bcbpy

Python client for the BCB SGS (Sistema Gerenciador de Series Temporais) API from the Banco Central do Brasil.

Fetch Brazilian economic and financial time series as pandas DataFrames with a simple, Pythonic interface. Includes 114 curated series codes covering exchange rates, interest rates, inflation, GDP, employment, and more.

Installation

pip install bcbpy

Or from source:

git clone https://github.com/rteoo/bcbpy.git
cd bcbpy
pip install .

Requirements

• Python 3.10+
• pandas
• requests

Quick Start

from bcbpy import fetch_series, fetch_last, fetch_multiple, INTEREST_RATES, EXCHANGE_RATES

# Last 10 CDI daily rates
cdi = fetch_last(INTEREST_RATES["CDI_DAILY"], n=10)
print(cdi)

# USD/BRL exchange rate for 2024
usd = fetch_series(EXCHANGE_RATES["USD_SALE_DAILY"], start_date="2024-01-01", end_date="2024-12-31")
print(usd)

# Multiple series merged into one DataFrame
df = fetch_multiple(
    {"CDI": INTEREST_RATES["CDI_DAILY"], "SELIC": INTEREST_RATES["SELIC_DAILY"]},
    start_date="2024-01-01",
    end_date="2024-12-31",
)
print(df.tail())

API Reference

Functions

fetch_series(code, start_date=None, end_date=None)

Fetch a time series by its SGS numeric code. Returns a pandas DataFrame indexed by date.

from bcbpy import fetch_series

# Accepts YYYY-MM-DD or DD/MM/YYYY date formats
ipca = fetch_series(433, start_date="2023-01-01", end_date="2024-12-31")

Daily series (CDI, Selic, USD/BRL, …) require a start_date: BCB rejects undated daily queries with HTTP 406, which surfaces as SGSHTTPError carrying BCB's explanation.

fetch_last(code, n=10)

Fetch the last N observations of a series.

from bcbpy import fetch_last

selic = fetch_last(11, n=5)

fetch_multiple(codes_dict, start_date=None, end_date=None)

Fetch multiple series and merge them into a single DataFrame, one column per series.

from bcbpy import fetch_multiple

df = fetch_multiple({"CDI": 12, "SELIC": 11, "TR": 226}, start_date="2024-01-01")

fetch_raw(code, start_date=None, end_date=None, transport=None)

Fetch one SGS window as a RawResult (payload bytes plus request metadata). Same 10-year single-call limit as fetch_series. Does not parse the body.

from bcbpy import fetch_raw

raw = fetch_raw(12, start_date="2024-01-01", end_date="2024-01-31")
print(raw.sha256, raw.source_url, raw.params)

fetch_raw_range(code, start_date=None, end_date=None, transport=None)

Like fetch_raw, but splits ranges longer than 10 years into bounded partitions. Adjacent partitions do not share a calendar day.

from bcbpy import fetch_raw_range

parts = fetch_raw_range(433, start_date="2010-01-01", end_date="2024-12-31")

list_codes(category=None)

Print all available series codes. Pass a category name to filter.

from bcbpy import list_codes

list_codes()                        # all 114 codes across 14 categories
list_codes("INTEREST_RATES")        # only interest rate codes

search_codes(keyword)

Search codes by keyword (case-insensitive). Returns a dict of matches.

from bcbpy import search_codes

results = search_codes("IPCA")      # finds 15 IPCA-related codes
results = search_codes("USD")       # finds USD exchange rate codes

Exceptions

Exception	When
SGSError	Base class for every error raised by bcbpy, including malformed or non-JSON responses (e.g. an unknown series code)
SGSHTTPError	Any other HTTP error status, with BCB's error text in the message. Also a requests.HTTPError.
SGSRateLimitError	API returns HTTP 429 (too many requests). retry_after is set from Retry-After when present.
SGSEmptyResponseError	No data returned for the given query

Network failures (timeouts, connection errors) are raised by requests unchanged.

Error Handling

from bcbpy import fetch_series, SGSRateLimitError, SGSEmptyResponseError

try:
    df = fetch_series(433, start_date="2024-01-01")
except SGSRateLimitError:
    print("Rate limited — wait and retry")
except SGSEmptyResponseError:
    print("No data for this date range")

Available Series Codes

114 curated codes organized in 14 categories:

Category	Series	Examples
EXCHANGE_RATES	6	USD/BRL daily sale/purchase, monthly averages
INTEREST_RATES	10	Selic, CDI, TR, TBF, TJLP
INFLATION	17	IPCA, INPC, IGP-M, IGP-DI, IPC-Fipe
IPCA_BREAKDOWN	11	Tradeable, non-tradeable, durables, services, cores
IPCA_CATEGORIES	9	Food, housing, transport, health, education
GDP	13	GDP current/constant/USD, per capita, quarterly components
EMPLOYMENT	7	Unemployment rate, labor force, income
INDUSTRIAL_PRODUCTION	6	Manufacturing, mining, capital/intermediate/consumer goods
FINANCIAL_MARKETS	7	Gold, Bovespa, IMA-B
SAVINGS	2	Savings rate and return
CONFIDENCE	4	Consumer (ICC) and business (ICEI) confidence
ECONOMIC_ACTIVITY	1	IBC-Br (GDP proxy, seasonally adjusted)
BASIC_BASKET	16	Cost of living by capital city
EXCHANGE_RATE_INDEX	5	Effective currency basket and bilateral real indices (USD, JPY, DEM, ARS)

Use any code directly by number or via the category dictionaries:

from bcbpy import INFLATION, GDP

# These are equivalent:
fetch_series(433)
fetch_series(INFLATION["IPCA"])

Migrating to 3.0

Version 3.0 renames seven registry keys to match the SGS series names. Update dictionary lookups and any saved key names using this table:

Category	Old key (2.x)	New key (3.0)	SGS code
EMPLOYMENT	AVG_NOMINAL_INCOME	AVG_REAL_HABITUAL_INCOME	24382
INTEREST_RATES	SELIC_OVERNIGHT_ANNUAL	SELIC_MONTHLY_ANNUALIZED	4189
INTEREST_RATES	CDI_OVERNIGHT	CDI_MONTHLY_ANNUALIZED	4392
EXCHANGE_RATE_INDEX	REER_USD	RER_USD	11753
EXCHANGE_RATE_INDEX	REER_JPY	RER_JPY	11754
EXCHANGE_RATE_INDEX	REER_EUR	RER_DEM	11755
EXCHANGE_RATE_INDEX	REER_ARS	RER_ARS	11756

The old keys are removed from the category dictionaries and ALL_CODES; lookups raise KeyError. list_codes and search_codes expose the new names. Numeric SGS codes, the 114-series count, and fetch behavior are unchanged.

Code 24382 measures real habitual income of employed people. Codes 4189 and 4392 measure Selic and CDI accumulated over the month, annualized on a 252-day basis. RER_DEM is the Deutsche mark index. The four RER_* indices are bilateral; REER_BASKET (11752) remains the effective currency-basket index. All five exchange-rate indices are IPCA-based, with June 1994 = 100.

from bcbpy import fetch_last, EMPLOYMENT, INTEREST_RATES, EXCHANGE_RATE_INDEX

income = fetch_last(EMPLOYMENT["AVG_REAL_HABITUAL_INCOME"])
selic = fetch_last(INTEREST_RATES["SELIC_MONTHLY_ANNUALIZED"])
dem = fetch_last(EXCHANGE_RATE_INDEX["RER_DEM"])

Discontinued series

These registered series have stopped updating in SGS (last observation as of September 2026). Historical data is still available; recent windows return SGSEmptyResponseError.

Series	Last observation
FINANCIAL_MARKETS: GOLD_BMF_GRAM, GOLD_LONDON_OZ, BOVESPA_INDEX, BOVESPA_VOLUME	Sep 2019
EMPLOYMENT["FORMAL_EMPLOYMENT_TOTAL"]	Dec 2019
INFLATION["ICV_DIEESE"]	Feb 2020
FINANCIAL_MARKETS: IMA_B, IMA_B5, IMA_B5_PLUS	May 2023
BASIC_BASKET (all cities)	Jun 2025
INFLATION: IGP_M_1ST_DECENNIAL, IGP_M_2ND_DECENNIAL, IPC_FIPE_1ST_QUAD, IPC_FIPE_2ND_QUAD, IPC_FIPE_3RD_QUAD	Jul 2025

API Limits

• Date range: max 10 years per single query (BCB restriction since March 2025). fetch_series / fetch_raw still enforce that limit. fetch_raw_range splits longer windows into bounded requests.
• Rate limiting: HTTP 429 on excessive requests (no official limit documented). SGSRateLimitError.retry_after carries Retry-After when the API sends it; the client does not auto-retry.
• Daily series: a start_date is mandatory; undated queries return HTTP 406.
• Unknown series codes: SGS answers with an HTML page (HTTP 200) after about 30 seconds instead of a 404. With the client's 30-second timeout this usually surfaces as requests.ReadTimeout; when the page arrives in time it raises SGSError.
• Date formats: the client accepts both YYYY-MM-DD and DD/MM/YYYY

Project Structure

bcbpy/
├── bcbpy/
│   ├── __init__.py      # Public API exports
│   ├── artifacts.py     # RawResult descriptor
│   ├── client.py        # API client functions and exceptions
│   ├── codes.py         # 114 curated series codes in 14 categories
│   └── constants.py     # Base URLs and API configuration
├── pyproject.toml       # PyPI packaging metadata
├── BCB_API_REFERENCE.md # SGS API reference and series code table
└── README.md

Data Source

All data is fetched from the BCB Open Data Portal under the Open Database License (ODbL).

License

MIT (see LICENSE). The BCB data accessed through this client remains under ODbL; users must comply with ODbL when redistributing data.

Releasing

The version in bcbpy/__init__.py must already be merged to main. From a clean checkout matching origin/main, run python release.py --tag vX.Y.Z --dry-run, then rerun without --dry-run and type the tag to confirm.

The release helper runs these gates locally: python -m pytest -m "not integration" -v and python -m build. It never bumps or commits main; PyPI publication remains the OIDC GitHub Actions workflow. A retry is safe only for the same tag when the existing tag points at the exact merged commit and no GitHub release exists. PyPI releases are immutable; rollback means following the package-recovery process rather than deleting or replacing a published version. The helper is platform-neutral Python, but hosted Actions behavior is not proven by local execution.