Multi-Source Data Aggregator: Combining Two Public APIs Into One Clean Dataset

What We're Building

A dataset combining country information from the REST Countries API (population, capital, currency — free, no signup) with live currency exchange rates from an open exchange-rate API (also free, no signup) — producing one table showing each country's population alongside its own currency's current value against the US dollar. Neither API alone can answer "which populous countries currently have the weakest currency" — only the combination can.

Step 1: Fetching From the First API

import requests
import pandas as pd

response = requests.get(
    "https://restcountries.com/v3.1/region/europe",
    params={"fields": "name,capital,population,currencies"},
)
countries_data = response.json()
print(len(countries_data), "countries returned")

Same requests.get(url, params={...}) pattern Chapter 3 introduced — just a different service, restricted to one region to keep this example a manageable size.

Step 2: Extracting a Currency Code From Nested JSON

records = []
for c in countries_data:
    currencies = c.get("currencies", {})
    currency_code = list(currencies.keys())[0] if currencies else None
    records.append({
        "country": c["name"]["common"],
        "capital": c.get("capital", [None])[0],
        "population": c["population"],
        "currency_code": currency_code,
    })

countries_df = pd.DataFrame(records)
print(countries_df.head())

Each country's currency arrives as a nested dict keyed by its own three-letter code (e.g. {"EUR": {...}}) — currency_code here isn't a field this API hands over directly, it's derived by pulling the first key out of that nested structure. This derived value is exactly what makes the merge in Step 4 possible at all.

Step 3: Fetching From the Second, Unrelated API

rates_response = requests.get("https://open.er-api.com/v6/latest/USD")
rates_data = rates_response.json()["rates"]

rates_df = pd.DataFrame(list(rates_data.items()), columns=["currency_code", "usd_rate"])
print(rates_df.head())

This API has never heard of REST Countries and doesn't organize its data by country at all — it's a flat dict of currency codes to exchange rates. rates_data.items() turns that dict into a list of (code, rate) pairs, which becomes a two-column DataFrame — the same currency-code column countries_df already has, arrived at completely independently.

Step 4: The Merge — ds1-5's Own Material, on a Derived Key

combined = countries_df.merge(rates_df, on="currency_code", how="left")
print(combined.sort_values("population", ascending=False).head(10))

A Quick Combined Insight

top_by_population = combined.sort_values("population", ascending=False).head(10)
print(top_by_population[["country", "population", "currency_code", "usd_rate"]])

A table like this — population from one API, live currency strength from a completely different one — genuinely doesn't exist as a single downloadable file anywhere. It only exists because this chapter built it, on the spot, from two independent sources.

The Complete Aggregator

import requests, pandas as pd

countries_data = requests.get(
    "https://restcountries.com/v3.1/region/europe",
    params={"fields": "name,capital,population,currencies"},
).json()

records = []
for c in countries_data:
    currencies = c.get("currencies", {})
    records.append({
        "country": c["name"]["common"],
        "capital": c.get("capital", [None])[0],
        "population": c["population"],
        "currency_code": list(currencies.keys())[0] if currencies else None,
    })
countries_df = pd.DataFrame(records)

rates_data = requests.get("https://open.er-api.com/v6/latest/USD").json()["rates"]
rates_df = pd.DataFrame(list(rates_data.items()), columns=["currency_code", "usd_rate"])

combined = countries_df.merge(rates_df, on="currency_code", how="left")
combined.to_csv("country_currency_combined.csv", index=False)
print(f"Combined {len(combined)} countries across two independent APIs.")

What's Next

Chapter 7: A First Real Classifier — this course's own single, deliberately light touch of ml1, a small, approachable classification project on a well-known dataset.