Michael
6415be8bf4
Apply the Clean Text page's post-run UX pattern to every other Ready
tool page (Find Duplicates, Standardize Formats, Fix Missing Values,
Map Columns, Automated Workflows) for consistency and ease of use.
Per page:
1. Preview wrapped in ``st.expander(f"Preview: {filename}",
expanded=not _has_result)``. Open before a result exists, folded
afterwards.
2. Options / configuration controls wrapped in
``st.expander("Options", expanded=not _has_result)``. Inner
sub-expanders preserved (Streamlit 1.36+ supports nesting).
3. After the primary action stashes the result, set a one-shot
``_<tool>_scroll_to_results`` flag in session state and call
``st.rerun()`` so the preview + options expanders see the new
state on the next pass and collapse themselves.
4. ``<div id="<tool>-results-anchor" style="height:1px">`` placed
immediately before the Results subheader.
5. End-of-page: pop the scroll flag and inject a tiny
``streamlit.components.v1.html`` iframe whose ``<script>`` calls
``scrollIntoView`` on the parent document's anchor. One-shot, so
unrelated reruns (toggling Show-hidden, etc.) don't yank the
viewport.
6. Download buttons hardened against the multi-button Streamlit
footgun: byte buffers pre-computed outside the column scopes,
explicit unique ``key="<tool>_dl_<purpose>"`` per button,
``use_container_width=True``, and previously-conditional buttons
now render unconditionally with ``disabled=True`` + a help
tooltip when the underlying data is empty so layout stays steady.
Per-page judgment calls (already noted in agent reports):
- Find Duplicates: sheet picker and delimiter selector kept OUTSIDE
expanders (the user still needs to see them when a file fails to
parse).
- Fix Missing Values: missingness profile wrapped INSIDE the Options
expander together with Strategy — the Results section already
shows a before/after missingness comparison that supersedes the
static input profile.
- Map Columns: all three subsections (Target schema, Strategy,
Mapping) wrapped under one outer Options expander, matching the
Text Cleaner pattern.
- Automated Workflows: inner "Recommended tool order" expander stays
nested inside the outer Options wrap; Run button stays outside
Options so the user can re-run after tweaking the (collapsed)
editor.
2008 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.