Country

Indicator

Value type

Values present

Net financial flows, IBRD (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2004

Country	Value	Value change, %	Rank
Argentina	-61,027,000	-93.9%	67
Armenia	-586,000	+14.2%	28
Bangladesh	-7,278,000	+15.5%	43
Bosnia & Herzegovina	-24,381,000	+1.18%	57
Belarus	-13,512,000	-5.83%	51
Belize	-1,737,000	-37.1%	33
Bolivia	150,000	-162%	23
Brazil	-116,344,000	-66%	71
Barbados	-6,000	-100%	26
Botswana	-1,679,000	-44.8%	31
Chile	18,803,000	-114%	12
China	306,268,000	-137%	3
Côte d’Ivoire	-38,363,000	-53.3%	61
Cameroon	-29,661,000	+3,252%	59
Congo - Brazzaville	-3,153,000	-50.9%	38
Colombia	199,990,000	-74.2%	4
Costa Rica	-8,734,000	-24.5%	46
Czechia	-18,862,000	-86%	55
Dominica	4,000	-99.1%	24
Dominican Republic	29,312,000	-2,493%	11
Algeria	-149,239,000	-1.75%	73
Ecuador	-52,933,000	-189%	65
Egypt	-51,783,000	+7.93%	64
Estonia	-4,137,000	-316%	40
Fiji	-2,251,000	+6.63%	35
Gabon	-11,684,000	+262%	49
Ghana	-1,607,000	-16.6%	30
Equatorial Guinea	0		25
Greece	0		25
Grenada	1,456,000	-33.5%	18
Guatemala	50,017,000	+84.6%	7
Guyana	-913,000	-60.8%	29
Honduras	-15,056,000	-27%	52
Croatia	35,945,602	-68.1%	9
Hungary	-39,497,000	-85.9%	62
Indonesia	-825,623,000	-13.1%	79
India	748,821,000	-126%	2
Iran	-34,301,000	-30.4%	60
Jamaica	-40,715,000	+29.6%	63
Jordan	-56,730,000	+89.1%	66
Kazakhstan	-27,365,000	+562%	58
Kenya	-4,764,000	-38.2%	42
St. Kitts & Nevis	1,309,000	-59.3%	19
South Korea	-1,806,789,000	-23.3%	82
Lebanon	14,174,000	-37.4%	13
St. Lucia	160,000	-95.6%	22
Sri Lanka	-1,968,000	+9.03%	34
Lesotho	-2,368,000	-87.2%	36
Lithuania	-92,766,000	+364%	70
Latvia	-8,001,000	-92.5%	44
Morocco	-292,593,000	+79.6%	76
Moldova	-10,398,000	+14.6%	48
Mexico	-1,150,418,000	+1,342%	80
North Macedonia	32,185,000	+77.1%	10
Malta	0		25
Mauritius	-9,034,000	-28.8%	47
Malawi	-485,000	-72.2%	27
Malaysia	-69,787,000	+555%	68
Nigeria	-216,424,000	+6.79%	74
Oman	0		25
Pakistan	-303,118,000	+25.8%	77
Peru	45,445,000	-74.8%	8
Philippines	-226,433,000	+65.4%	75
Papua New Guinea	-12,957,000	-13.2%	50
Poland	-676,540,000	+223%	78
Paraguay	-16,663,000	-173%	54
El Salvador	-24,280,000	-2,426%	56
Serbia	0		25
Slovakia	74,425,000	+31.9%	5
Slovenia	-8,396,872	-1.94%	45
Eswatini	8,815,000	+52.2%	14
Seychelles	0	-100%	25
Syria	-4,450,000	-41%	41
Chad	5,919,000	-72.6%	16
Thailand	-1,614,990,000	+545%	81
Turkmenistan	1,304,000	-183%	20
Trinidad & Tobago	-15,240,000	+705%	53
Tunisia	-88,681,000	-294%	69
Turkey	919,467,000	-518%	1
Tanzania	-2,865,000	+8.52%	37
Ukraine	-132,855,000	+280%	72
Uruguay	63,575,000	+230%	6
Uzbekistan	4,510,000	+62.5%	17
St. Vincent & Grenadines	360,000	+253%	21
South Africa	6,961,000	+122%	15
Zambia	-3,283,000	-54.4%	39
Zimbabwe	-1,694,000	+2,129%	32

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 = 'DT.NFL.MIBR.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 <- 'DT.NFL.MIBR.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

Net financial flows, IBRD (NFL, current US$)

Year: 2004

Acquire full dataset with Python or R from source