messy-table

pandas.read_excel assumes your spreadsheet is well-behaved. messy-table assumes it is not.

Turn messy real-world spreadsheets — Excel/CSV exported from ERPs, legacy systems, hand-made reports — into clean, typed data, and get back an auditable report of every fix that was applied.

from messy_table import clean

result = clean("relatorio_vendas.xlsx")

result.data        # list[dict] — clean, typed rows
result.columns     # per-column name / dtype / null summary
result.report      # every transformation that was applied
result.warnings    # low-confidence decisions
result.to_pandas() # DataFrame (optional extra)

Why

AI agents and data pipelines receive arbitrary spreadsheets from users and today re-implement, by hand and fragilely, the same heuristics: find where the table starts, fix the headers, convert Excel serial dates, interpret 1.234,56. messy-table is the canonical answer to that step — deterministic, dependency-light, and fully auditable.

Install

pip install messy-table            # core — depends only on openpyxl
pip install 'messy-table[pandas]'  # adds result.to_pandas()

Python ≥ 3.10. Ships py.typed — fully type-checked.

Before & after

A typical ERP export — a title banner, a blank row, pt-BR numbers, an N/A, and a trailing totals row:

Relatório de Vendas 2024
(gerado em 01/02/2024)

Produto      | Valor (R$) | Qtd | Ativo
Café         | 1.234,56   | 10  | sim
Chá          | 2.000,00   | 5   | nao
Açúcar       | -          | 3   | sim
TOTAL        | 3.234,56   | 18  |

>>> result = clean("vendas.xlsx")
>>> result.data
[{'produto': 'Café',   'valor_r': 1234.56, 'qtd': 10, 'ativo': True},
 {'produto': 'Chá',    'valor_r': 2000.0,  'qtd': 5,  'ativo': False},
 {'produto': 'Açúcar', 'valor_r': None,    'qtd': 3,  'ativo': True}]
>>> [(c.name, c.dtype) for c in result.columns]
[('produto', 'str'), ('valor_r', 'float'), ('qtd', 'int'), ('ativo', 'bool')]

The title, blank row and TOTAL line are gone; numbers are parsed in the column's inferred locale; - is a null; sim/nao became booleans — and the report says so.

Features (v0.1)

The report

Nothing changes without a record. Per-cell fixes are aggregated per column with a count and sample locations, so the report stays small even on huge files:

>>> print(result.report.to_json())
{
  "summary": {"table_start_detected": 1, "table_end_trimmed": 1,
              "header_renamed": 1, "null_normalized": 1,
              "number_parsed": 4, "type_coerced": 2},
  "actions": [
    {"kind": "table_start_detected", "rule": "density", "confidence": 0.8,
     "detail": "skipped 3 leading row(s) (title/metadata/blank); table starts at row 3"},
    {"kind": "number_parsed", "rule": "locale:pt_BR", "column": "valor_r",
     "count": 4, "confidence": 1.0,
     "examples": [{"row": 0, "original": "1.234,56", "final": 1234.56}]},
    ...
  ]
}

Configuration

The 80% case needs no config. For the rest:

from messy_table import clean, Config

result = clean(
    "dados.csv",
    config=Config(
        locale="pt_BR",            # force number/date interpretation; default "auto"
        header="auto",             # "auto" | int (row index) | None (no header)
        sheet=0,                   # index or name of the worksheet
        merged_cells="fill",       # "fill" | "first-only"
        null_values_extra=["s/i"], # add to the built-in null list
        strict=False,              # True: raise AmbiguityError instead of warning
    ),
)

In strict=True, a low-confidence decision raises AmbiguityError — always with a copy-pasteable Config suggestion to resolve it.

Security

messy-table parses untrusted files, so it defends against it: .xlsx archives are checked for decompression-bomb shape (absolute size and ratio) before they are opened, and a hard cell ceiling bounds memory. See ARCHITECTURE.md.

Docs

• ARCHITECTURE.md — pipeline, stack rationale, security decisions.
• docs/heuristics.md — every heuristic and its thresholds.

License

MIT — see LICENSE.