python学习的第四天
爬虫
1.结点选择语法
2.Requests
注：无requests即下载安装：pip install requests
response = requests.get(url)
response的常用方法：
①response.text：获取str类型的响应
②response.content：获取bytes类型的响应
③response.status_code：获取状态码
④response.headers：获取响应头
⑤response.request：获取响应对应的请求
2.1 导入
import requests
2.2 方法
# Requests
# 导入
import requests
url = 'http://www.baidu.com/'
response = requests.get(url)
print(response)
# 获取str类型的响应
print(response.text)
# 获取bytes类型的响应
print(response.content)
# 获取响应头
print(response.headers)
# 获取状态码
print(response.status_code)
# 获取编码方式
print(response.encoding)
print(response.apparent_encoding)
# 200 Ok 404 500
# 没有添加请求头的知乎网站
resp = requests.get('https://www.zhihu.com/',)
print(resp.status_code)
#使用字典定义请求头
header = {"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get('https://www.zhihu.com/',headers=header)
print(resp.status_code)
2.2.1百度
import requests
url = 'https://www.baidu.com'
response = requests.get(url)
print(response.encoding)
2.2.2淘宝
import requests
url = 'https://www.taobao.com'
response = requests.get(url)
print(response.encoding)
2.2.3响应头
# 200 ok、404 网页不存在、500 服务器错误、503 服务器超时
# 没有添加请求头的知乎网站
# resp = requests.get('https://www.zhihu.com')
# print(resp.status_code) # 显示400
# 使用字典定义请求头
headers = {"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get('https://www.zhihu.com', headers = headers)
print(resp.status_code)
# 显示200
提取本地HTML中的数据
1.建立一个html文件
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Title</title>
</head>
<body>
<h1>欢迎来到王者荣耀！</h1>
<ul>
<li><a href="https://pvp.qq.com/web201605/herodetail/508.shtml"><img src="https://game.gtimg.cn/images/yxzj/img201606/heroimg/508/508.jpg" alt="">伽罗</a></li>
<li>苏烈</li>
<li>孙策</li>
<li>大乔</li>
</ul>
<ol>
<li>射手</li>
<li>坦克</li>
<li>战士</li>
<li>辅助</li>
</ol>
<!-- div + css 布局 -->
<div>这是div标签</div>
<div id="container">
<p>被动：伽罗的普攻与技能伤害将会优先对目标的护盾效果造成一次等额的伤害</p>
<a href="https://www.baidu.com">点击跳转至百度</a>
</div>
<div>这是第二个div标签</div>
</body>
</html>
2.读取html文件——使用xpath语法进行提取
没有lxml即下载安装：pip install lxml
from lxml import html
# 读取html文件
with open('./index.html', 'r', encoding='utf-8') as f:
html_date = f.read()
# print(html_date)
# 解析html文件，获得selector对象
selector = html.fromstring(html_date)
# selector中调用xpath方法
# 要获取标签中的内容，末尾要添加text()
h1 = selector.xpath('/html/body/h1/text()')
print(h1[0])
# // 可以代表从任意位置出发
# //标签1[@属性=属性值]/标签2[@属性=属性值]..../text()
a = selector.xpath('//div[@id="container"]/a/text()')
print(a)
# 获取 p标签的内容
p = selector.xpath('//div[@id="container"]/p/text()')
print(p)
# 获取属性
link = selector.xpath('//div[@id="container"]/a/@href')
print(link[0])
当当网为例
import requests
from lxml import html
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def spider_dangdang(isbn):
book_list = []
# 目标站点地址
url = 'http://search.dangdang.com/?key={}&act=input'.format(isbn)
# print(url)
# 获取站点str类型的响应
headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get(url, headers=headers)
html_data = resp.text
# 将html页面写入本地
with open('dangdang.html', 'w', encoding='utf-8') as f:
f.write(html_data)
# 提取目标站的信息
selector = html.fromstring(html_data)
ul_list = selector.xpath('//div[@id="search_nature_rg"]/ul/li')
print('您好，共有{}家店铺售卖此图书'.format(len(ul_list)))
# 遍历 ul_list
for li in ul_list:
# 图书名称
title = li.xpath('./a/@title')[0].strip()
# print(title)
# 图书购买链接
link = li.xpath('./a/@href')[0]
# print(link)
# 图书价格
price = li.xpath('./p[@class="price"]/span[@class="search_now_price"]/text()')[0]
price = float(price.replace('¥', ''))
# print(price)
# 图书卖家名称
store = li.xpath('./p[@class="search_shangjia"]/a/text()')
# if len(store) == 0:
# store = '当当自营'
# else:
# store = store[0]
store = '当当自营' if len(store) == 0 else store[0]
# print(store)
# 添加每一个商家的图书信息
book_list.append({
'title':title,
'price':price,
'link':link,
'store':store
})
# 按照价格进行排序
book_list.sort(key=lambda x:x['price'])
# 遍历book_list
for book in book_list:
print(book)
# 展示价格最低的前10家 柱状图
# 店铺的名称
top10_store = [book_list[i] for i in range(10)]
# x = []
# for store in top10_store:
# x.append(store['store'])
x = [x['store'] for x in top10_store]
print(x)
# 图书的价格
y = [x['price'] for x in top10_store]
print(y)
# plt.bar(x, y)
# plt.bar(x, y, rotation=75)
plt.barh(x, y) # 横向
plt.show()
# 存储成csv文件
df = pd.DataFrame(book_list)
df.to_csv('dangdang.csv')
spider_dangdang('9787115428028') # 调用
练习
要求：
1.电影名，上映日期，类型，上映国家，想看人数
2.根据想看人数进行排序
3.绘制即将上映电影想看人数占比图和即将上映电影国家的占比图
4.绘制top5最想看的电影
import requests
from lxml import html
from wordcloud import WordCloud
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def spider_movie(isbn):
movie_list = []
url = 'https://movie.douban.com/cinema/later/{}'.format(isbn)
headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
resp = requests.get(url, headers=headers)
html_data = resp.text
selector = html.fromstring(html_data)
div_list = selector.xpath('//div[@id="showing-soon"]/div')
print('共有{}部电影即将上映'.format(len(div_list)))
for div in div_list:
# 电影名
name = div.xpath('./div[@class="intro"]/h3/a/text()')[0]
# print(name)
# 上映日期
day = div.xpath('./div[@class="intro"]/ul/li/text()')[0]
# print(day)
# 类型
type = div.xpath('./div[@class="intro"]/ul/li/text()')[1]
# print(type)
# 上映国家
country = div.xpath('./div[@class="intro"]/ul/li/text()')[2]
# print(country)
# 想看人数
div_three = div.xpath('./div[@class="intro"]/ul/li')[3]
number = div_three.xpath('./span/text()')[0]
number = str(number).replace('人想看', '')
number = int(number)
# print(number)
# 添加电影信息
movie_list.append({
'name':name,
'day':day,
'type':type,
'country':country,
'number':number
})
# 排序
movie_list.sort(key=lambda x:x['number'], reverse=True)
# 遍历
for movie in movie_list:
print(movie)
# 绘制即将上映电影最想看前五人数占比图
top5_movie = [movie_list[i] for i in range(4)]
labels = [x['name'] for x in top5_movie]
# print(labels)
counts = [x['number'] for x in top5_movie]
# print(counts)
colors = ['red', 'purple', 'yellow', 'gray', 'green']
plt.pie(counts, labels=labels, autopct='%1.2f%%', colors=colors)
plt.legend(loc=2)
plt.axis('equal')
plt.show()
# 绘制即将上映电影国家的占比图
total = [x['country'] for x in movie_list]
text = ''.join(total)
print(text)
words_list = jieba.lcut(text)
print(words_list)
counts = {}
excludes ={"大陆"}
for word in words_list:
if len(word) <= 1:
continue
else:
counts[word] = counts.get(word, 0) + 1
print(counts)
for word in excludes:
del counts[word]
items = list(counts.items())
print(items)
items.sort(key=lambda x: x[1], reverse=True)
print(items)
numm = [] # 数量
labels = [] # 国家
for i in range(len(items)):
x, y = items[i]
numm.append(y)
if(x == "中国"):
x = "中国大陆"
labels.append(x)
plt.pie(numm, labels=labels, autopct='%1.2f%%')
plt.legend(loc=2)
plt.axis('equal')
plt.show()
# top5.png
text = ' '.join(labels)
WordCloud(
font_path='MSYH.TTC',
background_color='white',
width=800,
height=600,
collocations=False
).generate(text).to_file('top5.png')
spider_movie('chongqing')
