Overview

The following data contains records collected on different countries and geographic locations from 1980 - 2013 from the World Bank. Included is different data about urban development, agriculture and rural development, health, and infrastructure.

https://ndb.nal.usda.gov/

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"Country" str "Canada"
[Preview ]
"Data" dict { } Based on the nature of the data, some of the numbers that are reported are very small. For example a field reported as 0.15 is 0.15 %, not 15 %.
"Year" int 1980
Value Count
"Heavily indebted poor countries (HIPC)" 34
"Botswana" 34
"Paraguay" 34
"Greece" 34
"High income: nonOECD" 34
"Gambia, The" 34
"United Kingdom" 34
"Comoros" 34
"Congo, Dem. Rep." 34
"Nicaragua" 34
"Tunisia" 34
"Mexico" 34
"Myanmar" 34
"Honduras" 34
"Japan" 34
"Trinidad and Tobago" 34
"New Caledonia" 34
"Small states" 34
"Mauritania" 34
"Poland" 34
"Jordan" 34
"Zimbabwe" 34
"Malawi" 34
"Papua New Guinea" 34
"Senegal" 34
"Sub-Saharan Africa (all income levels)" 34
"Zambia" 34
"Iceland" 34
"Malaysia" 34
"Israel" 34
"Iran, Islamic Rep." 34
"Middle East & North Africa (developing only)" 34
"Suriname" 34
"Netherlands" 34
"Malta" 34
"Singapore" 34
"Sub-Saharan Africa (developing only)" 34
"Sudan" 34
"Bhutan" 34
"Italy" 34
"Qatar" 34
"Barbados" 34
"Cyprus" 34
"Least developed countries: UN classification" 34
"Burundi" 34
"High income" 34
"Low income" 34
"Antigua and Barbuda" 34
"Djibouti" 34
"Korea, Rep." 34
"Equatorial Guinea" 34
"Uruguay" 34
"Chad" 34
"Iraq" 34
"Yemen, Rep." 34
"Grenada" 34
"Switzerland" 34
"Kiribati" 34
"Guinea-Bissau" 34
"French Polynesia" 34
"Namibia" 34
"Morocco" 34
"Portugal" 34
"Philippines" 34
"Denmark" 34
"East Asia & Pacific (developing only)" 34
"Nepal" 34
"Sierra Leone" 34
"Fragile and conflict affected situations" 34
"Belize" 34
"Haiti" 34
"Thailand" 34
"Puerto Rico" 34
"Chile" 34
"El Salvador" 34
"Algeria" 34
"Australia" 34
"Ecuador" 34
"Lower middle income" 34
"Nigeria" 34
"Pakistan" 34
"Ireland" 34
"Euro area" 34
"Costa Rica" 34
"Guyana" 34
"Panama" 34
"Kuwait" 34
"Virgin Islands (U.S.)" 34
"Brazil" 34
"United States" 34
"Niger" 34
"Hungary" 34
"Uganda" 34
"Mozambique" 34
"High income: OECD" 34
"Austria" 34
"Middle income" 34
"Brunei Darussalam" 34
"Caribbean small states" 34
"Fiji" 34
... ...
Key Type Example Value Comment
"Arable Land Percent" float 4.87974390527 Percent of land area
"Arable Land" float 1.82782057091 Hectacres per person
"Rural Population" int 5918004 Value of rural population
"Agricultural Land Percent" float 7.3572250979 Percent of land area
"Rural Population Growth" float 0.833711883207 Annual Percent
"Surface Area" float 9984670.0 Square kilometers
"Agricultural Land" float 669030.0 Square kilometers
"Land Area" float 9093510.0 Square kilometers
Key Type Example Value Comment
"Urban Population Percent" float 75.623 Percent of total population
"Population Density" float 2.66970619706 People per square kilometer of land area
"Urban Population Percent Growth" float 1.05057823382
Key Type Example Value Comment
"Life Expectancy at Birth, Total" float 74.8663414634 Years
"Total Population" float 24277000.0
"Life Expectancy at Birth, Male" float 71.32 Years
"Population Growth" float 0.997669360129 Annual Percent
"Life Expectancy at Birth, Female" float 78.59 Years
"Birth Rate" float 15.4 Crude, per 1000 People
"Death Rate" float 7.0 Crude, per 100 People
"Fertility Rate" float 1.754 Total, Births per woman
Key Type Example Value Comment
"Infrastructure" dict { }
"Health" dict { }
"Rural Development" dict { }
"Urban Development" dict { }
Key Type Example Value Comment
"Telephone Lines" float 9595000.0
"Mobile Cellular Subscriptions" float 0.0
"Telephone Lines per 100 People" float 39.5623509248
"Mobile Cellular Subscriptions per 100 People" float 0.0

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import global_development
list_of_report = global_development.get_reports()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import global_development
# These may be slow!
list_of_report = global_development.get_reports(test=True)

Documentation

 global_development.get_reports(test=False)

Returns global development reports from the dataset.