Adds src/core/format_standardize.py — a per-cell standardizer for dates,
phones, emails, addresses, names, currencies, booleans — wired through
StandardizeOptions / standardize_dataframe with FieldType registry.
Includes:
- Date parser handles ISO/US/EU/longform/excel-serial/unix-timestamp/
partial-precision/quarter notation; opt-in French/German/Spanish month
dictionaries via month_locales.
- Phone via libphonenumber with extension preservation (;ext=N), 001
international prefix handling, error sentinels for placeholders /
multi-number cells.
- Email lowercase/trim/mailto/angle-bracket strip with optional
--gmail-canonical mode.
- Address USPS abbreviation expansion or compression (expand=False per
corpus § 6.3), state-name → 2-letter conversion, multi-line collapse,
PO Box normalization, state-code preservation regardless of input case.
- Name handler: Mc/Mac/O'/D' inner caps, hyphen segments, particle
lowercasing (von/van/de/da), comma-format reversal, period stripping
for titles/suffixes/initials, PhD/MD acronym preservation, conservative
mode for mixed-case input.
- Currency: auto-detect EU vs US separators, space-thousands, Swiss
apostrophe, accounting parens, optional ISO code preservation, error
sentinels for percentages/ranges/word-values/ambiguous separators.
- Per-domain error_policy ("passthrough" | "sentinel") for surfacing
malformed values as <error: reason> per corpus § 0.3.
Test corpus from Business/DataTools/test-cases-format-cleaner copied to
test-cases/format-cleaner-corpus/ — 7 fixtures plus FORMATS-CASES.md.
tests/test_format_standardize_corpus.py drives all 199 rows through the
per-cell standardizers; 0 xfailed.
Wires the GUI page (3_Format_Standardizer.py) to "Ready" status.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a Review & Normalize page that sits between upload and every tool
page. The analyzer now tags each finding with confidence (high/medium/low)
and a fix_action; the gate auto-applies high-confidence fixes, surfaces
medium/low ones for user review, and blocks tool pages on error-level
findings until resolved or waived.
Core (src/core/):
- analyze.py: Finding gains confidence, fix_action, pre_applied; new
detectors for encoding_uncertain, encoding_decode_failed; new top-
level encoding_override parameter.
- fixes.py: registry of fix algorithms keyed by fix_action id.
- normalize.py: auto_fix(), apply_decisions(), is_normalized(), and
the NormalizationResult / Decision dataclasses the gate consumes.
- io.py: detect_encoding tries strict UTF-8 first; repair_bytes now
transcodes UTF-16/32 to UTF-8 before NUL-strip (fixes UTF-16 corruption)
and normalizes line endings (fixes bare-CR parser crash); empty file
handled gracefully instead of EmptyDataError traceback.
GUI (src/gui/):
- pages/0_Review.py: gate page with per-finding decision controls,
encoding override picker (16 codepages + custom), and Advanced output
options (encoding, delimiter, line terminator) on the download.
- components.py: require_normalization_gate() helper.
- pages/1-9: gate guard wired on every tool page.
Test corpora:
- test-cases/encodings-corpus/: 31 encoded CSV fixtures + 9 reference
UTF-8 files + manifest, synced from Business/DataTools.
- test-cases/text-cleaner-corpus/test_data/17: synced malformed input
(unquoted $1,500.00) for the unquoted-delimiter detector.
Tests (94 new):
- test_normalize.py (48): finding fields, fix registry, auto_fix scope,
decision paths, gate idempotency, output-options helper.
- test_encodings_corpus.py (90, 16 xfailed): parametric detection +
decode + analyzer-no-crash sweep against the manifest.
- test_analyze.py: encoding override + encoding_uncertain detectors.
- test_corpus.py: pre-parse repair in the strict reader.
run_tests.py: new aliases --tool normalize, --tool encodings, --tool gate;
encodings corpus added to --fixtures category.
Docs: USER-GUIDE §3.3 covers the gate workflow, encoding override, and
output options; TECHNICAL §10.2.1-10.2.4 documents the analyzer schema,
gate API, Review page, and pre-parse repair pipeline; CLI-REFERENCE adds
the analyzer JSON schema with the new fields; README links to all of it.
Suite: 765 passed, 17 xfailed (was 458 passed).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The analyzer's "Run Analysis" panel rendered sample cells via st.dataframe,
which (a) silently collapses leading/trailing ASCII whitespace and (b)
displays NBSP/ZWSP/control chars as nothing. The user couldn't see the
exact pollution they were being told about.
visualize_hidden_html gains a mark_outer_whitespace=True option that
wraps each leading and trailing ASCII space/tab in its own badge with a
"SP LEAD" / "SP TRAIL" tooltip. The badges are per-character so the
user can count exactly how much padding the cleaner will strip.
components.render_findings_panel now:
- injects hidden_char_css() once at the top of the panel
- replaces st.dataframe(samples) with a custom HTML table
- renders the value column with mark_outer_whitespace=True
- applies white-space: pre-wrap on value cells so any internal ASCII
whitespace also stays visible (browsers collapse runs by default)
Four new tests cover: leading+trailing badge counts, default-off
behaviour, leading tab badge, all-whitespace string treated entirely
as leading.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The whole point of the cleaner is to remove characters the user can't
see — which makes the "before / after" preview nearly useless by default.
A cell with NBSP padding looks identical to a cell with regular spaces.
Two new helpers in src.core.text_clean:
visualize_hidden_text(s)
Plain-text rendering: each invisible/control/smart character is
replaced by a glyph + [LABEL] (e.g. "·[NBSP]", "→[TAB]", "∅[ZWSP]",
"""[L DQUOTE]"). Suitable for terminal output, CSV exports, anywhere
HTML is wrong. Unmapped C0 controls render as [U+XXXX].
visualize_hidden_html(s) + hidden_char_css()
HTML rendering: every flagged character is wrapped in a <span> with
a CSS class and a tooltip showing the codepoint and label. Pair with
hidden_char_css() to inject the matching styles. Three colour bands
(whitespace, special, control) so the user can scan an audit table
and spot what's being changed at a glance.
Mapping covers: ASCII tab/LF/CR, every NBSP variant (U+00A0, U+202F,
U+2009, …), zero-width family (ZWSP/ZWNJ/ZWJ/WJ/BOM/SHY), bidi marks
(LRM/RLM), all smart quotes, en/em dashes, ellipsis, prime/double-prime,
and guillemets. ASCII printable text passes through; HTML output also
escapes &/</> .
GUI wiring (src/gui/pages/2_Text_Cleaner.py)
The "Examples" changes table now defaults to a hidden-char-rendered
HTML view: every NBSP/ZWSP/smart-quote/control char is shown with its
badge and codepoint tooltip. A "Show hidden characters" toggle lets
the user fall back to the raw st.dataframe view if they prefer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two more detectors close the analyzer gap list:
mixed_line_endings (warn, tool=02): scans raw bytes for combinations of
CRLF / LF / bare CR. Disaster pattern after multi-source concat
(Windows + macOS + Linux exports stitched together). Operates on raw
bytes only — DataFrame-mode analyze() skips it because raw bytes
aren't available. _load_for_analysis now returns the raw bytes
alongside the DataFrame and repair result so the detector has them.
near_duplicate_rows (info, tool=01): cheap dedup signal — strip and
lowercase every string column, then count df.duplicated(). Catches the
most common case (same customer entered twice with subtle formatting
differences) without paying for fuzzy matching. Anything more
sophisticated stays in tool 01.
Six new tests cover both detectors plus the dataframe-mode skip path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previously only analyze() and direct read_csv_repaired() callers got the
byte-level repair pass (BOM strip, NUL strip, smart-double-quote fold,
unquoted-delimiter merge). The dedup CLI and any other read_file consumer
silently missed it.
read_file gains a repair=True default. CSV/TSV inputs run through
repair_bytes before pandas sees them; Excel inputs still pass through
unchanged. Chunked reads (chunk_size set) bypass repair because the pre-
parse pass loads the whole file — preserving streaming behavior on huge
files. Repair actions and unrepairable lines are logged at INFO/WARNING.
cli_text_clean opts out (repair=False): the cleaner offers fine-grained
control via --preset and per-op flags, and a byte-level smart-quote fold
under the user's "minimal" preset would violate that contract. The
cell-level cleaner does the equivalent work itself when its options ask
for it.
Tests: read_file default strips BOM and folds curly double quotes;
repair=False preserves smart quotes; chunked reads still work and skip
repair as documented.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the §4.17 spec gap that test_gap_coverage.py was tracking via xfail:
collapse_whitespace must NOT touch cells whose shape carries meaningful
internal whitespace.
Adds _looks_structured(s) — returns True when s matches:
- numeric (currency optional, thousand-grouping by , . or single space)
- date (ISO/slash/dot separator, or 'Mon DD YYYY' / 'DD Mon YYYY')
- phone (digits + parens/dots/dashes/+/spaces, >= 7 digits, no letters)
The pipeline uses a new _smart_collapse_whitespace wrapper that defers to
collapse_whitespace only when _looks_structured returns False. The raw
collapse_whitespace function is unchanged so direct callers and existing
unit tests remain valid.
Five new positive tests replace the xfail:
- "(555) 123-4567" preserved (phone, double space inside)
- "1 234" preserved (European thousands)
- "2024-01-15" preserved (ISO date)
- "Jan 15 2024" preserved (textual date)
- "hello world" still collapsed to "hello world" (free-text negative case)
Conservative on purpose: a false negative just collapses (existing
behavior); a false positive leaves intentional double spaces in prose.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a top-level test infrastructure layer addressing four needs at once:
a single command to run anything, cross-platform automation, install/e2e
sanity, and zero-config pickup of new fixtures dropped into test-cases/.
Top-level runner — run_tests.py
python run_tests.py # everything (default)
python run_tests.py --tool dedup # one tool's tests
python run_tests.py --unit # category scopes
python run_tests.py --e2e # end-to-end CLI
python run_tests.py --install # import / dependency sanity
python run_tests.py --fixtures # corpus + dropped-file sweep
python run_tests.py --coverage # term-missing report
python run_tests.py --quick # skip @pytest.mark.slow
Tools: analyze, cli, config, dedup, io, normalizers, text_clean.
Cross-platform — tox.ini
Envs for py310-py313 plus install / e2e / fixtures / coverage / lint.
Forces UTF-8 (PYTHONUTF8=1, PYTHONIOENCODING=utf-8) so identical fixture
bytes parse the same on Linux/macOS/Windows.
Shared config — pytest.ini
testpaths, python_files conventions, custom markers (slow, e2e, install,
fixture_sweep), warning filters that fail on our own DeprecationWarnings
while tolerating third-party ones.
New test layers
tests/test_install.py — required deps import; project modules import;
src.core public API surface; CLI --help exits 0; streamlit app.py
parses as valid Python; run_tests.py --help works.
tests/test_e2e.py — CLI roundtrips: cli_analyze table + JSON, cli_text_clean
--apply writes a real file with NBSP/smart-quote folded, dedup CLI
removes duplicates, run_tests.py self-tests.
tests/test_fixtures_sweep.py — parametrizes over every CSV/TSV/XLSX
inside test-cases/ (excluding text-cleaner-corpus/, which has its own
suite). Each fixture must: load through repair_bytes, run analyze()
cleanly, and survive clean_dataframe() with row/col counts unchanged
plus idempotency. Drop a CSV in, re-run — no test code changes needed.
tests/test_gap_coverage.py — closes audit gaps: clean_headers=False
toggle, repair_bytes with tab/semicolon delimiters, BOM+NUL+smart-
quote combined-fix scenario, analyze() over an XLSX path, sample_rows
larger than the DataFrame, mid-cell BOM, findings_by_tool edges, plus
a strict xfail documenting the known §4.17 numeric/phone whitespace
heuristic gap.
Test count
Before: 288 passed + 1 xfailed
After: 475 passed + 2 xfailed (the second xfail is the documented
collapse_whitespace gap on phone-shaped cells; spec §4.17 calls
for a heuristic that hasn't been implemented yet).
Functional gaps surfaced (not fixed in this commit):
- Text cleaner: collapse_whitespace runs unconditionally on every string
cell, including phone/numeric/date-shaped ones. Spec §4.17 requires a
skip heuristic. Captured as strict xfail so the gap stays visible.
- io.read_file does not run pre-parse repair; only analyze() and direct
callers of read_csv_repaired() get it. CLI tool pages and the dedup
CLI miss the safety net.
- Analyzer has no mixed_line_endings detector or near_duplicate_rows
detector; both planned but require additional plumbing.
- GUI tool pages each have their own uploader instead of picking up the
home-page upload through session_state.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
python -m src.cli_analyze input.csv # rich table per tool
python -m src.cli_analyze input.csv --json # array of finding dicts
python -m src.cli_analyze input.csv --strict # exit 1 on warn/error
python -m src.cli_analyze input.csv -n 50000 # cap rows scanned
Findings are grouped by destination tool so the user can see at a glance
which tool to open next. Read-only; exit code 0 unless --strict is set.
The CLI keeps its own tool-id -> display-name map so it doesn't depend on
the GUI module.
7 tests cover: clean-file passthrough, dirty-file table, --json round-trip,
missing-file (exit 2), --strict exit code, --sample-rows cap.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Pure, advisory scan over an uploaded file or DataFrame that returns a list of
Finding objects naming each issue, the affected count, and which downstream
tool can fix it. The GUI uses this to badge tool nav items at upload; the CLI
will print findings as a table or JSON.
src/core/analyze.py:
Finding dataclass (id, severity, tool, count, description, column, samples)
analyze(source, *, sample_rows=1000, repair_result=None) -> list[Finding]
- source: DataFrame, path, or str. Path scans first 1000 rows.
- When source is a path, runs the same pre-parse repair the tool pages
will use; the resulting RepairResult is auto-surfaced as csv_*
findings. A caller-supplied repair_result wins so non-default repair
flags are respected.
Detectors (each independent, samples capped at 5):
- smart_punctuation_in_data -> 02
- nbsp_or_unicode_whitespace -> 02
- zero_width_or_invisible -> 02
- dirty_column_headers -> 02
- whitespace_padding -> 02
- null_like_sentinels -> 04
- suspected_mojibake -> 02 (Tier 2)
- mixed_case_email_column -> 02 case op
- leading_zero_ids -> informational, no tool
Helpers: findings_by_tool() for sidebar grouping, to_dict() for JSON.
Detectors are decoupled from the GUI display layer — they emit stable tool
ids ("02_text_cleaner") and the GUI maps those to display names.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Some pollution patterns block pandas before the cell-level cleaner can run.
Add a pre-parse pass on raw bytes that fixes only what breaks parsing, and
returns a structured action log the GUI/CLI can surface to the user.
repair_bytes(raw, *, encoding, delimiter, fold_quotes, strip_nul, repair_delims):
1. Strip leading UTF-8 BOM.
2. Strip embedded NUL bytes (the C parser truncates fields at NUL).
3. Fold smart double quotes (curly, guillemet, double-prime) to ASCII '"'.
Curly singles are NOT folded here; they don't conflict with CSV and the
cell-level cleaner handles them more accurately.
4. Per-row repair when one rogue delimiter is embedded in a field that
looks like currency or thousands-grouped digits. Tiered scoring keeps
" $1,500.00 ,7" unambiguous: the strict currency regex match wins
over the loose digit/sigil heuristic.
read_csv_repaired(path) -> (DataFrame, RepairResult). RepairResult exposes
.actions, .unrepairable_lines, and a summary() grouped by kind.
Out of scope for this pass: encoding repair, delimiter conversion, multi-
delimiter merges (k>1) — logged as unrepairable so callers can see what was
left alone instead of silently parsing wrong.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The 21-fixture corpus (test-cases/text-cleaner-corpus/) exercises the cleaner
end-to-end against the spec in TEST-CASES.md. Closing the failing cases drove
five small cleaner fixes plus two fixture-generation fixes:
- _SMART_CHARS: add prime, double prime, guillemets (case 03)
- _ZERO_WIDTH: add soft hyphen U+00AD (case 05)
- clean_dataframe: clean column headers via the same pipeline (cases 16/19/20),
with a clean_headers toggle on CleanOptions
- smart_title_case: title-case full-shout strings ("ALICE SMITH" -> "Alice
Smith") while still preserving embedded acronyms; preserve uppercase after
apostrophe in names ("O'CONNOR" -> "O'Connor", "o'neil" -> "O'neil")
- test_corpus.py reader: pre-strip NUL bytes (C parser truncates at NUL,
python engine is too strict about embedded literal "), per spec case 06
- generate_test_data.py: properly CSV-escape literal-quote cells in case 03
expected; quote the rogue-comma price field in case 17 input
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Rewrite README.md with project overview, quick-start, and CLI summary
- Add docs/CLI-REFERENCE.md with full flag reference and 8 recipe sections
- Add docs/DEVELOPER.md with architecture, data flow, and extension guides
- Rewrite src/core/__init__.py with public API exports and module docstring
- Add Streamlit GUI (src/gui/) with file upload, advanced options, interactive
match group review with side-by-side diff, and download buttons
- Add .gitignore, requirements.txt, all source code, tests, and sample data
- Add streamlit to requirements.txt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
