This tutorial demonstrates how to use Python's requests, lxml, and pprint libraries to crawl the Hao123 travel website, extract attraction names, opening times, reviews, and prices, and display the results, while providing complete code examples and practical tips.
This article details a complete workflow for extracting nationwide tourist attraction data from Qunar, cleaning and enriching it with geographic coordinates, and performing multi‑level statistical analysis and visualizations—including sales rankings, popularity metrics, heatmaps, and word clouds—to reveal regional tourism patterns across China.
This article demonstrates how to crawl Qunar’s nationwide tourism listings with Python, extract key fields such as name, level, location and price, handle anti‑scraping measures, and then perform comprehensive data analysis and visualisation—including sales rankings, popularity scores, geographic distribution and geocoding using the Amap API.
