Primary completion rate, female (% of relevant age group)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 91.7 -5.95% 46
Andorra Andorra 68.2 +13.4% 77
United Arab Emirates United Arab Emirates 101 +26.1% 18
Armenia Armenia 92.4 +1.42% 42
Azerbaijan Azerbaijan 95.9 -3.1% 32
Burkina Faso Burkina Faso 57.3 -10.2% 82
Bangladesh Bangladesh 116 +0.778% 2
Bahrain Bahrain 89.7 -4% 54
Bosnia & Herzegovina Bosnia & Herzegovina 86.4 -0.175% 58
Belarus Belarus 91.6 +0.66% 47
Belize Belize 92.7 -10.8% 39
Bolivia Bolivia 96.3 -0.626% 30
Barbados Barbados 97.9 +9.05% 26
Brunei Brunei 97.7 -2.67% 27
Côte d’Ivoire Côte d’Ivoire 80.1 +17.6% 67
Cameroon Cameroon 68.2 +2.95% 76
Congo - Kinshasa Congo - Kinshasa 74.1 -6.37% 71
Congo - Brazzaville Congo - Brazzaville 71.7 +8.62% 73
Cuba Cuba 93.3 -0.141% 37
Cayman Islands Cayman Islands 86.6 -5.72% 57
Dominica Dominica 85.7 +0.485% 61
Dominican Republic Dominican Republic 85.9 -4.65% 59
Algeria Algeria 101 +4.82% 20
Ecuador Ecuador 101 +0.702% 17
Ethiopia Ethiopia 55.4 -8.95% 84
Fiji Fiji 110 +2.47% 5
Georgia Georgia 106 -4.26% 9
Gibraltar Gibraltar 140 -5.65% 1
Gambia Gambia 82.6 +0.944% 63
Guatemala Guatemala 87.4 +0.229% 56
Honduras Honduras 77.7 +7.96% 68
Indonesia Indonesia 101 -0.753% 19
India India 93.6 -6.02% 35
Jamaica Jamaica 80.2 +7.11% 66
Jordan Jordan 97.6 +2.24% 28
Kazakhstan Kazakhstan 106 +1.81% 12
Kyrgyzstan Kyrgyzstan 92.3 -1.46% 43
Cambodia Cambodia 92.7 -1.14% 41
Kiribati Kiribati 106 +2.92% 10
Laos Laos 90.5 +1.54% 50
Lebanon Lebanon 69.7 -0.327% 74
St. Lucia St. Lucia 89.7 -16.4% 55
Lesotho Lesotho 75.7 -4.53% 69
Macao SAR China Macao SAR China 92.2 -1.68% 44
Morocco Morocco 106 +3.3% 11
Madagascar Madagascar 63.3 +2.05% 80
Maldives Maldives 98.2 -3.27% 25
Mali Mali 48.4 +8.71% 85
Montenegro Montenegro 109 -0.864% 6
Mongolia Mongolia 96 +0.466% 31
Mozambique Mozambique 56.2 -19.2% 83
Mauritius Mauritius 89.9 -5.31% 52
Malawi Malawi 73 -2.13% 72
Malaysia Malaysia 100 +3.85% 21
Niger Niger 46.8 -7.05% 86
Nepal Nepal 114 +19.8% 3
Nauru Nauru 114 +16.3% 4
Oman Oman 94.3 +8.66% 33
Panama Panama 92.7 -0.186% 40
Peru Peru 104 +1.43% 13
Philippines Philippines 80.9 -9.27% 65
Palau Palau 82.2 -0.0665% 64
Puerto Rico Puerto Rico 64.5 -26.8% 79
Paraguay Paraguay 89.8 +4.7% 53
Palestinian Territories Palestinian Territories 90.8 -3.45% 49
Rwanda Rwanda 65.1 -35.8% 78
Senegal Senegal 69.4 +16.3% 75
Solomon Islands Solomon Islands 75 -10.1% 70
Sierra Leone Sierra Leone 98.4 -4.28% 24
El Salvador El Salvador 82.7 +0.373% 62
San Marino San Marino 98.7 +4.46% 22
Eswatini Eswatini 91.1 -9.58% 48
Seychelles Seychelles 93.5 -7.38% 36
Syria Syria 63.1 +5.83% 81
Togo Togo 90.3 +3.18% 51
Thailand Thailand 102 -0.0651% 16
Timor-Leste Timor-Leste 93.1 -4.97% 38
Tonga Tonga 102 -8.77% 14
Trinidad & Tobago Trinidad & Tobago 85.8 -2.24% 60
Tuvalu Tuvalu 94 -3.37% 34
Tanzania Tanzania 91.9 +21.9% 45
Uzbekistan Uzbekistan 96.3 -1.97% 29
St. Vincent & Grenadines St. Vincent & Grenadines 107 -8.61% 8
Venezuela Venezuela 98.7 +9.66% 23
Vanuatu Vanuatu 108 +3.34% 7
Samoa Samoa 102 -1.99% 15 

                    
# 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.CMPT.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.CMPT.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))