Country

Indicator

Value type

Values present

Intentional homicides, female (per 100,000 female)

Source: worldbank.org, 19.12.2024

Year: 2021

Country	Value	Value change, %	Rank
Albania	0.84	+33.7%	47
United Arab Emirates	0.35	-29.6%	82
Argentina	1.35	-17.4%	39
Armenia	0.912	-12.2%	45
Antigua & Barbuda	10.3	+397%	1
Australia	0.375	-34.4%	81
Austria	0.883	+7.96%	46
Azerbaijan	1.32	-12.9%	40
Belgium	0.187	-80.1%	87
Bulgaria	0.79	+9.13%	50
Bahrain	0.18		88
Bahamas	2.82	-45.7%	22
Bosnia & Herzegovina	0.421	+42.1%	76
Belize	5.53	-1.35%	10
Bolivia	2.91	+12.9%	21
Barbados	4.1	+99.7%	13
Botswana	7.63	-13.7%	4
Canada	1.03	+9.91%	44
Switzerland	0.571	-7.93%	65
Chile	0.672	-41.1%	55
Colombia	4.1	+8.34%	14
Costa Rica	2.17	-10.3%	31
Cyprus	1.13	+39.2%	41
Czechia	0.506	-30.7%	73
Germany	0.798	-5.69%	49
Dominica	5.5	+98.6%	11
Denmark	0.51	-12.2%	72
Dominican Republic	2.57	+9.67%	24
Algeria	0.738	+29%	52
Ecuador	2.55	+35.8%	26
Egypt	0.548	+76.4%	68
Spain	0.401	-18.7%	78
Estonia	0.573	-59.9%	64
France	0.684	+17.4%	54
Ghana	0.577	+23.2%	63
Greece	0.619	+74.8%	59
Grenada	1.61	-66.9%	37
Guatemala	6.13	+19.1%	9
Guyana	2.19	-77.1%	30
Hong Kong SAR China	0.272	+22.2%	85
Honduras	6.49	-0.95%	7
Croatia	0.672	-25.6%	56
Haiti	1.87	+22.6%	34
Hungary	0.514	-33%	71
India	2.5	+2.97%	27
Ireland	0.278	+15.7%	84
Iceland	0.554	-75.3%	67
Israel	0.605	+15.6%	60
Italy	0.392	+3.08%	80
Jamaica	9.34	+17.4%	2
Jordan	0.577	+21.5%	62
Japan	0.209	-28%	86
Kenya	2.64	+54.2%	23
St. Kitts & Nevis	4.07		15
South Korea	0.493	-7.25%	74
St. Lucia	8.82	+59.5%	3
Liechtenstein	5.08	-0.775%	12
Lithuania	1.49	-23.1%	38
Latvia	3.58	-13.1%	17
Macao SAR China	0.823	+47.7%	48
Morocco	0.538	+16.6%	70
Mexico	6.17	+0.454%	8
Malta	0	-100%	91
Myanmar (Burma)	7.29	+652%	5
Montenegro	0.621	-33.2%	58
Mongolia	2.55	+5.71%	25
Mauritius	1.82	+8.92%	36
Namibia	6.8	+1.78%	6
Nicaragua	3.11	+98.2%	19
Netherlands	0.42	-16.2%	77
Norway	0.598	+6.24%	61
Oman	0.171	-26.5%	89
Panama	2.02	-5.63%	32
Poland	0.399	-11%	79
Paraguay	1.92	-8.46%	33
Palestinian Territories	0.35	-37.1%	83
Qatar	0.545	+106%	69
Romania	0.761	-20.4%	51
Russia	3.3	-6.74%	18
Singapore	0.0706	-60.2%	90
El Salvador	4.02	+1.15%	16
Serbia	0.711	+23.7%	53
Suriname	2.28	-30.7%	28
Slovakia	0.646	-35.6%	57
Slovenia	0.569	+0.0386%	66
Sweden	0.462	-4.83%	75
Turkey	1.03	+13%	43
Uruguay	2.26	+5.39%	29
United States	2.92	+13.9%	20
Uzbekistan	1.05	+9.39%	42
St. Vincent & Grenadines	0	-100%	91
Zimbabwe	1.87	+6.1%	35

Python
R

                    
# 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 = 'VC.IHR.PSRC.FE.P5'

# 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 <- 'VC.IHR.PSRC.FE.P5'

# 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))

Compare

Intentional homicides, female (per 100,000 female)

Year: 2021

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