Michael
84e4665ab0
Reported: the "✕" buttons on the uploaded file list removed files
inconsistently — some clicks took, some didn't.
Two compounding causes:
1. ``key=f"_home_remove_{name}"`` embedded the raw filename in the
Streamlit widget key. Streamlit's widget-identity machinery
normalizes keys differently across reruns when they contain
spaces, dots, brackets, or non-ASCII characters, so a button's
identity could shift between the render where the user clicked
it and the rerun that should have processed the click. The click
was registered, but the post-rerun render produced a new widget
under a new effective key, and the original click was "lost".
2. The handler mutated ``home_uploads`` mid-loop while subsequent
iterations were still creating buttons. ``st.rerun()`` raises
synchronously, but if ANOTHER button's state changed in the same
pass (e.g. a stale click held over from a fast double-tap), the
ordering of state-mutation vs widget-key-update vs rerun could
race.
Fixes:
- Stable widget keys: ``f"_home_remove_{sha1(name)[:10]}"``. The
hash is identifier-safe regardless of spaces / dots / Unicode in
the filename. Verified across "sample with spaces.csv",
"sample.csv", and "日本語.csv" — three sequential Remove clicks
each remove exactly one file with no clicks lost.
- Two-phase capture: the loop collects the target ``to_remove``
filename, finishes rendering every other row at consistent widget
identity, THEN mutates state once and reruns. No more mid-loop
``del`` racing other widgets' click handlers.
- Wider click target: column ratio ``[8, 1]`` (was ``[12, 1]``) and
``use_container_width=True`` on the Remove button so the click
surface fills the entire column. Label changed to "Remove" for
the same reason — "✕" is a thin glyph that compressed the
hit-test region.
2220 tests pass.
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.