Uncover Global Economic Patterns: Analyzing World Bank GDP Data with Python

Discover a rich source of economic data on the World Bank website and download historical GDP totals and growth‑rate files for all countries.

https://data.worldbank.org/

Data File Overview

The main files are:

GDP total data (GDP_data.csv) containing historical GDP values for each country.

GDP growth‑rate data (growth_data.csv) recording annual growth percentages.

Country income‑group classification (Country_data.csv) labeling nations as high, upper‑middle, lower‑middle, or low income.

Country code reference table for mapping country names to ISO codes.

Income‑Group Analysis

Distribution of Income Levels

Visualization shows a large disparity between high‑income and low‑income nations.

High‑Income Countries

Top‑10 GDP totals for high‑income nations are extracted by merging the income‑group file with the GDP data.

# 高收入国家2018年的GDP
high = country_data[country_data['Income_Group'] == '高收入国家']
high_gdp = pd.merge(high, gdp, how='inner')
high_gdp['2018'] = high_gdp['2018'].apply(lambda x: x/1000000000000)
high_gdp_top10 = high_gdp[['Country Name','Country Code','2018']].sort_values(by='2018', ascending=False)[:10]

The United States dominates the list, followed by other developed economies.

Middle‑Income Countries

Top‑10 middle‑income nations are largely large developing economies such as India, Brazil, and China (13.6 trillion RMB).

Lower‑Middle and Low‑Income Countries

These groups consist mainly of Asian nations and many small or conflict‑affected states with modest GDP totals.

Overall GDP Rankings

2018 GDP Top‑10

Besides the traditional Western powers, China, India and Brazil also appear among the leaders.

Bottom‑10 GDP Totals

Historical GDP Trends

Trend lines for the top five economies (USA, China, Japan, Germany, UK) show steady growth for the United States and China, while others exhibit larger fluctuations.

World GDP Map

By merging country codes with GDP data, a choropleth map highlights the United States and China as the deepest‑colored regions.

country_code = pd.read_json('countries.json')
country_code.rename(columns={'iso3':'Country Code'}, inplace=True)
conutry_code_name = country_code[['name','Country Code']]
country_gdp_code = pd.merge(country_gdp, conutry_code_name, on='Country Code', how='inner')

Removing the US and China reveals distinct regional clusters (Japan/Western Europe/India, Russia/Canada/Australia/Brazil, and the rest of the developing world).

GDP Growth‑Rate Analysis

Growth‑rate top‑10 are mostly low‑GDP countries, indicating higher relative growth potential, while India shows strong growth despite its large GDP base.

Bottom‑10 growth‑rate countries suffer negative growth, highlighting economic contraction.

China‑US‑India Comparison

The chart shows India’s high growth‑rate but modest total GDP compared with the massive economies of the United States and China.

Growth‑Rate World Map

A global map visualises most countries with 1‑4% growth, while some Central Asian and Southeast Asian nations reach 4‑7%.