Trained teachers in preprimary education, female (% of female teachers)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Andorra Andorra 100 0% 1
Armenia Armenia 72.8 -2.39% 26
Azerbaijan Azerbaijan 91 -0.518% 13
Burkina Faso Burkina Faso 20.9 -22% 45
Bahrain Bahrain 100 0% 1
Belarus Belarus 95.1 +0.43% 8
Belize Belize 70.9 +4.77% 29
Bolivia Bolivia 87.6 +1.58% 14
Barbados Barbados 72.2 +0.141% 28
Brunei Brunei 62 +44.2% 33
Botswana Botswana 92.4 +67.9% 12
Côte d’Ivoire Côte d’Ivoire 100 0% 1
Cameroon Cameroon 73.3 -0.794% 25
Colombia Colombia 97.5 +10.1% 5
Cayman Islands Cayman Islands 85.3 -14.7% 16
Dominica Dominica 46.2 +65.6% 38
Dominican Republic Dominican Republic 0 -100% 47
Algeria Algeria 94.8 -2.95% 9
Ecuador Ecuador 92.6 +0.548% 11
Eritrea Eritrea 39.3 +3.22% 40
Spain Spain 100 0% 1
Fiji Fiji 87.5 -7.89% 15
Grenada Grenada 51 +32.7% 36
Hong Kong SAR China Hong Kong SAR China 97.9 +0.157% 4
India India 96 +7.3% 6
Jordan Jordan 100 0% 1
Kazakhstan Kazakhstan 100 0% 1
Kyrgyzstan Kyrgyzstan 93.5 +0.101% 10
Cambodia Cambodia 100 0% 1
South Korea South Korea 100 0% 1
Lebanon Lebanon 33.7 +47% 44
Liberia Liberia 60.4 +3.15% 34
Sri Lanka Sri Lanka 47.8 +2.22% 37
Macao SAR China Macao SAR China 99.8 -0.00561% 2
Morocco Morocco 100 0% 1
Monaco Monaco 76.8 -2.61% 21
Moldova Moldova 100 0% 1
Maldives Maldives 79.5 +20.3% 20
Mongolia Mongolia 98.2 +2.19% 3
Malaysia Malaysia 36.4 -1.06% 41
Niger Niger 96 +0.656% 7
Nicaragua Nicaragua 70.5 +112% 30
Oman Oman 100 0% 1
Philippines Philippines 100 0% 1
Palau Palau 84.6 17
Palestinian Territories Palestinian Territories 100 0% 1
Qatar Qatar 100 0% 1
Rwanda Rwanda 53.2 +24.6% 35
Saudi Arabia Saudi Arabia 100 0% 1
Senegal Senegal 34 -5.01% 43
San Marino San Marino 35.9 -22.2% 42
Somalia Somalia 40.6 -34% 39
Seychelles Seychelles 69.5 +1.74% 31
Syria Syria 8.74 +3.69% 46
Chad Chad 80.1 +195% 19
Togo Togo 72.6 +12.1% 27
Thailand Thailand 100 0% 1
Tonga Tonga 62 +8.02% 32
Trinidad & Tobago Trinidad & Tobago 75.3 +0.0927% 23
Tuvalu Tuvalu 75.4 -24.6% 22
United States United States 100 0% 1
Uzbekistan Uzbekistan 100 0% 1
Vietnam Vietnam 82.7 +1.16% 18
Vanuatu Vanuatu 100 0% 1
Zimbabwe Zimbabwe 75.2 +4.55% 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.TCAQ.FE.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.PRE.TCAQ.FE.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))