RFBpy

RFBpy downloads, extracts and loads the open CNPJ (Brazilian company registry) data published by the Receita Federal, into SQLite or a parquet folder.

It replaces the old set of numbered scripts with a single object — RFBData — that owns all the error handling and the decision of what still needs to be downloaded.

• Discovers the monthly folders on the public Nextcloud/WebDAV share.
• Downloads only what is missing or out of date (size + ETag check), with retries, backoff and resumable .part files.
• Extracts the archives idempotently and guards against Zip Slip.
• Loads the CSVs into a SQLite database (via DuckDB) or into one parquet file per table.
• Works around the share's flaky load balancer and the occasional non-latin-1 bytes.

Installation

pip install rfbpy

or, with uv:

uv add rfbpy

Requires Python 3.10+.

Quick start

from rfbpy import RFBData

rfb = RFBData(
    processing_dir="~/Desktop/rfb_processing",
    files_to_download=["Cnaes.zip", "Estabelecimentos", "Empresas"],
)

rfb.list_months()      # -> ["2026-07", "2026-08", ...]
rfb.check()            # prints a summary (returns the CheckReport too)

rfb.download_and_unzip()   # downloads what is missing and extracts it
rfb.to_sqlite()        # -> LoadResult(path=.../20261012_RF-CNPJ-database.db)
rfb.to_parquet()       # -> LoadResult(path=.../04_parquet_files)

check() prints something like:

RFB CNPJ — check
  Share      : https://arquivos.receitafederal.gov.br/public.php/webdav/Dados/Cadastros/CNPJ/
  Month      : latest
  Zip folder : /home/me/Desktop/rfb_processing/01_zip-files

  Files offered : 3
  Up to date    : 1
  To download   : 2

  Pending files:
    - Motivos.zip      1.2 KB
    - Cnaes.zip       21.6 KB

  Download need : 22.8 KB (total 25.4 KB)
  Disk free     : 15.5 GB  [ok]

Pass print_report=False to suppress the output and use the returned object programmatically.

Or run the whole pipeline in one call:

result = rfb.run(destination="parquet")   # or destination="sqlite"
print(result.download.downloaded)
print(result.load.tables)                 # {'cnae': 1, 'estabelecimento': 2500000, ...}

Selecting files

rfb.download_and_unzip(only="Cnaes,Paises")   # comma-separated substrings
rfb.download_and_unzip(only=["Empresas"])     # or a list
rfb.download_and_unzip(month="2026-08")       # a specific folder instead of "latest"
rfb.download_and_unzip(force=True)            # re-download everything
rfb.download_and_unzip(dry_run=True)          # just report what would be downloaded

If an entry in files_to_download (or in only=) matches no file on the share, discovery fails with UnavailableFilesError listing the missing entries, the files that are available, and how to fix it — so typos fail loudly instead of silently downloading nothing. Entries are matched case-insensitively as substrings of the remote file names ("Socios" matches Socios0.zip …).

Reading the result

report = rfb.check()
report.to_download    # list[FileInfo] still missing
report.up_to_date     # list[FileInfo] already present
report.needed_bytes   # bytes still to download
report.enough_disk    # needed_bytes <= free_bytes

download_and_unzip() returns a DownloadResult; the extraction outcome is in result.extract (extracted / skipped / failed).

Configuration

Every option that used to live in config.toml can be passed straight to the constructor:

rfb = RFBData(
    processing_dir="~/Desktop/rfb_processing",
    zip_dir="01_zip-files",
    unzip_dir="02_unzipped_files",
    sqlite_dir="03_sqlite_file",
    parquet_dir="04_parquet_files",

    base_url="https://arquivos.receitafederal.gov.br/public.php/webdav/Dados/Cadastros/CNPJ/",
    share_token="gn672Ad4CF8N6TK",     # or set RFB_SHARE_TOKEN
    month="latest",

    files_to_download=["Cnaes.zip", "Estabelecimentos", "Empresas"],

    max_concurrent_downloads=3,
    request_timeout=15,
    connect_timeout=10,
    read_timeout=60,
    max_attempts=3,
    backoff=2,
    large_file_threshold_mb=100,
    max_large_downloads=2,
    resume=True,
    verify_zip=True,
    user_agent="rfbpy/1.0",

    skip_existing=True,
    remove_zip_after_extract=False,
    remove_extracted_after_use=False,
    encoding="latin1",
    separator=";",

    duckdb_threads=0,
    duckdb_memory_limit="0",
    duckdb_temp_dir="",
    duckdb_null_padding=False,
    show_progress=True,
    db_filename="{date}_RF-CNPJ-database.db",
    db_date_format="%Y%m%d",

    parquet_compression="zstd",
    cache_enabled=True,
    cache_ttl_hours=24,
    cache_dir="cache",
)

You can also use the grouped dataclasses (dirs=, urls=, downloads=, arquivos=, unzip=, leitura=, sqlite=, parquet=, colunas=), pass a whole RFBConfig, or load an existing file:

rfb = RFBData.from_toml("examples/config.toml")

See examples/config.toml for the full template. Unknown keys from older configs are ignored with a warning. RFB_SHARE_TOKEN overrides share_token.

Outputs

to_sqlite() and to_parquet() print a summary when they start and finish (pass print_report=False to silence them):

RFB CNPJ — parquet
  Output : /home/me/Desktop/rfb_processing/04_parquet_files
  Tables : 3
  Size   : 15.1 GB
  Time   : 842.3 s

  cnae                 1,141 rows       41.8 KB
  empresas        60,000,000 rows        6.2 GB
  estabelecimento 71,000,000 rows        8.9 GB

SQLite

to_sqlite() writes a database with the tables:

Only the tables whose files are in files_to_download are created. Indexes are created on cnpj/cnpj_basico and ANALYZE is run at the end. Canceled establishments (situacao_cadastral == "08") are dropped.

Parquet

to_parquet() writes <parquet_dir>/<table>.parquet (Zstandard by default), one file per table, with the same cleaning applied. Read them with DuckDB, pandas or polars:

import duckdb
duckdb.sql("SELECT uf, count(*) FROM 'parquet/*.parquet' GROUP BY 1").show()

Command line

rfbpy --config examples/config.toml months
rfbpy --config examples/config.toml check
rfbpy --config examples/config.toml download-and-unzip --dry-run
rfbpy --config examples/config.toml download-and-unzip --jobs 6
rfbpy --config examples/config.toml unzip
rfbpy --config examples/config.toml load --dest parquet
rfbpy --config examples/config.toml run --dest sqlite

download-and-unzip also accepts the shorter alias download.

The CLI reads --config, then $RFB_CONFIG, then ./config.toml.

Notes

• File-manifest cache. Listing the share is the slow/flaky part, so the discovered .zip manifest is cached on disk at <processing_dir>/<cache_dir>/files_manifest.json and reused for cache_ttl_hours (default 24 h). Logs say when it is saved ("Saved file manifest cache …") and when it is used ("Using cached file manifest …"). force=True bypasses it, and cache_enabled=False turns it off.
• Resumable downloads. Files are written to a .part and only renamed after they pass verification (size, and zip validity when verify_zip=True), so a partial file is never accepted. Interrupted or failed transfers keep the .part and are resumed with an HTTP Range request on the next attempt or the next call to download_and_unzip(); if the server ignores Range, the file restarts safely. Set resume=False (CLI --no-resume) to disable.
• Flaky WebDAV. The share sits behind a load balancer whose nodes sometimes answer PROPFIND with an empty HTTP 200 (or reset the connection) instead of the expected 207 Multistatus. Directory listing is retried until a valid response arrives (usually ~20 s).
• Non-latin-1 bytes. The RFB CSVs are latin-1. Rarely, big files contain C1 control bytes (0x80–0x9F) that DuckDB's latin-1 reader rejects; a temporary sanitized copy is used and removed afterwards, leaving the originals untouched.
• DuckDB SQLite extension. to_sqlite() downloads the DuckDB sqlite extension on first use (needs internet once). to_parquet() does not.

Development

uv sync --extra dev
.venv/bin/python -m pytest

The tests are offline: network access is mocked and DuckDB's SQLite extension tests are skipped when it is unavailable.

Releasing

Releases are published to PyPI by .github/workflows/release.yml using trusted publishing (OIDC, no tokens). The package version comes from __version__ in src/rfbpy/__init__.py (via hatch's dynamic version), so:

1. Bump __version__, commit and push to main.
2. Tag and push:

   git tag v0.1.0
   git push origin v0.1.0
   

The workflow builds the sdist/wheel, smoke-tests them in an isolated environment, checks the tag matches __version__, and publishes to PyPI. Use Actions → Release → Run workflow (with the testpypi input) to publish to TestPyPI first.

One-time setup: add a pending trusted publisher on PyPI for project rfbpy (owner msperlin, repo rfbpy, workflow release.yml, environment pypi), and create the matching pypi environment in the GitHub repository settings.

License

MIT — see LICENSE. Original downloader idea from rictom/cnpj-sqlite.