Michael e282f061dc test(home): move marker to true bottom of main content
User reported the previous TEST #2 banner appeared at the *top* of
the main content area instead of the bottom. Root cause: on the home
page, ``render_sticky_footer()`` is called at line 107 — before
``st.title()`` — so anything that function injects in document flow
lands at the top of ``stAppViewBlockContainer``. Other pages call
``render_sticky_footer()`` at the end of their script, so the flow
content lands at the bottom there.

Remove the marker from ``render_sticky_footer`` and add it directly
at the very end of ``_home._home_page()`` — after the findings
panels. If this banner lines up with the offending white strip when
scrolled to the bottom, the strip is something rendered at the tail
of the page (likely an iframe wrapper from ``render_findings_panel``
or the block container's ``padding-bottom``).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-18 22:11:24 +00:00

🌐 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 ~150200 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 trail
  • logs/<tool>_YYYYMMDD_HHMMSS.log — debug-level run log

Original input file is never modified.

Docs

Dependencies

pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.

License

Proprietary.

Description
Data tools development
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