1.目标
python爬取三国演义，生成词云、图表
2.码前须知
项目目标：三国人物名称及出现次数-----数据统计分析
提出问题：哪个人物在三国演义中出现的次数最多?，我们希望通过数据分析来获得答案。
分析工具：pandas,Matplotlib
pip install bs4
pip install lxml
pip install pandas
pip install Matplotlib
bs4数据解析必备知识点：标签定位,提取标签中的数据值
1.实例化一个BeautifulSoup对象，并将页面源码数据加载到该对象中,lxml是解析器，固定的参数,下面是举例
本地html加载到该对象:
fp = open(’./test.html’,‘r’,encoding=‘utf-8’)
soup = BeautifulSoup(fp,‘lxml’)
print(soup)
互联网上获取的源码数据(常用)
page_text = response.text
soup = BeautifulSoup(page_text,‘lxml’)
2.通过调用BeautifulSoup对象中相关的属性或者方法对标签进行定位和提取
bs4具体属性的用法
1.标签 ，如< p > < a >< div >等等
soup.tagName
例如
soup.a #返回的是html第一次出现的tagName的a标签
soup.div #返回的是html第一次出现的tagName的div标签
2.查找
soup.find(‘div’) 用法相当于soup.div
属性定位, < div class=‘song’ >
soup.find(‘div’,class_=‘song’) class_需要带下划线
class_/id/attr
3.所有符合条件的标签
soup.find_all(‘a’) #返回符合所有的a标签，也可以属性定位
4.select放置选择器 类选择器. .代表的就是tang
soup.select(’.tang’)
soup.select(‘某种选择器(id,class,标签，选择器)’)
返回的是一个列表
定位到标签下面的标签 >表示标签一个层级选择器
soup.select(’.tang > ul >li >a’[0])
空格表示多级选择器
soup.select(‘tang’ > ul a’[0]) 与上述的表达式相同
常用层级选择器
5.获取标签中间的文本数据
soup.a.text/string/get_text()
区别
text/get_text():可以获取某一个标签中所有的文本内容，即使不是直系的文本
string:只可以获取直系文本
6.获取标签中的属性值
soup.a[‘href’] 相当于列表操作
3.操作流程
1.爬取数据来源： 古诗词网《三国演义》
2.编码流程：
发起请求–requests
获取响应数据—页面信息
数据解析(通过bs4) --1进行指定标签的定位;2取得标签当中的文本内容
持久化存储–保存文件
2.文本词频统计：中文分词库–jieba库，具体的解释已在代码处声明
3.生成词云：wordcloud，具体的解释已在代码处声明
4.生成柱状分析图:matplotlib，具体的解释已在代码处声明
4.完整代码
外汇经纪商对比http://www.fx61.com/brokerlist
from bs4 import BeautifulSoup
import requests
import jieba#优秀的中文分词第三方库
import wordcloud
import pandas as pd
from matplotlib import pyplot as plt
1.对首页html进行爬取
headers={
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36',
}
fp = open('./sanguo.txt','w',encoding='utf-8')
page_text = requests.get(url=url,headers=headers).text
2.数据解析
实例化对象
soup = BeautifulSoup(page_text,'lxml')
获得li标签
li_list = soup.select('.book-mulu > ul > li')
取得li标签里的属性
for li in li_list:
通过bs4的方法直接获取a标签直系文本
title = li.a.string
对url进行拼接得到详情页的url
detail_url = 'http://www.shicimingju.com'+li.a['href']
对详情页发起请求
detail_page_text = requests.get(url=detail_url,headers=headers).text
解析详情页的标签内容,重新实例化一个详情页bs对象,lxml解析器
detail_soup = BeautifulSoup(detail_page_text,'lxml')
属性定位
div_tag = detail_soup.find('div',class_='chapter_content')
解析到了章节的内容，利用text方法获取
content = div_tag.text
持久化存储
fp.write(title+':'+content+'\n')
print(title,'爬取成功!!!')
print('爬取文本成功，进行下一步，jieba分词，并生成一个sanguo.xlsx文件用于数据分析')
排除一些不是人名，但是出现次数比较靠前的单词
excludes = {"将军","却说","荆州","二人","不可","不能","如此","商议","如何","主公","军士", "左右","军马","引兵","次日","大喜","天下","东吴","于是","今日","不敢","魏兵", "陛下","一人","都督","人马","不知","汉中","只见","众将","后主","蜀兵","上马", "大叫","太守","此人","夫人","先主","后人","背后","城中","天子","一面","何不", "大军","忽报","先生","百姓","何故","然后","先锋","不如","赶来","原来","令人", "江东","下马","喊声","正是","徐州","忽然","因此","成都","不见","未知","大败", "大事","之后","一军","引军","起兵","军中","接应","进兵","大惊","可以","以为", "大怒","不得","心中","下文","一声","追赶","粮草","曹兵","一齐","分解","回报", "分付","只得","出马","三千","大将","许都","随后","报知","前面","之兵","且说", "众官","洛阳","领兵","何人","星夜","精兵","城上","之计","不肯","相见","其言", "一日","而行","文武","襄阳","准备","若何","出战","亲自","必有","此事","军师", "之中","伏兵","祁山","乘势","忽见","大笑","樊城","兄弟","首级","立于","西川","朝廷","三军","大王","传令","当先","五百","一彪","坚守","此时","之间","投降","五千","埋伏","长安","三路","遣使","英雄"}
打开爬取下来的文件，并设置编码格式
txt = open("sanguo.txt", "r", encoding='utf-8').read()
精确模式，把文本精确的切分开，不存在冗余单词，返回列表类型
words = jieba.lcut(txt)
构造一个字典，来表达单词和出现频率的对应关系
counts = {}
逐一从words中取出每一个元素
for word in words:
已经有这个键的话就把相应的值加1，没有的话就取值为0，再加1
if len(word) == 1:
continue
elif word == "诸葛亮" or word == "孔明曰":
rword = "孔明"
elif word == "关公" or word == "云长":
rword = "关羽"
elif word == "玄德" or word == "玄德曰":
rword = "刘备"
elif word == "孟德" or word == "丞相":
rword = "曹操"
else:
rword = word
如果在里面返回他的次数，如果不在则添加到字典里面并加一
counts[rword] = counts.get(rword,0) + 1
删除停用词
for word in excludes:
del counts[word]
排序，变成list类型，并使用sort方法
items = list(counts.items())
对一个列表按照键值对的2个元素的第二个元素进行排序
Ture从大到小，结果保存在items中，第一个元素就是出现次数最多的元素
items.sort(key=lambda x:x[1], reverse=True)
将前十个单词以及出现的次数打印出来
name=[]
times=[]
for i in range(30):
word, count = items[i]
print ("{0:<10}{1:>5}".format(word, count))
name.append(word)
times.append(count)
print(name)
print(times)
创建索引
id=[]
for i in range(1,31):
id.append(i)
数据帧，相当于Excel中的一个工作表
df = pd.DataFrame({
'id':id,
'name':name,
'times':times,
})
自定义索引，不然pandas会使用默认的索引，这会导致生成的工作表
也会存在这些索引，默认从0开始
df = df.set_index('id')
print(df)
df.to_excel('sanguo.xlsx')
print("DONE!")
print('生成文件成功，进行下一步，生成词云')
词云部分
w=wordcloud.WordCloud(
font_path="C:\\Windows\\Fonts\\simhei.ttf", #设置字体
background_color="white", #设置词云背景颜色
max_words=1000, #词云允许最大词汇数
max_font_size=100, #最大字体大小
random_state=50 #配色方案的种数
)
txt=" ".join(name)
w.generate(txt)
w.to_file("ciyun.png")
print("done!")
print("词云生成并保存成功!!!,进行下一步生成柱状图")
dirpath = 'sanguo.xlsx'
data = pd.read_excel(dirpath,index_col='id',sheet_name='Sheet1')#指定id列为索引
print(data.head())#到此数据正常
print('OK!，到此数据正常')
柱状图部分
直接使用plt.bar() 绘制柱状图,颜色紫罗兰
plt.bar(data.name,data.times,color="#87CEFA")
添加中文字体支持
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
设置标题，x轴，y轴，fontsize设置字号
plt.title('三国人物名字前三十名出现的次数',fontsize=16)
plt.xlabel('人名')
plt.ylabel('统计次数')
因为X轴字体太长，利用rotation将其旋转90度
plt.xticks(data.name,rotation='90')
紧凑型布局，x轴太长为了显示全
plt.tight_layout()
imgname = 'sanguo.jpg'#设置图片保存的位置
plt.savefig(imgname)#保存图片
plt.show()
print('柱状图生成完毕!!!')
print('所有程序执行完成')