S1到S9，545名职业选手，有多少人折戟全球总决赛？

import requests
from bs4 import BeautifulSoup

headers = {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'
}

for k in range(2011, 2020):
    print(str(k) + '年')
    # 获取信息
    url = 'https://liquipedia.net/leagueoflegends/World_Championship/' + str(k)
    response = requests.get(url=url, headers=headers)
    soup = BeautifulSoup(response.text, 'html.parser')
    table = soup.find_all(class_='sortable wikitable')
    tbody = table[0].find('tbody')

    for i in tbody.find_all('tr')[1:]:
        td = i.find_all('td')
        item = {}
        item['range'] = td[0].get_text()
        # 地区
        item['country'] = td[1].get_text().replace(' ', '')
        # 数量
        item['number'] = td[2].get_text().replace(' ', '').split('/')[0]
        # 名字
        item['names'] = td[3].get_text().replace(' ', '').replace(',', ' ')
        # print(item)
        # 写入csv文件中
        with open('lol_message.csv', 'a+') as f:
            f.write(item['country'] + ',' + item['number'] + ',' + item['names'] + ',' + str(k) + 'n')

import pandas as pd

# 设置列名与数据对齐
pd.set_option('display.unicode.ambiguous_as_wide', True)
pd.set_option('display.unicode.east_asian_width', True)
# 显示10行
pd.set_option('display.max_rows', 10)

# 读取csv文件
df = pd.read_csv('lol_message.csv', header=None, names=['country', 'number', 'names', 'year'])

# 对名字出现次数进行统计
list_names = []
for name in df['names']:
    res = name.split(' ')
    for i in res:
        list_names.append(i)
# 计数
dict = {}
for key in list_names:
    dict[key] = dict.get(key, 0) + 1
# 排序
rank = sorted(dict.items(), key=lambda dict: dict[1], reverse=True)

# 保存为csv文件
for item in rank:
    print(item[1], item[0])
    with open('lol_player.csv', 'a+') as f:
        f.write(item[0] + ',' + str(item[1]) + 'n')

# 历年各地区参赛人数数据透视表
df1 = df.pivot_table(index=['country'], columns='year', values='number', fill_value=0).reset_index()
print(df1)
df1.to_csv('lol_year.csv')

import pandas as pd

# 设置列名与数据对齐
pd.set_option('display.unicode.ambiguous_as_wide', True)
pd.set_option('display.unicode.east_asian_width', True)
# 显示10行
pd.set_option('display.max_rows', 10)

# 读取数据
df = pd.read_csv('lol_top.csv', header=None, names=['range', 'country', 'name', 'position', 'team', 'year'])
# 修改数据
df = df.replace('AD Cary', 'AD Carry')
df = df.replace('Bottom', 'AD Carry')
df = df.replace('Jungler', 'Jungle')
df = df.replace('Middle', 'Mid')
print(df)

# 统计选手出现在榜单的次数
df1 = df.groupby(['name'])[['country']].count().reset_index().sort_values(by='country', ascending=False)
print(df1)
df1.to_csv('lol_name.csv', encoding='utf-8-sig')

# 统计队伍出现在榜单的次数
df2 = df.groupby(['team'])[['name']].count().reset_index().sort_values(by='name', ascending=False)
print(df2)
df2.to_csv('lol_team.csv', encoding='utf-8-sig')

# 统计地区出现在榜单的次数
df3 = (df.groupby(['country'])[['name']].count().reset_index().sort_values(by='name', ascending=False))
print(df3)
df3.to_csv('lol_country.csv', encoding='utf-8-sig')

# 统计位置出现在榜单的次数
df4 = (df.groupby(['position'])[['country']].count().reset_index().sort_values(by='country', ascending=False))
print(df4)
df4.to_csv('lol_position.csv', encoding='utf-8-sig')