How to Scrape Qunar Tourist Spot Data and Visualize It with Baidu Maps & ECharts

This tutorial shows how to use Python to scrape Qunar’s tourist‑spot listings, extract each spot’s sales volume and address, convert the addresses to latitude/longitude via Baidu Map’s Geocoder API, and finally visualise the data with Baidu Maps heatmaps and ECharts ranking charts.

1. Prepare APIs and Tools

We use Baidu Map API for geographic visualisation and ECharts for charting. Both are free to use and widely adopted in web applications.

2. Determine Output Files

The scraped data will be saved as JSON files for the front‑end visualisation and as an Excel file (via pandas) for offline inspection.

3. Crawl Data

Steps:

Analyse the Qunar URL pattern, e.g.

http://piao.qunar.com/ticket/list.htm?keyword=PLACE&page=PAGE

.

Iterate through pages, fetch each page with requests, and extract the required fields using xpath (preferred over regular expressions).

Extract the spot name, address, and sales number. Example extraction code:

import requests, lxml.etree as ET
url = "http://piao.qunar.com/ticket/list.htm?keyword=北京&page={}" 
for i in range(1, 401):
    html = requests.get(url.format(i)).text
    tree = ET.HTML(html)
    spots = tree.xpath('//div[@class="list_item"]')
    for s in spots:
        name = s.xpath('.//a[@class="name"]/text()')[0]
        address = s.xpath('.//p[@class="address"]/text()')[0]
        sales = s.xpath('.//span[@class="sale_num"]/text()')[0]
        # store the data …

4. Save to Local Files

Collected records are written to an Excel file with pandas.DataFrame.to_excel() and also dumped to a JSON file for later use.

5. Get Coordinates via Baidu Geocoder

For each address we call the Baidu Geocoder API:

import requests, json
api = "http://api.map.baidu.com/geocoder/v2/"
params = {"address": address, "output": "json", "ak": "YOUR_BAIDU_AK"}
resp = requests.get(api, params=params).json()
lng, lat = resp["result"]["location"]["lng"], resp["result"]["location"]["lat"]

The latitude, longitude and the previously obtained sales number are combined into a JSON object of the form {"x": lng, "y": lat, "value": sales}.

6. Generate JSON for Map and Chart

Three JSON files are produced:

One for Baidu Map heatmap (array of {x, y, value}).

Two for ECharts – one for the heatmap layer and another for the ranking bar chart.

7. Render in Browser

We create a simple HTML page that loads the JSON files, initialises the Baidu Map, adds a HeatmapOverlay, and draws an ECharts bar chart. Because the page loads local files, it must be served by a local HTTP server, e.g.: python -m http.server 8000 Open http://127.0.0.1:8000/ and the heatmap and ranking chart appear.

Postscript

Using a free Baidu developer account (limited to 6 000 requests per day) we scraped the first 400 pages (≈4 500 spots) on 2017‑09‑10. The resulting visualisations show the most popular tourist destinations during the National Day holiday. The demo is available at easyinfo.online and the source code has been uploaded to GitHub.