Brazilian economic data, from endpoint to answer.

01 / Python

Fetch the latest Brazilian inflation rate

This example uses the 12-month IPCA change. The response preserves the period, unit, and official-source links.

import requests

url = "https://open-economics-data.knbf982hkn.chatgpt.site/api/v1/indicators/br-ipca-12m/latest"
response = requests.get(url, timeout=30)
response.raise_for_status()

payload = response.json()
latest = payload["data"][0]
print(f"IPCA 12 months: {latest['value']}% ({latest['period']})")

pip install requests

02 / JavaScript

Get the current Selic target

Works in modern Node.js and in the browser. CORS is enabled on every public endpoint.

const url = "https://open-economics-data.knbf982hkn.chatgpt.site/api/v1/indicators/br-selic-target/latest";
const response = await fetch(url);
if (!response.ok) throw new Error(`HTTP ${response.status}`);

const { data, meta } = await response.json();
console.log({
  value: data[0].value,
  period: data[0].period,
  unit: meta.indicator.unit_symbol,
  source: meta.provenance.source_url,
});

03 / CSV

Download IPCA for Excel, Sheets, or R

Add format=csv to an observations request. Every row repeats source IDs and URLs, preserving provenance outside JSON.

https://open-economics-data.knbf982hkn.chatgpt.site/api/v1/indicators/br-ipca-monthly/observations?start=2024-01-01&format=csv

04 / pandas

Compare Selic and inflation by month

Selic is daily while 12-month IPCA is monthly. This example selects the final Selic target in each month before joining the series—an explicit choice, not a hidden transformation.

import requests
import pandas as pd

API = "https://open-economics-data.knbf982hkn.chatgpt.site/api/v1"

def series(indicator):
    response = requests.get(
        f"{API}/indicators/{indicator}/observations",
        params={"start": "2020-01-01", "order": "asc", "limit": 5000},
        timeout=30,
    )
    response.raise_for_status()
    return pd.DataFrame(response.json()["data"])

inflation = series("br-ipca-12m").assign(month=lambda x: x["date"].str[:7])
selic = series("br-selic-target").assign(month=lambda x: x["date"].str[:7])
selic_monthly = selic.groupby("month", as_index=False).last()

comparison = inflation[["month", "value"]].merge(
    selic_monthly[["month", "value"]], on="month", suffixes=("_ipca", "_selic")
)
print(comparison.tail(12).to_string(index=False))

pip install requests pandas