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下面的脚本提取数据并将数据保存到csv文件中。import requests
from bs4 import BeautifulSoup
import pandas as pd
res = requests.get('https://gol.gg/teams/list/season-ALL/split-ALL/region-ALL/tournament-LCS%20Summer%202020/week-ALL/')
soup = BeautifulSoup(res.text, 'html.parser')
table = soup.find("table", class_="table_list playerslist tablesaw trhover")
columns = [i.get_text(strip=True) for i in table.find("thead").find_all("th")]
data = []
table.find("thead").extract()
for tr in table.find_all("tr"):
data.append([td.get_text(strip=True) for td in tr.find_all("td")])
df = pd.DataFrame(data, columns=columns)
df.to_csv("data.csv", index=False)
输出:Name Season Region Games Win rate K:D GPM GDM Game duration Kills / game Deaths / game Towers killed Towers lost FB% FT% DRAPG DRA% HERPG HER% DRA@15 TD@15 GD@15 NASHPG NASH% CSM DPM WPM VWPM WCPM
0 100 Thieves S10 NA 14 35.7% 0.91 1744 -48 33:19 11.2 12.4 5.5 7.0 50.0 64.3 2.71 54.2 1.00 57.1 1.14 0.4 -378 0.64 42.9 33.2 1937 3.0 1.19 1.31
1 CLG S10 NA 14 35.7% 0.81 1705 -120 35:25 10.6 13.2 4.9 7.9 28.6 28.6 1.93 31.5 0.57 28.6 0.64 -0.6 -1297 0.57 30.4 32.6 1826 3.2 1.17 1.37
2 Cloud9 S10 NA 14 78.6% 1.91 1922 302 28:52 15.0 7.9 8.3 3.1 64.3 64.3 3.07 72.5 1.43 71.4 1.29 0.7 2410 1.00 78.6 33.3 1921 3.0 1.10 1.26
3 Dignitas S10 NA 14 28.6% 0.86 1663 -147 32:44 8.9 10.4 3.9 8.1 42.9 35.7 2.14 41.7 0.57 28.6 0.79 -0.7 -796 0.36 25.0 32.5 1517 3.1 1.28 1.23
4 Evil Geniuses S10 NA 14 50.0% 0.85 1738 -0 34:09 11.1 13.1 6.5 6.0 64.3 57.1 2.36 48.5 1.00 53.6 1.00 0.5 397 0.50 46.5 32.3 1895 3.2 1.36 1.34
5 FlyQuest S10 NA 14 57.1% 1.28 1770 65 34:55 13.4 10.4 6.5 5.2 71.4 35.7 2.86 53.4 1.00 50.0 0.79 -0.1 69 0.71 69.2 32.7 1801 3.2 1.16 1.72
6 Golden Guardians S10 NA 14 50.0% 0.96 1740 6 36:13 10.7 11.1 6.3 6.1 50.0 35.7 3.29 62.8 0.86 42.9 1.43 0.1 711 0.50 43.6 33.7 1944 3.2 1.27 1.53
7 Immortals S10 NA 14 21.4% 0.54 1609 -246 33:54 7.5 14.0 4.3 7.9 35.7 35.7 2.29 39.9 1.00 53.6 0.79 -0.4 -1509 0.36 25.0 31.4 1734 3.3 1.37 1.47
8 Team Liquid S10 NA 14 78.6% 1.31 1796 135 35:07 11.4 8.6 7.9 4.4 42.9 64.3 2.36 43.6 0.93 50.0 1.14 0.2 522 1.21 78.6 33.1 1755 3.5 1.27 1.42
9 TSM S10 NA 14 64.3% 1.12 1768 52 34:20 11.6 10.4 7.2 5.7 50.0 78.6 2.79 51.9 1.21 64.3 0.93 0.1 -129 0.86 57.1 32.6 1729 3.2 1.33 1.33