Unemployment, female (% of female labor force)

Source: worldbank.org, 19.12.2024

Year: 2023

Flag Country Value Value change, % Rank
United Arab Emirates United Arab Emirates 4.27 -39.6% 49
Argentina Argentina 6.78 -11.5% 28
Australia Australia 3.57 -2.72% 61
Austria Austria 5.03 +4.73% 45
Belgium Belgium 5.05 -3.94% 44
Burkina Faso Burkina Faso 5.74 +195% 37
Bulgaria Bulgaria 4.22 +3.99% 50
Bahamas Bahamas 8.2 -15.3% 22
Bosnia & Herzegovina Bosnia & Herzegovina 12.8 -17% 7
Belarus Belarus 2.78 -4.23% 76
Bolivia Bolivia 3.42 -17.7% 64
Brazil Brazil 9.68 -15.8% 14
Brunei Brunei 5.32 -10.5% 40
Bhutan Bhutan 4.21 -47% 52
Botswana Botswana 26.9 +3.73% 2
Canada Canada 5.26 +2.36% 42
Switzerland Switzerland 4.28 -2.26% 48
Chile Chile 9.36 +6.21% 16
Colombia Colombia 11.8 -10.2% 10
Costa Rica Costa Rica 10.3 -32.7% 13
Cyprus Cyprus 6.17 -20.3% 33
Czechia Czechia 3.05 +10.1% 70
Germany Germany 2.84 +1.39% 73
Denmark Denmark 5.27 +17.4% 41
Dominican Republic Dominican Republic 8.33 -2.08% 21
Ecuador Ecuador 4.31 -2.95% 47
Spain Spain 13.9 -6.01% 6
Estonia Estonia 6.64 +31.1% 29
Finland Finland 6.41 +0.722% 31
France France 7.21 +0.953% 26
United Kingdom United Kingdom 3.69 +2.02% 60
Gambia Gambia 5.81 +83.1% 35
Greece Greece 14.3 -13% 5
Guatemala Guatemala 2.92 -35.7% 72
Hong Kong SAR China Hong Kong SAR China 2.42 -31.4% 78
Honduras Honduras 8.6 -32.6% 19
Croatia Croatia 6.62 -16.2% 30
Hungary Hungary 4.15 +19.1% 53
Indonesia Indonesia 3.08 -2.35% 69
India India 4.06 -11.4% 54
Ireland Ireland 4.21 -9.85% 51
Iceland Iceland 3.1 -8.45% 68
Israel Israel 3.2 -10.8% 66
Italy Italy 8.76 -6.31% 17
Jamaica Jamaica 3.92 -23.5% 56
Japan Japan 2.3 -4.17% 80
South Korea South Korea 2.72 -10.2% 77
Lithuania Lithuania 6.38 +16.2% 32
Luxembourg Luxembourg 5.44 +14.7% 38
Latvia Latvia 5.35 -3.1% 39
Moldova Moldova 1.21 +77.4% 82
Mexico Mexico 2.8 -15.5% 74
North Macedonia North Macedonia 11.4 -8.47% 12
Malta Malta 2.94 +11.6% 71
Mongolia Mongolia 4.37 -11.1% 46
Mauritius Mauritius 7.44 -12.2% 24
Netherlands Netherlands 3.75 -1.83% 57
Norway Norway 3.45 +12.5% 63
New Zealand New Zealand 3.97 +12.3% 55
Panama Panama 8.57 -12.8% 20
Peru Peru 5.8 +10.5% 36
Poland Poland 2.79 -2.21% 75
Portugal Portugal 6.88 +6.2% 27
Paraguay Paraguay 7.32 -11.2% 25
Romania Romania 5.12 +1.53% 43
Russia Russia 3.19 -19.5% 67
Rwanda Rwanda 14.3 -13.7% 4
Saudi Arabia Saudi Arabia 12.8 -16.8% 8
Singapore Singapore 3.69 -3.38% 59
El Salvador El Salvador 3.36 +1.85% 65
Serbia Serbia 8.65 -2.61% 18
Slovakia Slovakia 5.93 -7.05% 34
Slovenia Slovenia 3.71 -14% 58
Sweden Sweden 7.87 -0.531% 23
Seychelles Seychelles 2.41 -32.1% 79
Thailand Thailand 0.807 -23.6% 83
Tunisia Tunisia 20.5 +1.47% 3
Turkey Turkey 12.7 -5.56% 9
Uruguay Uruguay 11.5 +26.8% 11
United States United States 3.45 -4.43% 62
Vietnam Vietnam 1.51 +6.04% 81
South Africa South Africa 34.6 -2.32% 1
Zimbabwe Zimbabwe 9.52 -9.93% 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 = 'SL.UEM.TOTL.FE.NE.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 <- 'SL.UEM.TOTL.FE.NE.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))