School enrollment, preprimary, male (% gross)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 69.3 -7.83% 27
Armenia Armenia 28 -39.5% 54
Azerbaijan Azerbaijan 46.4 +3.18% 40
Burundi Burundi 16.2 0% 64
Benin Benin 22.9 +3.97% 60
Burkina Faso Burkina Faso 6.46 +1.71% 68
Bangladesh Bangladesh 35.4 -20.3% 46
Bosnia & Herzegovina Bosnia & Herzegovina 26.2 -4.81% 58
Belarus Belarus 99.6 -2.17% 7
Belize Belize 33.5 -27.9% 47
Barbados Barbados 73.5 -10.5% 26
Bhutan Bhutan 52.1 +59% 35
China China 92.6 +2.6% 11
Côte d’Ivoire Côte d’Ivoire 10 -2.63% 66
Costa Rica Costa Rica 94.7 -2.48% 10
Cuba Cuba 100 +3.22% 6
Djibouti Djibouti 12.7 +11.7% 65
Dominica Dominica 66.5 -8.91% 29
Dominican Republic Dominican Republic 32.8 -42.4% 49
Ecuador Ecuador 54.1 -1.22% 33
Ethiopia Ethiopia 30.9 -9.5% 52
Fiji Fiji 32.2 -7.86% 51
Micronesia (Federated States of) Micronesia (Federated States of) 5.46 -9.66% 69
Gibraltar Gibraltar 102 -9.4% 4
Guinea Guinea 19.9 +11.3% 61
Gambia Gambia 39.9 -5.82% 42
Guatemala Guatemala 49.3 +4.56% 37
Honduras Honduras 33.2 -14% 48
India India 51.2 -15.9% 36
Jordan Jordan 26.4 -16.5% 57
Kyrgyzstan Kyrgyzstan 38.4 -5.34% 44
Cambodia Cambodia 32.7 +21.2% 50
St. Kitts & Nevis St. Kitts & Nevis 98.1 -14.3% 8
Laos Laos 48.7 +0.246% 38
Macao SAR China Macao SAR China 87.8 -4.32% 17
Morocco Morocco 60.1 -4.68% 32
Moldova Moldova 90.7 -4.28% 15
Marshall Islands Marshall Islands 67.3 -7.35% 28
Montenegro Montenegro 73.9 -6.57% 24
Mongolia Mongolia 80.8 -6.66% 20
Mauritius Mauritius 104 0% 2
Malaysia Malaysia 86.5 -9.51% 18
Namibia Namibia 35.7 +3.54% 45
Niger Niger 6.79 -6.05% 67
Nepal Nepal 112 -0.0867% 1
Oman Oman 27.8 -51.4% 55
Peru Peru 95.6 -6.76% 9
Philippines Philippines 90.7 -0.08% 14
Palestinian Territories Palestinian Territories 48.6 -16.3% 39
Qatar Qatar 53.8 -13.6% 34
Rwanda Rwanda 27.3 +0.915% 56
Saudi Arabia Saudi Arabia 17.7 -16.8% 62
Senegal Senegal 16.4 +2.68% 63
Sierra Leone Sierra Leone 24 +21.2% 59
San Marino San Marino 101 +1.51% 5
Somalia Somalia 1.27 +39.1% 71
Serbia Serbia 63.7 -2.45% 31
Suriname Suriname 76.3 -10.5% 22
Seychelles Seychelles 104 +9.35% 3
Turks & Caicos Islands Turks & Caicos Islands 92.5 -0.516% 12
Chad Chad 1.31 +17.2% 70
Togo Togo 28.6 -2.15% 53
Thailand Thailand 73.8 -1.72% 25
Trinidad & Tobago Trinidad & Tobago 63.9 -8.4% 30
Tuvalu Tuvalu 90.5 +8.04% 16
Tanzania Tanzania 77.6 -0.929% 21
Uzbekistan Uzbekistan 44.6 +5.8% 41
British Virgin Islands British Virgin Islands 85.5 -33.2% 19
Vietnam Vietnam 91.2 -0.416% 13
Samoa Samoa 39.6 +0.47% 43
Zimbabwe Zimbabwe 74.2 +2.47% 23 

                    
# 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.MA'

# 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.MA'

# 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))