1. 页面分析

• ① 获取每支球员页面的url；
• ② 利用Python代码获取每个网页中的数据；
• ③ 将获取到的数据，存储至不同的数据库；

# 76人
https://nba.stats.qq.com/player/list.htm#teamId=20

# 火箭
https://nba.stats.qq.com/player/list.htm#teamId=10

# 热火
https://nba.stats.qq.com/player/list.htm#teamId=14

部分截图如下

2. 数据爬取

from selenium import webdriver

# 创建浏览器对象，该操作会自动帮我们打开Google浏览器窗口
browser = webdriver.Chrome()

# 调用浏览器对象，向服务器发送请求。该操作会打开Google浏览器，并跳转到“百度”首页
browser.get("https://nba.stats.qq.com/player/list.htm#teamId=20")

# 最大化窗口
browser.maximize_window()

# 获取球员中文名
chinese_names = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[2]/a')
chinese_names_list  = [i.text for i in chinese_names]

# 获取球员英文名
english_names = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[3]/a')
english_names_list = [i.get_attribute('title') for i in english_names] # 获取属性

# 获取球员号码
numbers = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[4]')
numbers_list = [i.text for i in numbers]

# 获取球员位置
locations = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[5]')
locations_list = [i.text for i in locations]

# 获取球员身高
heights = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[6]')
heights_list = [i.text for i in heights]

# 获取球员体重
weights = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[7]')
weights_list = [i.text for i in weights]

# 获取球员年龄
ages = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[8]')
ages_list = [i.text for i in ages_list]

# 获取球员球龄
qiu_lings = browser.find_elements_by_xpath('//div[@class="players"]//tr[@class="show"]/td[9]')
qiu_lings_list = [i.text for i in qiu_lings_list]

3. 存储至txt

for i in zip(chinese_names_list,english_names_list,numbers_list,locations_list,heights_list,weights_list,ages_list,qiu_lings_list):
    with open("NBA.txt","a+",encoding="utf-8") as f:
        # zip函数，得到的是一个元组，我们需要将它转换为一个字符串
        f.write(str(i)[1:-1])
        # 自动换行，好写入第2行数据
        f.write("\n")
        f.write("\n")

4. 存储至excel

import pandas as pd

# 一定要学会组织数据
df = pd.DataFrame({"中文名": chinese_names_list,
                   "英文名": english_names_list,
                   "球员号码": numbers_list,
                   "位置": locations_list,
                   "身高": heights_list,
                   "体重": weights_list,
                   "年龄": ages_list,
                   "球龄": qiu_lings_list})
 
# to_excel()函数
df.to_excel("NBA.xlsx",encoding="utf-8",index=None)

5. 存储至mysql

① 创建一个表nba

import pymysql

# 1. 连接数据库
db = pymysql.connect(host='localhost',user='root', password='123456',port=3306, db='demo', charset='utf8')

# 2. 创建一个表
# 创建一个游标对象；
cursor = db.cursor()
 
# 建表语句；
sql = """
        create table NBA(
            chinese_names_list varchar(20),
            english_names_list varchar(20),
            numbers_list varchar(20),
            locations_list varchar(20),
            heights_list varchar(20),
            weights_list varchar(20),
            ages_list varchar(20),
            qiu_lings_list varchar(20)
        )charset=utf8
      """
# 执行sql语句；
cursor.execute(sql)

# 断开数据库的连接；
db.close()

② 往表nba中插入数据

import pymysql

# 1. 组织数据
data_list = []
for i in zip(chinese_names_list,english_names_list,numbers_list,locations_list,heights_list,weights_list,ages_list,qiu_lings_list):
    data_list.append(i)

# 2. 连接数据库
db = pymysql.connect(host='localhost',user='root', password='123456',port=3306, db='demo', charset='utf8')

# 创建一个游标对象；
cursor = db.cursor()

# 3. 插入数据
sql = 'insert into nba(chinese_names_list,english_names_list,numbers_list,locations_list,heights_list,weights_list,ages_list,qiu_lings_list) values(%s,%s,%s,%s,%s,%s,%s,%s)'

try:
    cursor.executemany(sql,data_list)
    db.commit()
    print("插入成功")
except:
    print("插入失败")
    db.rollback()
db.close()