Michael
155dd30746
Two related additions for the accountant workflow:
**1. Statement header extraction.** New
``extract_statement_metadata(pages)`` pulls the account number
and statement period out of the first page (falls back to
page 1+2 if either is missing on page 1 — Wells Fargo business
accounts put header info on page 2). Detected fields are
stamped onto EVERY transaction row so a multi-statement CSV is
self-attributing per row::
{
"date": "20250113",
"description": "Coffee Shop",
"amount_1": -4.50,
"account_number": "****5678",
"statement_period_start": "20250101",
"statement_period_end": "20250131",
...
}
Account-number regex is tolerant of masks (``****1234``),
hyphens (``1234-5678-9012``), and spaces. Period regex looks
for "Statement Period" / "From" / "Period Covered" labels plus
the first 1-2 full-year dates that follow. If only one date is
present near the label, it's used for both start and end (some
statements show only the closing date).
**2. Year inference for short dates.** When the row date is a
short ``01/13`` or ``Jan 13`` without a year, the scanner now
binds the year from the statement period's end date BEFORE
formatting. Doesn't handle the December-in-January-statement
cross-year case (rare; user can edit in the table).
**3. Configurable output date format.** New
``output_date_format`` parameter on ``scan_pdf_for_transactions``
defaults to ``%Y%m%d``. Applied to: the transaction date column
AND the statement period start/end fields. The page surfaces a
dropdown in Scan options with common presets (YYYYMMDD,
YYYY-MM-DD, MM/DD/YYYY, DD/MM/YYYY, ``Mon DD, YYYY``) plus a
Custom option that accepts a raw strftime string.
New helper: ``format_date(iso_str, fmt)`` converts ISO
``YYYY-MM-DD`` to any strftime; passes invalid input through
unchanged so the user can see what was actually there rather
than getting silent empties.
20 new tests cover: format_date, account-number extraction
(masked / hyphenated / spaced / no-label / short), period
extraction (standard / from-to / single-date / no-label),
metadata orchestrator (full header / no pages / page-2
fallback), year inference (US / dash / month-name / no-period /
unparseable), plus an end-to-end class that builds a header'd
PDF with short-date transactions and confirms metadata
attribution + year inference + format round-trip.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
🌐 Language: English · Español
DataTools
Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.
Tools
| # | Tool | Status |
|---|---|---|
| 01 | Find Duplicates — exact + fuzzy match, 5 normalizers, survivor rules, audit | Ready |
| 02 | Clean Text — whitespace, smart chars, BOM, line endings, case ops | Ready |
| 03 | Standardize Formats — dates, phones, emails, addresses, names, currencies, booleans | Ready |
| 04 | Fix Missing Values — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies | Ready |
| 05 | Map Columns — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder | Ready |
| 06 | Find Unusual Values | Coming Soon |
| 07 | Combine Files | Coming Soon |
| 08 | Quality Check | Coming Soon |
| 09 | Automated Workflows — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups | Ready |
Download (non-technical users)
Pre-built installers — no Python required:
| Platform | Download | First-launch note |
|---|---|---|
| macOS | DataTools-X.Y.Z-mac.dmg |
Drag DataTools.app into /Applications, then double-click. |
| Windows | DataTools-X.Y.Z-win-setup.exe |
Run the installer; launches from Start Menu. |
| Linux | DataTools-X.Y.Z-linux-x86_64.AppImage |
chmod +x the file, then double-click. |
Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150–200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.
Install from source (developers)
pip install -r requirements.txt
Python 3.10+ required.
Run
GUI (recommended):
streamlit run src/gui/app.py
CLI — seven entry points:
python -m src.cli customers.csv [--apply] # dedup
python -m src.cli_text_clean messy.csv [--apply] # text clean
python -m src.cli_format intl.csv [--apply] # format standardize (auto-streams >100 MB)
python -m src.cli_missing holes.csv [--apply] # missing values
python -m src.cli_column_map vendor.csv [--apply] # column mapper
python -m src.cli_pipeline any_file.csv [--apply] # chain tools end-to-end
python -m src.cli_analyze any_file.csv [--json] # scan only
Every CLI runs preview-only by default; add --apply to write output.
Language
The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.
Review & Normalize gate
Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.
Output
Every run writes:
{input}_<tool>.csv— the cleaned data{input}_changes.csv(text cleaner) or{input}_match_groups.csv(dedup) — audit traillogs/<tool>_YYYYMMDD_HHMMSS.log— debug-level run log
Original input file is never modified.
Docs
- User Guide — install, GUI workflow, gate
- CLI Reference — every flag with recipes
- Requirements — file sizes, encodings, detectors, perf targets
- Technical — architecture, gate internals, fix registry
- Developer Guide — adding fixes / detectors / standardizers
Dependencies
pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.
License
Proprietary.