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>
