Getting GDP data

We’ll show two options: World Bank and FRED

World Bank GDP per capita via WB API

API-based, no key needed, works in both R and Python.

• Same data source (World Bank).
• Official packages in Python, R
• No messy HTML scraping.
• Clear structure: select indicator → countries → year → get table.

📈 GDP per capita (constant 2015 US$), code: NY.GDP.PCAP.KD

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Python (using wbdata or pandas-datareader):

import wbdata
import pandas as pd
from datetime import datetime

# Set countries and indicator
countries = ['USA', 'HUN', 'DEU']
indicator = {'NY.GDP.PCAP.KD': 'GDP_per_capita'}

# Get data
df = wbdata.get_dataframe(indicator, country=countries, data_date=datetime(2021, 1, 1))
print(df.head())

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R (using wbstats):

install.packages("wbstats")
library(wbstats)

# Set indicator and countries
gdp_data <- wb(indicator = "NY.GDP.PCAP.KD", country = c("US", "HU", "DE"),
               startdate = 2021, enddate = 2021)

head(gdp_data)

FRED

FRED (Federal Reserve Economic Data) provides economic time series (e.g. GDP, inflation, interest rates).

• You need an API key, but the process is straightforward
• works in both R and Python with identical logic in both languages: set key → request series → get dataframe.

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🔑 Get API Key

1. Sign up at https://fred.stlouisfed.org/
2. Go to your account settings to get your API key.
3. Copy your API key, you’ll need it with code

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✅ Example: U.S. GDP per capita (A939RC0Q052SBEA)

Details at FRED site

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Python (using fredapi)

from fredapi import Fred

fred = Fred(api_key='your_api_key_here')

# GDP per capita
gdp_pc = fred.get_series('A939RC0Q052SBEA')
print(gdp_pc.tail())

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R (using fredr)

install.packages("fredr")
library(fredr)

fredr_set_key("your_api_key_here")

# GDP per capita
gdp_pc <- fredr(series_id = "A939RC0Q052SBEA")
head(gdp_pc)

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