Scrape Meituan for Shenzhen BBQ Shops and Analyze the Market with Python

Data Acquisition

Meituan's website is a dynamic page, so the data must be obtained by parsing its API or using Selenium. The real API URL is

https://apimobile.meituan.com/group/v4/poi/pcsearch/30?uuid=YOUR_UUID&userid=-1&limit=32&offset=OFFSET&cateId=-1&q=%E7%83%A4%E8%82%89&areaId=AREA_ID

. Important parameters include city ID (30 for Shenzhen), limit (items per page), offset (page offset), and query keyword ("烤肉"). To retrieve the full dataset, the areaId for each sub‑region must be iterated.

def get_meituan():
    try:
        for areaId in areaId_list:
            for x in range(0, 2000, 32):
                time.sleep(random.uniform(2,4))
                print('Extracting areaId %d page %d' % (areaId, int((x+32)/32)))
                url = f'https://apimobile.meituan.com/group/v4/poi/pcsearch/30?uuid=YOUR_UUID&userid=-1&limit=32&offset={x}&cateId=-1&q=%E7%83%A4%E8%82%89&areaId={areaId}'
                print(url)
                headers = {
                    'Accept':'*/*',
                    'Accept-Encoding':'gzip, deflate, br',
                    'Accept-Language':'zh-CN,zh;q=0.9',
                    'Connection':'keep-alive',
                    'Cookie':'YOUR_COOKIE',
                    'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.116 Safari/537.36',
                    'Host':'apimobile.meituan.com',
                    'Origin':'https://sr.meituan.com',
                    'Referer':'https://sr.meituan.com/s/%E7%83%A4%E8%82%89/'
                }
                response = requests.get(url, headers=headers)
                print(response.status_code)

Data Processing

The scraped data (over 20,000 records) is loaded with Pandas for cleaning. Steps include importing the CSV, adding column names, dropping duplicates, filling missing contact information, extracting the district from the address, categorising ratings, and converting data types.

import pandas as pd
import numpy as np
df = pd.read_csv('深圳烤肉1.csv', names=['店铺名称','店铺地址','人均消费','店铺评分','评论人数','所在商圈','图片链接','店铺类型','联系方式'])
df = df.drop_duplicates()
df = df.fillna('暂无数据')
df['所属区县'] = df['店铺地址'].str[:3].str.replace('南澳大','龙岗区')
cut = lambda x: '一般' if x<=3.5 else ('不错' if x<=4.0 else ('好' if x<=4.5 else '很好'))
df['评分类型'] = df['店铺评分'].map(cut)

Descriptive statistics are obtained with df.describe() and correlation with df.corr().

Data Analysis

Visualization is performed with pyecharts and matplotlib. Key findings include:

Geographic distribution: most BBQ shops are in Longgang, Longhua, Nanshan, and Futian districts.

Shop type distribution: skewers, grilled meat, and fusion BBQ dominate; Korean and Japanese styles are fewer.

Average spending: Nanshan and Futian have higher per‑capita spending, while Pinghu and Guangming are lower.

Ratings: overall average rating is below 3, with a positive correlation between spending and rating, and between comment count and rating.

Sample visualizations (maps, bar charts, pie charts, regression plots) illustrate these insights.

Conclusion

The analysis is for learning purposes only; opening a BBQ shop involves many additional factors. The data source is Meituan, which provides clean and comprehensive information, but other platforms can also be explored.