Mortality rate, neonatal (per 1,000 live births)

Source: worldbank.org, 01.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 34.3 -2.56% 4
Angola Angola 25.6 -1.92% 20
Albania Albania 6.9 -1.43% 92
Andorra Andorra 1.3 0% 133
United Arab Emirates United Arab Emirates 2.5 -3.85% 122
Argentina Argentina 5 -9.09% 102
Armenia Armenia 5.3 -3.64% 100
Antigua & Barbuda Antigua & Barbuda 5.4 -1.82% 99
Australia Australia 2.3 0% 124
Austria Austria 2 -4.76% 126
Azerbaijan Azerbaijan 12.8 -3.03% 62
Burundi Burundi 19.6 -2.49% 45
Belgium Belgium 2.1 -4.55% 125
Benin Benin 28 -1.75% 17
Burkina Faso Burkina Faso 24.5 -1.61% 25
Bangladesh Bangladesh 17.9 -0.556% 48
Bulgaria Bulgaria 2.6 -3.7% 121
Bahrain Bahrain 4.4 +2.33% 108
Bahamas Bahamas 9.2 -1.08% 79
Bosnia & Herzegovina Bosnia & Herzegovina 4.5 -2.17% 107
Belarus Belarus 0.8 0% 137
Belize Belize 9.3 -1.06% 78
Bolivia Bolivia 12.2 -3.94% 66
Brazil Brazil 7.6 -8.43% 88
Barbados Barbados 6.9 -2.82% 92
Brunei Brunei 4.9 -2% 103
Bhutan Bhutan 12.7 -3.79% 63
Botswana Botswana 20.9 -1.42% 41
Central African Republic Central African Republic 30.7 -15.2% 11
Canada Canada 3.4 0% 114
Switzerland Switzerland 2.8 0% 119
Chile Chile 4.6 +2.22% 106
China China 2.8 -6.67% 119
Côte d’Ivoire Côte d’Ivoire 28.2 -1.74% 16
Cameroon Cameroon 25.2 -1.95% 23
Congo - Kinshasa Congo - Kinshasa 25.3 -2.32% 22
Congo - Brazzaville Congo - Brazzaville 17.7 -1.67% 49
Colombia Colombia 6.5 -4.41% 95
Comoros Comoros 22.5 -2.17% 32
Cape Verde Cape Verde 8.3 -3.49% 86
Costa Rica Costa Rica 7.2 +4.35% 90
Cuba Cuba 4.2 +5% 110
Cyprus Cyprus 1.9 0% 127
Czechia Czechia 1.3 -7.14% 133
Germany Germany 2.3 0% 124
Djibouti Djibouti 28.2 -2.08% 16
Dominica Dominica 30.8 +0.984% 10
Denmark Denmark 1.8 -5.26% 128
Dominican Republic Dominican Republic 21.7 -2.69% 37
Algeria Algeria 15.3 -1.92% 55
Ecuador Ecuador 7.1 -1.39% 91
Egypt Egypt 9.3 -3.12% 78
Eritrea Eritrea 16.4 -3.53% 53
Spain Spain 1.7 0% 129
Estonia Estonia 1 0% 135
Ethiopia Ethiopia 27.4 -2.49% 18
Finland Finland 1.3 0% 133
Fiji Fiji 14.9 +0.676% 57
France France 2.7 0% 120
Micronesia (Federated States of) Micronesia (Federated States of) 12.1 -3.2% 67
Gabon Gabon 16.8 -2.89% 51
United Kingdom United Kingdom 2.7 0% 120
Georgia Georgia 5.3 -1.85% 100
Ghana Ghana 21.2 -2.3% 40
Guinea Guinea 30.3 -1.94% 12
Gambia Gambia 23.7 -2.47% 28
Guinea-Bissau Guinea-Bissau 32.7 -2.1% 7
Equatorial Guinea Equatorial Guinea 27.2 -2.51% 19
Greece Greece 2.3 0% 124
Grenada Grenada 12.4 -1.59% 65
Guatemala Guatemala 10 -3.85% 75
Guyana Guyana 15.6 -3.11% 54
Honduras Honduras 8.8 -3.3% 82
Croatia Croatia 2.8 -3.45% 119
Haiti Haiti 23.3 -1.69% 29
Hungary Hungary 2.1 -4.55% 125
Indonesia Indonesia 10.5 -3.67% 72
India India 17.3 -4.42% 50
Ireland Ireland 2.5 0% 122
Iran Iran 7.4 -3.9% 89
Iraq Iraq 12.9 -3.01% 61
Iceland Iceland 1.3 0% 133
Israel Israel 1.7 -5.56% 129
Italy Italy 1.6 -5.88% 130
Jamaica Jamaica 14.5 0% 58
Jordan Jordan 7.6 -2.56% 88
Japan Japan 0.8 0% 137
Kazakhstan Kazakhstan 4.3 -6.52% 109
Kenya Kenya 21.5 -1.38% 38
Kyrgyzstan Kyrgyzstan 10.8 -1.82% 71
Cambodia Cambodia 11.8 -4.07% 68
Kiribati Kiribati 22.4 -1.32% 33
St. Kitts & Nevis St. Kitts & Nevis 10.3 -2.83% 73
South Korea South Korea 1.2 0% 134
Kuwait Kuwait 4.8 -2.04% 104
Laos Laos 20 -2.44% 44
Lebanon Lebanon 11.1 +5.71% 70
Liberia Liberia 29.6 -1.99% 13
Libya Libya 5.7 +1.79% 98
St. Lucia St. Lucia 10 -1.96% 75
Sri Lanka Sri Lanka 4.1 -4.65% 111
Lesotho Lesotho 28.7 -2.38% 15
Lithuania Lithuania 1.9 -5% 127
Luxembourg Luxembourg 1.6 -5.88% 130
Latvia Latvia 1.5 -6.25% 131
Morocco Morocco 10.2 -3.77% 74
Monaco Monaco 1.5 0% 131
Moldova Moldova 10.8 0% 71
Madagascar Madagascar 23.8 -0.833% 27
Maldives Maldives 3.9 -4.88% 112
Mexico Mexico 7.6 -3.8% 88
Marshall Islands Marshall Islands 13 -3.7% 60
North Macedonia North Macedonia 1.4 -26.3% 132
Mali Mali 32.4 -1.52% 8
Malta Malta 3.7 -2.63% 113
Myanmar (Burma) Myanmar (Burma) 20.6 -2.37% 42
Montenegro Montenegro 1 0% 135
Mongolia Mongolia 7.4 -3.9% 89
Mozambique Mozambique 25.4 -1.55% 21
Mauritania Mauritania 21.5 -2.27% 38
Mauritius Mauritius 9 -3.23% 81
Malawi Malawi 18.8 -2.59% 46
Malaysia Malaysia 4.1 -2.38% 111
Namibia Namibia 24.1 -3.6% 26
Niger Niger 33.8 -0.88% 5
Nigeria Nigeria 33.7 -1.75% 6
Nicaragua Nicaragua 8.4 -5.62% 85
Netherlands Netherlands 2.6 0% 121
Norway Norway 1.3 0% 133
Nepal Nepal 16.6 -3.49% 52
Nauru Nauru 4.8 -5.88% 104
New Zealand New Zealand 2.7 -3.57% 120
Oman Oman 4.7 -2.08% 105
Pakistan Pakistan 37.6 -2.34% 2
Panama Panama 6.6 -4.35% 94
Peru Peru 7.8 -1.27% 87
Philippines Philippines 14 -1.41% 59
Palau Palau 11.7 -2.5% 69
Papua New Guinea Papua New Guinea 20.5 -2.38% 43
Poland Poland 2.4 -4% 123
North Korea North Korea 9.6 +1.05% 77
Portugal Portugal 1.6 -5.88% 130
Paraguay Paraguay 9.1 -3.19% 80
Palestinian Territories Palestinian Territories 8.7 -1.14% 83
Qatar Qatar 3.7 0% 113
Romania Romania 3.3 +3.12% 115
Russia Russia 1.7 -5.56% 129
Rwanda Rwanda 18.1 -1.09% 47
Saudi Arabia Saudi Arabia 3 -3.23% 117
Sudan Sudan 24.8 -2.36% 24
Senegal Senegal 22.3 -3.88% 34
Singapore Singapore 0.9 0% 136
Solomon Islands Solomon Islands 8.3 -3.49% 86
Sierra Leone Sierra Leone 29.3 -1.68% 14
El Salvador El Salvador 4.4 -4.35% 108
San Marino San Marino 0.6 0% 138
Somalia Somalia 34.9 -3.32% 3
Serbia Serbia 3.2 -3.03% 116
South Sudan South Sudan 40.2 -0.248% 1
São Tomé & Príncipe São Tomé & Príncipe 6.8 -4.23% 93
Suriname Suriname 9.9 -3.88% 76
Slovakia Slovakia 3 0% 117
Slovenia Slovenia 1.3 0% 133
Sweden Sweden 1.4 0% 132
Eswatini Eswatini 24.5 -2% 25
Seychelles Seychelles 8.5 -2.3% 84
Syria Syria 10 -2.91% 75
Turks & Caicos Islands Turks & Caicos Islands 2.9 -3.33% 118
Chad Chad 31.4 -1.57% 9
Togo Togo 23.1 -1.7% 30
Thailand Thailand 5.2 -3.7% 101
Tajikistan Tajikistan 12.6 -3.82% 64
Turkmenistan Turkmenistan 22.8 -2.15% 31
Timor-Leste Timor-Leste 22.2 -1.77% 35
Tonga Tonga 4.3 -4.44% 109
Trinidad & Tobago Trinidad & Tobago 12.9 -2.27% 61
Tunisia Tunisia 8.4 -6.67% 85
Turkey Turkey 4.9 -2% 103
Tuvalu Tuvalu 9 -3.23% 81
Tanzania Tanzania 20.6 -2.37% 42
Uganda Uganda 17.9 -2.72% 48
Ukraine Ukraine 4.7 -2.08% 105
Uruguay Uruguay 4.1 -4.65% 111
United States United States 3.4 0% 114
Uzbekistan Uzbekistan 7.6 -3.8% 88
St. Vincent & Grenadines St. Vincent & Grenadines 5.8 -4.92% 97
Venezuela Venezuela 15 0% 56
British Virgin Islands British Virgin Islands 6.6 -2.94% 94
Vietnam Vietnam 10.3 -1.9% 73
Vanuatu Vanuatu 8.7 -2.25% 83
Samoa Samoa 6.2 -3.13% 96
Kosovo Kosovo 6.8 -4.23% 93
Yemen Yemen 21.4 -1.83% 39
South Africa South Africa 11.7 +0.862% 69
Zambia Zambia 22 -1.79% 36
Zimbabwe Zimbabwe 22.4 -2.18% 33 

                    
# 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 = 'SH.DYN.NMRT'

# 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 <- 'SH.DYN.NMRT'

# 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))