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dataconvert: spreadsheets to SQL seeds, layouts from config, row cap, SCHEMA.md

Browse Source 
Converts CSV, xlsx/xls/ods, directories, ZIPs and globs into one INSERT
file per table. Producer-specific layouts (header/data rows found by a
marker cell) and sheet naming live in a gitignored dataconvert.json, with
dataconvert-example.json as the template. --max-rows samples each table
and SCHEMA.md records columns, types, row counts and full sizes.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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# Local layouts: how one producer's spreadsheets are shaped is not a fact about
# the tool, and the marker values name whoever that is. Copy
# dataconvert-example.json to dataconvert.json and edit that; it stays here.
dataconvert.json

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# dataconvert
Spreadsheets and CSV into schema-agnostic SQL seed files, one per table or sheet,
plus a `SCHEMA.md` describing every table.
```bash
uv run dataconvert.py --input data/ --out-dir seed/ # full seeds
uv run dataconvert.py --input data/ "*.xlsx" --out-dir seed/ # dirs, files, globs, .zip
uv run dataconvert.py --input data/ --out-dir sample/ --max-rows 20 # to understand the data
uv run dataconvert.py --input export.xlsx --header-row 2 --data-row 6
uv run dataconvert.py --input data/ --config their-exports.json
```
Reads `.csv`, `.xlsx`, `.xls` and `.ods`: single files, directories (recursively),
ZIP archives and wildcard patterns. Sheets of a multi-sheet workbook are written as
`<workbook>_<sheet>.sql` (see `bare_sheet_prefixes` below). Several
sources feeding the same table accumulate in one file, and so does re-running into
the same `--out-dir`, so point a fresh run at an empty directory.
## Layouts: config, not code
By default row 1 holds the column names and the data starts on row 2. Exports that
put a title above the header, or description rows between it and the data, are
described in `dataconvert.json` beside the script (or `--config FILE`). It is
gitignored because the marker values name whoever produced the files; start from
`dataconvert-example.json`.
```json
{
"layouts": [
{ "name": "catalogue",
"match": { "row": 2, "column": 1, "in": ["CODE", "ITEM_CODE"] },
"header_row": 2, "data_row": 6 }
],
"bare_sheet_prefixes": ["ref_"]
}
```
- **layouts** are checked against every sheet and CSV, first match wins. A layout
matches when the cell at `match.row`/`match.column` (counted from 1, trimmed) is one
of `match.in`; then the names come from `header_row` and the data from `data_row`
on. The run prints which layout each sheet got. A sheet nothing matches is read
normally.
- **bare_sheet_prefixes**: sheets whose name starts with one of these are written as
`<sheet>.sql` instead of `<workbook>_<sheet>.sql`.
For a one-off, `--header-row 2 --data-row 6` applies one layout to every file in the
run and skips detection.
## Sampling for a web LLM
Full seed files get large fast, and a model only needs to see the shape of the data.
With `--max-rows N`:
- every table still gets its `.sql` file, holding only its first N rows, with a header
line such as `-- SAMPLE: first 20 of 184233 rows. The full file would be ~48.1M`;
- `SCHEMA.md` lists every table with its total rows and the size the full seed file
would be, then each table's columns: original header, inferred type, null count
and one example value.
`SCHEMA.md` alone is often enough to hand to the model. Full sizes of sampled tables
are estimated from the rows written, so they carry a `~`. Types are inferred from
what pandas read: a starting point, not DDL. `--no-schema` skips the file.
## Layout
| file | does |
|---|---|
| `dataconvert.py` | command line |
| `config.py` | `dataconvert.json`: layouts and sheet naming |
| `readers.py` | files, directories, ZIPs and globs into DataFrames |
| `sqlgen.py` | DataFrames into INSERT statements, with the row cap |
| `output.py` | file naming and writing |
| `schema.py` | `SCHEMA.md` |
The modules import each other by name, so the folder works wherever it is copied:
`uv run dataconvert.py` from inside it, or `python3 path/to/dataconvert.py` with
pandas, openpyxl and odfpy installed.

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"""
Config: what a particular set of spreadsheets looks like.
The tool knows nothing about any one exporter. Where a header sits, which
sheets are recognised by a marker cell, and which sheet names are already
unique enough to keep as they are, are facts about whoever produced the files,
so they live in dataconvert.json beside this script. That file is gitignored:
copy dataconvert-example.json and edit it. Without one, every file is read with
its header on row 1.
{
"layouts": [
{
"name": "catalogue",
"match": {"row": 2, "column": 1, "in": ["CODE", "ITEM"]},
"header_row": 2,
"data_row": 6
}
],
"bare_sheet_prefixes": ["ref_"]
}
Rows and columns are counted as the spreadsheet shows them, from 1. A layout
applies to a sheet (or CSV) when the cell at match.row/match.column, trimmed,
is one of match.in. The first layout that matches wins.
"""
import json
from pathlib import Path
DEFAULT_PATH = Path(__file__).resolve().parent / "dataconvert.json"
class ConfigError(Exception):
pass
class Layout:
"""Where the column names are and where the data starts, from row 1."""
def __init__(self, header_row=1, data_row=None, name="default"):
self.name = name
self.header_row = int(header_row)
self.data_row = int(data_row) if data_row is not None else self.header_row + 1
if self.header_row < 1:
raise ConfigError(f"layout '{name}': header_row must be 1 or more")
if self.data_row <= self.header_row:
raise ConfigError(f"layout '{name}': the data has to start below the header row")
@property
def is_default(self):
return self.header_row == 1 and self.data_row == 2
class Rule:
def __init__(self, raw, index):
name = raw.get("name") or f"layout {index + 1}"
match = raw.get("match") or {}
values = match.get("in")
if not isinstance(values, list) or not values:
raise ConfigError(f"layout '{name}': match.in must be a non-empty list")
self.row = int(match.get("row", 1))
self.column = int(match.get("column", 1))
if self.row < 1 or self.column < 1:
raise ConfigError(f"layout '{name}': match.row and match.column count from 1")
self.values = {str(v).strip() for v in values}
self.layout = Layout(raw.get("header_row", 1), raw.get("data_row"), name)
def matches(self, raw_df):
r, c = self.row - 1, self.column - 1
if len(raw_df) <= r or raw_df.shape[1] <= c:
return False
return str(raw_df.iloc[r, c]).strip() in self.values
class Config:
def __init__(self, rules=(), bare_sheet_prefixes=(), source=None, forced=None):
self.rules = list(rules)
self.bare_sheet_prefixes = tuple(bare_sheet_prefixes)
self.source = source
# --header-row / --data-row on the command line: one layout for every
# file, no detection.
self.forced = forced
@property
def detects(self):
return self.forced is None and bool(self.rules)
def layout_for(self, raw_df):
"""The layout for a sheet read without a header, or None for the default read."""
if self.forced is not None:
return None if self.forced.is_default else self.forced
for rule in self.rules:
if rule.matches(raw_df):
return rule.layout
return None
def load(path=None, forced=None):
"""Read the given config, else dataconvert.json beside this script if there is one."""
explicit = path is not None
path = Path(path) if explicit else DEFAULT_PATH
if not path.exists():
if explicit:
raise ConfigError(f"no such config file: {path}")
return Config(forced=forced)
try:
raw = json.loads(path.read_text(encoding="utf-8"))
except json.JSONDecodeError as e:
raise ConfigError(f"{path} is not valid JSON: {e}")
rules = [Rule(r, i) for i, r in enumerate(raw.get("layouts", []))]
prefixes = raw.get("bare_sheet_prefixes", [])
if not isinstance(prefixes, list):
raise ConfigError(f"{path}: bare_sheet_prefixes must be a list")
return Config(rules, [str(p) for p in prefixes], source=path, forced=forced)

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{
"_comment": "Template for dataconvert.json, which dataconvert.py reads from beside itself (or --config FILE). Copy this to dataconvert.json (gitignored) and describe the files you actually convert there. Rows and columns count from 1, as the spreadsheet shows them.",
"_layouts": "Checked in order against every sheet and CSV; the first match wins, and a sheet nothing matches is read with its header on row 1. A layout matches when the cell at match.row / match.column, trimmed, is one of match.in. header_row holds the column names; data starts at data_row, and anything in between is skipped.",
"layouts": [
{
"name": "catalogue",
"match": { "row": 2, "column": 1, "in": ["CODE", "ITEM_CODE"] },
"header_row": 2,
"data_row": 6
}
],
"_bare_sheet_prefixes": "Sheets of a multi-sheet workbook are written as <workbook>_<sheet>.sql. A sheet whose name starts with one of these is written as <sheet>.sql instead, because its name is already unique.",
"bare_sheet_prefixes": ["ref_"]
}

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#!/usr/bin/env python3
"""
dataconvert
Converts CSV, Excel (.xlsx, .xls), OpenDocument (.ods), directories, ZIP archives,
or wildcard file patterns into schema-agnostic, individual SQL seed files, plus a
SCHEMA.md describing every table.
Usage:
python3 dataconvert.py --input "data/*" "*.xlsx" --out-dir seed/
python3 dataconvert.py --input path/to/folder/ --out-dir seed/
python3 dataconvert.py --input path/to/folder/ --out-dir sample/ --max-rows 20
python3 dataconvert.py --input export.xlsx --header-row 2 --data-row 6
python3 dataconvert.py --input data/ --config their-exports.json
How a given producer lays out its sheets is not built in: see config.py and
dataconvert-example.json.
--max-rows is for understanding the data rather than loading it: every table
still gets its file and its SCHEMA.md entry, with only the first N rows, and a
note of how many rows there are and how big the full file would be.
The modules beside this file are imported by name, so the folder works wherever
it is copied: run this script from anywhere, no install step.
"""
import argparse
from pathlib import Path
from output import write_tables
import config as cfg
from readers import expand_inputs, iter_sources
from schema import SchemaReport
def positive_int(value):
n = int(value)
if n < 1:
raise argparse.ArgumentTypeError("must be 1 or more")
return n
def main():
parser = argparse.ArgumentParser(description="Convert data sources into schema-agnostic SQL seed files.")
parser.add_argument("--input", nargs="+", required=True, help="Input file(s), directory, wildcard pattern(s), or ZIP archive(s)")
parser.add_argument("--out-dir", default="seed", help="Directory where individual .sql files will be written")
parser.add_argument("--max-rows", type=positive_int, default=None,
help="Write at most N rows per table; the header and SCHEMA.md note the full row count and size")
parser.add_argument("--no-schema", action="store_true", help="Do not write SCHEMA.md")
parser.add_argument("--header-row", type=positive_int, default=1,
help="Spreadsheet row holding the column names, for every file; overrides the config layouts (default 1)")
parser.add_argument("--data-row", type=positive_int, default=None,
help="First row of data, when rows sit between it and the header (default: the row after the header)")
parser.add_argument("--config", default=None,
help="Layouts and naming for these files (default: dataconvert.json beside this script, if present)")
args = parser.parse_args()
try:
forced = None
if args.header_row != 1 or args.data_row is not None:
forced = cfg.Layout(args.header_row, args.data_row, "command line")
config = cfg.load(args.config, forced)
except cfg.ConfigError as e:
parser.error(str(e))
if config.source is not None:
print(f"[dataconvert] config: {config.source}")
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
report = None if args.no_schema else SchemaReport()
for in_path in expand_inputs(args.input):
for source_name, dfs in iter_sources(in_path, config):
write_tables(dfs, out_dir, source_name, args.max_rows, report, config.bare_sheet_prefixes)
if report is not None and report.entries:
print(f"[dataconvert] Generated: {report.write(out_dir, args.max_rows)}")
if __name__ == "__main__":
main()

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"""
Output: one .sql file per table or sheet, named after it.
"""
from pathlib import Path
from sqlgen import render_table, sanitize_identifier
def table_filename(raw_name: str, source_name: str, sheet_count: int, bare_prefixes=()) -> str:
"""
Sheets of a multi-sheet workbook are prefixed with the workbook, so two
workbooks cannot collide, unless the config names the sheet as already
unique (bare_sheet_prefixes).
"""
clean = sanitize_identifier(raw_name)
if sheet_count > 1 and not clean.startswith(tuple(bare_prefixes)):
return f"{sanitize_identifier(source_name)}_{clean}.sql"
return f"{clean}.sql"
def write_tables(dfs: dict, out_dir: Path, source_name: str, max_rows=None, report=None, bare_prefixes=()):
"""Write individual .sql files per table/sheet into the output directory."""
for raw_name, df in dfs.items():
if df.empty:
continue
table = sanitize_identifier(raw_name)
filename = table_filename(raw_name, source_name, len(dfs), bare_prefixes)
sql, total, full_bytes, exact = render_table(df, table, max_rows)
out_file = out_dir / filename
# Several sources can feed the same table; they accumulate in one file.
mode = "a" if out_file.exists() else "w"
with open(out_file, mode, encoding="utf-8") as f:
f.write(sql)
shown = total if exact else max_rows
if report is not None:
report.add(source_name, table, filename, df, total, full_bytes, exact, shown)
suffix = "" if exact else f" ({shown} of {total} rows)"
print(f"[dataconvert] Generated: {out_file}{suffix}")

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[project]
name = "dataconvert"
version = "0.1.0"
description = "Spreadsheets and CSV into SQL seed files and a schema summary"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"pandas>=2.2.0",
"openpyxl>=3.1.2",
"odfpy>=1.4.1",
]
[tool.uv]
package = false

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"""
Readers: files, directories, ZIP archives and wildcards into DataFrames.
Each input becomes zero or more (source_name, {entity_name: DataFrame}) pairs,
one per spreadsheet or CSV. Nothing here knows about SQL or output files.
"""
import glob
import os
import tempfile
import zipfile
from pathlib import Path
import pandas as pd
SUPPORTED = {".csv", ".xlsx", ".xls", ".ods"}
def expand_inputs(inputs):
"""Wildcard patterns become the paths they match; everything else passes through."""
paths = []
for in_str in inputs:
if any(c in in_str for c in ["*", "?", "["]):
matched = glob.glob(in_str, recursive=True)
if not matched:
print(f"[Warning] No files matched wildcard pattern: '{in_str}'")
paths.extend(Path(m) for m in sorted(matched))
else:
paths.append(Path(in_str))
return paths
def read_sheet(read, config):
"""
One sheet or CSV, laid out as the config says.
With nothing to detect, a plain read, exactly as before. Otherwise the sheet
is read once without a header, checked against the layouts, and when one
matches the header and data rows are sliced out of that same read.
"""
if not config.detects and config.forced is None:
return read(), None
raw = read(header=None)
layout = config.layout_for(raw)
if layout is None:
return read(), None
data = raw.iloc[layout.data_row - 1:].copy()
data.columns = [str(c).strip() for c in raw.iloc[layout.header_row - 1]]
return data, layout
def load_dataframes_from_file(file_path: Path, config) -> dict:
"""Load a file (.csv, .xlsx, .xls, .ods) into {entity_name: DataFrame}."""
ext = file_path.suffix.lower()
dfs = {}
try:
if ext == ".csv":
dfs[file_path.stem], layout = read_sheet(lambda **kw: pd.read_csv(file_path, **kw), config)
note_layout(file_path.name, None, layout)
elif ext in [".xlsx", ".xls", ".ods"]:
xls = pd.ExcelFile(file_path, engine="odf") if ext == ".ods" else pd.ExcelFile(file_path)
for sheet in xls.sheet_names:
dfs[sheet], layout = read_sheet(
lambda sheet=sheet, **kw: pd.read_excel(xls, sheet_name=sheet, **kw), config)
note_layout(file_path.name, sheet, layout)
except Exception as e:
print(f"[Warning] Could not read '{file_path.name}': {e}")
return dfs
def note_layout(file_name, sheet, layout):
if layout is not None:
where = f"{file_name} [{sheet}]" if sheet else file_name
print(f"[dataconvert] {where}: layout '{layout.name}', header row {layout.header_row}, data from row {layout.data_row}")
def iter_sources(path: Path, config):
"""Yield (source_name, dfs) for a file, a directory (recursively) or a ZIP archive."""
if path.is_file() and path.suffix.lower() == ".zip":
with tempfile.TemporaryDirectory() as tmp_dir:
with zipfile.ZipFile(path, "r") as zip_ref:
zip_ref.extractall(tmp_dir)
yield from iter_sources(Path(tmp_dir), config)
elif path.is_dir():
for root, _, files in os.walk(path):
for f in sorted(files):
f_path = Path(root) / f
if f_path.suffix.lower() in SUPPORTED:
yield f_path.stem, load_dataframes_from_file(f_path, config)
elif path.is_file() and path.suffix.lower() in SUPPORTED:
yield path.stem, load_dataframes_from_file(path, config)

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"""
SCHEMA.md: what each table looks like, without its rows.
Written for reading, by a person or a web LLM that has to understand the data
before anything else: columns, an inferred type, how many are empty, one
example value, and how big the table really is. Types are inferred from what
pandas read, so they are a starting point, not a DDL.
"""
from pathlib import Path
import pandas as pd
from sqlgen import human_bytes, sanitize_identifier
EXAMPLE_MAX = 40
def infer_type(series: pd.Series) -> str:
values = series.dropna()
if values.empty:
return "unknown (all null)"
if pd.api.types.is_bool_dtype(values):
return "boolean"
if pd.api.types.is_integer_dtype(values):
return "integer"
if pd.api.types.is_float_dtype(values):
return "integer" if (values % 1 == 0).all() else "numeric"
if pd.api.types.is_datetime64_any_dtype(values):
return "timestamp"
# Object columns from spreadsheets mix types; name the one that is there.
kinds = {type(v).__name__ for v in values}
if kinds <= {"int", "bool"}:
return "integer"
if kinds <= {"int", "float"}:
return "numeric"
if kinds <= {"datetime", "Timestamp"}:
return "timestamp"
longest = values.astype(str).str.len().max()
return f"text (max {longest})" if kinds == {"str"} else f"mixed ({', '.join(sorted(kinds))})"
def example(series: pd.Series) -> str:
values = series.dropna()
if values.empty:
return ""
text = str(values.iloc[0]).replace("\n", " ").replace("|", "\\|")
return text if len(text) <= EXAMPLE_MAX else text[: EXAMPLE_MAX - 1] + "…"
class SchemaReport:
"""Collects one entry per written table, then writes them as one document."""
def __init__(self):
self.entries = []
def add(self, source, table, filename, df, total_rows, full_bytes, exact, shown_rows):
self.entries.append(dict(
source=source, table=table, filename=filename, df=df,
total_rows=total_rows, full_bytes=full_bytes, exact=exact, shown_rows=shown_rows,
))
def tables(self):
"""
One entry per output file. Several sources can feed the same table, and
the file holds all of them, so the report does too: rows and sizes are
summed, and the columns come from the first source.
"""
merged = {}
for e in self.entries:
m = merged.get(e["filename"])
if m is None:
merged[e["filename"]] = dict(e, sources=[e["source"]])
continue
m["sources"].append(e["source"])
m["total_rows"] += e["total_rows"]
m["full_bytes"] += e["full_bytes"]
m["shown_rows"] += e["shown_rows"]
m["exact"] = m["exact"] and e["exact"]
return list(merged.values())
def write(self, out_dir: Path, max_rows):
tables = self.tables()
lines = ["# Data schema", ""]
total_rows = sum(t["total_rows"] for t in tables)
total_bytes = sum(t["full_bytes"] for t in tables)
lines.append(
f"{len(tables)} tables · {total_rows} rows · full seed files "
f"{'~' if any(not t['exact'] for t in tables) else ''}{human_bytes(total_bytes)}"
)
lines.append("")
if max_rows is not None:
lines += [
f"The .sql files beside this one hold at most {max_rows} rows per table from each",
"source: they are samples for understanding the data, not seeds to load. Sizes",
"marked ~ are estimated from the rows that were written.",
"",
]
lines += ["| table | file | rows | full size | in the file |", "|---|---|---:|---:|---|"]
for t in tables:
size = ("" if t["exact"] else "~") + human_bytes(t["full_bytes"])
kept = "all" if t["exact"] else f"{t['shown_rows']} rows"
lines.append(f"| `{t['table']}` | `{t['filename']}` | {t['total_rows']} | {size} | {kept} |")
lines.append("")
for t in tables:
df = t["df"]
sources = ", ".join(f"`{src}`" for src in t["sources"])
lines += [f"## {t['table']}", "", f"From {sources} · {t['total_rows']} rows · {len(df.columns)} columns", ""]
lines += ["| column | source header | type | nulls | example |", "|---|---|---|---:|---|"]
for col in df.columns:
series = df[col]
header = str(col).replace("|", "\\|")
lines.append(
f"| `{sanitize_identifier(col)}` | {header} | {infer_type(series)} | "
f"{int(series.isna().sum())} | {example(series)} |"
)
lines.append("")
path = out_dir / "SCHEMA.md"
path.write_text("\n".join(lines), encoding="utf-8")
return path

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"""
SQL rendering: DataFrames into schema-agnostic INSERT statements.
With a row cap, only the first rows are rendered, and the size the full output
would have had is estimated from them, so a sample still says how big the real
thing is.
"""
import re
import pandas as pd
def sanitize_identifier(identifier: str) -> str:
"""Sanitize names for SQL tables, columns, and filenames."""
clean = re.sub(r"[^\w]", "_", str(identifier).strip().lower())
clean = re.sub(r"_+", "_", clean)
return clean.strip("_")
def sql_value(v) -> str:
if pd.isna(v):
return "NULL"
if isinstance(v, (bool, int)):
return str(v)
if isinstance(v, float):
return str(int(v)) if v.is_integer() else str(v)
escaped = str(v).replace("'", "''")
return f"'{escaped}'"
def render_table(df: pd.DataFrame, table_name: str, max_rows=None):
"""
Return (sql_text, total_rows, full_bytes, exact).
full_bytes is what the file would weigh with every row: measured when every
row was rendered, extrapolated from the average rendered row otherwise.
"""
total = len(df)
if total == 0:
return "", 0, 0, True
table_ref = f'"{table_name}"'
cols = ", ".join(f'"{sanitize_identifier(c)}"' for c in df.columns)
shown = df if max_rows is None else df.head(max_rows)
rows = []
# iterrows, not itertuples: it hands values over the way the original tool
# did, and seed files people already load depend on exactly that quoting.
for _, row in shown.iterrows():
vals = ", ".join(sql_value(v) for v in row)
rows.append(f"INSERT INTO {table_ref} ({cols}) VALUES ({vals}) ON CONFLICT DO NOTHING;\n")
head = f"-- Generated seed data for table: {table_ref}\n"
begin, commit = "BEGIN;\n\n", "\nCOMMIT;\n"
rows_bytes = sum(len(r.encode("utf-8")) for r in rows)
fixed = len((head + begin + commit).encode("utf-8"))
exact = len(rows) == total
if exact:
full_bytes = fixed + rows_bytes
else:
full_bytes = fixed + round(rows_bytes / len(rows) * total)
note = ""
if not exact:
note = (
f"-- SAMPLE: first {len(rows)} of {total} rows. The full file would be "
f"~{human_bytes(full_bytes)}; run without --max-rows for all of it.\n"
)
return head + note + begin + "".join(rows) + commit, total, full_bytes, exact
def human_bytes(n: int) -> str:
size = float(n)
for unit in ("B", "K", "M", "G"):
if size < 1000 or unit == "G":
return f"{size:.0f}{unit}" if unit == "B" else f"{size:.1f}{unit}"
size /= 1000
return f"{n}B"

320
soleprint/station/tools/dataconvert/uv.lock generated Normal file
View File

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