How to Scrape and Analyze Taobao Snack Sales Data with Python

Preface

The author was hired by a client who wants to open a Taobao store for "Little Fish Snacks" and needed market analysis of existing products. Manual statistics are possible but cumbersome, so the author was asked to automate the process.

1. Project Requirements

Search "小鱼零食" on Taobao and collect sales and revenue for all items in the first 10 pages, then count items within predefined price ranges.

Identify the geographic distribution of the sellers across the country.

Find the most frequently commented product among the results.

Extract the names and links of the top‑selling 10 stores.

2. Result Preview

After gathering the data, the author performed analysis and produced a bar chart (mouse‑over shows exact item counts). The chart shows that most products are priced between 10–30 CNY, indicating a low‑end market positioning.

The geographic analysis reveals that sellers are concentrated along the coast and the middle‑lower Yangtze River region, with the coastal area being the densest.

Comment analysis shows that the most frequent words are related to taste, packaging quality, portion size, and shelf life. These insights can guide product packaging and marketing.

The final section lists the top‑selling 10 stores with their links.

The author reflects that, with the data, one could explore price‑based entry points, geographic differentiation, or user‑centric marketing strategies, though they admit to being a non‑expert in snack products.

3. Scraper Source Code

The full Python source code is provided below. It uses selenium for web navigation, csv for data storage, and wordcloud for visualizing comment keywords. The code is split into several functions:

import csv
import os
import time
import wordcloud
from selenium import webdriver
from selenium.webdriver.common.by import By

def tongji():
    prices = []
    with open('前十页销量和金额.csv', 'r', encoding='utf-8', newline='') as f:
        fieldnames = ['价格', '销量', '店铺位置']
        reader = csv.DictReader(f, fieldnames=fieldnames)
        for index, i in enumerate(reader):
            if index != 0:
                price = float(i['价格'].replace('¥', ''))
                prices.append(price)
    DATAS = {'<10': 0, '10~30': 0, '30~50': 0,
             '50~70': 0, '70~90': 0, '90~110': 0,
             '110~130': 0, '130~150': 0, '150~170': 0, '170~200': 0, }
    for price in prices:
        if price < 10:
            DATAS['<10'] += 1
        elif 10 <= price < 30:
            DATAS['10~30'] += 1
        elif 30 <= price < 50:
            DATAS['30~50'] += 1
        elif 50 <= price < 70:
            DATAS['50~70'] += 1
        elif 70 <= price < 90:
            DATAS['70~90'] += 1
        elif 90 <= price < 110:
            DATAS['90~110'] += 1
        elif 110 <= price < 130:
            DATAS['110~130'] += 1
        elif 130 <= price < 150:
            DATAS['130~150'] += 1
        elif 150 <= price < 170:
            DATAS['150~170'] += 1
        elif 170 <= price < 200:
            DATAS['170~200'] += 1
    for k, v in DATAS.items():
        print(k, ':', v)

# ... (other functions: get_the_top_10, get_top_10_comments, get_top_10_comments_wordcloud, get_10_pages_datas)

if __name__ == '__main__':
    url = 'https://s.taobao.com/search?q=%E5%B0%8F%E9%B1%BC%E9%9B%B6%E9%A3%9F&...'
    # get_10_pages_datas()
    # tongji()
    # get_the_top_10(url)
    # get_top_10_comments(url)
    get_top_10_comments_wordcloud()

Using the above code, one can retrieve the required data and then visualize it with bar charts and geographic maps.

Through this project, the author demonstrates a practical workflow for market analysis on e‑commerce platforms using Python.