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

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Andorra Andorra 100 0% 1
United Arab Emirates United Arab Emirates 100 0% 1
Armenia Armenia 85.2 +3.4% 28
Azerbaijan Azerbaijan 99.4 -0.391% 4
Burkina Faso Burkina Faso 95.2 +2.38% 15
Bangladesh Bangladesh 77.4 +5.98% 39
Bahrain Bahrain 100 0% 1
Bahamas Bahamas 93 +1.14% 17
Belarus Belarus 99.4 +0.0482% 3
Belize Belize 90.4 +0.698% 23
Bolivia Bolivia 90.1 +0.736% 24
Barbados Barbados 77.5 -0.421% 37
Brunei Brunei 84.1 +0.591% 29
Côte d’Ivoire Côte d’Ivoire 100 0% 1
Congo - Kinshasa Congo - Kinshasa 16.9 -83.1% 56
Comoros Comoros 77.4 +29.8% 38
Cuba Cuba 100 0% 1
Cayman Islands Cayman Islands 98.7 +1.79% 6
Djibouti Djibouti 56.5 -43.5% 50
Dominica Dominica 66.6 -1.6% 47
Dominican Republic Dominican Republic 0 -100% 59
Algeria Algeria 91.7 -4.21% 20
Ecuador Ecuador 91.2 +0.77% 22
Fiji Fiji 96 +5.16% 12
Guyana Guyana 67.5 -3.95% 46
Hong Kong SAR China Hong Kong SAR China 94.6 -1.5% 16
Indonesia Indonesia 35.3 54
India India 91.2 +3.25% 21
Jamaica Jamaica 96.6 -2.97% 11
Jordan Jordan 100 0% 1
Kazakhstan Kazakhstan 100 0% 1
Kyrgyzstan Kyrgyzstan 95.9 -0.645% 13
Cambodia Cambodia 100 0% 1
Kiribati Kiribati 92.5 -0.159% 18
Laos Laos 77.5 -13.2% 36
Lebanon Lebanon 40.5 +17.3% 52
St. Lucia St. Lucia 81.3 -3.1% 32
Lesotho Lesotho 99.6 +1.34% 2
Macao SAR China Macao SAR China 99.3 +0.0219% 5
Morocco Morocco 100 0% 1
Monaco Monaco 76.7 +5.01% 41
Moldova Moldova 100 0% 1
Mali Mali 34.1 -39.7% 55
Mongolia Mongolia 98.2 -1.53% 9
Mauritius Mauritius 100 0% 1
Malaysia Malaysia 88.7 +1.8% 26
Niger Niger 98.5 +0.244% 7
Nicaragua Nicaragua 63.4 -12.8% 49
Nepal Nepal 98.2 +2.31% 8
Nauru Nauru 8.33 -91.7% 58
Oman Oman 100 0% 1
Peru Peru 12.3 57
Palau Palau 95.3 +0.144% 14
Palestinian Territories Palestinian Territories 100 0% 1
Qatar Qatar 100 0% 1
Rwanda Rwanda 67.9 -11.7% 45
Senegal Senegal 69.3 +4.64% 43
Sierra Leone Sierra Leone 72.3 -8.42% 42
El Salvador El Salvador 98 +1.82% 10
San Marino San Marino 36.1 +0.687% 53
Seychelles Seychelles 81.4 +37.2% 31
Syria Syria 43.2 -51.3% 51
Turks & Caicos Islands Turks & Caicos Islands 79.7 -10.5% 34
Chad Chad 77.1 +0.135% 40
Togo Togo 68.6 -0.319% 44
Thailand Thailand 100 0% 1
Tonga Tonga 92.4 -4.45% 19
Trinidad & Tobago Trinidad & Tobago 79.9 +1.42% 33
Tunisia Tunisia 100 0% 1
Tuvalu Tuvalu 65.9 +35.1% 48
Ukraine Ukraine 89.9 -0.155% 25
Uzbekistan Uzbekistan 100 0% 1
St. Vincent & Grenadines St. Vincent & Grenadines 79.6 -0.548% 35
Venezuela Venezuela 88.3 27
Vietnam Vietnam 82.8 +10.8% 30 

                    
# 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.PRM.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.PRM.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))