Secondary education, teachers (% female)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 70.5 +1.14% 16
Andorra Andorra 66.2 -0.467% 28
United Arab Emirates United Arab Emirates 70 -3.38% 18
Armenia Armenia 88.4 +0.109% 1
Azerbaijan Azerbaijan 77.4 +0.562% 6
Burkina Faso Burkina Faso 18.1 +0.375% 69
Bangladesh Bangladesh 27.3 +1.41% 66
Bahrain Bahrain 59.8 +0.594% 40
Bahamas Bahamas 76.4 +3.76% 7
Bosnia & Herzegovina Bosnia & Herzegovina 64.2 +0.697% 35
Belarus Belarus 80.6 +0.0979% 5
Belize Belize 64.9 +1.32% 32
Bolivia Bolivia 52.1 -0.196% 54
Barbados Barbados 64.7 +0.789% 34
Brunei Brunei 69.7 -0.103% 20
China China 59.2 +1.69% 42
Côte d’Ivoire Côte d’Ivoire 16.4 +5.61% 71
Cameroon Cameroon 40.9 +0.464% 61
Congo - Kinshasa Congo - Kinshasa 17.6 +7.57% 70
Congo - Brazzaville Congo - Brazzaville 11.8 +18.8% 73
Cuba Cuba 65.1 +2.35% 31
Cayman Islands Cayman Islands 70.2 +0.682% 17
Djibouti Djibouti 27.8 +7.34% 65
Dominica Dominica 69.9 -3.65% 19
Dominican Republic Dominican Republic 63.8 +0.814% 36
Ecuador Ecuador 59.3 -0.00211% 41
Egypt Egypt 51.6 +0.997% 56
Gibraltar Gibraltar 63.7 -3.98% 37
Guatemala Guatemala 53.6 +0.956% 53
Guyana Guyana 75.7 +3.67% 9
Hong Kong SAR China Hong Kong SAR China 55.3 -0.714% 51
Indonesia Indonesia 63.1 +4.42% 38
India India 48 +1.68% 57
Jamaica Jamaica 71.8 -0.476% 13
Jordan Jordan 57.6 -1.04% 45
Kazakhstan Kazakhstan 73.9 -2.01% 11
St. Lucia St. Lucia 71.6 +0.203% 14
Lesotho Lesotho 55.2 -0.0161% 52
Macao SAR China Macao SAR China 57.9 -0.787% 44
Morocco Morocco 39.4 +0.704% 62
Monaco Monaco 58 +3.7% 43
Moldova Moldova 81.6 +0.543% 4
Mali Mali 13.3 +1.26% 72
Montenegro Montenegro 76.3 -0.0406% 8
Mauritius Mauritius 64.8 +0.55% 33
Malaysia Malaysia 67.5 -0.873% 26
Niger Niger 26.9 +20.2% 67
Nicaragua Nicaragua 62.5 +3.5% 39
Nepal Nepal 28.8 +9.75% 64
Nauru Nauru 68.4 +6.91% 22
Oman Oman 69.2 +0.0989% 21
Peru Peru 45.3 -0.238% 60
Palau Palau 65.1 -0.946% 30
Palestinian Territories Palestinian Territories 56.1 +0.0663% 48
Qatar Qatar 51.8 +0.8% 55
Russia Russia 82.8 +0.066% 3
Rwanda Rwanda 34.1 +3.17% 63
Senegal Senegal 18.3 -0.274% 68
El Salvador El Salvador 55.6 +1.63% 49
San Marino San Marino 75.4 +1.06% 10
Seychelles Seychelles 55.5 +0.301% 50
Syria Syria 70.9 +38.2% 15
Turks & Caicos Islands Turks & Caicos Islands 65.4 +2.46% 29
Chad Chad 9.08 +4.46% 74
Togo Togo 45.4 +540% 59
Thailand Thailand 66.6 -1.81% 27
Trinidad & Tobago Trinidad & Tobago 72.1 -0.147% 12
Tunisia Tunisia 56.6 +1.2% 46
Tuvalu Tuvalu 56.1 -9.35% 47
Ukraine Ukraine 83.2 +1.04% 2
Uzbekistan Uzbekistan 68.3 +1.19% 23
St. Vincent & Grenadines St. Vincent & Grenadines 68.1 -1.13% 24
Venezuela Venezuela 67.7 +53.1% 25
Vanuatu Vanuatu 47 +3.87% 58 

                    
# 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.TCHR.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.TCHR.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))