School enrollment, preprimary, female (% gross)

Source: worldbank.org, 03.09.2025

Year: 2021

Flag Country Value Value change, % Rank
Albania Albania 69.2 -7.9% 27
Armenia Armenia 29.6 -36.7% 53
Azerbaijan Azerbaijan 46.2 +4.07% 40
Burundi Burundi 16.7 0% 64
Benin Benin 23.4 +4.25% 60
Burkina Faso Burkina Faso 6.55 +2.41% 68
Bangladesh Bangladesh 37.4 -20.2% 45
Bosnia & Herzegovina Bosnia & Herzegovina 24.7 -8.59% 59
Belarus Belarus 93.8 -2.56% 11
Belize Belize 34.8 -25.4% 47
Barbados Barbados 76.8 -11.1% 23
Bhutan Bhutan 51.8 +58.6% 36
China China 93.6 +3.13% 12
Côte d’Ivoire Côte d’Ivoire 10.9 -0.842% 65
Costa Rica Costa Rica 95.8 -1.47% 9
Cuba Cuba 99.2 +1.67% 6
Djibouti Djibouti 10.6 +9.62% 66
Dominica Dominica 65.6 -17.2% 29
Dominican Republic Dominican Republic 33.9 -41.8% 50
Ecuador Ecuador 56.5 -1.49% 33
Ethiopia Ethiopia 29.3 -9.37% 54
Fiji Fiji 30.5 -7.47% 51
Micronesia (Federated States of) Micronesia (Federated States of) 6.34 +7.95% 69
Gibraltar Gibraltar 110 -12.4% 1
Guinea Guinea 19.4 +11.9% 61
Gambia Gambia 43.2 -4.09% 43
Guatemala Guatemala 50.4 +4.8% 38
Honduras Honduras 34.3 -14.4% 49
India India 52.3 -14.8% 35
Jordan Jordan 26.8 -14.8% 57
Kyrgyzstan Kyrgyzstan 38.7 -5.35% 44
Cambodia Cambodia 34.5 +24.5% 48
St. Kitts & Nevis St. Kitts & Nevis 89.3 -18.9% 16
Laos Laos 49.9 +0.159% 39
Macao SAR China Macao SAR China 85 -4.56% 18
Morocco Morocco 59.6 +3.45% 32
Moldova Moldova 90 -3.86% 14
Marshall Islands Marshall Islands 68.2 +9.13% 28
Montenegro Montenegro 71.8 -6.75% 26
Mongolia Mongolia 79.5 -6.67% 21
Mauritius Mauritius 99.6 0% 4
Malaysia Malaysia 88.6 -9.61% 17
Namibia Namibia 37.4 +7.07% 46
Niger Niger 7.27 -4.43% 67
Nepal Nepal 97.6 +0.901% 7
Oman Oman 27 -52% 56
Peru Peru 97.3 -5.79% 8
Philippines Philippines 89.7 +3.05% 15
Palestinian Territories Palestinian Territories 50.4 -13.4% 37
Qatar Qatar 54.2 -13.5% 34
Rwanda Rwanda 29.1 +3.07% 55
Saudi Arabia Saudi Arabia 19 -15.1% 62
Senegal Senegal 18.8 +5.07% 63
Sierra Leone Sierra Leone 26 +18.8% 58
San Marino San Marino 99.2 -8.52% 5
Somalia Somalia 1.02 +15.3% 71
Serbia Serbia 63.9 -2.4% 31
Suriname Suriname 80.1 -12.7% 20
Seychelles Seychelles 105 +6.85% 2
Turks & Caicos Islands Turks & Caicos Islands 100 +3.62% 3
Chad Chad 1.23 +14.1% 70
Togo Togo 29.8 -1.6% 52
Thailand Thailand 74.2 -1.5% 25
Trinidad & Tobago Trinidad & Tobago 65.1 -8.5% 30
Tuvalu Tuvalu 81.1 +10.1% 19
Tanzania Tanzania 76.9 -1.36% 22
Uzbekistan Uzbekistan 43.4 +6.63% 42
British Virgin Islands British Virgin Islands 90.3 -29.9% 13
Vietnam Vietnam 93.9 -0.264% 10
Samoa Samoa 43.4 +4.15% 41
Zimbabwe Zimbabwe 74.4 +2.46% 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.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.PRE.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))