我们这次的案例网址是:https://book.douban.com/tag/%E7%BC%96%E7%A8%8B。
最近在研究爬虫和数据可视化的结合,所以,时间上分配的有点不太宽裕。
爬虫部分的完整代码:
from selenium import webdriver
from lxml import etree
import requests
import time
import os
options = webdriver.ChromeOptions()
options.add_argument('--headless')
options.add_argument('--disable-gpu')
driver = webdriver.Chrome(options=options)
headers = {
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 Edg/84.0.522.52'
}
url = 'https://book.douban.com/tag/%E7%BC%96%E7%A8%8B'
driver.get(url)
books = driver.find_element_by_xpath('//*[@id="content"]/div/div[1]').text
#print(books)
with open('example_book.txt','a',encoding='utf-8') as f:
f.write(books)
运行结果,去编辑器路径下面找到文件打开:
接着,我们就要用jieba库(第三方库),来进行分词,分词后进行词频统计,所以有:
import jieba
txt = open("D:/pycharm_project/example_book.txt","r",encoding="utf-8").read()
words = jieba.lcut(txt,cut_all=True)
counts = {}
py_num = 0
book_num = 0
computer_num = 0
board_num = 0
for word in words:
if len(word) == 1:
continue
elif word == "python编程" or word == "python":
word1 = "python"
py_num += 1
counts[word1] = py_num
elif word == "本书" or word == "书":
word2 = "书"
book_num += 1
counts[word2] = book_num
elif word == "计算机" or word == "算法":
word3 = "计算机"
computer_num += 1
counts[word3] = computer_num
elif word == "出版社" or word == "出版":
word4 = "出版"
board_num += 1
counts[word4] = board_num
else:
continue
print(counts)
结果:
然后,我们再做修改,得到如下结果,这样我们就完成了分词和词频统计。
接下来就是数据可视化部分了。我们要用到pyecharts库(要在联网情况下操作)和OS系统模块来创建文件夹存放HTML文件,所以有:
from pyecharts.charts import Bar
import os
B = Bar()
B.add_xaxis(['出版','书','计算机'])
B.add_yaxis('词语出现次数',[42,13,19])
os.mkdir('E:/Example')
B.render('E:/Example/豆瓣图书爬取可视化.html')
我们现在去E盘的Example目录下面打开,效果如下:
这次案例还可以,第一次做全套,从数据爬取,到数据预处理,再到数据可视化,觉得还是很有收获的,pyecharts库很不错,这将是我陷入pyecharts不能自拔的开始!!
最后,感谢大家前来观看鄙人的文章,文中或有诸多不妥之处,还望指出和海涵。
转载:https://blog.csdn.net/weixin_43408020/article/details/107848326
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