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feat(format): per-cell standardizers + 199-row buyer corpus

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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>
This commit is contained in:
Michael
2026-05-01 02:11:24 +00:00
parent 3f007ef3d6
commit 4adeb5c7f3
14 changed files with 4364 additions and 69 deletions
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639
src/gui/pages/3_Format_Standardizer.py
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@@ -1,91 +1,594 @@
"""DataTools Format Standardizer — stub page."""
"""DataTools Format Standardizer — Streamlit page."""
from __future__ import annotations
import io
import json
import sys
from pathlib import Path
import pandas as pd
import streamlit as st
_project_root = Path(__file__).resolve().parent.parent.parent.parent
if str(_project_root) not in sys.path:
sys.path.insert(0, str(_project_root))
from src.gui.components import hide_streamlit_chrome, require_normalization_gate
from src.gui.components import (
hide_streamlit_chrome,
pickup_or_upload,
require_normalization_gate,
)
from src.core.format_standardize import (
PRESETS,
FieldType,
StandardizeOptions,
standardize_dataframe,
)
hide_streamlit_chrome()
require_normalization_gate()
# ---------------------------------------------------------------------------
# Header
# ---------------------------------------------------------------------------
st.title("📐 Format Standardizer")
st.caption("Standardize formats across columns for consistency.")
st.info("This tool is under development.")
# ---------------------------------------------------------------------------
# What this tool will do
# ---------------------------------------------------------------------------
st.markdown("""
**Features:**
- Date format standardization (e.g., MM/DD/YYYY → YYYY-MM-DD)
- Phone number formatting (E.164, national, international)
- Currency normalization ($1,000.00 → 1000.00)
- Name casing (JOHN DOE → John Doe)
- Address abbreviation expansion (St. → Street, Ave. → Avenue)
- Boolean standardization (Yes/No/Y/N/1/0 → True/False)
""")
st.divider()
# ---------------------------------------------------------------------------
# File upload (functional)
# ---------------------------------------------------------------------------
uploaded = st.file_uploader(
"Upload CSV or Excel file",
type=["csv", "tsv", "xlsx", "xls"],
help="Upload a file to preview. Processing is not yet available.",
key="fmtstd_file_upload",
)
if uploaded is not None:
import pandas as pd
try:
if uploaded.name.endswith((".xlsx", ".xls")):
df = pd.read_excel(uploaded)
else:
df = pd.read_csv(uploaded)
st.subheader(f"Preview: {uploaded.name}")
st.caption(f"{len(df)} rows, {len(df.columns)} columns")
st.dataframe(df.head(10), use_container_width=True)
except Exception as e:
st.error(f"Failed to read file: {e}")
# ---------------------------------------------------------------------------
# Placeholder options
# ---------------------------------------------------------------------------
st.subheader("Format Rules")
st.selectbox("Date format", ["YYYY-MM-DD", "MM/DD/YYYY", "DD/MM/YYYY", "DD-Mon-YYYY"], disabled=True)
st.selectbox("Phone format", ["E.164 (+15551234567)", "National ((555) 123-4567)", "Digits only"], disabled=True)
st.selectbox("Currency handling", ["Strip symbols, keep number", "Normalize to 2 decimals", "Keep as-is"], disabled=True)
st.selectbox("Name casing", ["Title Case", "UPPER", "lower", "As-is"], disabled=True)
st.checkbox("Expand address abbreviations", value=False, disabled=True)
st.divider()
st.button("Standardize Formats", type="primary", use_container_width=True, disabled=True)
# ---------------------------------------------------------------------------
# Footer
# ---------------------------------------------------------------------------
st.divider()
st.caption(
"Runs locally. Your data never leaves this computer. "
"| DataTools v3.0"
"Canonicalize dates, phone numbers, currency, names, addresses, and "
"booleans on a per-column basis. Runs locally — your data never leaves "
"this computer."
)
# ---------------------------------------------------------------------------
# File upload
# ---------------------------------------------------------------------------
uploaded = pickup_or_upload(
label="Upload CSV or Excel file",
key="fmtstd_file_upload",
types=["csv", "tsv", "xlsx", "xls"],
)
if uploaded is None:
st.info("Upload a CSV, TSV, or Excel file to begin.")
st.stop()
@st.cache_data(show_spinner=False)
def _read_uploaded(name: str, data: bytes) -> pd.DataFrame:
"""Read the uploaded bytes into a DataFrame, treating all cells as strings."""
suffix = Path(name).suffix.lower()
bio = io.BytesIO(data)
if suffix in (".xlsx", ".xls"):
return pd.read_excel(bio, dtype=str, keep_default_na=False)
for enc in ("utf-8", "utf-8-sig", "latin-1"):
try:
bio.seek(0)
sep = "\t" if suffix == ".tsv" else ","
return pd.read_csv(
bio, dtype=str, keep_default_na=False,
encoding=enc, sep=sep, on_bad_lines="warn",
)
except UnicodeDecodeError:
continue
bio.seek(0)
return pd.read_csv(bio, dtype=str, keep_default_na=False, encoding="latin-1")
try:
df = _read_uploaded(uploaded.name, uploaded.getvalue())
except Exception as e:
st.error(f"Failed to read file: {e}")
st.stop()
st.subheader(f"Preview: {uploaded.name}")
st.caption(f"{len(df)} rows, {len(df.columns)} columns")
st.dataframe(df.head(10), use_container_width=True)
st.divider()
# ---------------------------------------------------------------------------
# Auto-detect column types
# ---------------------------------------------------------------------------
#
# A first pass over a 200-row sample picks a likely field type per column.
# It's a hint, not a commitment — every column shows a selectbox the user
# can override. Heuristics deliberately err toward "(skip)" rather than
# guessing wrong, since wrong guesses produce misleading change audits.
import re as _re
_DATE_HINT_RE = _re.compile(
r"^\s*\d{1,4}[-/.]\d{1,2}[-/.]\d{1,4}\s*$"
r"|^\s*[A-Za-z]{3,9}\s+\d{1,2}[, ]+\d{2,4}\s*$"
r"|^\s*\d{1,2}\s+[A-Za-z]{3,9}\s+\d{2,4}\s*$"
)
_PHONE_HINT_RE = _re.compile(r"^[\s\d().+\-]+$")
_CURRENCY_HINT_RE = _re.compile(r"^[\s$€£¥]?\s*-?\d[\d,. ]*\d?\s*$|^\s*\(\s*[$€£¥]?\d.*\)\s*$")
_BOOL_TOKENS = {"yes", "no", "y", "n", "true", "false", "t", "f", "0", "1"}
def _detect_field_type(col: str, samples: list[str]) -> FieldType | None:
"""Return a likely :class:`FieldType` for *col*, or None when unsure.
Strategy: drop empties, then require ≥80% of remaining sample cells to
fit the type's hint regex. Boolean check runs first because ``0/1`` also
matches the currency regex; date/phone/currency next; address/name fall
back to header-name keywords because their cell shapes overlap with
plain free text.
"""
cells = [s.strip() for s in samples if isinstance(s, str) and s.strip()]
if not cells:
return None
n = len(cells)
threshold = max(1, int(n * 0.8))
bool_hits = sum(1 for c in cells if c.casefold() in _BOOL_TOKENS)
if bool_hits >= threshold:
return FieldType.BOOLEAN
date_hits = sum(1 for c in cells if _DATE_HINT_RE.match(c))
if date_hits >= threshold:
return FieldType.DATE
# Phone: digit-heavy, 7+ digits, no letters.
phone_hits = 0
for c in cells:
if _PHONE_HINT_RE.match(c) and sum(1 for ch in c if ch.isdigit()) >= 7:
phone_hits += 1
if phone_hits >= threshold:
return FieldType.PHONE
currency_hits = sum(1 for c in cells if _CURRENCY_HINT_RE.match(c))
if currency_hits >= threshold:
return FieldType.CURRENCY
header = col.lower()
if any(tok in header for tok in ("address", "addr", "street")):
return FieldType.ADDRESS
if any(tok in header for tok in ("name", "customer", "contact")):
return FieldType.NAME
if any(tok in header for tok in ("date", "dob", "birth", "joined", "created")):
return FieldType.DATE
if any(tok in header for tok in ("phone", "mobile", "tel")):
return FieldType.PHONE
if any(tok in header for tok in ("price", "amount", "cost", "total", "fee")):
return FieldType.CURRENCY
if any(tok in header for tok in ("active", "enabled", "is_", "has_", "flag")):
return FieldType.BOOLEAN
return None
# ---------------------------------------------------------------------------
# Options
# ---------------------------------------------------------------------------
st.subheader("Column types")
st.caption(
"Assign each column to a field type. Auto-detected suggestions are "
"pre-filled; pick **(skip)** to leave a column untouched."
)
_FIELD_LABELS = {
"(skip)": None,
"Date": FieldType.DATE,
"Phone": FieldType.PHONE,
"Currency": FieldType.CURRENCY,
"Name": FieldType.NAME,
"Address": FieldType.ADDRESS,
"Boolean": FieldType.BOOLEAN,
}
_LABEL_BY_TYPE = {v: k for k, v in _FIELD_LABELS.items()}
_LABELS = list(_FIELD_LABELS.keys())
sample_size = min(len(df), 200)
sample_df = df.head(sample_size)
column_types: dict[str, FieldType] = {}
cols_per_row = 3
columns_iter = list(df.columns)
for i in range(0, len(columns_iter), cols_per_row):
cols_block = st.columns(cols_per_row)
for j, col_name in enumerate(columns_iter[i:i + cols_per_row]):
with cols_block[j]:
detected = _detect_field_type(col_name, sample_df[col_name].tolist())
default_label = _LABEL_BY_TYPE.get(detected, "(skip)")
chosen = st.selectbox(
col_name,
_LABELS,
index=_LABELS.index(default_label),
key=f"fmtstd_type__{col_name}",
)
ft = _FIELD_LABELS[chosen]
if ft is not None:
column_types[col_name] = ft
st.divider()
st.subheader("Format options")
# ---------------------------------------------------------------------------
# Preset bundle picker
# ---------------------------------------------------------------------------
#
# Picking a preset rewrites every option below to that preset's defaults.
# It does NOT touch column-type assignments — those are user-driven and
# orthogonal. To make the rewrite stick across the rerun, we stash the
# preset values into the per-option session keys; the widgets below read
# those keys via their ``index``/``value`` arguments.
_PRESET_LABELS = {
"us-default": "US (default) — ISO 8601 dates · E.164 phones · USD",
"european": "European — DMY input · INTL phones · EUR comma decimal",
"uk": "UK — DD/MM/YYYY · GB phones · Yes/No booleans",
"iso-strict": "ISO Strict — ISO 8601 · bare-number currency · true/false",
"legacy-us": "Legacy US — MM/DD/YYYY · National phones · Yes/No",
"custom": "Custom — keep current settings",
}
preset_choice = st.radio(
"Standards preset",
list(_PRESET_LABELS.keys()),
format_func=lambda k: _PRESET_LABELS[k],
index=0,
horizontal=False,
key="fmtstd_preset",
help=(
"Pick a published standard or regional convention as the baseline. "
"Every option below is still individually overridable; choose "
"**Custom** to keep whatever you've manually adjusted."
),
)
# Detect a preset switch since the last rerun; when it changes (and the
# new choice isn't ``custom``), purge the dependent widget keys so
# Streamlit lets their ``index=``/``value=`` defaults take effect on the
# new render. Without this clear, prior session_state pins the widget to
# the previous preset's choice and the apparent picker becomes a no-op.
_DEPENDENT_KEYS = [
"fmtstd_date_format", "fmtstd_date_order",
"fmtstd_phone_format", "fmtstd_phone_region",
"fmtstd_currency_decimal", "fmtstd_currency_decimals",
"fmtstd_currency_preserve", "fmtstd_currency_preserve_code",
"fmtstd_name_case", "fmtstd_bool_style",
]
_last = st.session_state.get("fmtstd_preset_last")
if _last != preset_choice:
st.session_state["fmtstd_preset_last"] = preset_choice
if preset_choice != "custom":
for k in _DEPENDENT_KEYS:
st.session_state.pop(k, None)
st.rerun()
# Map preset → widget-state defaults. Done as labels so the radios/selects
# below pick up the right index without us re-implementing each map twice.
_PRESET_TO_WIDGETS: dict[str, dict[str, str]] = {
"us-default": {
"date_format": "YYYY-MM-DD (ISO)", "date_order": "MDY (US)",
"phone_format": "E.164 (+15551234567)", "phone_region": "US",
"currency_decimal": "dot (1,234.56)", "currency_decimals": 2,
"currency_preserve_code": False,
"name_case": "Title Case", "boolean_style": "True/False",
},
"european": {
"date_format": "YYYY-MM-DD (ISO)", "date_order": "DMY (EU)",
"phone_format": "International (+1 555-123-4567)", "phone_region": "DE",
"currency_decimal": "comma (1.234,56)", "currency_decimals": 2,
"currency_preserve_code": True,
"name_case": "Title Case", "boolean_style": "True/False",
},
"uk": {
"date_format": "DD/MM/YYYY", "date_order": "DMY (EU)",
"phone_format": "International (+1 555-123-4567)", "phone_region": "GB",
"currency_decimal": "dot (1,234.56)", "currency_decimals": 2,
"currency_preserve_code": False,
"name_case": "Title Case", "boolean_style": "Yes/No",
},
"iso-strict": {
"date_format": "YYYY-MM-DD (ISO)", "date_order": "MDY (US)",
"phone_format": "E.164 (+15551234567)", "phone_region": "US",
"currency_decimal": "dot (1,234.56)", "currency_decimals": 0,
"currency_preserve_code": True,
"name_case": "Title Case", "boolean_style": "true/false",
},
"legacy-us": {
"date_format": "MM/DD/YYYY", "date_order": "MDY (US)",
"phone_format": "National ((555) 123-4567)", "phone_region": "US",
"currency_decimal": "dot (1,234.56)", "currency_decimals": 2,
"currency_preserve_code": False,
"name_case": "Title Case", "boolean_style": "Yes/No",
},
}
# ``iso-strict`` wants currency with no rounding; the GUI exposes that via
# the "preserve original precision" checkbox rather than a sentinel value
# in the number-input. Map that here.
_PRESET_PRESERVE_DECIMALS: dict[str, bool] = {
"iso-strict": True,
}
def _preset_default(key: str, fallback):
"""Pull the preset-driven default for *key*, or *fallback* on Custom."""
if preset_choice == "custom":
return fallback
return _PRESET_TO_WIDGETS[preset_choice].get(key, fallback)
opt_cols = st.columns(2)
with opt_cols[0]:
st.markdown("**Dates**")
_DATE_LABELS = ["YYYY-MM-DD (ISO)", "MM/DD/YYYY", "DD/MM/YYYY", "DD-Mon-YYYY", "Mon DD, YYYY"]
date_format_label = st.selectbox(
"Output format",
_DATE_LABELS,
index=_DATE_LABELS.index(_preset_default("date_format", "YYYY-MM-DD (ISO)")),
key="fmtstd_date_format",
)
date_format_map = {
"YYYY-MM-DD (ISO)": "%Y-%m-%d",
"MM/DD/YYYY": "%m/%d/%Y",
"DD/MM/YYYY": "%d/%m/%Y",
"DD-Mon-YYYY": "%d-%b-%Y",
"Mon DD, YYYY": "%b %d, %Y",
}
_DATE_ORDER_LABELS = ["MDY (US)", "DMY (EU)"]
date_order = st.radio(
"Ambiguous input order (e.g. 01/02/2024)",
_DATE_ORDER_LABELS,
index=_DATE_ORDER_LABELS.index(_preset_default("date_order", "MDY (US)")),
horizontal=True,
key="fmtstd_date_order",
)
st.markdown("**Phones**")
_PHONE_LABELS = [
"E.164 (+15551234567)", "International (+1 555-123-4567)",
"National ((555) 123-4567)", "Digits only",
]
phone_format_label = st.selectbox(
"Output format",
_PHONE_LABELS,
index=_PHONE_LABELS.index(_preset_default("phone_format", "E.164 (+15551234567)")),
key="fmtstd_phone_format",
)
phone_format_map = {
"E.164 (+15551234567)": "E164",
"International (+1 555-123-4567)": "INTERNATIONAL",
"National ((555) 123-4567)": "NATIONAL",
"Digits only": "DIGITS",
}
phone_region = st.text_input(
"Default region (ISO-2)",
value=_preset_default("phone_region", "US"),
max_chars=2,
help="Region used when the input has no country code. ``US``, ``GB``, ``DE``, etc.",
key="fmtstd_phone_region",
).upper() or "US"
with opt_cols[1]:
st.markdown("**Currency**")
_CURR_DECIMAL_LABELS = ["dot (1,234.56)", "comma (1.234,56)"]
currency_decimal = st.radio(
"Decimal separator in input",
_CURR_DECIMAL_LABELS,
index=_CURR_DECIMAL_LABELS.index(_preset_default("currency_decimal", "dot (1,234.56)")),
horizontal=True,
key="fmtstd_currency_decimal",
)
currency_decimals = st.number_input(
"Round to decimals",
min_value=0, max_value=8,
value=int(_preset_default("currency_decimals", 2)),
step=1,
key="fmtstd_currency_decimals",
)
preserve_decimals = st.checkbox(
"Preserve original precision (don't round)",
value=_PRESET_PRESERVE_DECIMALS.get(preset_choice, False),
key="fmtstd_currency_preserve",
)
currency_preserve_code = st.checkbox(
"Preserve currency code (emit `USD 1234.56`, `EUR 99.00`, etc.)",
value=bool(_preset_default("currency_preserve_code", False)),
help=(
"Detects an ISO 4217 code or symbol in the input ($/€/£/¥/USD/"
"EUR/...) and re-emits it as a space-separated prefix on the "
"standardized number. Cells without a currency marker emit "
"just the number."
),
key="fmtstd_currency_preserve_code",
)
st.markdown("**Names**")
_NAME_CASE_LABELS = ["Title Case", "UPPER", "lower"]
name_case_label = st.selectbox(
"Casing",
_NAME_CASE_LABELS,
index=_NAME_CASE_LABELS.index(_preset_default("name_case", "Title Case")),
key="fmtstd_name_case",
)
name_case_map = {"Title Case": "title", "UPPER": "upper", "lower": "lower"}
st.markdown("**Booleans**")
_BOOL_LABELS = ["True/False", "true/false", "Yes/No", "Y/N", "1/0"]
boolean_style = st.selectbox(
"Output style",
_BOOL_LABELS,
index=_BOOL_LABELS.index(_preset_default("boolean_style", "True/False")),
key="fmtstd_bool_style",
)
# ---------------------------------------------------------------------------
# Address abbreviations — built-in USPS table is editable
# ---------------------------------------------------------------------------
#
# Users with international addresses (German Strasse, Spanish-language
# Avenida, French Boulevard variants) need to override the built-in
# table. Show it in a data_editor so the override is visible — the table
# is small, this is the right surface.
extra_abbreviations: dict[str, str] = {}
if any(ft == FieldType.ADDRESS for ft in column_types.values()):
with st.expander("Custom address abbreviations (advanced)", expanded=False):
st.caption(
"Add or override entries in the address abbreviation table. "
"Each row maps a short form (case-insensitive, periods OK) to "
"the long form the standardizer should emit. Built-in USPS "
"Pub. 28 entries (`St` → `Street`, `Ave` → `Avenue`, …) apply "
"automatically; rows here merge on top and can override them."
)
starter = pd.DataFrame(
[
{"abbreviation": "", "expansion": ""},
{"abbreviation": "", "expansion": ""},
{"abbreviation": "", "expansion": ""},
]
)
edited = st.data_editor(
starter,
num_rows="dynamic",
use_container_width=True,
column_config={
"abbreviation": st.column_config.TextColumn(
"Short form",
help="Case-insensitive, trailing period optional. e.g. ``Strasse``",
),
"expansion": st.column_config.TextColumn(
"Long form",
help="What the standardizer emits. e.g. ``Straße``",
),
},
key="fmtstd_extra_abbrev",
)
for _, row in edited.iterrows():
k = str(row.get("abbreviation") or "").strip()
v = str(row.get("expansion") or "").strip()
if k and v:
extra_abbreviations[k] = v
if extra_abbreviations:
st.success(
f"{len(extra_abbreviations)} custom mapping(s) will merge "
"with the built-in table."
)
options = StandardizeOptions(
column_types=column_types,
date_output_format=date_format_map[date_format_label],
date_order="MDY" if date_order.startswith("MDY") else "DMY",
phone_format=phone_format_map[phone_format_label], # type: ignore[arg-type]
phone_region=phone_region,
currency_decimal="dot" if currency_decimal.startswith("dot") else "comma",
currency_decimals=None if preserve_decimals else int(currency_decimals),
currency_preserve_code=currency_preserve_code,
name_case=name_case_map[name_case_label], # type: ignore[arg-type]
boolean_style=boolean_style, # type: ignore[arg-type]
extra_abbreviations=extra_abbreviations,
)
# ---------------------------------------------------------------------------
# Run
# ---------------------------------------------------------------------------
st.divider()
if not column_types:
st.warning("Pick a field type for at least one column to enable standardization.")
run_disabled = not column_types
if st.button(
"Standardize Formats",
type="primary",
use_container_width=True,
disabled=run_disabled,
):
with st.spinner("Standardizing..."):
try:
result = standardize_dataframe(df, options)
except ValueError as e:
st.error(str(e))
st.stop()
st.session_state["fmtstd_result"] = result
st.session_state["fmtstd_input_name"] = uploaded.name
result = st.session_state.get("fmtstd_result")
if result is None:
st.stop()
# ---------------------------------------------------------------------------
# Results
# ---------------------------------------------------------------------------
st.subheader("Results")
pct = (result.cells_changed / result.cells_total * 100.0) if result.cells_total else 0.0
m1, m2, m3, m4 = st.columns(4)
m1.metric("Cells scanned", result.cells_total)
m2.metric("Cells changed", result.cells_changed)
m3.metric("% changed", f"{pct:.1f}%")
m4.metric("Unparseable", result.cells_unparseable)
if result.cells_unparseable:
st.info(
f"{result.cells_unparseable} cell(s) in typed columns didn't match a "
"recognizable shape and were left as-is. Check the changes audit "
"below to find them, or re-classify the column to **(skip)**."
)
if result.cells_changed:
counts = result.changes.groupby(["column", "field_type"]).size()
st.markdown("**Changes by column**")
st.dataframe(
counts.rename("cells_changed").to_frame(),
use_container_width=True,
)
st.markdown("**Examples (first 25 changes)**")
examples = result.changes.head(25).copy()
examples["row"] = examples["row"] + 1
st.dataframe(examples, use_container_width=True, hide_index=True)
st.markdown("**Standardized preview (first 10 rows)**")
st.dataframe(result.standardized_df.head(10), use_container_width=True)
# ---------------------------------------------------------------------------
# Downloads
# ---------------------------------------------------------------------------
st.divider()
stem = Path(st.session_state.get("fmtstd_input_name", "input")).stem
dl_a, dl_b, dl_c = st.columns(3)
with dl_a:
standardized_bytes = result.standardized_df.to_csv(index=False).encode("utf-8-sig")
st.download_button(
"Download standardized CSV",
data=standardized_bytes,
file_name=f"{stem}_standardized.csv",
mime="text/csv",
)
with dl_b:
if not result.changes.empty:
changes_bytes = result.changes.to_csv(index=False).encode("utf-8-sig")
st.download_button(
"Download changes audit",
data=changes_bytes,
file_name=f"{stem}_changes.csv",
mime="text/csv",
)
with dl_c:
config_bytes = json.dumps(options.to_dict(), indent=2).encode("utf-8")
st.download_button(
"Download config JSON",
data=config_bytes,
file_name="format_standardize_config.json",
mime="application/json",
)
st.divider()
st.caption("Runs locally. Your data never leaves this computer. | DataTools v3.0")