pixbr

Python client for the Brazilian Central Bank (BCB) PIX Open Data API (Olinda / OData service). It hides the BCB's unusual OData URL syntax and returns pandas DataFrames.

📖 Documentation: https://strategicprojects.github.io/pixbr/

This is the Python counterpart of the R package pixr.

Installation

pip install pixbr        # once published
# or, from source:
pip install -e ".[dev]"

Quick start

Reusable client (recommended for multiple 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)

Module-level convenience functions mirror the pixr names:

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'

Worked example

A small end-to-end analysis: which Brazilian regions paid the most via PIX in September 2025, and what was the average ticket per person-to-person transfer?

from pixbr import PixClient, format_brl

client = PixClient()

# 1. Pull person-to-person transaction statistics for the month.
stats = client.transaction_stats(
    "202509",
    filter="NATUREZA eq 'P2P'",
    columns=["PAG_REGIAO", "VALOR", "QUANTIDADE"],
)

# 2. Aggregate by payer region (the data comes pre-broken-down, so we sum it).
by_region = (
    stats.groupby("PAG_REGIAO", as_index=False)
    .agg(total_value=("VALOR", "sum"), total_count=("QUANTIDADE", "sum"))
    .assign(avg_ticket=lambda d: d["total_value"] / d["total_count"])
    .sort_values("total_value", ascending=False)
)

# 3. Present it with Brazilian currency formatting.
by_region["total_value"] = by_region["total_value"].map(format_brl)
by_region["avg_ticket"] = by_region["avg_ticket"].map(format_brl)
print(by_region.to_string(index=False))

The same aggregation is available as a one-liner via the convenience helper:

from pixbr import get_pix_summary

summary = get_pix_summary("202509", group_by="PAG_REGIAO")
# columns: PAG_REGIAO, total_value, total_count, avg_value, n_records

Fetching several months at once and combining into a single DataFrame:

from pixbr import get_pix_transaction_stats_multi

q3 = get_pix_transaction_stats_multi(["202507", "202508", "202509"])
monthly = q3.groupby("AnoMes")["VALOR"].sum()

Debugging a request without sending it (useful to inspect the OData URL):

client.build_url("EstatisticasTransacoesPix", {"Database": "202509"},
                 filter="NATUREZA eq 'P2P'", top=10)
# https://olinda.bcb.gov.br/.../EstatisticasTransacoesPix(Database=@Database)
#   ?$format=json&@Database='202509'&$filter=NATUREZA%20eq%20'P2P'&$top=10

Endpoints

Use pix_endpoints() and pix_columns("keys"|"municipality"|"stats"|"fraud") to inspect available endpoints and columns.

OData query parameters

All endpoint methods accept the common OData parameters:

• filter — OData filter expression, e.g. "NATUREZA eq 'P2P' and PAG_REGIAO eq 'SUDESTE'"
• columns — list of columns to select (unknown columns are dropped with a warning)
• orderby — "Column" (asc) or "Column desc"
• top — maximum number of records

Note: skip is not supported by the BCB PIX API; passing it emits a warning and is ignored. Use top to limit results.

Notes

• PixClient(timeout=..., max_retries=..., verbose=...) configures the HTTP session. The default timeout is 120s — the BCB API can be slow for large queries.
• client.build_url(...) / pix_url(...) return the request URL without sending it (handy for debugging).
• client.ping() / pix_ping() test connectivity to all four endpoints.

License

MIT