Massive refactoring. Csv import still works
This commit is contained in:
Jonas Linter
@@ -161,6 +161,47 @@ class CSVImporter:
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self.customer_service = CustomerService(db_session)
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self.reservation_service = ReservationService(db_session)
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def _dryrun_csv_file(self, csv_file_path: str) -> dict[str, Any]:
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"""Parse CSV file and return first 10 rows without importing.
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Args:
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csv_file_path: Path to CSV file
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Returns:
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Dictionary with headers and rows
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"""
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df = pd.read_csv(csv_file_path, encoding="utf-8-sig", nrows=10).fillna("")
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df = self._normalize_csv_columns(df)
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return {
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"headers": df.columns.tolist(),
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"rows": df.to_dict(orient="records"),
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}
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def _normalize_csv_columns(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Normalize and rename CSV columns based on mapping.
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Handles both standard column renames and positional renaming for child age columns
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that appear in the landing page form CSV format.
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"""
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# Apply standard column rename mapping
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rename_dict = {col: self.COLUMN_RENAME_MAP.get(col, col) for col in df.columns}
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df = df.rename(columns=rename_dict)
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# Handle positional renaming for child age columns (landing page form format)
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# These appear as unnamed columns immediately after num_children
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col_list = list(df.columns)
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if "num_children" in col_list and "kind_ages_csv" not in col_list:
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num_children_idx = col_list.index("num_children")
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# Rename the next 10 columns as child ages (1-10)
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for i in range(1, 11):
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if num_children_idx + i < len(col_list):
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col_name = col_list[num_children_idx + i]
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if not col_name.startswith("child_"):
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df.rename(columns={col_name: f"child_{i}_age"}, inplace=True)
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return df
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def _get_hotel_info(self, hotel_code: str) -> tuple[str, str]:
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"""Get hotel name from config by hotel_code.
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@@ -270,67 +311,13 @@ class CSVImporter:
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await self.db_session.begin()
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try:
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# Handle dry-run mode
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if dryrun:
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df = pd.read_csv(path, encoding="utf-8-sig", nrows=10).fillna("")
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return self._dryrun_csv_file(path)
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# Rename columns based on mapping
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rename_dict = {col: self.COLUMN_RENAME_MAP.get(col, col) for col in df.columns}
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df = df.rename(columns=rename_dict)
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dryrun_data = {
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"headers": df.columns.tolist(),
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"rows": df.to_dict(orient="records"),
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}
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# Print formatted output
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print("\n=== CSV Import Dry Run ===")
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print(f"\nHeaders ({len(df.columns)} columns):")
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for i, header in enumerate(df.columns, 1):
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print(f" {i}. {header}")
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print(f"\nFirst {len(df)} rows:")
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print(df.to_string())
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# Find and print rows with num_children > 0
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print("\n=== Rows with num_children > 0 ===")
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for row_num, row in df.iterrows():
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try:
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num_children = int(row.get("num_children", 0) or 0)
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if num_children > 0:
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print(f"\nRow {row_num + 2}:")
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print(row.to_string())
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except:
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pass
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return dryrun_data
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# Load CSV with pandas
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# Load and prepare CSV
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df = pd.read_csv(path, encoding="utf-8-sig").fillna("")
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# Rename columns based on mapping
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rename_dict = {col: self.COLUMN_RENAME_MAP.get(col, col) for col in df.columns}
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df = df.rename(columns=rename_dict)
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# Handle positional renaming for child age columns
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# After "num_children" (column 5, 0-indexed), the next 10 columns are child ages
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# and columns after that are duplicates (child_1_age_duplicate, child_2_age_duplicate)
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# BUT only if we don't already have kind_ages_csv (from leads export format)
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col_list = list(df.columns)
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if "num_children" in col_list and "kind_ages_csv" not in col_list:
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num_children_idx = col_list.index("num_children")
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# The 10 columns after num_children are child ages (1-10)
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for i in range(1, 11):
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if num_children_idx + i < len(col_list):
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col_name = col_list[num_children_idx + i]
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# Only rename if not already renamed
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if not col_name.startswith("child_"):
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df.rename(columns={col_name: f"child_{i}_age"}, inplace=True)
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col_list[num_children_idx + i] = f"child_{i}_age"
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# Debug: log the column names after renaming
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_LOGGER.debug("CSV columns after rename: %s", list(df.columns))
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df = self._normalize_csv_columns(df)
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stats = {
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"total_rows": 0,
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@@ -343,41 +330,24 @@ class CSVImporter:
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"errors": [],
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}
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# Helper function to parse dates
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def parse_date_str(date_str: str) -> Optional[date]:
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"""Parse date string in various formats."""
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if not date_str or not isinstance(date_str, str):
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return None
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date_str = date_str.strip()
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for fmt in ["%Y-%m-%d", "%d.%m.%Y", "%d/%m/%Y"]:
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try:
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return datetime.strptime(date_str, fmt).date()
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except ValueError:
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continue
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return None
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# Process each row - stop on first error for debugging
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# Process each row
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for row_num, row in df.iterrows():
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stats["total_rows"] += 1
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row_num += 2 # Convert to 1-based and account for header
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# Extract required fields (using renamed column names)
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# Extract and validate required fields
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first_name = str(row.get("first_name", "")).strip()
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last_name = str(row.get("last_name", "")).strip()
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email = str(row.get("email", "")).strip()
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# Validate required name fields
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if not first_name or not last_name:
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_LOGGER.warning("Skipping row %d: missing name", row_num)
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stats["skipped_empty"] += 1
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continue
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# Parse and validate dates
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start_date_str = str(row.get("check_in_date", "")).strip()
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end_date_str = str(row.get("check_out_date", "")).strip()
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start_date = parse_date_str(start_date_str)
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end_date = parse_date_str(end_date_str)
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start_date = self._parse_date(str(row.get("check_in_date", "")).strip())
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end_date = self._parse_date(str(row.get("check_out_date", "")).strip())
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if not start_date or not end_date:
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_LOGGER.warning("Skipping row %d: invalid or missing dates", row_num)
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@@ -402,199 +372,43 @@ class CSVImporter:
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stats["skipped_duplicates"] += 1
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continue
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# Build customer data from CSV row
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customer_data = {
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"given_name": first_name,
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"surname": last_name,
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"name_prefix": str(row.get("salutation", "")).strip() or None,
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"email_address": email or None,
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"phone": str(row.get("phone", "")).strip() or None,
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"email_newsletter": self._parse_bool(row.get("newsletter_opt_in")),
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"address_line": None,
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"city_name": None,
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"postal_code": None,
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"country_code": None,
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"gender": None,
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"birth_date": None,
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"language": "de",
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"address_catalog": False,
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"name_title": None,
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}
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# Get or create customer (without committing)
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# Get or create customer
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customer_data = self._build_customer_data(first_name, last_name, email, row)
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customer = await self._find_or_create_customer(customer_data, auto_commit=False)
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if customer.id is None:
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await self.db_session.flush() # Flush to get customer.id
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await self.db_session.flush()
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stats["created_customers"] += 1
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else:
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stats["existing_customers"] += 1
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# Build reservation data from CSV row
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try:
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num_adults = int(row.get("num_adults", 1) or 1)
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except (ValueError, TypeError):
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num_adults = 1
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# Parse adult/children counts and extract ages
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num_adults = self._parse_int(row.get("num_adults", 1), default=1)
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num_children = self._parse_int(row.get("num_children", 0), default=0)
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children_ages, age_adjustment, adjusted_num_children = self._extract_children_ages(row, num_children)
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num_adults += age_adjustment
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num_children = adjusted_num_children if adjusted_num_children > 0 else num_children
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try:
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num_children = int(row.get("num_children", 0) or 0)
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except (ValueError, TypeError):
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num_children = 0
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# Extract children ages from columns (including duplicates)
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children_ages = []
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# Check if we have kind_ages_csv (from leads export format)
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kind_ages_csv = str(row.get("kind_ages_csv", "")).strip()
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if kind_ages_csv and kind_ages_csv.lower() != "nan":
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# Parse comma-separated ages
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try:
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ages_list = [int(age.strip()) for age in kind_ages_csv.split(",") if age.strip()]
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# Separate valid children (0-17) from young adults (18+)
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# 18-year-olds are counted as adults, not children
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valid_children = [age for age in ages_list if 0 <= age <= 17]
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young_adults = [age for age in ages_list if age >= 18]
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children_ages = valid_children
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# If we found 18+ year olds, adjust num_children and num_adults accordingly
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if young_adults:
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num_children = len(valid_children)
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num_adults += len(young_adults)
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_LOGGER.debug(
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f"Row {row_num}: Found {len(young_adults)} young adults (18+). "
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f"Adjusted num_children to {num_children}, num_adults to {num_adults}"
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)
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except (ValueError, TypeError):
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pass
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# If no kind_ages_csv, try to extract ages from individual columns
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if not children_ages:
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young_adults = []
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# Try to extract ages from renamed columns first
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# Check primary child age columns (1-10)
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for i in range(1, 11):
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age_key = f"child_{i}_age"
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age_val = row.get(age_key, "")
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if age_val != "" and age_val is not None:
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try:
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# Handle both int and float values (e.g., 3, 3.0)
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age = int(float(age_val))
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if 0 <= age <= 17:
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children_ages.append(age)
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elif age >= 18:
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young_adults.append(age)
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except (ValueError, TypeError):
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pass
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# Check for duplicate child age columns (e.g., child_1_age_duplicate, child_2_age_duplicate)
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for i in range(1, 3): # Only 1.1 and 2.1 duplicates mentioned
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age_key = f"child_{i}_age_duplicate"
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age_val = row.get(age_key, "")
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if age_val != "" and age_val is not None:
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try:
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# Handle both int and float values (e.g., 3, 3.0)
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age = int(float(age_val))
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if 0 <= age <= 17:
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children_ages.append(age)
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elif age >= 18:
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young_adults.append(age)
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except (ValueError, TypeError):
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pass
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# Adjust num_children and num_adults if we found 18+ year olds
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if young_adults:
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num_children = len(children_ages)
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num_adults += len(young_adults)
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_LOGGER.debug(
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f"Row {row_num}: Found {len(young_adults)} young adults (18+) in individual columns. "
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f"Adjusted num_children to {num_children}, num_adults to {num_adults}"
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)
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# Debug: log extraction details
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_LOGGER.debug(
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"Row %d: num_children=%d, extracted %d ages: %s, kind_ages_csv=%s",
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row_num,
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num_children,
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len(children_ages),
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children_ages,
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kind_ages_csv,
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# Build and create reservation
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reservation = self._build_reservation_data(
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row, start_date, end_date, num_adults, num_children,
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children_ages, fbclid, gclid, hotel_code, row_num
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)
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# If we extracted ages but num_children says there are different number,
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# compact the list to match num_children. Remove ages "0" first
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if len(children_ages) > num_children:
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# Remove ages "0" first, but only as many as needed
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num_to_remove = len(children_ages) - num_children
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for _ in range(num_to_remove):
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if 0 in children_ages:
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children_ages.remove(0)
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else:
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# If no "0" ages left, just remove the last one
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children_ages.pop()
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# Generate unique ID (use submission timestamp if available, else row number)
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submission_ts = str(row.get("submission_timestamp", "")).strip()
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if submission_ts:
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submission_id = submission_ts
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else:
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submission_id = f"csv_import_{row_num}_{datetime.now().isoformat()}"
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# Determine hotel code and name (from config)
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final_hotel_code, final_hotel_name = self._get_hotel_info(hotel_code)
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# Parse room type fields if available
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room_type_code = str(row.get("room_type_code", "")).strip() or None
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room_class_code = str(row.get("room_classification_code", "")).strip() or None
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# Build and validate ReservationData
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reservation = ReservationData(
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unique_id=submission_id,
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start_date=start_date,
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end_date=end_date,
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num_adults=num_adults,
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num_children=num_children,
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children_ages=children_ages,
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hotel_code=final_hotel_code,
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hotel_name=final_hotel_name,
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offer=str(row.get("room_offer", "")).strip() or None,
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user_comment=str(row.get("message", "")).strip() or None,
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fbclid=fbclid,
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gclid=gclid,
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utm_source=str(row.get("utm_source", "")).strip() or None,
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utm_medium=str(row.get("utm_medium", "")).strip() or None,
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utm_campaign=str(row.get("utm_campaign", "")).strip() or None,
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utm_term=str(row.get("utm_term", "")).strip() or None,
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utm_content=str(row.get("utm_content", "")).strip() or None,
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room_type_code=room_type_code,
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room_classification_code=room_class_code,
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db_reservation = await self.reservation_service.create_reservation(
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reservation, customer.id, auto_commit=False
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)
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stats["created_reservations"] += 1
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_LOGGER.info("Created reservation for %s %s", first_name, last_name)
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# Create reservation if customer exists (without committing)
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if customer.id:
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db_reservation = await self.reservation_service.create_reservation(
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reservation, customer.id, auto_commit=False
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# Pre-acknowledge if requested
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if pre_acknowledge and db_reservation.md5_unique_id:
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await self.reservation_service.record_acknowledgement(
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client_id=client_id,
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unique_id=db_reservation.md5_unique_id,
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username=username,
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auto_commit=False
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)
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stats["created_reservations"] += 1
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_LOGGER.info("Created reservation for %s %s", first_name, last_name)
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# Pre-acknowledge if requested
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if pre_acknowledge and db_reservation.md5_unique_id:
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await self.reservation_service.record_acknowledgement(
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client_id=client_id,
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unique_id=db_reservation.md5_unique_id,
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username=username,
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auto_commit=False
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)
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stats["pre_acknowledged"] += 1
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_LOGGER.debug(
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"Pre-acknowledged reservation %s for client %s",
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db_reservation.md5_unique_id,
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username or client_id
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)
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else:
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raise ValueError("Failed to get or create customer")
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stats["pre_acknowledged"] += 1
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@@ -611,6 +425,148 @@ class CSVImporter:
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return stats
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def _parse_int(self, value: Any, default: int = 0) -> int:
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"""Parse value to int, returning default if parsing fails."""
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try:
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return int(value) if value else default
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except (ValueError, TypeError):
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return default
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def _build_customer_data(self, first_name: str, last_name: str, email: str, row: Any) -> dict:
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"""Build customer data dictionary from CSV row."""
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return {
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"given_name": first_name,
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"surname": last_name,
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"name_prefix": str(row.get("salutation", "")).strip() or None,
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"email_address": email or None,
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"phone": str(row.get("phone", "")).strip() or None,
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"email_newsletter": self._parse_bool(row.get("newsletter_opt_in")),
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"address_line": None,
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"city_name": None,
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"postal_code": None,
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"country_code": None,
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"gender": None,
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"birth_date": None,
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"language": "de",
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"address_catalog": False,
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"name_title": None,
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}
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def _build_reservation_data(
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self, row: Any, start_date: date, end_date: date, num_adults: int,
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num_children: int, children_ages: list[int], fbclid: Optional[str],
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gclid: Optional[str], hotel_code: str, row_num: int
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) -> ReservationData:
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"""Build ReservationData from CSV row."""
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submission_ts = str(row.get("submission_timestamp", "")).strip()
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submission_id = submission_ts if submission_ts else f"csv_import_{row_num}_{datetime.now().isoformat()}"
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final_hotel_code, final_hotel_name = self._get_hotel_info(hotel_code)
|
||||
room_type_code = str(row.get("room_type_code", "")).strip() or None
|
||||
room_class_code = str(row.get("room_classification_code", "")).strip() or None
|
||||
|
||||
return ReservationData(
|
||||
unique_id=submission_id,
|
||||
start_date=start_date,
|
||||
end_date=end_date,
|
||||
num_adults=num_adults,
|
||||
num_children=num_children,
|
||||
children_ages=children_ages,
|
||||
hotel_code=final_hotel_code,
|
||||
hotel_name=final_hotel_name,
|
||||
offer=str(row.get("room_offer", "")).strip() or None,
|
||||
user_comment=str(row.get("message", "")).strip() or None,
|
||||
fbclid=fbclid,
|
||||
gclid=gclid,
|
||||
utm_source=str(row.get("utm_source", "")).strip() or None,
|
||||
utm_medium=str(row.get("utm_medium", "")).strip() or None,
|
||||
utm_campaign=str(row.get("utm_campaign", "")).strip() or None,
|
||||
utm_term=str(row.get("utm_term", "")).strip() or None,
|
||||
utm_content=str(row.get("utm_content", "")).strip() or None,
|
||||
room_type_code=room_type_code,
|
||||
room_classification_code=room_class_code,
|
||||
)
|
||||
|
||||
def _parse_date(self, date_str: str) -> Optional[date]:
|
||||
"""Parse date string in various formats.
|
||||
|
||||
Supports: YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY
|
||||
"""
|
||||
if not date_str or not isinstance(date_str, str):
|
||||
return None
|
||||
date_str = date_str.strip()
|
||||
for fmt in ["%Y-%m-%d", "%d.%m.%Y", "%d/%m/%Y"]:
|
||||
try:
|
||||
return datetime.strptime(date_str, fmt).date()
|
||||
except ValueError:
|
||||
continue
|
||||
return None
|
||||
|
||||
def _extract_children_ages(self, row: Any, num_children: int) -> tuple[list[int], int, int]:
|
||||
"""Extract and parse children ages from CSV row.
|
||||
|
||||
Handles both CSV format (comma-separated) and individual columns.
|
||||
Returns (children_ages, adjusted_num_adults, adjusted_num_children) where:
|
||||
- adjusted_num_adults accounts for 18+ year-olds in the ages list
|
||||
- adjusted_num_children is the actual count of extracted children ages
|
||||
"""
|
||||
children_ages = []
|
||||
num_adults_adjustment = 0
|
||||
|
||||
# Try comma-separated ages first (from leads export format)
|
||||
kind_ages_csv = str(row.get("kind_ages_csv", "")).strip()
|
||||
if kind_ages_csv and kind_ages_csv.lower() != "nan":
|
||||
try:
|
||||
ages_list = [int(age.strip()) for age in kind_ages_csv.split(",") if age.strip()]
|
||||
children_ages = [age for age in ages_list if 0 <= age <= 17]
|
||||
young_adults = [age for age in ages_list if age >= 18]
|
||||
num_adults_adjustment = len(young_adults)
|
||||
adjusted_num_children = len(children_ages)
|
||||
return children_ages, num_adults_adjustment, adjusted_num_children
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
# Try individual column ages if no CSV format found
|
||||
young_adults = []
|
||||
for i in range(1, 11): # Check child_1_age through child_10_age
|
||||
age_val = row.get(f"child_{i}_age", "")
|
||||
if age_val != "" and age_val is not None:
|
||||
try:
|
||||
age = int(float(age_val))
|
||||
if 0 <= age <= 17:
|
||||
children_ages.append(age)
|
||||
elif age >= 18:
|
||||
young_adults.append(age)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
# Check for duplicate child age columns
|
||||
for i in range(1, 3): # child_1_age_duplicate, child_2_age_duplicate
|
||||
age_val = row.get(f"child_{i}_age_duplicate", "")
|
||||
if age_val != "" and age_val is not None:
|
||||
try:
|
||||
age = int(float(age_val))
|
||||
if 0 <= age <= 17:
|
||||
children_ages.append(age)
|
||||
elif age >= 18:
|
||||
young_adults.append(age)
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
num_adults_adjustment = len(young_adults)
|
||||
|
||||
# Trim ages list if it exceeds num_children
|
||||
if len(children_ages) > num_children:
|
||||
num_to_remove = len(children_ages) - num_children
|
||||
for _ in range(num_to_remove):
|
||||
if 0 in children_ages:
|
||||
children_ages.remove(0)
|
||||
else:
|
||||
children_ages.pop()
|
||||
|
||||
adjusted_num_children = len(children_ages)
|
||||
return children_ages, num_adults_adjustment, adjusted_num_children
|
||||
|
||||
def _parse_bool(self, value: Any) -> Optional[bool]:
|
||||
"""Parse various boolean representations to bool or None.
|
||||
|
||||
@@ -671,19 +627,9 @@ class CSVImporter:
|
||||
|
||||
if existing:
|
||||
# Update customer data if needed
|
||||
try:
|
||||
existing_customer = await self.customer_service.update_customer(
|
||||
existing, customer_data, auto_commit=auto_commit
|
||||
)
|
||||
except Exception as e:
|
||||
|
||||
print(customer_data)
|
||||
print("---")
|
||||
print(existing)
|
||||
|
||||
|
||||
raise
|
||||
|
||||
existing_customer = await self.customer_service.update_customer(
|
||||
existing, customer_data, auto_commit=auto_commit
|
||||
)
|
||||
return existing_customer
|
||||
|
||||
# Create new customer
|
||||
|
||||
Reference in New Issue
Block a user