 46
Bosnia & Herzegovina Bosnia & Herzegovina 25.5 -6.63% 58
Belarus Belarus 96.8 -2.36% 7
Belize Belize 34.2 -26.7% 47
Barbados Barbados 75.1 -10.8% 23
Bhutan Bhutan 51.9 +58.8% 35
China China 93.1 +2.85% 12
Côte d’Ivoire Côte d’Ivoire 10.4 -1.71% 66
Costa Rica Costa Rica 95.2 -1.99% 10
Cuba Cuba 99.6 +2.47% 6
Djibouti Djibouti 11.6 +10.7% 65
Dominica Dominica 66 -13.1% 29
Dominican Republic Dominican Republic 33.4 -42.1% 50
Ecuador Ecuador 55.3 -1.36% 33
Ethiopia Ethiopia 30.1 -9.44% 52
Fiji Fiji 31.3 -7.67% 51
Micronesia (Federated States of) Micronesia (Federated States of) 5.89 -1.29% 69
Gibraltar Gibraltar 106 -10.9% 1
Guinea Guinea 19.7 +11.6% 61
Gambia Gambia 41.5 -4.94% 42
Guatemala Guatemala 49.8 +4.68% 37
Honduras Honduras 33.8 -14.2% 48
India India 51.7 -15.4% 36
Jordan Jordan 26.6 -15.7% 57
Kyrgyzstan Kyrgyzstan 38.5 -5.35% 44
Cambodia Cambodia 33.6 +22.8% 49
St. Kitts & Nevis St. Kitts & Nevis 93.7 -16.6% 11
Laos Laos 49.3 +0.203% 39
Macao SAR China Macao SAR China 86.4 -4.44% 18
Morocco Morocco 59.9 -0.902% 32
Moldova Moldova 90.3 -4.08% 14
Marshall Islands Marshall Islands 67.8 +0.104% 28
Montenegro Montenegro 72.9 -6.66% 26
Mongolia Mongolia 80.1 -6.66% 20
Mauritius Mauritius 102 0% 4
Malaysia Malaysia 87.5 -9.56% 17
Namibia Namibia 36.5 +5.32% 45
Niger Niger 7.03 -5.23% 67
Nepal Nepal 105 +0.4% 2
Oman Oman 27.4 -51.7% 56
Peru Peru 96.4 -6.28% 8
Philippines Philippines 90.2 +1.41% 15
Palestinian Territories Palestinian Territories 49.5 -14.9% 38
Qatar Qatar 54 -13.5% 34
Rwanda Rwanda 28.2 +2.01% 55
Saudi Arabia Saudi Arabia 18.3 -15.9% 62
Senegal Senegal 17.6 +3.93% 63
Sierra Leone Sierra Leone 25 +20% 59
San Marino San Marino 100 -3.57% 5
Somalia Somalia 1.15 +27.5% 71
Serbia Serbia 63.8 -2.43% 31
Suriname Suriname 78.2 -11.6% 21
Seychelles Seychelles 104 +8.12% 3
Turks & Caicos Islands Turks & Caicos Islands 96.2 +1.55% 9
Chad Chad 1.27 +15.7% 70
Togo Togo 29.2 -1.87% 53
Thailand Thailand 74 -1.61% 25
Trinidad & Tobago Trinidad & Tobago 64.5 -8.45% 30
Tuvalu Tuvalu 85.9 +8.95% 19
Tanzania Tanzania 77.3 -1.14% 22
Uzbekistan Uzbekistan 44 +6.2% 41
British Virgin Islands British Virgin Islands 87.9 -31.6% 16
Vietnam Vietnam 92.4 -0.343% 13
Samoa Samoa 41.4 +2.29% 43
Zimbabwe Zimbabwe 74.3 +2.47% 24 

                    
# 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.PRE.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.PRE.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))