Country

Indicator

Value type

Values present

Physicians (per 1,000 people)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
United Arab Emirates	2.91	+1.15%	42
Australia	3.98	+1.92%	19
Austria	5.43	+1.99%	4
Burundi	0.065	-7.14%	96
Belgium	3.24	+1.03%	38
Benin	0.212	+242%	85
Burkina Faso	0.101	+5.21%	92
Bangladesh	0.67	+2.13%	75
Bulgaria	4.29	+0.965%	14
Bolivia	1.27	+33.3%	67
Brazil	2.14	-6.99%	58
Barbados	2.88	+9.71%	43
Brunei	1.91	+7.59%	62
Bhutan	0.56	+15.7%	76
Botswana	0.392	-2.49%	80
Central African Republic	0.021	-68.2%	100
Canada	2.46	+1.27%	52
Switzerland	4.44	+1.23%	11
Chile	2.97	+5.88%	40
China	2.52	+5.49%	50
Côte d’Ivoire	0.18	+9.09%	86
Cameroon	0.124	+27.8%	90
Congo - Kinshasa	0.177	+5.99%	87
Colombia	2.45	+3.03%	53
Costa Rica	2.52	+2.73%	50
Cuba	9.43	+2.62%	1
Cyprus	3.55	+5.59%	26
Czechia	4.25	+2.26%	16
Germany	4.52	+1.32%	7
Dominican Republic	2.03	+4.16%	60
Eritrea	0.079	-3.66%	95
Spain	4.48	-2.12%	10
Estonia	3.44	-1.21%	29
Ethiopia	0.101	-2.88%	92
Finland	4.38	+21.3%	13
France	3.34	+0.602%	33
United Kingdom	3.17	+4.27%	39
Georgia	5.33	+3.27%	5
Ghana	0.127	-22.6%	89
Gambia	0.102	+34.2%	91
Guinea-Bissau	0.22	+15.2%	84
Greece	6.37	+1.13%	2
Croatia	3.61	-48.1%	24
Hungary	3.3	+5.07%	35
Indonesia	0.695	+10.8%	74
Ireland	4.06	+16.6%	18
Iraq	0.972	+6.46%	70
Iceland	4.41	+2.04%	12
Israel	3.65	+1.95%	23
Italy	4.1	+2.5%	17
Kenya	0.098	-39.1%	93
Kyrgyzstan	2.15	-1.06%	56
South Korea	2.52	+0.359%	51
Laos	0.327	-8.4%	81
Liberia	0.171	+11.8%	88
Sri Lanka	1.19	-1.65%	68
Lithuania	4.5	+1.24%	9
Latvia	3.38	+0.957%	32
Moldova	3.25	-20%	36
Mexico	2.56	+4.83%	49
Malta	4.28	+2.13%	15
Montenegro	2.77	+1.17%	47
Mongolia	3.87	+2.65%	21
Mozambique	0.081	-2.41%	94
Mauritania	0.224	+7.69%	83
Malaysia	2.32	+3.95%	54
Namibia	0.467	+12.3%	78
Nigeria	0.395	+7.63%	79
Netherlands	3.91	+1.48%	20
Norway	5.17	+1.61%	6
Nepal	0.867	+2.48%	71
New Zealand	3.52	+1.97%	27
Oman	2	+0.15%	61
Panama	1.73	+6.45%	63
Peru	1.65	+21%	64
Philippines	0.786	+4.52%	73
Papua New Guinea	0.063	+3.28%	97
Poland	3.39	+3.35%	31
Portugal	5.77	+2.71%	3
Paraguay	3.24	+186%	37
Romania	3.47	+5.25%	28
Saudi Arabia	2.79	+5.32%	46
Senegal	0.101	+20.2%	92
Singapore	2.6	+3.51%	48
El Salvador	2.91	-0.783%	41
Serbia	2.84	+5.11%	44
Suriname	1.48	+85.9%	65
Slovakia	3.68	+0.272%	22
Slovenia	3.33	+1.43%	34
Chad	0.058	-3.33%	99
Togo	0.059	-27.2%	98
Tajikistan	2.13	+0.661%	59
Turkmenistan	2.14	-1.61%	57
Tonga	1.01	+6.21%	69
Trinidad & Tobago	3.41	-18.6%	30
Tunisia	1.29	-0.232%	66
Turkey	2.17	+6.34%	55
Uruguay	4.51	-9.13%	8
United States	3.61	+1.49%	25
Uzbekistan	2.81	+0.754%	45
Samoa	0.548	-1.08%	77
South Africa	0.809	+1.25%	72
Zambia	0.297	+16.5%	82

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 = 'SH.MED.PHYS.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 <- 'SH.MED.PHYS.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))

Compare

Physicians (per 1,000 people)

Year: 2021

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