Trained teachers in secondary 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 85.8 +3.32% 22
Azerbaijan Azerbaijan 99.2 +0.126% 4
Burkina Faso Burkina Faso 65.2 -6.49% 33
Bangladesh Bangladesh 71 +0.393% 30
Bahrain Bahrain 100 0% 1
Bahamas Bahamas 91.9 +1.96% 16
Belarus Belarus 97.3 -0.153% 6
Belize Belize 76.1 +2.04% 26
Bolivia Bolivia 91.5 +0.886% 17
Barbados Barbados 52.2 +0.4% 39
Brunei Brunei 91.2 -0.0334% 18
Côte d’Ivoire Côte d’Ivoire 100 0% 1
Cameroon Cameroon 64.1 +1.44% 36
Cuba Cuba 100 0% 1
Cayman Islands Cayman Islands 99.2 -0.772% 3
Djibouti Djibouti 73.4 -26.6% 28
Dominica Dominica 52.7 +9.54% 38
Dominican Republic Dominican Republic 0 -100% 48
Ecuador Ecuador 80.3 +1.61% 23
Gibraltar Gibraltar 7.69 +13.8% 46
Guyana Guyana 73.9 +1.67% 27
Hong Kong SAR China Hong Kong SAR China 92 -2.41% 15
Indonesia Indonesia 38 +0.189% 42
India India 92.4 +1.79% 14
Jamaica Jamaica 100 0% 1
Jordan Jordan 100 0% 1
Kazakhstan Kazakhstan 100 0% 1
St. Lucia St. Lucia 71.5 -6.12% 29
Lesotho Lesotho 98.1 +12.2% 5
Macao SAR China Macao SAR China 94.9 +0.702% 9
Morocco Morocco 100 0% 1
Monaco Monaco 64.7 -12.9% 34
Moldova Moldova 100 0% 1
Mali Mali 41 -52.3% 41
Mauritius Mauritius 55.6 +17.8% 37
Malaysia Malaysia 90.1 +4.66% 20
Niger Niger 28.4 +70.5% 43
Nicaragua Nicaragua 18.8 -51.9% 45
Nepal Nepal 95.2 +20.5% 8
Oman Oman 99.9 -0.0571% 2
Peru Peru 25.2 44
Palau Palau 92.9 +0.238% 12
Palestinian Territories Palestinian Territories 100 0% 1
Qatar Qatar 100 0% 1
Rwanda Rwanda 78.1 -2.37% 24
Senegal Senegal 77.3 -1.44% 25
El Salvador El Salvador 95.8 +1.77% 7
San Marino San Marino 2.84 -13.1% 47
Seychelles Seychelles 91 -0.191% 19
Syria Syria 44.9 40
Turks & Caicos Islands Turks & Caicos Islands 93.8 -1.28% 11
Chad Chad 68.2 -0.115% 31
Thailand Thailand 100 0% 1
Tunisia Tunisia 92.8 -7.16% 13
Tuvalu Tuvalu 68.1 +109% 32
Ukraine Ukraine 94.2 +0.62% 10
Uzbekistan Uzbekistan 100 0% 1
St. Vincent & Grenadines St. Vincent & Grenadines 64.6 -5.13% 35
Venezuela Venezuela 88.5 21 

                    
# 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.SEC.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.SEC.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))