mirror of
https://github.com/mblanke/Gov_Travel_App.git
synced 2026-03-01 14:10:22 -05:00
mblanke
15094ac94b
- 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.
36 lines
900 B
Python
36 lines
900 B
Python
import sqlite3
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conn = sqlite3.connect('data/travel_rates_scraped.sqlite3')
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cursor = conn.cursor()
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print("Argentina entries with breakfast:")
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cursor.execute("""
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SELECT country, city, rate_type, rate_amount, currency
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FROM rate_entries
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WHERE country LIKE '%Argentina%' AND rate_type LIKE '%breakfast%'
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LIMIT 5
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""")
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for row in cursor.fetchall():
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print(f" {row}")
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print("\nAlbania entries with breakfast:")
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cursor.execute("""
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SELECT country, city, rate_type, rate_amount, currency
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FROM rate_entries
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WHERE country LIKE '%Albania%' AND rate_type LIKE '%breakfast%'
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LIMIT 5
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""")
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for row in cursor.fetchall():
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print(f" {row}")
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print("\nAll Argentina city entries:")
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cursor.execute("""
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SELECT DISTINCT city, currency
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FROM rate_entries
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WHERE country LIKE '%Argentina%'
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""")
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for row in cursor.fetchall():
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print(f" {row[0]}: {row[1]}")
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conn.close()
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