Michael
263af3c7c2
User uploaded a real Chase statement and got "0 rows detected."
Two bugs the rewrite shipped with, plus a diagnostic:
**1. Short dates without year weren't recognized.** Most bank
statements (Chase, Wells, BofA, …) display transaction dates as
``01/13`` or ``Jan 13`` because the year is implied by the
statement period. The original regex required ``\d{2,4}`` after
the second slash, so ``01/13`` failed to match and rows with no
detected date got dropped.
Split ``_DATE_RES`` into ``_FULL`` (with year) and ``_SHORT``
(no year), with a two-pass detector: pass 1 tries full-year
patterns across the whole row; pass 2 only tries short patterns
if pass 1 found nothing. This prevents a stray ``Page 1/2`` from
shadowing the real dated transaction on the same line.
Short patterns:
- ``\d{1,2}/\d{1,2}`` — Chase, etc.
- ``\d{1,2}-\d{1,2}``
- ``[A-Z][a-z]{2}\s+\d{1,2}`` — "Jan 13"
When parsing, short dates pass through ``parse_date`` and
return None (no year to bind to), so the scanner falls back to
the raw text — the user sees ``01/13`` in the date column and
can correct in the editor.
**2. Multi-word dates leaked the day token into the description.**
A pre-existing bug: ``_find_dates_in_words`` returned only the
START word index, and ``_description_from_row`` only excluded
that single word. For "Jan 13 Coffee $4.50", the description
became "13 Coffee" instead of "Coffee". Fixed by returning
``(start, end, text)`` with ``end`` exclusive (computed from
``len(m.group(1).split())`` so window-overrun doesn't
over-consume), and the description builder now skips the full
range.
**3. New diagnostic: ``diagnose_pdf_lines(pdf_bytes)``.** Returns
every clustered text line the scanner saw with ``has_date`` /
``has_amount`` flags. When the page's scan returns 0 rows, an
auto-expanded "what the scanner saw" expander now renders a
table of all extracted lines so the user can:
- Spot scanned-PDF cases (empty result → enable OCR)
- See which lines have a date but no amount (or vice versa)
- Eyeball the date / amount format the scanner missed
Without leaving the app or asking the developer for help.
Eight new tests cover: short US date (``01/13``), short month-
name date with two-word consumption (``Jan 13``), the
``Page 1/2 ... 01/13/2026`` shadowing case, and the multi-word-
date description fix.
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.