Country

Indicator

Value type

Values present

Trademark applications, nonresident, by count

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Albania	7,596	+8.33%	63
Andorra	2,294	+61.8%	102
United Arab Emirates	17,295	+41.7%	28
Argentina	15,713	+11.5%	32
Armenia	7,648	-0.378%	61
Antigua & Barbuda	1,826	+16.1%	105
Australia	76,801	+29%	6
Austria	8,815	-0.0227%	50
Azerbaijan	9,023	+8.09%	49
Bangladesh	4,696	+20.1%	77
Bulgaria	4,076	+15.4%	84
Bahrain	13,703	+44%	39
Bosnia & Herzegovina	9,935	+11.7%	44
Belarus	14,934	+4.08%	34
Bolivia	3,661	+34.3%	90
Brazil	47,273	+27.2%	13
Barbados	793	+5.45%	111
Brunei	4,179	+12.7%	82
Bhutan	2,045	-3.58%	103
Canada	109,335	+29.1%	4
Switzerland	70,091	+25.1%	7
Chile	15,595	+18%	33
China	261,982	+14.3%	2
Colombia	21,964	+18.5%	25
Cape Verde	206	+51.5%	119
Costa Rica	7,091	+15.6%	64
Cuba	3,822	+10.9%	88
Curaçao	2,626	+31.8%	99
Cyprus	2,307	+9.23%	101
Czechia	5,010	+5.41%	72
Germany	26,098	-0.874%	24
Djibouti	315	+0.962%	117
Dominica	182	+5.2%	121
Denmark	4,288	+9.36%	80
Dominican Republic	5,970	+18.8%	68
Algeria	9,197	+18.4%	48
Ecuador	7,701	+21.8%	60
Egypt	17,325	+23.2%	27
Spain	9,520	+11.4%	46
Estonia	3,043	+14.1%	97
Ethiopia	878	+1.86%	110
Finland	3,571	+18.4%	92
France	16,868	+4.49%	30
United Kingdom	227,170	+145%	3
Georgia	7,870	+9.43%	59
Gambia	3,076	+57.6%	96
Grenada	289	+39.6%	118
Hong Kong SAR China	45,546	+13%	14
Honduras	5,075	+38.7%	70
Croatia	3,973	+17.4%	86
Hungary	4,089	+11.2%	83
Indonesia	35,760	-17.3%	18
India	52,878	+25.1%	11
Iran	17,194	+12%	29
Iraq	1,064	-16.5%	108
Iceland	9,266	+14.9%	47
Israel	20,976	+22.7%	26
Italy	11,299	+5.59%	42
Jamaica	4,219	+14.6%	81
Jordan	4,522	+32.9%	79
Japan	94,825	+21.3%	5
Kazakhstan	16,019	+4.49%	31
Kyrgyzstan	7,616	+11.2%	62
South Korea	60,825	+20.1%	9
Liechtenstein	8,801	+5.43%	51
Sri Lanka	3,626	-2.24%	91
Lithuania	3,237	+4.25%	94
Latvia	3,303	+10.2%	93
Macao SAR China	12,239	+9.79%	41
Morocco	14,579	+24.7%	35
Monaco	8,517	+18.8%	52
Moldova	8,438	+3.52%	54
Madagascar	3,686	+24%	89
Mexico	57,324	+29.7%	10
North Macedonia	8,445	-20.9%	53
Malta	327	-9.67%	116
Mongolia	4,985	-2.25%	73
Mozambique	5,037	+47.1%	71
Mauritius	2,483	+26.2%	100
Malaysia	32,586	+21.3%	21
Norway	40,720	+30.8%	16
New Zealand	42,569	+28.9%	15
Oman	9,634	+33.5%	45
Pakistan	8,079	+95.6%	55
Panama	6,953	+24.7%	65
Peru	13,048	+18.9%	40
Philippines	29,927	+16.1%	22
Papua New Guinea	906	+83.4%	109
Poland	6,889	+13.3%	66
Portugal	5,406	+15.2%	69
Paraguay	10,627	+14.8%	43
Qatar	8,042	+37%	56
Romania	4,581	+2.16%	78
Russia	68,261	+20.2%	8
Rwanda	4,039	+68.7%	85
Saudi Arabia	13,719	+38.6%	38
Singapore	47,358	+20.1%	12
El Salvador	7,895	+28.1%	57
Somalia	140		123
Serbia	13,759	+6.63%	37
São Tomé & Príncipe	2,793	+93.6%	98
Suriname	782	+13.8%	112
Slovakia	4,769	+12.3%	75
Slovenia	3,211	-0.124%	95
Sweden	4,781	+19.8%	74
Seychelles	411	-12.2%	115
Thailand	34,688	+14.4%	20
Tonga	482	+39.7%	114
Trinidad & Tobago	3,836	+132%	87
Turkey	39,220	+21.8%	17
Tuvalu	186	+1,140%	120
Uganda	1,765	+17.1%	106
Ukraine	26,987	+11.7%	23
Uruguay	6,240	+19.1%	67
United States	347,735	+8.38%	1
Uzbekistan	7,879	+12.5%	58
St. Vincent & Grenadines	541	+90.5%	113
Vietnam	35,648	+12.1%	19
Vanuatu	146	-19.8%	122
Samoa	1,567	-3.09%	107
Yemen	1,906	+24.6%	104
South Africa	14,077	-0.999%	36
Zambia	4,760	+43.9%	76

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 = 'IP.TMK.NRCT'

# 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 <- 'IP.TMK.NRCT'

# 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))

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Trademark applications, nonresident, by count

Year: 2021

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