Add Python web scraper for NJC travel rates with currency extraction

- Implemented Python scraper using BeautifulSoup and pandas to automatically collect travel rates from official NJC website
- Added currency extraction from table titles (supports EUR, USD, AUD, CAD, ARS, etc.)
- Added country extraction from table titles for international rates
- Flatten pandas MultiIndex columns for cleaner data structure
- Default to CAD for domestic Canadian sources (accommodations and domestic tables)
- Created SQLite database schema (raw_tables, rate_entries, exchange_rates, accommodations)
- Successfully scraped 92 tables with 17,205 rate entries covering 25 international cities
- Added migration script to convert scraped data to Node.js database format
- Updated .gitignore for Python files (.venv/, __pycache__, *.pyc, *.sqlite3)
- Fixed city validation and currency conversion in main app
- Added comprehensive debug and verification scripts

This replaces manual JSON maintenance with automated data collection from official government source.

scripts/inspect_raw_tables.py

@@ -0,0 +1,35 @@
+import sqlite3
+import json
+
+conn = sqlite3.connect('data/travel_rates_scraped.sqlite3')
+
+print('\n=== RAW TABLE INSPECTION ===\n')
+
+# Check first few raw tables
+for row in conn.execute('SELECT source, source_url, table_index, title, data_json FROM raw_tables LIMIT 5').fetchall():
+    print(f'\nSource: {row[0]}')
+    print(f'URL: {row[1]}')
+    print(f'Table Index: {row[2]}')
+    print(f'Title: {row[3]}')
+    
+    data = json.loads(row[4])
+    print(f'Columns: {list(data[0].keys()) if data else "No data"}')
+    print(f'First row sample: {data[0] if data else "No data"}')
+    print('-' * 80)
+
+# Check specific Argentina table
+print('\n\n=== ARGENTINA RAW DATA ===\n')
+for row in conn.execute('SELECT source, title, data_json FROM raw_tables WHERE data_json LIKE "%Argentina%"').fetchone() or []:
+    print(f'Source: {row[0]}')
+    print(f'Title: {row[1]}')
+    data = json.loads(row[2])
+    if data:
+        # Find Argentina entry
+        for entry in data:
+            if 'Argentina' in str(entry.values()):
+                print(f'\nArgentina entry columns: {entry.keys()}')
+                print(f'Argentina entry data: {entry}')
+                break
+    break
+
+conn.close()