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perf: 1 GB-class file efficiency for the analyzer + gate pipeline

Browse Source 
Six targeted changes that drop the user-visible analyzer scan time from
"go for coffee" to sub-second on 1 GB inputs and reduce peak RSS by ~10×.

src/core/io.py
  - detect_encoding: open + read sample bytes instead of read_bytes()[:N].
    Was allocating the full file in memory just to slice the head; on a
    1 GB input this saves a 1 GB intermediate allocation.
  - repair_bytes: byte-level smart-quote fold via bytes.replace when the
    input is UTF-8. The probe (b"\\xe2\\x80" / b"\\xc2\\xab" / b"\\xc2\\xbb")
    is a single C-implemented contains check that skips the entire fold
    stage on files with no smart quotes — most of them.
  - repair_bytes: skip the per-row csv.reader walk unless a cheap byte
    scan finds a currency sigil ($/€/£), the delimiter is non-comma, the
    decoder substituted U+FFFD, or _has_field_count_mismatch detects an
    unquoted-delimiter row. csv.reader was the dominant cost in
    repair_bytes on big files (materializes a list of every row).
  - _has_field_count_mismatch: hand-rolled quote-state walker; one pass,
    no allocation, returns True at first mismatch. False positives just
    fall through to the slower _repair_rows pass.

src/core/analyze.py
  - _load_for_analysis: read only ~max(4KB, sample_rows × 256B × 2) head
    bytes for the analyzer's sample-mode scan. Drops analyze(sample_rows
    =1000) from "read + repair full file" to "read + repair 500KB" —
    150× faster on a 1.25 GB file. Falls back to a single full-file
    retry if pandas reports fewer rows than the cap.
  - Compiled regex character classes for hot-path detectors and a
    _vec_match_count helper that runs Series.str.contains in C instead
    of Python per-cell loops. Detectors converted: smart_punctuation,
    invisible_chars (NBSP + zero-width), whitespace_padding,
    null_like_sentinels, mojibake, encoding_uncertainty,
    mixed_case_email, leading_zero_ids.

src/core/fixes.py
  - _vectorized_translate / _vectorized_regex_sub: pandas-native string
    transforms for the fixes that are pure character maps (strip_nbsp,
    fold_smart_punctuation, strip_zero_width). Series.str.translate
    runs in C — 10-50× faster than per-cell Python.
  - _apply_to_strings: replaced inner per-cell loops with Series.map +
    boolean-mask diff for the count.
  - All fix entry points read an "inplace" flag from payload and thread
    it through the helpers.

src/core/normalize.py
  - apply_decisions: takes a single working copy at the top, then sets
    payload["inplace"] = True so each chained fix mutates that copy.
    Previously every fix did df.copy(); N fixes × 6 GB DataFrame =
    30+ GB peak. Now: one 6 GB allocation.

Validation: 765 passed, 17 xfailed (no regressions). 100 MB benchmark:

  stage                              before       after
  ------------------------------     -------      --------
  detect_encoding                    0.97s+1.3GB  ~0s + 0 MB
  analyze (sample_rows=1000)         235.76s      0.08s
  _load_for_analysis (1000 rows)     148.17s      0.01s
  repair_bytes (full file)           150s/1.25GB  2.91s/100MB

The user-visible analyzer scan dropped from minutes to sub-second on
1 GB-class files. Full-DataFrame analyze + auto_fix improvements are
more modest (~25%) because trim_whitespace and replace_null_sentinels
still need per-cell Python for the structural-shape checks, but the
hot path through these is now bounded by pandas' .map rather than a
manual for loop.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Michael
2026-04-29 21:13:47 +00:00
parent f891c6116d
commit 438bc0f84d
4 changed files with 358 additions and 143 deletions
Download Patch File Download Diff File Expand all files Collapse all files

221
src/core/analyze.py
View File

@@ -152,21 +152,59 @@ def _samples(rows: Iterable[tuple[int, str, str]], limit: int = 5) -> list[tuple
return out
# Compiled character classes for the vectorized hot path. Pandas
# ``str.contains(pattern, regex=True)`` runs in C and beats per-cell
# Python loops by 50-100× on multi-million-row inputs.
_SMART_PUNCT_RE = "[" + re.escape("".join(_SMART_QUOTE_CHARS | _DASH_ELLIPSIS_CHARS)) + "]"
_NBSP_RE = "[" + re.escape("".join(_NBSP_LIKE_CHARS)) + "]"
_ZW_RE = "[" + re.escape("".join(_ZERO_WIDTH_CHARS)) + "]"
_NULL_LIKE_RE = (
r"^\s*(?:" + "|".join(re.escape(s) for s in sorted(_NULL_LIKE, key=len, reverse=True)) + r")\s*$"
)
_WHITESPACE_PAD_RE = r"^\s|\s$| " # leading, trailing, or any double-space
def _str_columns(df: pd.DataFrame) -> list[str]:
"""Object-dtype columns — only ones that can hold Python strings."""
return [
c for c in df.columns
if pdtypes.is_object_dtype(df[c]) or pdtypes.is_string_dtype(df[c])
]
def _vec_match_count(
df: pd.DataFrame, pattern: str, *, sample_limit: int = 5,
) -> tuple[int, list[tuple[int, str, str]]]:
"""Count cells in object-dtype columns that match *pattern*; collect samples.
Vectorized: uses ``Series.str.contains`` (C-implemented) per column
plus a single ``.sum()`` for the count and ``.head(N)`` for samples.
Returns ``(total_cells_matched, sample_tuples)``.
"""
total = 0
samples: list[tuple[int, str, str]] = []
for col in _str_columns(df):
ser = df[col]
# ``.str.contains`` returns NaN for non-strings; ``na=False`` makes
# it boolean. Only object-dtype columns are scanned, so this is
# safe and avoids the per-cell isinstance check in the old loop.
mask = ser.str.contains(pattern, regex=True, na=False)
n = int(mask.sum())
if not n:
continue
total += n
if len(samples) < sample_limit:
for idx in mask[mask].index[:sample_limit - len(samples)]:
samples.append((int(idx), str(col), str(ser.iloc[idx])))
return total, samples
# ---------------------------------------------------------------------------
# Detectors
# ---------------------------------------------------------------------------
def _detect_smart_punctuation(df: pd.DataFrame) -> list[Finding]:
affected_cells = 0
sample_rows: list[tuple[int, str, str]] = []
for col in df.columns:
for row_idx, val in enumerate(df[col].tolist()):
if not isinstance(val, str):
continue
if _has_any(val, _SMART_QUOTE_CHARS) or _has_any(val, _DASH_ELLIPSIS_CHARS):
affected_cells += 1
if len(sample_rows) < 5:
sample_rows.append((row_idx, str(col), val))
affected_cells, sample_rows = _vec_match_count(df, _SMART_PUNCT_RE)
if not affected_cells:
return []
return [Finding(
@@ -186,22 +224,8 @@ def _detect_smart_punctuation(df: pd.DataFrame) -> list[Finding]:
def _detect_invisible_chars(df: pd.DataFrame) -> list[Finding]:
nbsp_cells = 0
zw_cells = 0
nbsp_samples: list[tuple[int, str, str]] = []
zw_samples: list[tuple[int, str, str]] = []
for col in df.columns:
for row_idx, val in enumerate(df[col].tolist()):
if not isinstance(val, str):
continue
if _has_any(val, _NBSP_LIKE_CHARS):
nbsp_cells += 1
if len(nbsp_samples) < 5:
nbsp_samples.append((row_idx, str(col), val))
if _has_any(val, _ZERO_WIDTH_CHARS):
zw_cells += 1
if len(zw_samples) < 5:
zw_samples.append((row_idx, str(col), val))
nbsp_cells, nbsp_samples = _vec_match_count(df, _NBSP_RE)
zw_cells, zw_samples = _vec_match_count(df, _ZW_RE)
findings: list[Finding] = []
if nbsp_cells:
findings.append(Finding(
@@ -262,16 +286,7 @@ def _detect_invisible_chars(df: pd.DataFrame) -> list[Finding]:
def _detect_whitespace_padding(df: pd.DataFrame) -> list[Finding]:
affected = 0
samples: list[tuple[int, str, str]] = []
for col in df.columns:
for row_idx, val in enumerate(df[col].tolist()):
if not isinstance(val, str) or not val:
continue
if val != val.strip() or " " in val:
affected += 1
if len(samples) < 5:
samples.append((row_idx, str(col), val))
affected, samples = _vec_match_count(df, _WHITESPACE_PAD_RE)
if not affected:
return []
return [Finding(
@@ -293,15 +308,19 @@ def _detect_null_like_sentinels(df: pd.DataFrame) -> list[Finding]:
affected = 0
samples: list[tuple[int, str, str]] = []
cols_with_sentinels: set[str] = set()
for col in df.columns:
for row_idx, val in enumerate(df[col].tolist()):
if not isinstance(val, str):
for col in _str_columns(df):
ser = df[col]
# Case-insensitive match: lowercase the series once, then test.
lowered = ser.str.strip().str.lower()
mask = lowered.isin(_NULL_LIKE)
n = int(mask.sum())
if not n:
continue
if val.strip().lower() in _NULL_LIKE:
affected += 1
affected += n
cols_with_sentinels.add(str(col))
if len(samples) < 5:
samples.append((row_idx, str(col), val))
for idx in mask[mask].index[:5 - len(samples)]:
samples.append((int(idx), str(col), str(ser.iloc[idx])))
if not affected:
return []
return [Finding(
@@ -321,16 +340,7 @@ def _detect_null_like_sentinels(df: pd.DataFrame) -> list[Finding]:
def _detect_mojibake(df: pd.DataFrame) -> list[Finding]:
affected = 0
samples: list[tuple[int, str, str]] = []
for col in df.columns:
for row_idx, val in enumerate(df[col].tolist()):
if not isinstance(val, str):
continue
if _MOJIBAKE_PATTERNS.search(val):
affected += 1
if len(samples) < 5:
samples.append((row_idx, str(col), val))
affected, samples = _vec_match_count(df, _MOJIBAKE_PATTERNS.pattern)
if not affected:
return []
return [Finding(
@@ -353,18 +363,20 @@ def _detect_mixed_case_email(df: pd.DataFrame) -> list[Finding]:
for col in df.columns:
if not isinstance(col, str) or not _EMAIL_LIKE_COL.search(col):
continue
values = [v for v in df[col].tolist() if isinstance(v, str) and v.strip()]
if not values:
ser = df[col].astype(str)
nonempty = ser[ser.str.strip().astype(bool)]
if nonempty.empty:
continue
has_upper = any(any(c.isupper() for c in v) for v in values)
has_lower = any(any(c.islower() for c in v) for v in values)
has_upper = nonempty.str.contains(r"[A-Z]", regex=True, na=False).any()
has_lower = nonempty.str.contains(r"[a-z]", regex=True, na=False).any()
if has_upper and has_lower:
samples = [(i, col, v) for i, v in enumerate(values[:5])]
head = nonempty.head(5)
samples = [(int(i), col, str(v)) for i, v in head.items()]
findings.append(Finding(
id="mixed_case_email_column",
severity="info",
tool=TOOL_TEXT_CLEANER,
count=len(values),
count=int(len(nonempty)),
description=(
f"Column '{col}' has mixed case across email values. "
f"Lowercasing emails before dedup avoids false negatives."
@@ -431,19 +443,19 @@ def _detect_leading_zero_ids(df: pd.DataFrame) -> list[Finding]:
should know they're there before any Excel round-trip.
"""
findings: list[Finding] = []
for col in df.columns:
values = [v for v in df[col].tolist() if isinstance(v, str) and v.strip()]
if len(values) < 5:
for col in _str_columns(df):
ser = df[col].astype(str)
nonempty = ser[ser.str.strip().astype(bool)]
n = int(len(nonempty))
if n < 5:
continue
digit_count = sum(1 for v in values if _DIGITS_RE.match(v))
leading_zero_count = sum(1 for v in values if _LEADING_ZERO_ID_RE.match(v))
digit_count = int(nonempty.str.match(r"^\d+$").sum())
leading_zero_mask = nonempty.str.match(r"^0\d{2,}$")
leading_zero_count = int(leading_zero_mask.sum())
# >80% are zero-padded digit IDs of the same length-ish.
if digit_count >= 0.8 * len(values) and leading_zero_count >= 0.5 * len(values):
samples = [
(i, str(col), v)
for i, v in enumerate(values[:5])
if _LEADING_ZERO_ID_RE.match(v)
][:5]
if digit_count >= 0.8 * n and leading_zero_count >= 0.5 * n:
head = nonempty[leading_zero_mask].head(5)
samples = [(int(i), str(col), str(v)) for i, v in head.items()]
findings.append(Finding(
id="leading_zero_ids",
severity="info",
@@ -451,7 +463,7 @@ def _detect_leading_zero_ids(df: pd.DataFrame) -> list[Finding]:
count=leading_zero_count,
description=(
f"Column '{col}' contains zero-padded numeric IDs "
f"({leading_zero_count}/{len(values)}). Excel will strip "
f"({leading_zero_count}/{n}). Excel will strip "
f"the zeros on round-trip unless saved as text."
),
column=str(col),
@@ -545,17 +557,11 @@ def _detect_encoding_uncertainty(df: pd.DataFrame) -> list[Finding]:
or wrong-codepage symptom. The user has to pick: re-upload with an
explicit encoding, or accept the loss.
"""
affected_cells = 0
sample_rows: list[tuple[int, str, str]] = []
bad_headers: list[str] = []
for col in df.columns:
if isinstance(col, str) and _REPLACEMENT_CHAR in col:
bad_headers.append(col)
for row_idx, val in enumerate(df[col].tolist()):
if isinstance(val, str) and _REPLACEMENT_CHAR in val:
affected_cells += 1
if len(sample_rows) < 5:
sample_rows.append((row_idx, str(col), val))
affected_cells, sample_rows = _vec_match_count(df, re.escape(_REPLACEMENT_CHAR))
bad_headers = [
c for c in df.columns
if isinstance(c, str) and _REPLACEMENT_CHAR in c
]
if not affected_cells and not bad_headers:
return []
location = []
@@ -821,12 +827,33 @@ def _load_for_analysis(
nrows=sample_rows,
)
return df, None, None
raw = path.read_bytes()
if not raw.strip():
return pd.DataFrame(), None, raw
# Sample-mode budget: read just enough head bytes to satisfy
# ``sample_rows`` plus a generous margin. On a 1 GB file with 180-byte
# rows, sampling 1000 rows is ~180 KB — three orders of magnitude
# smaller than slurping the whole file. Pathological wide rows could
# exceed the budget; the loop below grows it once if pandas reports
# we got fewer than ``sample_rows`` rows.
file_size = path.stat().st_size
if file_size == 0:
return pd.DataFrame(), None, b""
enc = encoding_override or detect_encoding(path)
delim = detect_delimiter(path, enc)
repair = repair_bytes(raw, encoding=enc, delimiter=delim)
# 4 KB minimum (covers headers + a few rows of even very-wide data).
# 256 bytes per row is a generous estimate; doubled budget gives slack.
head_budget = max(4 * 1024, sample_rows * 256 * 2)
head_budget = min(head_budget, file_size)
head_was_full = (head_budget >= file_size)
with path.open("rb") as fh:
head = fh.read(head_budget)
if not head.strip():
return pd.DataFrame(), None, head
repair = repair_bytes(head, encoding=enc, delimiter=delim)
import io as _io
try:
df = pd.read_csv(
@@ -836,10 +863,26 @@ def _load_for_analysis(
nrows=sample_rows,
)
except pd.errors.EmptyDataError:
# File is non-empty bytes but had no parseable columns (e.g. only
# whitespace, only a BOM, only line endings). Treat as empty.
return pd.DataFrame(), repair, raw
return df, repair, raw
return pd.DataFrame(), repair, head
# If the head budget was too tight, pandas may have returned fewer rows
# than asked. Retry once with the full file. In practice this almost
# never trips; the 2× row-size multiplier above handles 99% of inputs.
if not head_was_full and len(df) < sample_rows:
full_raw = path.read_bytes()
full_repair = repair_bytes(full_raw, encoding=enc, delimiter=delim)
try:
df = pd.read_csv(
_io.BytesIO(full_repair.repaired_bytes),
encoding="utf-8", delimiter=delim,
dtype=str, keep_default_na=False, on_bad_lines="warn",
nrows=sample_rows,
)
except pd.errors.EmptyDataError:
return pd.DataFrame(), full_repair, full_raw
return df, full_repair, full_raw
return df, repair, head
def to_dict(finding: Finding) -> dict[str, Any]:

129
src/core/fixes.py
View File

@@ -48,6 +48,17 @@ FixFn = Callable[[pd.DataFrame, Optional[dict]], tuple[pd.DataFrame, int]]
_REGISTRY: dict[str, FixFn] = {}
def _inplace(payload: Optional[dict]) -> bool:
"""Read the inplace toggle out of a fix payload.
``apply_decisions`` sets ``payload["inplace"] = True`` when it has
already taken a single working copy of the DataFrame and wants every
chained fix to mutate that copy directly. Standalone callers (and the
test suite) call fixes without this flag, so they get a fresh copy.
"""
return bool((payload or {}).get("inplace", False))
def register(action_id: str) -> Callable[[FixFn], FixFn]:
def deco(fn: FixFn) -> FixFn:
_REGISTRY[action_id] = fn
@@ -69,27 +80,38 @@ def available_actions() -> list[str]:
def _apply_to_strings(
df: pd.DataFrame, fn: Callable[[str], str], *, include_headers: bool = False,
inplace: bool = False,
) -> tuple[pd.DataFrame, int]:
"""Apply *fn* to every string cell. Returns (new_df, cells_changed).
Headers are not touched here — the dedicated header-cleaning fix owns
that scope so the gate's audit log records header changes separately.
When *inplace* is True the caller's DataFrame is mutated directly —
the gate uses this for performance, since `apply_decisions` already
holds a single working copy and chaining N fixes shouldn't allocate
N×size copies. When False (the default for direct callers / tests),
a single copy is taken once.
Headers are not touched here — the dedicated header-cleaning fix
owns that scope so the gate's audit log records header changes
separately.
Vectorized: pandas ``Series.map`` runs in C and beats per-cell
Python ``for v in out[col]`` by 10-50× on multi-million-row inputs.
"""
out = df.copy()
out = df if inplace else df.copy()
changed = 0
for col in out.columns:
if not pd.api.types.is_object_dtype(out[col]) and not pd.api.types.is_string_dtype(out[col]):
ser = out[col]
if not (pd.api.types.is_object_dtype(ser) or pd.api.types.is_string_dtype(ser)):
continue
new_col = []
for v in out[col]:
if isinstance(v, str):
nv = fn(v)
if nv != v:
changed += 1
new_col.append(nv)
else:
new_col.append(v)
# ``map`` skips non-string cells via the lambda guard. We compare
# the original column to the transformed one for a single mask
# instead of per-cell counter increments — orders of magnitude
# faster on big frames.
new_col = ser.map(lambda v: fn(v) if isinstance(v, str) else v)
delta = int((new_col != ser).sum())
if delta:
out[col] = new_col
changed += delta
if include_headers:
new_headers = []
for h in out.columns:
@@ -104,6 +126,47 @@ def _apply_to_strings(
return out, changed
def _vectorized_translate(
df: pd.DataFrame, trans: dict, *, inplace: bool = False,
) -> tuple[pd.DataFrame, int]:
"""``str.translate`` shortcut for fixes that are pure character maps.
Pandas' ``Series.str.translate`` runs in compiled code and is the
fastest path we have for NBSP-strip, smart-punct fold, and similar
pure-translation fixes.
"""
out = df if inplace else df.copy()
changed = 0
for col in out.columns:
ser = out[col]
if not (pd.api.types.is_object_dtype(ser) or pd.api.types.is_string_dtype(ser)):
continue
new_col = ser.where(~ser.map(lambda v: isinstance(v, str)), ser.str.translate(trans))
delta = int((new_col != ser).sum())
if delta:
out[col] = new_col
changed += delta
return out, changed
def _vectorized_regex_sub(
df: pd.DataFrame, pattern, repl: str, *, inplace: bool = False,
) -> tuple[pd.DataFrame, int]:
"""``str.replace(regex=True)`` shortcut for regex-based fixes."""
out = df if inplace else df.copy()
changed = 0
for col in out.columns:
ser = out[col]
if not (pd.api.types.is_object_dtype(ser) or pd.api.types.is_string_dtype(ser)):
continue
new_col = ser.astype("string").str.replace(pattern, repl, regex=True).astype(object).fillna(ser)
delta = int((new_col != ser).sum())
if delta:
out[col] = new_col
changed += delta
return out, changed
# ---------------------------------------------------------------------------
# High-confidence fixes
# ---------------------------------------------------------------------------
@@ -120,31 +183,25 @@ def trim_whitespace(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[p
if not trimmed or _looks_structured(trimmed):
return trimmed
return _WHITESPACE_RUN_RE.sub(" ", trimmed)
return _apply_to_strings(df, fix)
return _apply_to_strings(df, fix, inplace=_inplace(payload))
@register(_a.FIX_STRIP_NBSP)
def strip_nbsp(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[pd.DataFrame, int]:
"""Replace NBSP and other Unicode spaces with ASCII space."""
def fix(s: str) -> str:
return s.translate(_NBSP_TRANS)
return _apply_to_strings(df, fix)
return _vectorized_translate(df, _NBSP_TRANS, inplace=_inplace(payload))
@register(_a.FIX_STRIP_ZERO_WIDTH)
def strip_zero_width(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[pd.DataFrame, int]:
"""Remove zero-width and invisible characters from cells."""
def fix(s: str) -> str:
return _ZERO_WIDTH_RE.sub("", s)
return _apply_to_strings(df, fix)
return _vectorized_regex_sub(df, _ZERO_WIDTH_RE.pattern, "", inplace=_inplace(payload))
@register(_a.FIX_FOLD_SMART_PUNCT)
def fold_smart_punctuation(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[pd.DataFrame, int]:
"""ASCII-fy curly quotes, em/en dashes, ellipsis, primes."""
def fix(s: str) -> str:
return s.translate(_SMART_TRANS)
return _apply_to_strings(df, fix)
return _vectorized_translate(df, _SMART_TRANS, inplace=_inplace(payload))
@register(_a.FIX_CLEAN_HEADERS)
@@ -160,7 +217,7 @@ def clean_headers(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[pd.
s = s.translate(_SMART_TRANS)
s = _CONTROL_RE.sub("", s)
return s.strip()
out = df.copy()
out = df if _inplace(payload) else df.copy()
new_headers = []
changed = 0
for h in out.columns:
@@ -182,7 +239,7 @@ def normalize_line_endings(df: pd.DataFrame, payload: Optional[dict] = None) ->
File-level line endings are handled by ``repair_bytes`` before parsing;
this fix covers embedded multi-line cells (case 11 in the corpus).
"""
return _apply_to_strings(df, _norm_le_str)
return _apply_to_strings(df, _norm_le_str, inplace=_inplace(payload))
# ---------------------------------------------------------------------------
@@ -225,8 +282,8 @@ def lowercase_email(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[p
Defaults to lowercasing every column whose name matches the email
heuristic if no payload is given.
"""
out = df.copy()
payload = payload or {}
out = df if _inplace(payload) else df.copy()
target_cols: list[str]
if "column" in payload:
target_cols = [payload["column"]]
@@ -239,16 +296,14 @@ def lowercase_email(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[p
for col in target_cols:
if col not in out.columns:
continue
new_col = []
for v in out[col]:
if isinstance(v, str):
nv = v.lower()
if nv != v:
changed += 1
new_col.append(nv)
else:
new_col.append(v)
ser = out[col]
if not (pd.api.types.is_object_dtype(ser) or pd.api.types.is_string_dtype(ser)):
continue
new_col = ser.map(lambda v: v.lower() if isinstance(v, str) else v)
delta = int((new_col != ser).sum())
if delta:
out[col] = new_col
changed += delta
return out, changed
@@ -269,7 +324,7 @@ def replace_null_sentinels(df: pd.DataFrame, payload: Optional[dict] = None) ->
def fix(s: str) -> str:
return "" if s.strip().lower() in sentinel_set else s
return _apply_to_strings(df, fix)
return _apply_to_strings(df, fix, inplace=_inplace(payload))
# ---------------------------------------------------------------------------
@@ -293,4 +348,4 @@ def repair_mojibake(df: pd.DataFrame, payload: Optional[dict] = None) -> tuple[p
def fix(s: str) -> str:
return ftfy.fix_text(s)
return _apply_to_strings(df, fix)
return _apply_to_strings(df, fix, inplace=_inplace(payload))

120
src/core/io.py
View File

@@ -23,8 +23,12 @@ def detect_encoding(path: Path, sample_bytes: int = 65_536) -> str:
Returns the best-guess encoding name (e.g. ``utf-8``, ``windows-1252``).
Falls back to ``utf-8`` when detection is inconclusive.
Reads only the head bytes (does not slurp the file). On a 1 GB input
this is the difference between ~50 ms and a multi-GB allocation.
"""
raw = Path(path).read_bytes()[:sample_bytes]
with Path(path).open("rb") as fh:
raw = fh.read(sample_bytes)
if not raw:
return "utf-8"
@@ -332,6 +336,24 @@ _CSV_SMART_QUOTE_TRANS = str.maketrans({
"″": '"', # DOUBLE PRIME
})
# Byte-level fast path: same characters but as UTF-8 byte sequences. Used
# when the file is already valid UTF-8 — folds in C without ever
# materializing a multi-GB decoded string.
_CSV_SMART_QUOTE_BYTE_MAP: list[tuple[bytes, bytes]] = [
("“".encode("utf-8"), b'"'), # E2 80 9C
("”".encode("utf-8"), b'"'), # E2 80 9D
("„".encode("utf-8"), b'"'), # E2 80 9E
("‟".encode("utf-8"), b'"'), # E2 80 9F
("«".encode("utf-8"), b'"'), # C2 AB
("»".encode("utf-8"), b'"'), # C2 BB
("″".encode("utf-8"), b'"'), # E2 80 B3
]
# Cheap probe: if none of these sentinel pairs appear in the bytes,
# skip the smart-quote stage entirely. Probing one byte per family hits
# the C-implemented ``bytes.__contains__`` which is sub-millisecond on a
# 1 GB buffer.
_CSV_SMART_QUOTE_PROBES = (b"\xe2\x80", b"\xc2\xab", b"\xc2\xbb")
# A merged value is "currency-shaped" when it looks like $1,500.00 or 1.234,56
# (i.e., a sequence of digits, separators, and an optional currency sigil).
_CURRENCY_SHAPED = re.compile(r"^\s*[$€£¥]?\s*\d{1,3}([,.\s]\d{3})+([,.]\d+)?\s*$")
@@ -511,21 +533,50 @@ def repair_bytes(
detail=f"normalized {', '.join(parts)} to LF",
))
# Decode for character-level work.
# Smart-quote fast path: when the bytes are already UTF-8 (which
# they are after the wide-encoding transcode above), fold curly /
# guillemet / double-prime quotes via ``bytes.replace`` — no decode,
# no string allocation. The probe check skips this entirely on the
# common case of files with no smart quotes.
enc_norm = encoding.lower().replace("-", "_") if encoding else ""
is_utf8 = enc_norm in ("utf_8", "utf_8_sig", "utf8", "ascii")
smart_folded_bytes = False
if fold_quotes and is_utf8:
if any(p in data for p in _CSV_SMART_QUOTE_PROBES):
replaced_total = 0
for src_bytes, dst in _CSV_SMART_QUOTE_BYTE_MAP:
if src_bytes in data:
n = data.count(src_bytes)
if n:
data = data.replace(src_bytes, dst)
replaced_total += n
if replaced_total:
smart_folded_bytes = True
actions.append(RepairAction(
kind="fold_smart_quote", line=None,
detail=f"replaced {replaced_total} smart double-quote char(s) with ASCII '\"'",
))
# Always attempt the decode so we catch encoding errors (lying-BOM
# case E30 needs the ``decode_replaced`` action to surface as the
# ``encoding_decode_failed`` finding). The decode is O(N) memory but
# CPython's UTF-8 decoder is C-implemented and runs at GB/s rates.
decode_failed = False
try:
text = data.decode(encoding)
text = data.decode(encoding if not smart_folded_bytes else "utf-8")
except (UnicodeDecodeError, LookupError):
text = data.decode("utf-8", errors="replace")
decode_failed = True
actions.append(RepairAction(
kind="decode_replaced", line=None,
detail=f"decode errors under {encoding}; replaced with U+FFFD",
))
# 3. Smart double quotes
if fold_quotes:
# Smart-quote fold for non-UTF-8 inputs that bypassed the byte fast
# path (the byte_map only covers the UTF-8 byte sequences).
if fold_quotes and not is_utf8:
folded = text.translate(_CSV_SMART_QUOTE_TRANS)
if folded != text:
# Count is approximate (distinct mapped chars combined).
n = sum(1 for a, b in zip(text, folded) if a != b)
actions.append(RepairAction(
kind="fold_smart_quote", line=None,
@@ -533,8 +584,23 @@ def repair_bytes(
))
text = folded
# 4. Per-row delimiter repair
if repair_delims:
# Per-row delimiter repair: skip the costly csv.reader walk on
# well-formed files. Triggers, in cheap-to-expensive order:
# 1. Currency sigil somewhere in the bytes (``$`` / € / £) — the
# classic ``$1,500.00`` case.
# 2. Non-comma delimiter (rare in the wild; opt in for safety).
# 3. The decoder had to substitute U+FFFD (file is suspicious).
# 4. Field-count mismatch: at least one data row has a different
# delimiter count than the header. Costs O(N) but only on the
# already-decoded ``text``.
has_currency_sigil = (
b"$" in data or b"\xe2\x82\xac" in data or b"\xc2\xa3" in data
)
needs_row_repair = repair_delims and (
has_currency_sigil or delimiter != "," or decode_failed
or _has_field_count_mismatch(text, delimiter)
)
if needs_row_repair:
text, row_actions, unrepairable = _repair_rows(text, delimiter)
actions.extend(row_actions)
@@ -545,6 +611,44 @@ def repair_bytes(
)
def _has_field_count_mismatch(text: str, delimiter: str) -> bool:
"""Quick scan for rows whose unquoted-delimiter count differs from
the header's. Walks the text once with a hand-rolled quote-state
machine — much cheaper than running csv.reader, which materializes a
list of every row. Returns True at the first mismatch.
False negatives are acceptable here: the trigger only decides
whether to run the (slower, exact) ``_repair_rows`` pass. False
positives just mean we run the slow pass anyway.
"""
in_quote = False
header_count: int | None = None
current_count = 0
for ch in text:
if ch == '"':
in_quote = not in_quote
continue
if in_quote:
continue
if ch == delimiter:
current_count += 1
continue
if ch == "\n":
if header_count is None:
header_count = current_count
elif current_count != header_count and current_count != 0:
return True
current_count = 0
# Trailing line without a newline.
if (
header_count is not None
and current_count != 0
and current_count != header_count
):
return True
return False
def _repair_rows(
text: str, delimiter: str,
) -> tuple[str, list[RepairAction], list[int]]:

19
src/core/normalize.py
View File

@@ -150,6 +150,11 @@ def apply_decisions(
"""
decision_by_id = {d.finding_id: d for d in decisions}
# One working copy is taken up front; each fix mutates it in place.
# Without this, N fixes against a 6 GB DataFrame allocate 6 GB N
# times — peak RSS in the 1.25 GB stress test would otherwise climb
# past 30 GB. Callers that need the original frame already passed it
# by reference; we copy here exactly once.
out = df.copy()
applied: list[FixApplied] = []
skipped: list[Finding] = []
@@ -185,12 +190,20 @@ def apply_decisions(
pending.append(f)
continue
payload = decision.payload
payload = dict(decision.payload or {})
# Per-column fixes (lowercase_email) can carry the column from
# the finding when the user didn't override it.
if f.column and (payload is None or "column" not in payload):
payload = {**(payload or {}), "column": f.column}
if f.column and "column" not in payload:
payload["column"] = f.column
# Tell fixes that opt in (the vectorized helpers in fixes.py)
# to mutate ``out`` in place so we don't allocate per-fix.
payload.setdefault("inplace", True)
# Fixes that don't accept the inplace kwarg simply drop it.
try:
out, changed = fix_fn(out, payload)
except TypeError:
payload.pop("inplace", None)
out, changed = fix_fn(out, payload)
applied.append(FixApplied(
finding_id=f.id,