Country

Indicator

Value type

Values present

Net financial flows, IMF nonconcessional (NFL, current US$)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Afghanistan	-4,202,255	-16.3%	42
Angola	-178,873,301		63
Argentina	-5,073,959,569	-177%	70
Armenia	-93,668,921	-258%	57
Benin	135,539,385	-29.9%	20
Burkina Faso	93,458,141	-500%	23
Bangladesh	921,914,618	-680%	2
Bosnia & Herzegovina	-116,646,582	+450%	60
Central African Republic	17,318,625	-272%	29
Côte d’Ivoire	668,479,980	-503%	3
Cameroon	165,688,894	-10.3%	17
Congo - Kinshasa	406,351,345	-0.314%	7
Congo - Brazzaville	172,892,759	-0.314%	16
Colombia	0		35
Comoros	2,944,246	-220%	33
Cape Verde	21,024,613	+39.5%	27
Costa Rica	213,521,223	-61.3%	13
Djibouti	0	-100%	35
Dominican Republic	-159,218,756		62
Ecuador	-228,950,835	-114%	64
Egypt	-2,595,708,627	+238%	69
Ethiopia	0		35
Gabon	-117,556,404	-195%	61
Georgia	-30,016,104	+79.4%	48
Ghana	478,139,194	-496%	5
Guinea	-45,821,917	-208%	50
Gambia	1,276,685	-95.9%	34
Guinea-Bissau	12,613,434	-568%	31
Grenada	-3,746,010	+7.33%	41
Honduras	45,419,034		24
Haiti	96,866,565	-873%	21
Jamaica	18,675,353	-115%	28
Jordan	-101,431,753	-120%	59
Kenya	380,342,725	-38.9%	8
Kyrgyzstan	-70,936,725	+228%	55
St. Lucia	0		35
Sri Lanka	505,497,872	-462%	4
Lesotho	-6,148,632	-5.23%	43
Morocco	0		35
Moldova	161,326,554	+14.2%	18
Madagascar	15,599,036	-79.9%	30
Maldives	0		35
North Macedonia	-46,791,771	-142%	51
Mali	-35,832,558	+76.2%	49
Myanmar (Burma)	-57,454,825		52
Montenegro	-20,177,492		47
Mongolia	-59,075,695	+146%	53
Mauritania	34,856,034	-493%	25
Malawi	5,344,201	-91.6%	32
Nigeria	-818,605,858		68
Nicaragua	0		35
Nepal	95,077,677	-5.19%	22
Pakistan	-82,767,072	-107%	56
Papua New Guinea	175,587,538		15
Rwanda	194,280,233	-704%	14
Sudan	0		35
Senegal	424,485,074	+63.4%	6
Solomon Islands	-237,461	+19.1%	36
El Salvador	-95,784,723		58
Somalia	-275,387,749	-1,080%	65
Serbia	218,383,832	-79.2%	12
São Tomé & Príncipe	-993,867	-122%	39
Suriname	157,684,600	+199%	19
Eswatini	-13,089,689		45
Togo	-16,789,008	+398%	46
Tajikistan	0	-100%	35
Tonga	0	-100%	35
Tunisia	-420,474,923	+247%	66
Tanzania	302,482,285	+95.9%	11
Uganda	361,193,785	+199%	10
Ukraine	1,914,253,685	+197%	1
Uzbekistan	-61,216,844		54
St. Vincent & Grenadines	-553,630	-0.314%	37
Vanuatu	-2,267,883	-0.314%	40
Samoa	-773,749	-50.2%	38
Kosovo	27,549,447	-308%	26
Yemen	-13,006,978	-11.3%	44
South Africa	-508,806,314		67
Zambia	373,213,567	+99.4%	9

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.IMFN.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.IMFN.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, IMF nonconcessional (NFL, current US$)

Year: 2023

Acquire full dataset with Python or R from source