猫眼电影之哪吒数据爬取、数据分析

最近哪吒大火,所以我们分析一波哪吒的影评信息,分析之前我们需要数据呀,所以开篇我们先讲一下爬虫的数据提取;话不多说,走着。

首先我们找到网站的url = "https://maoyan.com/films/1211270",找到评论区看看网友的吐槽,如下

F12打开看看有没有评论信息,我们发现还是有信息的。

但是现在的问题时,我们好像只有这几条评论信息,完全不支持我们的分析呀,我们只能另谋出路了;

f12中由手机测试功能,打开刷新页面,向下滚动看见查看好几十万的评论数据,点击进入后,在network中会看见url = "http://m.maoyan.com/review/v2/comments.json?movieId=1211270&userId=-1&offset=15&limit=15&ts=1568600356382&type=3" api,有这个的时候我们就可以搞事情了。

但是随着爬取,还是不能获取完整的信息,百度、谷歌、必应一下,我们通过时间段获取信息,这样我们不会被猫眼给墙掉,所以我们使用该url="http://m.maoyan.com/mmdb/comments/movie/1211270.json?_v_=yes&offset=0&startTime="

效果如下:

开始构造爬虫代码:

 1 #!/usr/bin/env python
 2 # -*- coding: utf-8 -*-
 3 # author:albert time:2019/9/3
 4 import  requests,json,time,csv
 5 from fake_useragent import  UserAgent  #获取userAgent
 6 from datetime import  datetime,timedelta
 7  8 def get_content(url):
 9     '''获取api信息的网页源代码'''
10     ua = UserAgent().random
11     try:
12         data = requests.get(url,headers={'User-Agent':ua},timeout=3 ).text
13         return data
14     except:
15         pass
16     
17 def  Process_data(html):
18     '''对数据内容的获取'''
19     data_set_list = []
20     #json格式化
21     data_list =  json.loads(html)['cmts']
22     for data in data_list:
23         data_set = [data['id'],data['nickName'],data['userLevel'],data['cityName'],data['content'],data['score'],data['startTime']]
24         data_set_list.append(data_set)
25     return  data_set_list
26 27 if __name__ == '__main__':
28     start_time = start_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')  # 获取当前时间,从当前时间向前获取
29     # print(start_time)
30     end_time = '2019-07-26 08:00:00'
31 32     # print(end_time)
33     while start_time > str(end_time):
34         #构造url
35         url = 'http://m.maoyan.com/mmdb/comments/movie/1211270.json?_v_=yes&offset=0&startTime=' + start_time.replace(
36             ' ', '%20')
37         print('........')
38         try:
39             html = get_content(url)
40         except Exception as e:
41             time.sleep(0.5)
42             html = get_content(url)
43         else:
44             time.sleep(1)
45         comments = Process_data(html)
46         # print(comments[14][-1])
47         if comments:
48             start_time = comments[14][-1]
49             start_time = datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') + timedelta(seconds=-1)
50             # print(start_time)
51             start_time = datetime.strftime(start_time,'%Y-%m-%d %H:%M:%S')
52             print(comments)
53             #保存数据为csv
54             with open("comments_1.csv", "a", encoding='utf-8',newline='') as  csvfile:
55                 writer = csv.writer(csvfile)
56                 writer.writerows(comments)
57

 

-----------------------------------数据分析部分-----------------------------------

我们手里有接近两万的数据后开始进行数据分析阶段:

工具:jupyter、库方法:pyecharts v1.0===> pyecharts 库向下不兼容,所以我们需要使用新的方式(链式结构)实现:

我们先来分析一下哪吒的等级星图,使用pandas 实现分组求和,正对1-5星的数据:

 1 from pyecharts import options as opts
 2 from pyecharts.globals import SymbolType
 3 from pyecharts.charts import Bar,Pie,Page,WordCloud
 4 from pyecharts.globals import ThemeType,SymbolType
 5 import numpy
 6 import pandas as pd
 7  8 df = pd.read_csv('comments_1.csv',names=["id","nickName","userLevel","cityName","score","startTime"])
 9 attr = ["一星", "二星", "三星", "四星", "五星"]
10 score = df.groupby("score").size()  # 分组求和
11 value = [
12     score.iloc[0] + score.iloc[1]+score.iloc[1],
13     score.iloc[3] + score.iloc[4],
14     score.iloc[5] + score.iloc[6],
15     score.iloc[7] + score.iloc[8],
16     score.iloc[9] + score.iloc[10],
17 ]
18 # 饼图分析
19 # 暂时处理,不能直接调用value中的数据
20 attr = ["一星", "二星", "三星", "四星", "五星"]
21 value = [286, 43, 175, 764, 10101]
22 23 pie = (
24     Pie(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
25     .add('',[list(z) for z in zip(attr, value)])
26     .set_global_opts(title_opts=opts.TitleOpts(title='哪吒等级分析'))
27     .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c}"))
28 )
29 pie.render_notebook()

 

实现效果:

然后进行词云分析:

 1 import jieba
 2 import matplotlib.pyplot as plt   #生成图形
 3 from  wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
 4  5 df = pd.read_csv("comments_1.csv",names =["id","nickName","userLevel","cityName","content","score","startTime"])
 6  7 comments = df["content"].tolist()
 8 # comments
 9 df
10 11 # 设置分词
12 comment_after_split = jieba.cut(str(comments), cut_all=False)  # 非全模式分词,cut_all=false
13 words = " ".join(comment_after_split)  # 以空格进行拼接
14 15 stopwords = STOPWORDS.copy()
16 stopwords.update({"电影","最后","就是","不过","这个","一个","感觉","这部","虽然","不是","真的","觉得","还是","但是"})
17 18 bg_image = plt.imread('bg.jpg')
19 #生成
20 wc=WordCloud(
21     width=1024,
22     height=768,
23     background_color="white",
24     max_words=200,
25     mask=bg_image,            #设置图片的背景
26     stopwords=stopwords,
27     max_font_size=200,
28     random_state=50,
29     font_path='C:/Windows/Fonts/simkai.ttf'   #中文处理,用系统自带的字体
30     ).generate(words)
31 32 #产生背景图片,基于彩色图像的颜色生成器
33 image_colors=ImageColorGenerator(bg_image)
34 #开始画图
35 plt.imshow(wc.recolor(color_func=image_colors))
36 #为背景图去掉坐标轴
37 plt.axis("off")
38 #保存云图
39 plt.show()
40 wc.to_file("评价.png")

 

效果如下:

初学者

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posted @ 2019-10-26 22:29  xbhog  阅读(623)  评论(3编辑  收藏  举报