哪吒数据提取、数据分析

xbhog

发布于 2019-09-18 13:09:35

7690

发布于 2019-09-18 13:09:35

文章被收录于专栏：开发技能乱炖开发技能乱炖

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：