Country

Indicator

Value type

Values present

Merchandise imports (current US$)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Aruba	1,602,000,000	+15.1%	168
Afghanistan	8,638,000,000	+7.01%	120
Angola	14,722,000,000	-6.21%	98
Albania	9,612,000,000	+10.6%	113
Andorra	2,033,000,000	+1.9%	161
United Arab Emirates	538,753,000,000	+14.5%	14
Argentina	60,822,000,000	-17.5%	53
Armenia	17,067,000,000	+33.8%	90
American Samoa	714,000,000	+12.3%	184
Antigua & Barbuda	816,000,000	-3.32%	183
Australia	296,369,000,000	+3.12%	24
Austria	210,865,000,000	-6.8%	30
Azerbaijan	21,053,000,000	+21.8%	82
Burundi	994,000,000	-17%	178
Belgium	512,661,000,000	-7.81%	15
Benin	5,230,000,000	-0.419%	134
Burkina Faso	6,732,000,000	+14.2%	129
Bangladesh	67,880,000,000	+1.52%	51
Bulgaria	53,817,000,000	+0.341%	57
Bahrain	15,610,000,000	+1.58%	96
Bahamas	4,271,000,000	+1.98%	142
Bosnia & Herzegovina	15,847,000,000	+3.2%	95
Belarus	45,700,000,000	+6.05%	61
Belize	1,403,000,000	+4.62%	172
Bermuda	1,271,000,000	+7.71%	174
Bolivia	9,624,000,000	-16.3%	112
Brazil	277,954,000,000	+9.99%	26
Barbados	2,154,000,000	+1.41%	159
Brunei	7,887,000,000	+5.38%	124
Bhutan	1,471,000,000	+12%	170
Botswana	7,055,000,000	+8.44%	127
Central African Republic	364,000,000	-28.1%	194
Canada	573,294,000,000	+0.443%	13
Switzerland	369,424,000,000	+1.04%	20
Chile	84,154,000,000	-1.35%	46
China	2,587,234,000,000	+1.18%	2
Côte d’Ivoire	19,397,000,000	+2.95%	84
Cameroon	8,500,000,000	+4.73%	123
Congo - Kinshasa	26,500,000,000	+3.8%	75
Congo - Brazzaville	3,100,000,000	-3.37%	152
Colombia	64,105,000,000	+2.08%	52
Comoros	377,000,000	+5.31%	193
Cape Verde	1,802,000,000	-1.69%	165
Costa Rica	26,484,000,000	+10.2%	76
Cuba	8,784,000,000	-2.11%	118
Curaçao	2,107,000,000	+15.1%	160
Cayman Islands	1,595,000,000	-12.9%	169
Cyprus	13,169,000,000	-6.83%	103
Czechia	232,446,000,000	+0.232%	28
Germany	1,424,545,000,000	-2.93%	3
Djibouti	4,385,000,000	-17.2%	141
Dominica	233,000,000	-22.8%	198
Denmark	121,875,000,000	+0.936%	37
Dominican Republic	29,764,000,000	+3.26%	70
Algeria	46,100,000,000	+7.3%	59
Ecuador	29,428,000,000	-4.77%	71
Egypt	86,333,000,000	+3.77%	43
Eritrea	571,000,000	+14.7%	185
Spain	471,912,000,000	+0.433%	16
Estonia	22,350,000,000	-2.21%	80
Ethiopia	21,489,000,000	+25.9%	81
Finland	80,379,000,000	-3.28%	47
Fiji	3,154,000,000	+1.74%	150
France	750,230,000,000	-4.81%	6
Micronesia (Federated States of)	292,000,000	-2.99%	196
Gabon	4,000,000,000	-5.15%	146
United Kingdom	815,973,000,000	+3.09%	4
Georgia	16,875,000,000	+8.15%	91
Ghana	16,199,000,000	+15.6%	92
Guinea	5,917,000,000	+22.1%	133
Gambia	1,264,000,000	+3.02%	175
Guinea-Bissau	487,000,000	+2.74%	189
Equatorial Guinea	2,700,000,000	+11%	154
Greece	91,428,000,000	+1.77%	42
Grenada	448,000,000	-26.4%	192
Greenland	932,000,000	+2.98%	180
Guatemala	32,488,000,000	+7.16%	69
Guam	887,000,000	-32.2%	181
Guyana	6,731,000,000	+1.43%	130
Hong Kong SAR China	704,141,000,000	+7.72%	8
Honduras	17,671,000,000	+1.84%	86
Croatia	45,721,000,000	+5.22%	60
Haiti	3,114,000,000	-7.82%	151
Hungary	148,974,000,000	-4.76%	32
Indonesia	233,659,000,000	+5.31%	27
India	701,596,000,000	+4.11%	9
Ireland	142,457,000,000	-5.72%	33
Iran	68,554,000,000	+3.78%	50
Iraq	85,923,000,000	+30.5%	44
Iceland	9,811,000,000	+3.71%	111
Israel	91,538,000,000	-0.369%	41
Italy	615,194,000,000	-3.86%	12
Jamaica	7,321,000,000	-3.57%	125
Jordan	26,792,000,000	+4.02%	73
Japan	742,608,000,000	-5.5%	7
Kazakhstan	59,133,000,000	-2.51%	54
Kenya	20,118,000,000	+8.25%	83
Kyrgyzstan	12,214,000,000	-2.43%	105
Cambodia	28,699,000,000	+17.7%	72
Kiribati	335,000,000	+8.77%	195
St. Kitts & Nevis	292,000,000	-12.3%	196
South Korea	631,767,000,000	-1.68%	11
Kuwait	38,242,000,000	+2.01%	66
Laos	8,624,000,000	+12.7%	122
Lebanon	17,309,000,000	-4.5%	88
Liberia	1,728,000,000	-9.67%	166
Libya	17,566,000,000	-3.46%	87
St. Lucia	1,028,000,000	+14.2%	177
Sri Lanka	18,842,000,000	+12.1%	85
Lesotho	1,935,000,000	+12.2%	164
Lithuania	44,748,000,000	-7.72%	62
Luxembourg	25,081,000,000	-4.62%	78
Latvia	23,706,000,000	-13.4%	79
Macao SAR China	16,011,000,000	-8.73%	93
Morocco	75,116,000,000	+9.45%	48
Moldova	9,065,000,000	+4.5%	116
Madagascar	4,833,000,000	-0.617%	135
Maldives	3,637,000,000	+4%	147
Mexico	643,974,000,000	+3.62%	10
Marshall Islands	98,000,000	-5.77%	201
North Macedonia	11,966,000,000	-0.747%	106
Mali	7,072,000,000	+1.22%	126
Malta	8,713,000,000	+3.7%	119
Myanmar (Burma)	13,570,000,000	-17.5%	101
Montenegro	4,141,000,000	+0.461%	144
Mongolia	11,613,000,000	+25.5%	108
Northern Mariana Islands	546,000,000	-2.15%	186
Mozambique	9,210,000,000	-8.74%	115
Mauritania	4,798,000,000	-0.415%	138
Mauritius	6,814,000,000	+8.62%	128
Malawi	3,264,000,000	+3.92%	149
Malaysia	300,355,000,000	+13.1%	23
Namibia	8,630,000,000	+19.2%	121
New Caledonia	2,303,000,000	-27.9%	156
Niger	2,742,000,000	-15.9%	153
Nigeria	40,907,000,000	-17.2%	65
Nicaragua	11,468,000,000	+5.06%	109
Netherlands	811,633,000,000	-3.67%	5
Norway	97,392,000,000	+2.01%	40
Nepal	13,273,000,000	+10.5%	102
Nauru	63,000,000	-10%	202
New Zealand	46,892,000,000	-6.14%	58
Oman	43,464,000,000	+12.1%	63
Pakistan	56,468,000,000	+12.6%	55
Panama	26,611,000,000	-22.4%	74
Peru	55,165,000,000	+5.29%	56
Philippines	134,301,000,000	+0.37%	35
Palau	138,000,000	-29.6%	200
Papua New Guinea	4,109,000,000	-24%	145
Poland	379,314,000,000	+2.5%	18
North Korea	2,374,000,000	-2.7%	155
Portugal	115,951,000,000	+2%	38
Paraguay	17,210,000,000	+6.86%	89
French Polynesia	2,174,000,000	-5.35%	158
Qatar	35,802,000,000	+13.9%	67
Romania	136,420,000,000	+3.4%	34
Russia	294,536,000,000	-2.75%	25
Rwanda	4,800,000,000	+24.4%	137
Saudi Arabia	232,028,000,000	+12.1%	29
Sudan	4,820,000,000	-36%	136
Senegal	11,807,000,000	-0.606%	107
Singapore	458,686,000,000	+8.32%	17
Solomon Islands	868,000,000	+22.8%	182
Sierra Leone	2,011,000,000	+4.63%	162
El Salvador	15,973,000,000	+2.08%	94
Somalia	4,200,000,000	-3.51%	143
Serbia	42,245,000,000	+6.04%	64
South Sudan	1,184,000,000	+1.98%	176
São Tomé & Príncipe	165,000,000	-10.3%	199
Suriname	1,648,000,000	-2.89%	167
Slovakia	113,511,000,000	-0.0115%	39
Slovenia	84,254,000,000	+17.6%	45
Sweden	187,958,000,000	-2.68%	31
Eswatini	1,950,000,000	-4.36%	163
Sint Maarten	1,275,000,000	+10.1%	173
Seychelles	1,450,000,000	-0.0689%	171
Syria	6,240,000,000	+6.3%	132
Turks & Caicos Islands	451,000,000	+1.81%	191
Chad	2,200,000,000	+5.36%	157
Togo	3,472,000,000	+6.54%	148
Thailand	306,810,000,000	+6.34%	22
Tajikistan	6,684,000,000	+13.7%	131
Turkmenistan	4,637,000,000	+15.3%	139
Timor-Leste	958,000,000	+5.97%	179
Tonga	266,000,000	-2.21%	197
Trinidad & Tobago	9,043,000,000	-1.23%	117
Tunisia	26,027,000,000	+2.13%	77
Turkey	344,020,000,000	-4.96%	21
Tanzania	14,302,000,000	+4.17%	99
Uganda	14,200,000,000	+20.5%	100
Ukraine	70,062,000,000	+10.3%	49
Uruguay	12,523,000,000	+0.296%	104
United States	3,359,319,000,000	+6.03%	1
Uzbekistan	35,238,000,000	-0.947%	68
St. Vincent & Grenadines	484,000,000	+6.14%	190
Venezuela	15,090,000,000	+37.9%	97
Vietnam	379,027,000,000	+16.3%	19
Vanuatu	529,000,000	+11.8%	187
Samoa	489,000,000	+4.49%	188
Yemen	4,552,000,000	-10.3%	140
South Africa	123,469,000,000	-5.57%	36
Zambia	11,194,000,000	+10.6%	110
Zimbabwe	9,499,000,000	+3.09%	114

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 = 'TM.VAL.MRCH.CD.WT'

# 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 <- 'TM.VAL.MRCH.CD.WT'

# 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

Merchandise imports (current US$)

Year: 2024

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