python crawler--618 e-commerce data crawling and analysis

Hello everyone, I am your Xiao Xiao. 618 is coming soon. Next, I will briefly introduce to you how to use python to crawl 618 promotion data!

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Introduction: This blog will introduce how to use Python’s Selenium library to crawl Taobao website’s 618 promotion activity data and conduct simple data analysis.

1 Introduction

As one of China's largest e-commerce platforms, Taobao's annual 618 promotions attract much attention. This article will use Python’s Selenium library to crawl Taobao website’s 618 promotion data and conduct a simple analysis of the data.

2. Crawl data

First, we need to install the Selenium library and download the Chrome browser driver. Then, we can use the following code to crawl the 618 promotion activity data of Taobao website:

# 导入必要的库
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import time
import re
import csv

# 定义爬取函数
def search_product(browser, key_word):
    # ... 省略代码 ...

def get_product(div):
    # ... 省略代码 ...

def save_to_csv(data, filename='data.csv'):
    # ... 省略代码 ...

def main(browser, key_word, wait_time=10, items_per_page=44, filename='data.csv'):
    # ... 省略代码 ...

# 创建浏览器对象
chrome_options = Options()
chrome_options.add_argument("--headless")
browser = webdriver.Chrome(options=chrome_options)

# 执行爬取操作
main(browser, '618促销')

# 关闭浏览器
browser.quit()

3. Data analysis

The data crawled using the above code is saved in data.csva file. We can use Python’s pandas library to read and analyze data. Here is a simple data analysis example:

import pandas as pd

# 读取数据
data = pd.read_csv('data.csv', header=None, names=['商品信息', '价格', '销量', '店铺名称'])

# 查看数据前几行
print(data.head())

# 统计销量前5的商品
top5 = data.sort_values(by='销量', ascending=False).head(5)
print(top5)

# 统计平均价格
average_price = data['价格'].mean()
print('平均价格:', average_price)

# 统计店铺数量
shop_count = data['店铺名称'].nunique()
print('店铺数量:', shop_count)

4 Conclusion

By crawling the 618 promotion data of Taobao website and conducting simple data analysis, we can draw the following conclusions:

The top 5 best-selling products are...
The average price is...
The number of stores participating in the promotion is...

Of course, the above is just a simple example, and you can perform more complex data analysis and visualization according to your own needs.

I hope this article will help you understand how to use Python’s Selenium library to crawl Taobao data and perform simple data analysis!