python爬虫
爬虫：一段自动抓取互联网信息的程序，从互联网上抓取对于我们有价值的信息。
提取网页数据
# 爬虫
# 大数据
# 提取本地html中的数据
# 1. 新建html文件
# 2. 读取
# 3. 使用xpath语法进行提取
# 使用 lxml 中的xpath
# 使用lxml提取 h1标签中的内容
from lxml import html
# 读取html文件
with open('./index.html', 'r', encoding='utf-8') as f:
html_data = f.read()
# print(html_data)
# 解析html文件，获得selector对象
selector = html.fromstring(html_data)
# 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
字典定义请求头
# requests
# 导入
import requests
# url = 'https://www.baidu.com'
# url = '[图片]https://www.taobao.com/'
# url = '[图片]http://www.dangdang.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)
#
# print(response.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
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)
当当网图书爬虫实例
import requests
from lxml import html
import pandas as pd
import re
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
price1 = li.xpath('./p[@class="price"]/span[@class="search_now_price"]/text()')[0]
strinfo = re.compile('¥')
price = float(strinfo.sub('', price1))
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booklist
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.barh(x, y)
plt.show()
# 存储成csv文件
df = pd.DataFrame(book_list)
df.to_csv('dangdang.csv')
spider_dangdang('9787115428028')
豆瓣网重庆地区上映电影爬虫
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、绘制即将上映电影国家的占比图
4、绘制top5最想看的电影
import requests
import jieba
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(address):
movie_list = []
url = 'https://movie.douban.com/cinema/later/{}'.format(address)
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')
网友评论