modification du clean csv et du makefile

This commit is contained in:
951095 2025-09-19 12:44:06 +02:00
parent 55d4b4ff85
commit eea1bd8c4b
2 changed files with 99 additions and 27 deletions

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@ -36,10 +36,18 @@ import:
@until docker exec -i $(PG_CONTAINER) pg_isready -U $(PG_USER) -d $(PG_DB); do \
sleep 2; \
done
@echo "🧹 Nettoyage des CSV avant import (local -> in-place)..."
@python3 scripts/clean_csv.py
@if [ $$? -ne 0 ]; then echo "❌ Erreurs lors du nettoyage des CSV - corrige les fichiers avant import"; exit 1; fi
@echo "📥 Import des données de démo..."
@docker exec -i $(PG_CONTAINER) psql -U $(PG_USER) -d $(PG_DB) -f $(IMPORT_SQL)
@echo "✅ Données importées."
set-dev-admin-pw:
@echo "🔐 Définir mot de passe dev pour admin@ptits-pas.fr (admin123)"
@docker exec -i $(PG_CONTAINER) psql -U $(PG_USER) -d $(PG_DB) -c "UPDATE utilisateurs SET password = crypt('admin123', gen_salt('bf')) WHERE email = 'admin@ptits-pas.fr';"
@echo "✅ Mot de passe admin dev mis à jour."
verify:
@echo "⏳ Attente que Postgres démarre pour vérifier..."
@until docker exec -i $(PG_CONTAINER) pg_isready -U $(PG_USER) -d $(PG_DB); do \

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@ -1,59 +1,123 @@
#!/usr/bin/env python3
"""scripts/clean_csv.py
Usage:
python3 scripts/clean_csv.py [input_dir] [--out-dir OUT] [--dry-run]
This script cleans CSV files (trim, remove 'NULL', fix column count) and
performs simple validations (UUID-looking columns, date columns).
"""
import csv
import sys
from pathlib import Path
from datetime import datetime
import argparse
import re
def clean_csv_file(file_path: Path):
"""
Nettoie un CSV directement en place :
- remplace "NULL" par ""
- supprime les espaces parasites
- force le même nombre de colonnes que l'en-tête
"""
UUID_RE = re.compile(r'^[0-9a-fA-F-]{36}$')
def is_uuid(s: str) -> bool:
return bool(UUID_RE.match(s))
def looks_like_date(s: str) -> bool:
if not s:
return False
# try ISO-like detection
try:
datetime.fromisoformat(s)
return True
except Exception:
return False
def clean_csv_file(file_path: Path, out_path: Path = None, dry_run: bool = False):
cleaned_rows = []
errors = []
with open(file_path, "r", encoding="utf-8-sig", newline="") as infile:
with file_path.open("r", encoding="utf-8-sig", newline="") as infile:
reader = csv.reader(infile)
try:
header = next(reader)
except StopIteration:
print(f"[⚠️] Fichier vide : {file_path}")
return
return {'file': str(file_path), 'error': 'empty', 'errors': []}
nb_cols = len(header)
cleaned_rows.append([h.strip() for h in header])
# Heuristics for validations
uuid_cols = [i for i, h in enumerate(header) if h.lower() == 'id' or h.lower().endswith('_id')]
date_cols = [i for i, h in enumerate(header) if any(k in h.lower() for k in ('date', '_le', '_at'))]
for i, row in enumerate(reader, start=2):
# Ajuste le nombre de colonnes
if len(row) < nb_cols:
row.extend([""] * (nb_cols - len(row)))
elif len(row) > nb_cols:
row = row[:nb_cols]
# Nettoyage cellule par cellule
row = [cell.strip().replace("NULL", "") for cell in row]
row = [cell.strip().replace('NULL', '') for cell in row]
# Validations simples
for ci in uuid_cols:
if ci < len(row) and row[ci]:
if not is_uuid(row[ci]):
errors.append(f"Line {i}: column {header[ci]} not UUID-like: {row[ci]!r}")
for ci in date_cols:
if ci < len(row) and row[ci]:
if not looks_like_date(row[ci]):
errors.append(f"Line {i}: column {header[ci]} not ISO-like date: {row[ci]!r}")
cleaned_rows.append(row)
# Réécriture dans le même fichier
with open(file_path, "w", encoding="utf-8", newline="") as outfile:
writer = csv.writer(outfile)
writer.writerows(cleaned_rows)
# Write output if not dry-run
if not dry_run:
target = out_path if out_path else file_path
with target.open('w', encoding='utf-8', newline='') as outfile:
writer = csv.writer(outfile)
writer.writerows(cleaned_rows)
print(f"[✔] Nettoyé : {file_path}")
return {'file': str(file_path), 'errors': errors}
def main():
if len(sys.argv) > 1:
base_dir = Path(sys.argv[1])
parser = argparse.ArgumentParser()
parser.add_argument('input_dir', nargs='?', default='bdd/data_test')
parser.add_argument('--out-dir', '-o', help='Optional output directory for cleaned files')
parser.add_argument('--dry-run', action='store_true', help='Do not write files, just report')
args = parser.parse_args()
base_dir = Path(args.input_dir)
out_dir = Path(args.out_dir) if args.out_dir else None
if out_dir and not out_dir.exists():
out_dir.mkdir(parents=True)
results = []
for file_path in sorted(base_dir.glob('*.csv')):
out_path = out_dir / file_path.name if out_dir else None
res = clean_csv_file(file_path, out_path, args.dry_run)
results.append(res)
# Report
any_errors = False
for r in results:
if 'error' in r:
print(f"[WARN] {r['file']}: {r['error']}")
if r.get('errors'):
any_errors = True
print(f"[ERR] {r['file']} ->")
for e in r['errors'][:20]:
print(' -', e)
else:
print(f"[OK] {r['file']}")
if any_errors:
print('\nSome files have validation issues. Fix them or run with --dry-run to inspect.')
sys.exit(2)
else:
base_dir = Path("bdd/data_test")
print(f"🔎 Nettoyage des CSV dans : {base_dir}")
for file_path in base_dir.glob("*.csv"):
clean_csv_file(file_path)
print(f"✅ Terminé. Les fichiers CSV ont été écrasés (nettoyés en place).")
print('\nAll files cleaned successfully.')
if __name__ == "__main__":
if __name__ == '__main__':
main()