Get Started¶
Installation¶
pixbr requires Python 3.9+ and depends only on
httpx and
pandas.
Two ways to use it¶
1. A reusable client (recommended)¶
PixClient keeps an HTTP connection pool open, so it is the right choice when
you make several requests.
from pixbr import PixClient
client = PixClient()
# PIX keys stock by participant (date in YYYY-MM-DD)
keys = client.keys("2025-12-01", filter="TipoChave eq 'CPF'", top=100)
# Transaction statistics (database in YYYYMM)
stats = client.transaction_stats("202509", filter="NATUREZA eq 'P2P'")
# Transactions by municipality
muni = client.transactions_by_municipality("202512", filter="Sigla_Regiao eq 'NE'")
# Fraud statistics (MED)
fraud = client.fraud_stats("202509", top=100)
PixClient is also a context manager:
2. Convenience functions¶
These mirror the pixr names and create a short-lived client per call.
from pixbr import get_pix_transaction_stats, get_pix_summary, format_brl
df = get_pix_transaction_stats("202509")
summary = get_pix_summary("202509", group_by="PAG_REGIAO")
format_brl(1234567.89) # 'R$ 1.234.567,89'
Endpoints at a glance¶
| Endpoint | Parameter | PixClient method |
Convenience function |
|---|---|---|---|
ChavesPix |
Data (YYYY-MM-DD) |
.keys() |
get_pix_keys() |
TransacoesPixPorMunicipio |
DataBase (YYYYMM) |
.transactions_by_municipality() |
get_pix_transactions_by_municipality() |
EstatisticasTransacoesPix |
Database (YYYYMM) |
.transaction_stats() |
get_pix_transaction_stats() |
EstatisticasFraudesPix |
Database (YYYYMM) |
.fraud_stats() |
get_pix_fraud_stats() |
Inspect endpoints and columns programmatically:
from pixbr import pix_endpoints, pix_columns
pix_endpoints() # DataFrame describing all endpoints
pix_columns("stats") # columns for the transaction-stats endpoint
Configuration¶
PixClient(
timeout=120, # seconds; the BCB API can be slow for large queries
max_retries=3, # retries on transport errors
verbose=True, # log progress at INFO level
)
skip is not supported
The BCB PIX API does not support $skip pagination. Passing skip emits a
warning and is ignored — use top to limit results.
Next steps¶
- Understanding PIX Data — what each dataset contains.
- Working with OData Queries — filtering, ordering, selecting.
- Examples — end-to-end analyses.