Individuals using the Internet, female (% of female population)

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 81.7 +0.222% 45
United Arab Emirates United Arab Emirates 100 0% 1
Argentina Argentina 89 +1.08% 27
Austria Austria 94.2 +1.4% 13
Belgium Belgium 94.7 +1.36% 11
Bangladesh Bangladesh 37.3 +14.2% 53
Bulgaria Bulgaria 79.8 +2.07% 48
Bahrain Bahrain 100 0% 1
Bosnia & Herzegovina Bosnia & Herzegovina 83 +7.85% 42
Belarus Belarus 92.1 +2.19% 19
Brazil Brazil 85.7 +5.56% 36
Switzerland Switzerland 96.5 +1.68% 7
Chile Chile 93.2 +15.5% 16
Costa Rica Costa Rica 85.5 +3% 37
Cyprus Cyprus 91.8 +1.06% 21
Czechia Czechia 84.8 +3.25% 39
Germany Germany 91.5 +1.1% 24
Denmark Denmark 98.7 +0.656% 4
Ecuador Ecuador 73 +4.81% 51
Spain Spain 95.6 +1.22% 9
Estonia Estonia 94.4 +2.61% 12
Finland Finland 92.6 -0.126% 17
France France 86.3 +1.76% 33
Georgia Georgia 82.1 +4.45% 44
Greece Greece 84.8 +2.67% 40
Croatia Croatia 79.8 -0.737% 47
Hungary Hungary 91.8 +2.47% 22
Indonesia Indonesia 66.3 +4.43% 52
Italy Italy 86.1 +2.73% 35
Kazakhstan Kazakhstan 91.8 -0.147% 23
South Korea South Korea 96.7 +0.17% 6
Kuwait Kuwait 100 +0.1% 1
Lithuania Lithuania 90.7 +2.63% 25
Luxembourg Luxembourg 99.3 +1.85% 2
Latvia Latvia 92.3 +0.752% 18
Mexico Mexico 81.4 +4.25% 46
Malta Malta 94 +3.11% 14
Malawi Malawi 16.7 54
Malaysia Malaysia 97.2 +1.39% 5
Netherlands Netherlands 95.9 +4.95% 8
Norway Norway 99 0% 3
Poland Poland 86.7 +0.611% 32
Portugal Portugal 85 +1.69% 38
Paraguay Paraguay 79.2 +2.12% 49
Palestinian Territories Palestinian Territories 86.3 -2.17% 34
Romania Romania 88.5 +4.39% 29
Russia Russia 92.1 +2.33% 20
Saudi Arabia Saudi Arabia 100 0% 1
Singapore Singapore 94 -1.11% 15
Serbia Serbia 84.6 +4.1% 41
Slovakia Slovakia 87.1 -3.03% 31
Slovenia Slovenia 89.8 +1.98% 26
Sweden Sweden 95.6 +0.0154% 10
Thailand Thailand 88.6 +2.16% 28
Turkey Turkey 82.1 +3.56% 43
Uzbekistan Uzbekistan 87.1 +7.68% 30
Vietnam Vietnam 75.9 +0.389% 50 

                    
# 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 = 'IT.NET.USER.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 <- 'IT.NET.USER.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))