Scraping Douban Top 250 Movies with Python and Analyzing Year Distribution in Excel

First, import the required modules with from bs4 import BeautifulSoup and import requests. Use requests.get to fetch the HTML of the Douban Top 250 page and parse it with BeautifulSoup(html.text, 'html.parser').

Iterate over each movie block using for item in soup.find_all('div', "info"):. Extract the title with title = item.div.a.span.string and the year line with yearline = item.find('div','bd').p.contents[2].string. Clean the year string by removing spaces and newline characters:

yearline = yearline.replace(' ', '')
yearline = yearline.replace('
', '')

Obtain the four‑digit year using slicing year = yearline[0:4] and print the result with a tab separator.

To scrape all 250 movies, wrap the request in a loop that increments the start parameter by 25 for each page (0, 25, 50, …). The full loop looks like:

import requests
from bs4 import BeautifulSoup
start = 0
for n in range(0, 10):
    html = requests.get('https://movie.douban.com/top250?start=' + str(start))
    start += 25
    soup = BeautifulSoup(html.text, 'html.parser')
    for item in soup.find_all('div', "info"):
        title = item.div.a.span.string
        yearline = item.find('div','bd').p.contents[2].string
        yearline = yearline.replace(' ', '')
        yearline = yearline.replace('
', '')
        year = yearline[0:4]
        print(title, '\t', year)

Copy the printed output into an Excel sheet, placing the movie titles and years in two columns. Insert a pivot table (Data → PivotTable) on a new worksheet, drag the year field to the Rows area and also to the Values area, then set the value field to “Count” to count how many movies were released each year.

Finally, create a column chart from the pivot table to visualize the distribution of high‑quality movies over the years, revealing trends such as a surge in the early 1990s and a peak around 2010.