哪吒数据提取、数据分析

• 2019 年 10 月 5 日
• 筆記

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本文链接：https://blog.csdn.net/weixin_43908900/article/details/100882598

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：

#!/usr/bin/env python  # -*- coding: utf-8 -*-  # author：albert time:2019/9/3  import  requests,json,time,csv  from fake_useragent import  UserAgent  #获取userAgent  from datetime import  datetime,timedelta    def get_content(url):      '''获取api信息的网页源代码'''      ua = UserAgent().random      try:          data = requests.get(url,headers={'User-Agent':ua},timeout=3 ).text          return data      except:          pass    def  Process_data(html):      '''对数据内容的获取'''      data_set_list = []      #json格式化      data_list =  json.loads(html)['cmts']      for data in data_list:          data_set = [data['id'],data['nickName'],data['userLevel'],data['cityName'],data['content'],data['score'],data['startTime']]          data_set_list.append(data_set)      return  data_set_list    if __name__ == '__main__':      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      # print(start_time)      end_time = '2019-07-26 08:00:00'        # print(end_time)      while start_time > str(end_time):          #构造url          url = 'http://m.maoyan.com/mmdb/comments/movie/1211270.json?_v_=yes&offset=0&startTime=' + start_time.replace(              ' ', '%20')          print('........')          try:              html = get_content(url)          except Exception as e:              time.sleep(0.5)              html = get_content(url)          else:              time.sleep(1)          comments = Process_data(html)          # print(comments[14][-1])          if comments:              start_time = comments[14][-1]              start_time = datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') + timedelta(seconds=-1)              # print(start_time)              start_time = datetime.strftime(start_time,'%Y-%m-%d %H:%M:%S')              print(comments)              #保存数据为csv              with open("comments_1.csv", "a", encoding='utf-8',newline='') as  csvfile:                  writer = csv.writer(csvfile)                  writer.writerows(comments)

———————————–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：

from pyecharts import options as opts  from pyecharts.globals import SymbolType  from pyecharts.charts import Bar,Pie,Page,WordCloud  from pyecharts.globals import ThemeType,SymbolType  import numpy  import pandas as pd    df = pd.read_csv('comments_1.csv',names=["id","nickName","userLevel","cityName","score","startTime"])  attr = ["一星", "二星", "三星", "四星", "五星"]  score = df.groupby("score").size()  # 分组求和  value = [      score.iloc[0] + score.iloc[1]+score.iloc[1],      score.iloc[3] + score.iloc[4],      score.iloc[5] + score.iloc[6],      score.iloc[7] + score.iloc[8],      score.iloc[9] + score.iloc[10],  ]

# 饼图分析  # 暂时处理，不能直接调用value中的数据  attr = ["一星", "二星", "三星", "四星", "五星"]  value = [286, 43, 175, 764, 10101]    pie = (      Pie(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))      .add('',[list(z) for z in zip(attr, value)])      .set_global_opts(title_opts=opts.TitleOpts(title='哪吒等级分析'))      .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c}"))  )  pie.render_notebook()

实现效果：

然后进行词云分析：

import jieba  import matplotlib.pyplot as plt   #生成图形  from  wordcloud import WordCloud,STOPWORDS,ImageColorGenerator    df = pd.read_csv("comments_1.csv",names =["id","nickName","userLevel","cityName","content","score","startTime"])    comments = df["content"].tolist()  # comments  df    # 设置分词  comment_after_split = jieba.cut(str(comments), cut_all=False)  # 非全模式分词，cut_all=false  words = " ".join(comment_after_split)  # 以空格进行拼接    stopwords = STOPWORDS.copy()  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"})    bg_image = plt.imread('bg.jpg')  #生成  wc=WordCloud(      width=1024,      height=768,      background_color="white",      max_words=200,      mask=bg_image,            #设置图片的背景      stopwords=stopwords,      max_font_size=200,      random_state=50,      font_path='C:/Windows/Fonts/simkai.ttf'   #中文处理，用系统自带的字体      ).generate(words)    #产生背景图片，基于彩色图像的颜色生成器  image_colors=ImageColorGenerator(bg_image)  #开始画图  plt.imshow(wc.recolor(color_func=image_colors))  #为背景图去掉坐标轴  plt.axis("off")  #保存云图  plt.show()  wc.to_file("哪吒.png")

效果如下：