Michael
3cf935c999
Two corrections from real-statement feedback:
**1. Drop rows where the transaction amount is exactly 0.**
Bank statements include date+amount-shaped noise like
"INTEREST EARNED 0.00", "PAGE TOTAL 0.00", "BALANCE FORWARD
0.00 1,234.56" — all match the date+amount heuristic but
aren't transactions. New filter in
``scan_pdf_for_transactions``: drop rows whose ``amount_1``
parses to exactly 0. Non-zero balances in ``amount_2`` don't
rescue a zero amount_1 — leftmost amount is the canonical
transaction amount. Unparsed-but-non-empty amount strings are
kept (user verifies in the editor).
**2. Multi-date rows: first date wins for the column, every
date excluded from the description.** Chase / BofA / Wells
commonly show both a transaction date and a posting date per
row:
01/13 01/14 COFFEE SHOP $4.50
Before this fix, ``_find_dates_in_words`` returned the first
date only and the second date leaked into description as
"01/14 COFFEE SHOP". Now it returns ALL dates with their word
ranges; the scanner uses ``dates[0]`` as the canonical date
and passes every range to the description builder for
exclusion.
The detector's two-pass strategy now also guards against
mixing full-year and short-date matches on the same row.
Previously, a header line like ``Page 1/2 of 3 ... Statement
Date 01/13/2026`` would return both ``1/2`` and ``01/13/2026``,
and ``1/2`` (being leftmost) would have won the date column.
Now: if any full-year date is found on the row, short patterns
are NOT also collected — full year anchors interpretation. A
row with no full-year date (Chase short-date case) still falls
back to short patterns and collects all of them.
New tests:
- ``test_multiple_dates_returned_in_position_order`` —
``01/13`` + ``01/14`` both returned, in order
- ``TestMultiDateRow.test_first_date_wins_second_excluded_from_description``
— end-to-end through ``scan_pdf_for_transactions``
- ``TestZeroAmountRowsAreDropped.test_zero_amount_row_dropped``
— "INTEREST EARNED 0.00" row dropped while real txn kept
- ``test_negative_amount_kept`` — pin that -40.00 is not
treated as zero by the filter
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.