Lower secondary completion rate, female (% of relevant age group)

Source: worldbank.org, 03.09.2025

Year: 2022

Flag Country Value Value change, % Rank
Aruba Aruba 103 +12.4% 11
Albania Albania 94.9 -1.46% 54
Argentina Argentina 101 -7.75% 18
Armenia Armenia 104 -0.953% 8
Austria Austria 97 -3.06% 42
Azerbaijan Azerbaijan 84 -3.35% 87
Belgium Belgium 95.4 +0.646% 48
Benin Benin 27.7 -17.6% 119
Burkina Faso Burkina Faso 35 -15.3% 117
Bangladesh Bangladesh 91.3 -5.91% 71
Bulgaria Bulgaria 95.2 -0.829% 51
Bahrain Bahrain 98.3 +0.303% 35
Bosnia & Herzegovina Bosnia & Herzegovina 85.6 -1.93% 84
Belarus Belarus 91.5 +1.12% 70
Belize Belize 67.2 -15.8% 100
Bolivia Bolivia 84.3 -5.66% 86
Barbados Barbados 87.5 -10.3% 81
Bhutan Bhutan 68.3 -23.5% 97
Botswana Botswana 90.3 -3.42% 74
Switzerland Switzerland 96.6 -1.13% 44
Chile Chile 100 +0.181% 23
Côte d’Ivoire Côte d’Ivoire 55.5 +10.1% 106
Cameroon Cameroon 35.6 +0.992% 116
Colombia Colombia 87.4 -3.52% 82
Costa Rica Costa Rica 68.2 -5.25% 98
Cuba Cuba 88.9 -0.616% 78
Cayman Islands Cayman Islands 122 -9.69% 4
Cyprus Cyprus 104 -0.0473% 9
Czechia Czechia 93.5 -4.07% 61
Germany Germany 61.9 -1.85% 102
Denmark Denmark 100 +0.588% 22
Dominican Republic Dominican Republic 82.3 +9.88% 88
Algeria Algeria 92.1 -1.05% 67
Ecuador Ecuador 95 -4.92% 53
Eritrea Eritrea 48.2 +0.965% 109
Spain Spain 93.2 -3.52% 62
Estonia Estonia 99.7 +0.819% 28
Finland Finland 99.7 +0.232% 29
Fiji Fiji 101 -0.153% 17
France France 99.9 -0.224% 24
Micronesia (Federated States of) Micronesia (Federated States of) 79.5 +3.59% 91
United Kingdom United Kingdom 99.3 +1.8% 31
Georgia Georgia 97.4 -3.68% 39
Gibraltar Gibraltar 127 +7.63% 3
Greece Greece 95.4 +1.45% 49
Guatemala Guatemala 51.3 -6.86% 107
Croatia Croatia 102 +2.27% 15
Hungary Hungary 96.9 -1.65% 43
Indonesia Indonesia 102 +1.61% 16
India India 91.3 -0.289% 72
Ireland Ireland 104 +3.91% 10
Israel Israel 93.8 -0.545% 60
Italy Italy 95.8 -3.38% 47
Jordan Jordan 94.8 -1.12% 55
Kazakhstan Kazakhstan 92 +0.832% 68
Kyrgyzstan Kyrgyzstan 92.4 -1.49% 65
Cambodia Cambodia 67.5 +5.55% 99
Kiribati Kiribati 96 -8.79% 45
South Korea South Korea 98.4 +3.95% 34
Laos Laos 58 -4.12% 104
Liberia Liberia 45.3 +2.75% 111
St. Lucia St. Lucia 92.5 +4.31% 64
Sri Lanka Sri Lanka 97 -0.573% 40
Lesotho Lesotho 56.8 -2.9% 105
Lithuania Lithuania 101 +1.43% 19
Luxembourg Luxembourg 98.8 +2.61% 33
Latvia Latvia 95.1 -2.7% 52
Macao SAR China Macao SAR China 88 -4.91% 79
Morocco Morocco 78 +1.39% 93
Madagascar Madagascar 34.6 -2.96% 118
Maldives Maldives 91 -3.19% 73
Mexico Mexico 94.6 +0.604% 57
Marshall Islands Marshall Islands 50.4 -71.2% 108
North Macedonia North Macedonia 91.9 +4.9% 69
Malta Malta 97.9 -1.38% 37
Montenegro Montenegro 99.8 +3.19% 26
Mongolia Mongolia 98.1 +3.95% 36
Mozambique Mozambique 40.8 +23.5% 115
Malawi Malawi 21.6 +6.91% 120
Malaysia Malaysia 84.6 +2.8% 85
Niger Niger 15.1 -4.45% 121
Norway Norway 99.5 +0.0184% 30
Nepal Nepal 101 +3.62% 20
Nauru Nauru 76.3 -23.7% 94
Oman Oman 89.3 -9.71% 76
Pakistan Pakistan 45 +2.38% 112
Panama Panama 82 -2.96% 89
Peru Peru 89 +0.94% 77
Philippines Philippines 102 +9.82% 13
Palau Palau 87.9 -16.8% 80
Poland Poland 135 +32.6% 1
Portugal Portugal 104 +3.57% 7
Paraguay Paraguay 69.8 -5.06% 96
Palestinian Territories Palestinian Territories 94.7 +1.6% 56
Qatar Qatar 94.5 +5.24% 58
Romania Romania 79.1 +18.5% 92
Rwanda Rwanda 42.2 -7.92% 114
Saudi Arabia Saudi Arabia 116 +14% 5
Senegal Senegal 43.2 -6.37% 113
Singapore Singapore 102 +4.84% 14
El Salvador El Salvador 73.8 -3.26% 95
San Marino San Marino 92.8 -2.03% 63
Serbia Serbia 97.7 -0.0349% 38
Slovakia Slovakia 81.2 -1.6% 90
Slovenia Slovenia 95.2 -1.87% 50
Sweden Sweden 99.8 -0.384% 27
Seychelles Seychelles 99.9 -22.8% 25
Syria Syria 46.8 +0.639% 110
Turks & Caicos Islands Turks & Caicos Islands 87.2 +3.08% 83
Togo Togo 59.9 +14.9% 103
Thailand Thailand 133 +5.07% 2
Tonga Tonga 105 +14.7% 6
Turkey Turkey 92.4 -0.726% 66
Tuvalu Tuvalu 89.6 +3.37% 75
Uruguay Uruguay 95.9 +38.1% 46
United States United States 101 -6.45% 21
Uzbekistan Uzbekistan 97 +0.91% 41
Vietnam Vietnam 102 +10.4% 12
Vanuatu Vanuatu 62.8 -0.0697% 101
Samoa Samoa 98.9 -4.91% 32
South Africa South Africa 94 -5.71% 59 

                    
# 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.CMPT.LO.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.CMPT.LO.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))