Scraping and Analyzing Douban Top250 Movies with Python

This article demonstrates how to use Python to crawl Douban's Top250 movie list, extract fields such as rank, title, director, year, country, genre, rating, number of reviews, and short comments, and then analyze and visualize the data.

It first explains pagination by modifying the start parameter in the URL and discusses anti‑scraping techniques, including setting appropriate User-Agent , Referer , and handling cookies or JavaScript‑generated parameters.

Basic extraction code using requests and lxml.etree is provided:

# -*- coding: utf-8 -*-
# @Author: Kun
import requests
from lxml import etree
import pandas as pd

df = []
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4343.0 Safari/537.36',
           'Referer': 'https://movie.douban.com/top250'}
columns = ['排名','电影名称','导演','上映年份','制作国家','类型','评分','评价分数','短评']

def get_data(html):
    xp = etree.HTML(html)
    lis = xp.xpath('//*[@id="content"]/div/div[1]/ol/li')
    for li in lis:
        ranks = li.xpath('div/div[1]/em/text()')
        titles = li.xpath('div/div[2]/div[1]/a/span[1]/text()')
        directors = li.xpath('div/div[2]/div[2]/p[1]/text()')[0].strip().replace("\xa0\xa0\xa0","\t").split("\t")
        infos = li.xpath('div/div[2]/div[2]/p[1]/text()')[1].strip().replace('\xa0','').split('/')
        dates, areas, genres = infos[0], infos[1], infos[2]
        ratings = li.xpath('.//div[@class="star"]/span[2]/text()')[0]
        scores = li.xpath('.//div[@class="star"]/span[4]/text()')[0][:-3]
        quotes = li.xpath('.//p[@class="quote"]/span/text()')
        for rank, title, director in zip(ranks, titles, directors):
            if len(quotes) == 0:
                quotes = None
            else:
                quotes = quotes[0]
            df.append([rank, title, director, dates, areas, genres, ratings, scores, quotes])
    d = pd.DataFrame(df, columns=columns)
    d.to_excel('Top250.xlsx', index=False)

for i in range(0, 251, 25):
    url = "https://movie.douban.com/top250?start={}&filter=".format(str(i))
    res = requests.get(url, headers=headers)
    html = res.text
    get_data(html)

A multithreaded version using threading and a queue is also shown to speed up crawling.

# -*- coding: utf-8 -*-
import pandas as pd
import time
import requests
from lxml import etree
from queue import Queue
from threading import Thread, Lock

class Movie():
    def __init__(self):
        self.df = []
        self.headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4343.0 Safari/537.36',
                        'Referer': 'https://movie.douban.com/top250'}
        self.columns = ['排名','电影名称','导演','上映年份','制作国家','类型','评分','评价分数','短评']
        self.lock = Lock()
        self.url_list = Queue()
    def get_url(self):
        url = 'https://movie.douban.com/top250?start={}&filter='
        for i in range(0, 250, 25):
            self.url_list.put(url.format(str(i)))
    def get_html(self):
        while True:
            if not self.url_list.empty():
                url = self.url_list.get()
                resp = requests.get(url, headers=self.headers)
                html = resp.text
                self.xpath_parse(html)
            else:
                break
    def xpath_parse(self, html):
        xp = etree.HTML(html)
        lis = xp.xpath('//*[@id="content"]/div/div[1]/ol/li')
        for li in lis:
            ranks = li.xpath('div/div[1]/em/text()')
            titles = li.xpath('div/div[2]/div[1]/a/span[1]/text()')
            directors = li.xpath('div/div[2]/div[2]/p[1]/text()')[0].strip().replace("\xa0\xa0\xa0","\t").split("\t")
            infos = li.xpath('div/div[2]/div[2]/p[1]/text()')[1].strip().replace('\xa0','').split('/')
            dates, areas, genres = infos[0], infos[1], infos[2]
            ratings = li.xpath('.//div[@class="star"]/span[2]/text()')[0]
            scores = li.xpath('.//div[@class="star"]/span[4]/text()')[0][:-3]
            quotes = li.xpath('.//p[@class="quote"]/span/text()')
            for rank, title, director in zip(ranks, titles, directors):
                if len(quotes) == 0:
                    quotes = None
                else:
                    quotes = quotes[0]
                self.df.append([rank, title, director, dates, areas, genres, ratings, scores, quotes])
        d = pd.DataFrame(self.df, columns=self.columns)
        d.to_excel('douban.xlsx', index=False)
    def main(self):
        start_time = time.time()
        self.get_url()
        th_list = []
        for i in range(5):
            th = Thread(target=self.get_html)
            th.start()
            th_list.append(th)
        for th in th_list:
            th.join()
        end_time = time.time()
        print(end_time - start_time)

if __name__ == '__main__':
    spider = Movie()
    spider.main()

After crawling, the data is read with pandas , cleaned (e.g., normalizing the release year), and visualized using pyecharts bar charts for year distribution, rating distribution, comment count top 10, and director ranking, as well as a word cloud for genres.

Additional snippets illustrate how to delete dictionary entries in Python using pop , del , and clear .