Country

Indicator

Value type

Values present

Imports of goods and services (BoP, current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	22,682,525,278	-4.24%	73
Albania	11,696,529,916	+12.8%	84
Argentina	79,998,522,783	-13.3%	45
Armenia	19,086,880,609	+31.3%	75
Antigua & Barbuda	1,282,230,314	+0.69%	105
Australia	405,336,357,225	+4.14%	19
Austria	284,467,308,595	-3.14%	27
Azerbaijan	27,338,808,000	+9.28%	71
Belgium	531,029,440,251	-2.82%	17
Bangladesh	74,959,772,435	+2.44%	47
Bulgaria	60,029,170,000	+1.47%	54
Bahrain	33,044,414,894	+2.07%	66
Bahamas	7,068,647,426	+12.7%	93
Bosnia & Herzegovina	16,202,034,953	+5.42%	81
Belarus	50,678,823,115	+6.78%	58
Belize	1,724,316,943	+9.61%	100
Brazil	377,050,080,479	+10.8%	22
Brunei	9,110,495,538	+0.369%	91
Bhutan	1,512,981,737	-14.5%	102
Canada	733,777,721,186	+1.43%	11
Switzerland	582,554,356,568	+4.71%	15
Chile	99,238,957,067	-0.842%	43
China	3,253,992,699,024	+4.22%	2
Colombia	78,632,817,462	+2.86%	46
Cape Verde	1,473,225,763	+3.14%	103
Costa Rica	30,459,218,674	+7.2%	69
Cyprus	33,801,825,003	+3.83%	65
Czechia	216,741,016,051	-1.07%	31
Germany	1,774,301,331,131	-0.383%	3
Djibouti	4,764,705,083	-9.57%	97
Dominica	387,532,461	-7.1%	113
Denmark	252,953,812,953	+3.89%	30
Dominican Republic	36,144,500,000	+4.92%	61
Ecuador	33,969,947,198	-4.1%	64
Spain	568,501,692,612	+2.81%	16
Estonia	32,374,650,458	+1.82%	67
Finland	122,643,844,271	-2.8%	39
France	1,074,434,908,748	-1.74%	5
United Kingdom	1,157,848,698,038	+3.92%	4
Georgia	18,914,647,054	+6.17%	76
Gambia	1,549,228,969	+14.5%	101
Greece	122,408,347,595	+2.66%	40
Grenada	990,587,401	+7.13%	107
Guatemala	35,576,104,500	+7.62%	62
Hong Kong SAR China	723,396,681,954	+7.73%	12
Honduras	18,234,549,521	+1.72%	79
Croatia	49,859,892,497	+6.51%	59
Hungary	154,076,950,204	-5.59%	36
Indonesia	279,418,843,146	+6.37%	28
India	923,080,597,400	+7.4%	7
Iceland	14,298,057,344	+4.9%	82
Israel	140,437,800,000	+0.00441%	37
Italy	717,277,529,572	-3.02%	13
Jamaica	9,524,445,779	-3.46%	89
Japan	965,047,346,579	-3.14%	6
Kazakhstan	74,245,641,889	+2.09%	48
Cambodia	34,329,032,394	+16.7%	63
St. Kitts & Nevis	642,934,302	-3.92%	109
South Korea	758,723,500,000	+0.0414%	10
Kuwait	61,520,891,186	-3.01%	53
St. Lucia	1,445,677,124	+11.9%	104
Lesotho	2,082,725,726	+0.282%	99
Lithuania	58,491,345,843	+1.02%	55
Luxembourg	160,032,087,317	+2.05%	34
Latvia	29,234,084,508	-2.14%	70
Moldova	10,418,370,000	+5.88%	87
Maldives	5,344,487,632	+7.24%	96
Mexico	697,066,723,384	+3.32%	14
North Macedonia	12,643,717,502	-0.819%	83
Malta	24,504,911,237	+8.25%	72
Montenegro	5,477,724,144	+6.01%	95
Mozambique	10,487,702,640	-6.19%	86
Malaysia	279,090,346,965	+10%	29
Namibia	9,199,425,098	+8.96%	90
Nigeria	57,730,449,623	-11.8%	56
Nicaragua	11,437,300,000	+8.73%	85
Netherlands	884,154,370,375	-1.01%	8
Norway	162,467,147,023	+4.07%	32
Nepal	17,777,041,054	+28.1%	80
New Zealand	67,998,492,485	-0.604%	50
Pakistan	66,843,574,086	+15.1%	52
Panama	30,886,790,934	-14%	68
Peru	67,159,919,996	+5.31%	51
Philippines	161,154,243,006	+6.41%	33
Poland	441,945,000,000	+4.28%	18
Portugal	136,976,460,933	+2.51%	38
Paraguay	18,376,906,674	+2.97%	77
Palestinian Territories	8,264,255,681	-29%	92
Qatar	69,692,032,967	-3.44%	49
Romania	159,575,094,722	+4.01%	35
Russia	381,450,480,000	+0.472%	21
Saudi Arabia	317,011,653,030	+9.35%	25
Singapore	786,020,430,564	+7.9%	9
Solomon Islands	857,128,261	-3%	108
El Salvador	18,354,040,667	+7.75%	78
Suriname	2,571,185,114	+16.7%	98
Slovakia	120,289,916,082	+0.46%	41
Slovenia	54,582,845,880	+2.39%	57
Sweden	309,525,682,943	+1.75%	26
Thailand	351,418,674,357	+7.46%	24
Tajikistan	6,906,919,389	+16.4%	94
Timor-Leste	1,197,306,932	+2.4%	106
Tonga	392,887,841	+2.45%	112
Trinidad & Tobago	10,189,963,668	+10.5%	88
Turkey	367,022,000,000	-5.06%	23
Ukraine	92,025,000,000	+3.21%	44
Uruguay	19,117,250,889	-0.738%	74
United States	4,108,442,000,000	+6.53%	1
Uzbekistan	43,624,151,241	+2.29%	60
St. Vincent & Grenadines	641,178,788	+8.88%	110
Vietnam	398,672,000,000	+17.3%	20
Samoa	575,749,112	+2.67%	111
South Africa	119,589,982,001	-3.13%	42

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 = 'BM.GSR.GNFS.CD'

# 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 <- 'BM.GSR.GNFS.CD'

# 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

Imports of goods and services (BoP, current US$)

Year: 2024

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