下面我们介绍几个非常简单入门的爬虫项目，相信不会再出现那种直接劝退的现象啦！
豆瓣
豆瓣作为国民级网站，在爬虫方面也非常友好，几乎没有设置任何反爬措施，以此网站来练手实在是在适合不过了。
评论爬取
我们以如下地址为例子
https://movie.douban.com/subject/3878007/
可以看到这里需要进行翻页处理，通过观察发现，评论的URL如下：
https://movie.douban.com/subject/3878007/comments?start=0&limit=20&sort=new_score&status=P&percent_type=l
每次翻一页，start都会增长20，由此可以写代码如下
def get_praise():
praise_list = []
for i in range(0, 2000, 20):
url = 'https://movie.douban.com/subject/3878007/comments?start=%s&limit=20&sort=new_score&status=P&percent_type=h' % str(i)
req = requests.get(url).text
content = BeautifulSoup(req, "html.parser")
check_point = content.title.string
if check_point != r"没有访问权限":
comment = content.find_all("span", attrs={"class": "short"})
for k in comment:
praise_list.append(k.string)
else:
break
return
使用range函数，步长设置为20，同时通过title等于“没有访问权限”来作为翻页的终点。
下面继续分析评论等级。
豆瓣的评论是分为三个等级的，这里分别获取，方便后面的继续分析
def get_ordinary():
ordinary_list = []
for i in range(0, 2000, 20):
url = 'https://movie.douban.com/subject/3878007/comments?start=%s&limit=20&sort=new_score&status=P&percent_type=m' % str(i)
req = requests.get(url).text
content = BeautifulSoup(req, "html.parser")
check_point = content.title.string
if check_point != r"没有访问权限":
comment = content.find_all("span", attrs={"class": "short"})
for k in comment:
ordinary_list.append(k.string)
else:
break
return
def get_lowest():
lowest_list = []
for i in range(0, 2000, 20):
url = 'https://movie.douban.com/subject/3878007/comments?start=%s&limit=20&sort=new_score&status=P&percent_type=l' % str(i)
req = requests.get(url).text
content = BeautifulSoup(req, "html.parser")
check_point = content.title.string
if check_point != r"没有访问权限":
comment = content.find_all("span", attrs={"class": "short"})
for k in comment:
lowest_list.append(k.string)
else:
break
return
其实可以看到，这里的三段区别主要在请求URL那里，分别对应豆瓣的好评，一般和差评。
最后把得到的数据保存到文件里。
if __name__ == "__main__":
print("Get Praise Comment")
praise_data = get_praise()
print("Get Ordinary Comment")
ordinary_data = get_ordinary()
print("Get Lowest Comment")
lowest_data = get_lowest()
print("Save Praise Comment")
praise_pd = pd.DataFrame(columns=['praise_comment'], data=praise_data)
praise_pd.to_csv('praise.csv', encoding='utf-8')
print("Save Ordinary Comment")
ordinary_pd = pd.DataFrame(columns=['ordinary_comment'], data=ordinary_data)
ordinary_pd.to_csv('ordinary.csv', encoding='utf-8')
print("Save Lowest Comment")
lowest_pd = pd.DataFrame(columns=['lowest_comment'], data=lowest_data)
lowest_pd.to_csv('lowest.csv', encoding='utf-8')
print("THE END!!!")
制作词云
这里使用jieba来分词，使用wordcloud库制作词云，还是分成三类，同时去掉了一些干扰词，比如“一部”、“一个”、“故事”和一些其他名词，操作都不是很难，直接上代码。
import jieba
import pandas as pd
from wordcloud import WordCloud
import numpy as np
from PIL import Image
font = r'C:\Windows\Fonts\FZSTK.TTF'
STOPWORDS = set(map(str.strip, open('stopwords.txt').readlines()))
def wordcloud_praise():
df = pd.read_csv('praise.csv', usecols=[1])
df_list = df.values.tolist()
comment_after = jieba.cut(str(df_list), cut_all=False)
words = ' '.join(comment_after)
img = Image.open('haiwang8.jpg')
img_array = np.array(img)
wc = WordCloud(width=2000, height=1800, background_color='white', font_path=font, mask=img_array, stopwords=STOPWORDS)
wc.generate(words)
wc.to_file('praise.png')
def wordcloud_ordinary():
df = pd.read_csv('ordinary.csv', usecols=[1])
df_list = df.values.tolist()
comment_after = jieba.cut(str(df_list), cut_all=False)
words = ' '.join(comment_after)
img = Image.open('haiwang8.jpg')
img_array = np.array(img)
wc = WordCloud(width=2000, height=1800, background_color='white', font_path=font, mask=img_array, stopwords=STOPWORDS)
wc.generate(words)
wc.to_file('ordinary.png')
def wordcloud_lowest():
df = pd.read_csv('lowest.csv', usecols=[1])
df_list = df.values.tolist()
comment_after = jieba.cut(str(df_list), cut_all=False)
words = ' '.join(comment_after)
img = Image.open('haiwang7.jpg')
img_array = np.array(img)
wc = WordCloud(width=2000, height=1800, background_color='white', font_path=font, mask=img_array, stopwords=STOPWORDS)
wc.generate(words)
wc.to_file('lowest.png')
if __name__ == "__main__":
print("Save praise wordcloud")
wordcloud_praise()
print("Save ordinary wordcloud")
wordcloud_ordinary()
print("Save lowest wordcloud")
wordcloud_lowest()
print("THE END!!!")
海报爬取
对于海报的爬取，其实也十分类似，直接给出代码
import requests
import json
def deal_pic(url, name):
pic = requests.get(url)
with open(name + '.jpg', 'wb') as f:
f.write(pic.content)
def get_poster():
for i in range(0, 10000, 20):
url = 'https://movie.douban.com/j/new_search_subjects?sort=U&range=0,10&tags=电影&start=%s&genres=爱情' % i
req = requests.get(url).text
req_dict = json.loads(req)
for j in req_dict['data']:
name = j['title']
poster_url = j['cover']
print(name, poster_url)
deal_pic(poster_url, name)
if __name__ == "__main__":
get_poster()
烂番茄网站
这是一个国外的电影影评网站，也比较适合新手练习，网址如下
https://www.rottentomatoes.com/tv/game_of_thrones
我们就以权力的游戏作为爬取例子。
import requests
from bs4 import BeautifulSoup
from pyecharts.charts import Line
import pyecharts.options as opts
from wordcloud import WordCloud
import jieba
baseurl = 'https://www.rottentomatoes.com'
def get_total_season_content():
url = 'https://www.rottentomatoes.com/tv/game_of_thrones'
response = requests.get(url).text
content = BeautifulSoup(response, "html.parser")
season_list = []
div_list = content.find_all('div', attrs={'class': 'bottom_divider media seasonItem '})
for i in div_list:
suburl = i.find('a')['href']
season = i.find('a').text
rotten = i.find('span', attrs={'class': 'meter-value'}).text
consensus = i.find('div', attrs={'class': 'consensus'}).text.strip()
season_list.append([season, suburl, rotten, consensus])
return season_list
def get_season_content(url):
# url = 'https://www.rottentomatoes.com/tv/game_of_thrones/s08#audience_reviews'
response = requests.get(url).text
content = BeautifulSoup(response, "html.parser")
episode_list = []
div_list = content.find_all('div', attrs={'class': 'bottom_divider'})
for i in div_list:
suburl = i.find('a')['href']
fresh = i.find('span', attrs={'class': 'tMeterScore'}).text.strip()
episode_list.append([suburl, fresh])
return episode_list[:5]
mylist = [['/tv/game_of_thrones/s08/e01', '92%'],
['/tv/game_of_thrones/s08/e02', '88%'],
['/tv/game_of_thrones/s08/e03', '74%'],
['/tv/game_of_thrones/s08/e04', '58%'],
['/tv/game_of_thrones/s08/e05', '48%'],
['/tv/game_of_thrones/s08/e06', '49%']]
def get_episode_detail(episode):
# episode = mylist
e_list = []
for i in episode:
url = baseurl + i[0]
# print(url)
response = requests.get(url).text
content = BeautifulSoup(response, "html.parser")
critic_consensus = content.find('p', attrs={'class': 'critic_consensus superPageFontColor'}).text.strip().replace(' ', '').replace('\n', '')
review_list_left = content.find_all('div', attrs={'class': 'quote_bubble top_critic pull-left cl '})
review_list_right = content.find_all('div', attrs={'class': 'quote_bubble top_critic pull-right '})
review_list = []
for i_left in review_list_left:
left_review = i_left.find('div', attrs={'class': 'media-body'}).find('p').text.strip()
review_list.append(left_review)
for i_right in review_list_right:
right_review = i_right.find('div', attrs={'class': 'media-body'}).find('p').text.strip()
review_list.append(right_review)
e_list.append([critic_consensus, review_list])
print(e_list)
if __name__ == '__main__':
total_season_content = get_total_season_content()
王者英雄网站
我这里选取的是如下网站
http://db.18183.com/
import requests
from bs4 import BeautifulSoup
def get_hero_url():
print('start to get hero urls')
url = 'http://db.18183.com/'
url_list = []
res = requests.get(url + 'wzry').text
content = BeautifulSoup(res, "html.parser")
ul = content.find('ul', attrs={'class': "mod-iconlist"})
hero_url = ul.find_all('a')
for i in hero_url:
url_list.append(i['href'])
print('finish get hero urls')
return url_list
def get_details(url):
print('start to get details')
base_url = 'http://db.18183.com/'
detail_list = []
for i in url:
# print(i)
res = requests.get(base_url + i).text
content = BeautifulSoup(res, "html.parser")
name_box = content.find('div', attrs={'class': 'name-box'})
name = name_box.h1.text
hero_attr = content.find('div', attrs={'class': 'attr-list'})
attr_star = hero_attr.find_all('span')
survivability = attr_star[0]['class'][1].split('-')[1]
attack_damage = attr_star[1]['class'][1].split('-')[1]
skill_effect = attr_star[2]['class'][1].split('-')[1]
getting_started = attr_star[3]['class'][1].split('-')[1]
details = content.find('div', attrs={'class': 'otherinfo-datapanel'})
# print(details)
attrs = details.find_all('p')
attr_list = []
for attr in attrs:
attr_list.append(attr.text.split('：')[1].strip())
detail_list.append([name, survivability, attack_damage,
skill_effect, getting_started, attr_list])
print('finish get details')
return detail_list
def save_tocsv(details):
print('start save to csv')
with open('all_hero_init_attr_new.csv', 'w', encoding='gb18030') as f:
f.write('英雄名字,生存能力,攻击伤害,技能效果,上手难度,最大生命,最大法力,物理攻击,'
'法术攻击,物理防御,物理减伤率,法术防御,法术减伤率,移速,物理护甲穿透,法术护甲穿透,攻速加成,暴击几率,'
'暴击效果,物理吸血,法术吸血,冷却缩减,攻击范围,韧性,生命回复,法力回复\n')
for i in details:
try:
rowcsv = '{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}'.format(
i[0], i[1], i[2], i[3], i[4], i[5][0], i[5][1], i[5][2], i[5][3], i[5][4], i[5][5],
i[5][6], i[5][7], i[5][8], i[5][9], i[5][10], i[5][11], i[5][12], i[5][13], i[5][14], i[5][15],
i[5][16], i[5][17], i[5][18], i[5][19], i[5][20]
)
f.write(rowcsv)
f.write('\n')
except:
continue
print('finish save to csv')
if __name__ == "__main__":
get_hero_url()
hero_url = get_hero_url()
details = get_details(hero_url)
save_tocsv(details)
好了，今天先分享这三个网站，咱们后面再慢慢分享更多好的练手网站与实战代码！
