Trained teachers in secondary education (% of total 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 +3.43% 22
Azerbaijan Azerbaijan 99.1 +0.115% 2
Burkina Faso Burkina Faso 64.9 -6.6% 33
Bangladesh Bangladesh 65 +1.16% 32
Bahrain Bahrain 100 0% 1
Bahamas Bahamas 91 +1.75% 14
Belarus Belarus 97.2 -0.15% 6
Belize Belize 73.3 +2.94% 27
Bolivia Bolivia 90.1 +1.09% 19
Barbados Barbados 51 +0.851% 39
Brunei Brunei 90.9 -0.0236% 15
Côte d’Ivoire Côte d’Ivoire 100 0% 1
Cameroon Cameroon 60 +12.9% 36
Cuba Cuba 100 0% 1
Cayman Islands Cayman Islands 98.9 -1.08% 3
Djibouti Djibouti 75.6 -24.4% 26
Dominica Dominica 44.4 +4.21% 40
Dominican Republic Dominican Republic 0 -100% 48
Ecuador Ecuador 77.2 +1.64% 24
Gibraltar Gibraltar 6.86 +9.31% 47
Guyana Guyana 71.2 +1.15% 30
Hong Kong SAR China Hong Kong SAR China 90.6 -2.93% 17
Indonesia Indonesia 38 -0.732% 43
India India 92 +1.89% 13
Jamaica Jamaica 100 0% 1
Jordan Jordan 100 0% 1
Kazakhstan Kazakhstan 100 0% 1
St. Lucia St. Lucia 68.3 -6.04% 31
Lesotho Lesotho 97.6 +13.7% 5
Macao SAR China Macao SAR China 94.2 +0.795% 11
Morocco Morocco 100 0% 1
Monaco Monaco 71.5 -10.4% 29
Moldova Moldova 100 0% 1
Mali Mali 40.5 -49.8% 42
Mauritius Mauritius 56.2 +16.3% 38
Malaysia Malaysia 87.4 +5.03% 21
Niger Niger 30.7 +85% 44
Nicaragua Nicaragua 18.4 -50.3% 46
Nepal Nepal 97.1 +16.5% 7
Oman Oman 98.1 -1.91% 4
Peru Peru 28.9 45
Palau Palau 90.8 +0.419% 16
Palestinian Territories Palestinian Territories 100 0% 1
Qatar Qatar 100 0% 1
Russia Russia 80.1 23
Rwanda Rwanda 76.3 -3.28% 25
Senegal Senegal 72.7 -0.193% 28
El Salvador El Salvador 95.3 +3.04% 9
Seychelles Seychelles 90.2 -0.739% 18
Syria Syria 44.2 -50.8% 41
Turks & Caicos Islands Turks & Caicos Islands 93.3 -1.55% 12
Chad Chad 61 -2.61% 35
Thailand Thailand 100 0% 1
Tunisia Tunisia 95.9 -4.05% 8
Tuvalu Tuvalu 57.7 +101% 37
Ukraine Ukraine 95 +0.477% 10
Uzbekistan Uzbekistan 100 0% 1
St. Vincent & Grenadines St. Vincent & Grenadines 61.2 -4.42% 34
Venezuela Venezuela 88.4 20 

                    
# 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.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.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))