Country

Indicator

Value type

Values present

Ores and metals imports (% of merchandise imports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Albania	1.27	-28.6%	60
Argentina	3.31	+13.5%	27
Armenia	5.71	+73.7%	15
Antigua & Barbuda	0.973	-11.2%	70
Australia	1.35	-1.27%	58
Azerbaijan	1.04	-1.02%	65
Belgium	4.59	+10.5%	19
Burkina Faso	0.378	-27%	85
Bulgaria	9.83	+12.9%	4
Bosnia & Herzegovina	5.04	-8.05%	16
Belize	0.554	+1.34%	83
Bolivia	0.882	+19.2%	73
Brazil	2.88	-1.71%	32
Barbados	0.737	+14.2%	78
Canada	3.25	-2.34%	28
Switzerland	2.25	+4.17%	43
Chile	1.89	+16.5%	47
China	15.1	+3.48%	1
Cyprus	0.748	+11.8%	76
Czechia	2.79	-4.36%	34
Germany	3.92	-5.26%	23
Denmark	2.08	+9.53%	45
Dominican Republic	1.11	-12.7%	63
Ecuador	1.12	-10.9%	61
Egypt	7.1	-0.608%	8
Spain	3.56	+7.69%	26
Estonia	1.55	+2.78%	56
Finland	7.2	+7.47%	6
Fiji	0.999	+26.3%	67
United Kingdom	2.7	-6.85%	36
Georgia	1.64	-42.1%	50
Greece	4.15	+14.1%	22
Grenada	0.999	-16.3%	68
Guatemala	1.07	-2.74%	64
Guyana	1.58	+1.86%	53
Hong Kong SAR China	1.6	+7.61%	51
Croatia	2.94	-2.16%	31
Hungary	2.75	+1.58%	35
India	6.86	+13.7%	10
Ireland	0.977	-21.5%	69
Iceland	8.91	+10.8%	5
Israel	1.39	+0.973%	57
Italy	4.81	+1.15%	18
Japan	7.2	+2.24%	7
Kyrgyzstan	0.637	+20%	81
South Korea	6.68	-0.253%	11
Sri Lanka	1.58	+14.5%	54
Lithuania	1.73	+26.5%	49
Luxembourg	5.75	+8.49%	14
Latvia	1.27	+0.668%	59
Macao SAR China	0.287	-8.47%	86
Moldova	0.743	-0.801%	77
Maldives	2.83	-27.2%	33
Mexico	2.6	+5.98%	40
North Macedonia	11.2	-11.7%	2
Malta	0.699	-17.9%	79
Myanmar (Burma)	0.928	+4.46%	72
Montenegro	1.6	-26.8%	52
Mauritius	1.03	+5.05%	66
Malaysia	5.03	-6.97%	17
Namibia	9.92	+66.4%	3
Netherlands	2.62	+0.85%	38
Norway	6.22	-1.67%	12
New Zealand	1.79	+5.92%	48
Pakistan	3.83	+4.8%	25
Panama	0.651	+10.8%	80
Philippines	4.16	-3.86%	21
Poland	3.13	-4.23%	30
Portugal	2.6	-2.16%	39
Paraguay	1.56	+24.8%	55
French Polynesia	0.421	-10.1%	84
Romania	2.44	+2.37%	42
El Salvador	0.933	-20.1%	71
Suriname	0.632	-9.37%	82
Slovakia	2.66	+1.79%	37
Slovenia	3.23	-15.4%	29
Sweden	3.87	+18.9%	24
Togo	0.765	-14.4%	75
Thailand	4.37	+7.92%	20
Trinidad & Tobago	5.9	+22.6%	13
Turkey	7.01	+6.35%	9
Ukraine	1.11	-5.04%	62
United States	2.08	-8.17%	44
Uzbekistan	1.92	-11.6%	46
South Africa	2.49	+10.4%	41
Zimbabwe	0.803	-2.48%	74

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.MMTL.ZS.UN'

# 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.MMTL.ZS.UN'

# 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

Ores and metals imports (% of merchandise imports)

Year: 2024

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