雪中悍刀行在腾讯热播，做篇关于python的作业。--Python爬虫与数据分析。
分为三个部分：
第一：爬虫部分；爬虫爬评论内容和评论时间；
第二：数据处理部分；将爬下来的数据进行整理清洗以便可视化；
第三：可视化分析；对清洗好的数据进行可视化分析，对爬取的内容做一个整体的分析；
项目结构分为三个文件夹：Spiders，dataProcess和echarts。如图：
因为爬取的是腾讯视频，腾讯视频的评论内容都封装在json数据里，只要找到相应的视频id即可。前面有一片关于各大视频网站的文章 - 爬取某些网站的弹幕和评论数据 - Python。其实也就是找到视频播放地址，然后F12，然后搜索部分评论内容，找到response，就能看到targetid，在response里的callback参数也有视频id。雪中悍刀行的视频播放地址和id如下：
- 视频网址：https://v.qq.com/x/cover/mzc0020020cyvqh.html；
- 评论json数据网址：https://video.coral.qq.com/varticle/7579013546/comment/v2；
- 注：只要替换视频数字id的值，即可爬取其他视频的评论 ；
第一部分，爬虫
1，爬取评论内容，spiders/contentSpider.py。
1 """
2 评论内容爬虫，爬取评论内容并保存到txt文件
3 """
4 import json
5 import pprint
6 import re
7 import requests
8
9 class contentSpider():
10 headers = {
11 "cookie": "",13 "referer": "https://v.qq.com/x/search/?q=%E9%9B%AA%E4%B8%AD%E6%82%8D%E5%88%80%E8%A1%8C&stag=101&smartbox_ab=",
14 "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
15 }
16
17 def __init__(self, url, params, headers=headers):
18 self.url = url
19 self.params = params
20 self.headers = headers
21
22 def get_html_content(self):
23 """
24 获取网页内容
25 """
26 response = requests.get(url=self.url, params=self.params, headers=self.headers)
27 response.raise_for_status() # 异常捕获
28 response.encoding = response.apparent_encoding # 自动识别编码
29 response.encoding = 'utf-8' # 将编码设置成utf-8
30 return response.text # 返回值
31
32 def parse_html_content(self):
33 """
34 解析数据
35 """
36 htmlText = self.get_html_content()
37 comment_content = re.findall('"content":"(.*?)"', htmlText, re.S) # 在网页文本中查找评论内容，忽略换行符，也就是匹配包括换行符在内的内容
38 # for comment in comment_content:
39 # print(comment)
40 return comment_content
41
42 def get_next_page_cursor(self):
43 htmlText = self.get_html_content()
44 nextPageCursor = re.findall('"last":"(.*?)"', htmlText, re.S) # 在文本中查找用于下一页的cursor，也就是产生的last参数
45 return nextPageCursor
46
47 def savetoTxt(self):
48 comments = self.parse_html_content()
49 fw = open('雪中悍刀行评论内容.txt', mode='a+', encoding='utf-8')
50 for comment in comments:
51 print(comment)
52 fw.write(comment + '\n')
53 fw.close()
54 if __name__ == "__main__":
55 for page in range(1, 100+1):
56 if page == 1:
57 params = {
58 "callback": "_varticle7579013546commentv2",
59 "orinum": "10",
60 "oriorder": "o",
61 "pageflag": "1",
62 "cursor": "0",
63 "scorecursor": "0",
64 "orirepnum": "2",
65 "reporder": "o",
66 "reppageflag": "1",
67 "source": "132",
68 "_": "1640918319760",
69 }
70 else:
71 params = {
72 "callback": "_varticle7579013546commentv2",
73 "orinum": "10",
74 "oriorder": "o",
75 "pageflag": "1",
76 "cursor": app.get_next_page_cursor(),
77 "scorecursor": "0",
78 "orirepnum": "2",
79 "reporder": "o",
80 "reppageflag": "1",
81 "source": "132",
82 "_": "1640918319760",
83 }
84 url = 'https://video.coral.qq.com/varticle/7579013546/comment/v2'
85 app = contentSpider(url=url, params=params)
86 app.savetoTxt()2，评论时间爬取spiders/timeSpider，我自己为了练手，把上面的代码重写写了一遍，其实上面的代码只需要加个类属性保存时间txt就好了，然后多运行一次app。
1 """
2 评论时间爬虫
3 """
4 import os.path
5
6 import requests
7 import re
8
9 class timeSpider():
10 headers = {13 "referer": "https://v.qq.com/x/search/?q=%E9%9B%AA%E4%B8%AD%E6%82%8D%E5%88%80%E8%A1%8C&stag=101&smartbox_ab=",
14 "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
15 }
16 def __init__(self, url, params, headers=headers):
17 self.url = url
18 self.params = params
19 self.headers = headers
20
21 def get_html_content(self):
22 response = requests.get(url=self.url, params=self.params, headers=self.headers)
23 response.raise_for_status()
24 response.encoding = response.apparent_encoding
25 response.encoding = 'utf-8'
26 return response.text
27
28 def parse_html_content(self):
29 htmlText = self.get_html_content()
30 timedata = re.findall('"time":"(\d+)",', htmlText)
31 return timedata
32
33 def get_next_page_cursor(self):
34 htmlText = self.get_html_content()
35 nextPageCursor = re.findall('"last":"(.*?)"', htmlText, re.S) # 在文本中查找用于下一页的cursor，也就是产生的last参数
36 return nextPageCursor
37
38 def savetoTxt(self):
39 times = self.parse_html_content() # 获取所有时间
40 for time in times:
41 with open('雪中悍刀行评论时间.txt', mode='a+', encoding='utf-8') as f:
42 f.write(time + '\n')
43
44 if __name__ == "__main__":
45 for page in range(1, 100+1):
46 if page == 1:
47 params = {
48 "callback": "_varticle7579013546commentv2",
49 "orinum": "10",
50 "oriorder": "o",
51 "pageflag": "1",
52 "cursor": "0",
53 "scorecursor": "0",
54 "orirepnum": "2",
55 "reporder": "o",
56 "reppageflag": "1",
57 "source": "132",
58 "_": "1640918319760",
59 }
60 else:
61 params = {
62 "callback": "_varticle7579013546commentv2",
63 "orinum": "10",
64 "oriorder": "o",
65 "pageflag": "1",
66 "cursor": app.get_next_page_cursor(),
67 "scorecursor": "0",
68 "orirepnum": "2",
69 "reporder": "o",
70 "reppageflag": "1",
71 "source": "132",
72 "_": "1640918319760",
73 }
74 url = 'https://video.coral.qq.com/varticle/7579013546/comment/v2'
75 app = timeSpider(url=url, params=params)
76 app.savetoTxt()3，还有一份代码spiders/infoSpider.py，一次性爬取所需信息。
1 """
2 爬取一些信息
3 """
4 import json
5 import pprint
6 import random
7 import re
8 import csv
9 import time
10 import requests
11
12
13 class commentSpider():
14 headers = {
15 "cookie": "",
16 "referer": "https://v.qq.com/",
17 "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36",
18 }
19 def __init__(self, url, params, headers=headers):
20 self.url = url
21 self.params = params
22 self.headers = headers
23
24 def requestsPage(self):
25 response = requests.get(url=self.url, params=self.params, headers=self.headers)
26 response.raise_for_status()
27 response.encoding = response.apparent_encoding
28 response.encoding = 'utf-8'
29 return response.text
30
31 def responseToJson(self):
32 htmlText = self.requestsPage()
33 # print(htmlText)
34 """
35 处理字符串，
36 # 第一种，算出_varticle7579013546commentv2(的长度，然后切片
37 # 第二种，用正则表达式提取_varticle7579013546commentv2((*.?))
38 # 第三种，用字符串方法lstrip('_varticle7579013546commentv2(')和rstrip(')')去除左右两边多余的东西
39 三种方法提取出来的字符串是一样的
40 """
41 # htmlString1 = htmlText[29:-1] # 第一种切片
42 # print(htmlString1)
43 # htmlString2 = re.findall('_varticle7579013546commentv2\((.*?)\)', htmlText)[0] # 正则提取
44 # print(htmlString2)
45 # htmlString3 = htmlText.replace('_varticle7579013546commentv2(', '').rstrip(')') # 替换
46 # print(htmlString3)
47 # print(htmlString1==htmlString2==htmlString3) # True
48 htmlString = htmlText[29:-1]
49 json_data = json.loads(htmlString) # 转换成json
50 return json_data
51
52 def parse_json_data(self):
53 userIds = []
54 contents = []
55 pictureNums = []
56 commentTimes = []
57 likeCounts = []
58 replyNums = []
59 indexScores = []
60 json_data = self.responseToJson()
61 results = json_data['data']['oriCommList'] # 要的评论结果
62 #
63 for item in results:
64 userId = item['userid'] # 评论者id
65 userIds.append(userId)
66
67 # 尝试获取一下是否有图片，以及评论的时候带了几张图片
68 try:
69 pictureNum = len(item['picture']) # 评论图片
70 except:
71 pictureNum = None
72 pictureNums.append(pictureNum)
73 commentTime = item['time'] # 十位时间戳，秒级，毫秒级是13位
74 commentTimes.append(commentTime)
75 likeCount = item['up'] # 评论获赞数
76 likeCounts.append(likeCount)
77 replyNum = item['orireplynum'] # 评论被回复的数量
78 replyNums.append(replyNum)
79 indexScore = item['indexscore'] # 排名评分
80 indexScores.append(indexScore)
81 content = item['content'] # 评论内容
82 contents.append(content)
83 # print(userId, pictureNum, commentTime, likeCount, replyNum, indexScore, content, sep = ' | ')
84 zipdata = zip(userIds, pictureNums, commentTimes, likeCounts, replyNums, indexScores, contents)
85
86 return zipdata
87
88 def get_next_cursor_id(self):
89 """
90 获取到请求下一页的cursor参数，也就是json文件里的last
91 """
92 json_data = self.responseToJson()
93 last = json_data['data']['last'] # 获取参数
94 cursor = last
95 return cursor
96
97 def saveToCsv(self, csvfile):
98 csvWriter = csv.writer(csvfile)
99 csvWriter.writerow(['评论者Id', '评论内容图片数', '评论时间', '评论获赞数', '评论被回复数', '评论rank值', '评论内容', ])
100 results = self.parse_json_data()
101 for userId, pictureNum, commentTime, likeCount, replyNum, indexScore, content in results:
102 csvWriter.writerow([userId, pictureNum, commentTime, likeCount, replyNum, indexScore, content])
103
104 def run(self):
105 self.saveToCsv(csvfile)
106 if __name__ == "__main__":
107 csvfile = open('雪中悍刀行评论信息.csv', mode='a', encoding='utf-8-sig', newline='')
108 page = 1
109 while page < 1000+1:
110 if page == 1:
111 params = {
112 "callback": "_varticle7579013546commentv2",
113 "orinum": "10",
114 "oriorder": "o",
115 "pageflag": "1",
116 "cursor": "0",
117 "scorecursor": "0",
118 "orirepnum": "2",
119 "reporder": "o",
120 "reppageflag": "1",
121 "source": "132",
122 "_": "1641368986144",
123 }
124 else:
125 params = {
126 "callback": "_varticle7579013546commentv2",
127 "orinum": "10",
128 "oriorder": "o",
129 "pageflag": "1",
130 "cursor": app.get_next_cursor_id(),
131 "scorecursor": "0",
132 "orirepnum": "2",
133 "reporder": "o",
134 "reppageflag": "1",
135 "source": "132",
136 "_": "1641368986144",
137 }
138 print(f'===================开始采集第{page}页数据。==================')
139 print() # 打印个空格
140 time.sleep(random.uniform(2, 5))
141 url = 'https://video.coral.qq.com/varticle/7579013546/comment/v2'
142 app = commentSpider(url=url, params=params)
143 cursor = app.get_next_cursor_id()
144 print(f'**********第{page}页产生的cursor为{cursor}，供第{page + 1}页使用！**********')
145 app.saveToCsv(csvfile=csvfile)
146 page += 1
147 csvfile.close()至此，爬虫部分完成。
第二部分，数据处理
数据处理部分主要分为时间处理和内容处理。时间处理要处理出日期和时段，内容处理的话就一个演员提及占比。所以先将txt转换成csv（早知道直接用csv保存了）。
1，评论时间戳转成正常的时间。我喜欢叫格式化时间。
1 """
2 爬取到的评论时间是时间戳，现在将它转换成正常的时间
3 这里把转换后的时间写入到csv文档，比较好读取
4 """
5 import csv
6 import time
7
8 csvFile = open('timedata.csv', mode='a', encoding='utf-8-sig', newline='') # csv文档用来存储正常时间的
9 csvWriter = csv.writer(csvFile)
10
11 with open('spider/雪中悍刀行评论时间.txt', mode='r', encoding='utf-8') as f:
12 for line in f:
13 line = line.strip() # 行去空格
14 line = int(line) # 强制转换成整数
15 timedata = time.localtime(line) # 转换成本地时间
16 localtime = time.strftime("%Y-%m-%d %H:%M:%S", timedata) # 格式化本地时间
17 # print(type(localtime))
18 localtime = localtime.split() # 以空格分割字符串
19 csvWriter.writerow(localtime)
20 csvFile.close()2，评论内容写入csv文档。
1 """
2 将评论内容读入csv
3 """
4 import csv
5
6 csvFile = open('content.csv', mode='a', encoding='utf-8-sig', newline='')
7 csvWriter = csv.writer(csvFile)
8
9 with open(r'../spider/雪中悍刀行评论内容.txt', mode='r', encoding='utf-8') as f:
10 for line in f:
11 csvWriter.writerow(line.split()) # 写入行的时候一定是个字典
12
13 csvFile.close()3，全局数据，按小时统计评论并写入csv。也就是全局数据内，0点有几条评论，1点有几条评论，以此类推，直到23点。
1 """
2 评论时间是每次评论产生的时间，所以将时间csv所有条目提取出来，用来统计24个小时，每小时产生的评论数
3 """
4
5 import csv
6 def getHourAndHourCount():
7 """
8 在总评论中获取每个点出现的次数
9 """
10 with open(r'timedata.csv', mode='r', encoding='utf-8') as f:
11 reader = csv.reader(f)
12 # for row in reader: # 读取行，每一行都是一个list
13 # print(row)
14 hourTime = [str(row[1])[0:2] for row in reader] # 读取时间列并提取小时
15 # print(hourTime)
16 # print(type(hourTime)) # <class 'list'>
17
18 # 建立集合
19 timeSet = set(hourTime)
20 hourCount = []
21 # 遍历集合
22 for item in timeSet:
23 hourCount.append((item, hourTime.count(item))) # 添加元素及出现的次数
24 # 排序
25 hourCount.sort()
26 # print(hourCount)
27 return hourCount
28
29 hourCounts = getHourAndHourCount()
30 print(hourCounts)
31 # 将函数得到的值存储到另外一个csv文档
32 with open('timedataprocessed.csv', mode='w+', encoding='utf-8-sig', newline='') as f:
33 csvWriter = csv.writer(f)
34 for hour in hourCounts:
35 csvWriter.writerow(hour) # hour是个元组，也就是在csv里写入两列
36
37
38 def setXandYdata():
39 # 开始提取x轴和y轴需要的数据
40 # 首先是x轴
41 with open('timedataprocessed.csv', mode='r', encoding='utf-8-sig') as f:
42 reader = csv.reader(f) # 读取csv文档
43 hour = [str(row[0]) for row in reader] # 将每个时间的数字提取出来用来做x轴数据和标签
44 print(hour)
45
46 #其次是y轴
47 with open('timedataprocessed.csv', mode='r', encoding='utf-8-sig') as f:
48 reader = csv.reader(f)
49 count = [float(row[1]) for row in reader]
50 print(count)4，全局数据，按天统计评论条数。比如全局数据采集到的时间是2021-12-24到2022-01-01，期间每一天产生的评论数量。
1 """
2 统计评论时间中每天出现的评论数量
3 如果不用utf-8-sig进行编码会出现\ufeff
4 """
5
6 import csv
7 def getLatestContent():
8 """
9 读取时间文档并按天提取参数
10 """
11 with open(r'timedata.csv', mode='r', encoding='utf-8-sig') as f:
12 reader = csv.reader(f)
13 dateTime = [str(row[0]) for row in reader]
14 print(dateTime)
15 # print(type(dateTime))
16 # 转换成集合
17 dateSet = set(dateTime)
18 # print(dateSet)
19 dateCount = []
20 # 开始统计出现的次数
21 for item in dateSet:
22 dateCount.append((item, dateTime.count(item)))
23 dateCount.sort()
24 return dateCount
25 # 写入到另外一个csv文档里
26 dateCounts = getLatestContent()
27 with open('dateprocessed.csv', mode='w+', encoding='utf-8-sig', newline='') as f:
28 csvWriter = csv.writer(f)
29 for date in dateCounts:
30 csvWriter.writerow(date) # date是元组类型
31
32 def setXandYdata():
33 """
34 利用新csv获取x轴和y轴数据
35 """
36 with open('dateprocessed.csv', mode='r', encoding='utf-8-sig') as f:
37 reader = csv.reader(f)
38 date = [str(row[0]) for row in reader]
39 print(date)
40
41 # 提取数量
42 with open('dateprocessed.csv', mode='r', encoding='utf-8') as f:
43 reader = csv.reader(f)
44 count = [float(row[1]) for row in reader]
45 print(count)数据处理完毕，接下来就是数据可视化以及简单的数据分析。
第三部分，数据可视化和简单的数据分析
1，词云展示
1 """
2 词云制作
3 """
4 import jieba
5 import numpy as np
6 import re
7 from PIL import Image
8 from wordcloud import WordCloud
9 from matplotlib import pyplot as plt
10
11 # f = open('../spider/雪中悍刀行评论内容.txt', mode='r', encoding='utf-8-sig') # 打开文档，原始评论数据，是词的来源
12 # txt = f.read() # 读取打开的文档
13 # print(txt)
14 # f.close() # 关闭文档
15
16 with open('../spider/雪中悍刀行评论内容.txt', mode='r', encoding='utf-8') as f:
17 txt = f.read() # 读取文档
18
19 newTxt = re.sub(r"[A-Za-z0-9\!\%\[\]\,\。#😁🦵☁？😂⛽🌚👍🏻💩😄💕😊✨\\n]", "", txt) # 将txt中的特殊字符替换一下
20 print(newTxt)
21
22 # 分词
23 words = jieba.lcut(newTxt) # 利用jieba进行分词
24 # 背景图
25 bgimg = Image.open('../wc.jpg')
26 img_array = np.array(bgimg) # 二值化
27
28 wordcloud = WordCloud(
29 background_color='Yellow',
30 width=1080,
31 height=960,
32 font_path="../文悦新青年体.otf",
33 max_words=150, # 最大词数
34 scale=10, #清晰度
35 max_font_size=100,
36 mask=img_array,
37 collocations=False).generate(newTxt)
38
39 plt.imshow(wordcloud)
40 plt.axis('off')
41 plt.show()
42 wordcloud.to_file('wc.png')2，全局数据，各个小时产生的评论条形图。
1 """
2 每小时评论总数的条形图统计
3 """
4 from pyecharts.charts import Bar
5 from pyecharts.globals import ThemeType
6 import pyecharts.options as opts
7 import csv
8
9 class drawBar():
10 """
11 绘制每个小时所产生评论的条形图
12 """
13 def __init__(self):
14 self.bar = Bar(init_opts=opts.InitOpts(width='1200px', height='700px', theme=ThemeType.LIGHT))
15
16 def add_x(self):
17 with open('../dataProcess/timedataprocessed.csv', mode='r', encoding='utf-8-sig') as f:
18 reader = csv.reader(f)
19 x = [str(row[0]) for row in reader]
20 print(x)
21
22 # 添加x轴数据
23 self.bar.add_xaxis(
24 xaxis_data=x,
25 )
26
27 def add_y(self):
28 with open('../dataProcess/timedataprocessed.csv', mode='r', encoding='utf-8-sig') as f:
29 reader = csv.reader(f)
30 y = [float(row[1]) for row in reader]
31 print(y)
32
33 self.bar.add_yaxis(
34 series_name='每小时评论数', # 第一个系列
35 y_axis = y, # 第一个系列数据
36 label_opts=opts.LabelOpts(is_show=True, color='Black'), # y轴标签属性
37 bar_max_width='100px', # 柱子最大宽度
38 )
39
40 # 为了区分y轴上数据的系列，这里再加一个空系列
41 self.bar.add_yaxis(
42 series_name='这是第二个系列',
43 y_axis=None,
44 )
45
46 def set_gloabl(self):
47 self.bar.set_global_opts(
48 title_opts=opts.TitleOpts(
49 title='雪中悍刀行每小时产生的评论总数',
50 title_textstyle_opts=opts.TextStyleOpts(font_size=35),
51 ),
52 # 提示框配置，鼠标移动到上面所提示的东西
53 tooltip_opts=opts.TooltipOpts(
54 is_show=True,
55 trigger='axis', # 触发类型，axis坐标轴触发，鼠标移动到的时候会有一个x轴的实线跟随鼠标移动，并显示提示信息。
56 axis_pointer_type='cross', # 指示器类型（cross将会生成两条分别垂直于X轴和Y轴的虚线，不启用trigger才会显示完全）
57 ),
58 toolbox_opts=opts.ToolboxOpts(), # 工具栏选项，什么都不填会显示所有工具
59 legend_opts=opts.LegendOpts( # 调整图例的位置，也就是y轴seriesname的位置
60 pos_left='30%',
61 pos_top='110px',
62 ), #
63 )
64
65 def draw(self):
66 # 开始绘制
67 self.add_x()
68 self.add_y()
69 self.set_gloabl()
70 self.bar.render('雪中悍刀行每个小时点产生的评论汇总.html')
71
72 def run(self):
73 # 一个运行函数
74 self.draw()
75
76 if __name__ == "__main__":
77 app = drawBar()
78 app.run()3，全局数据内，各天产生数据的条形图。
1 """
2 统计评论时间内，每天所产生的的评论数
3 """
4 from pyecharts.charts import Bar
5 from pyecharts.globals import ThemeType
6 import pyecharts.options as opts
7 import csv
8
9 class drawBar():
10 """
11 一个统计评论时间内，每天产生评论总数的条形图的类
12 """
13 def __init__(self):
14 self.bar = Bar(init_opts=opts.InitOpts(width='1280px', height='960px', theme=ThemeType.VINTAGE))
15
16 def add_x(self):
17 """
18 x轴数据
19 """
20 # 先打开csv文档读取数据
21 with open('../dataProcess/dateprocessed.csv', mode='r', encoding='utf-8-sig') as f:
22 reader = csv.reader(f) # csv阅读对象
23 x = [str(row[0]) for row in reader] # 提取csv的第一列
24 print(x) # 是个列表
25
26 # 将数据添加到x轴
27 self.bar.add_xaxis(
28 xaxis_data=x,
29 )
30
31 def add_y(self):
32 # 先读出数据
33 with open('../dataProcess/dateprocessed.csv', mode='r', encoding='utf-8-sig') as f:
34 reader = csv.reader(f)
35 y = [float(row[1]) for row in reader]
36 print(y) # 列表
37
38 # 添加到y轴
39 self.bar.add_yaxis(
40 series_name='每天产生的评论数',
41 y_axis=y,
42 label_opts=opts.LabelOpts(is_show=True, color='Red'), # y轴标签设置
43 bar_width='20px', # 设置柱子宽度
44 )
45
46 def set_global(self):
47 """
48 设置全局属性
49 """
50 self.bar.set_global_opts(
51 xaxis_opts=opts.AxisOpts(
52 axislabel_opts=opts.LabelOpts(rotate=45), # 让x轴呈45度倾斜
53 ), # 设置x轴坐标
54 # datazoom_opts=opts.DataZoomOpts(), # 水平方向有拖块儿
55 title_opts=opts.TitleOpts(
56 title='雪中悍刀行评论内容每天产生的评论数',
57 title_textstyle_opts=opts.TextStyleOpts(font_size=28)
58 ),
59 # 提示设置
60 tooltip_opts=opts.TooltipOpts(
61 is_show=True, # 是否出现
62 trigger='axis', # 触发类型，(axis表示坐标轴触发，鼠标移动上去的时候会有一条垂直于x轴的实线跟随鼠标移动，并且提示信息)
63 axis_pointer_type='cross', # 指示器类型，(Cross表示生成两条分别垂直于x轴和y轴的虚线，不启用trigger才会显示完全)
64 ),
65 toolbox_opts=opts.ToolboxOpts(), # 工具箱配置，不填参数默认表示所有都开启
66 legend_opts=opts.LegendOpts(
67 pos_left='10%',
68 pos_top='110px',
69 ),
70 )
71
72 def draw(self):
73 # 开始绘制
74 self.add_x()
75 self.add_y()
76 self.set_global()
77 self.bar.render('每天评论总数统计.html')
78
79 def run(self):
80 self.draw()
81
82 if __name__ == "__main__":
83 app = drawBar()
84 app.run()4，全局数据，每小时产生评论的饼图。
1 """
2 评论时间内，每个小时产生的评论的饼图
3 """
4 from pyecharts.charts import Pie
5 from pyecharts.globals import ThemeType
6 import pyecharts.options as opts
7 import csv
8
9 class drawPie():
10 """
11 评论时间内，每个小时产生的评论数量饼图
12 """
13 def __init__(self):
14 self.pie = Pie(init_opts=opts.InitOpts(width='1700px', height='500px', theme=ThemeType.VINTAGE))
15
16 def set_global(self):
17 # 设置全局属性
18 self.pie.set_global_opts(
19 # 标题属性
20 title_opts=opts.TitleOpts(
21 title='每小时产生的评论饼图',
22 title_textstyle_opts=opts.TextStyleOpts(font_size=28),
23 ),
24 # 图例位置
25 legend_opts=opts.LegendOpts(
26 pos_left='20%',
27 pos_top='10%',
28 ),
29 )
30 self.pie.set_series_opts(
31 label_opts=opts.LabelOpts(formatter="{b}:{c}"),
32 )
33 def get_data_and_add(self):
34 # 首先读取的是小时
35 with open('../dataProcess/timedataprocessed.csv', mode='r', encoding='utf-8-sig') as f:
36 reader = csv.reader(f)
37 hour = [str(row[0]) for row in reader] # 读取到的是小时
38 print(hour)
39 # 其次读取的是次数
40 with open('../dataProcess/timedataprocessed.csv', mode='r', encoding='utf-8-sig') as f:
41 reader = csv.reader(f)
42 count = [float(row[1]) for row in reader] # 读取到的是统计的次数
43 print(count)
44
45 # 将他们添加到饼图里
46 self.pie.add('饼图', center=[280, 270], data_pair=[(i, j) for i, j in zip(hour, count)]) # 饼图
47 self.pie.add('环图', center=[845, 270], data_pair=[(i, j) for i, j in zip(hour, count)], radius=['40%', '75%'])
48 self.pie.add('南丁格尔图', center=[1350, 260], data_pair=[(i, j) for i, j in zip(hour, count)], rosetype='radius')
49
50 def draw(self):
51 self.set_global()
52 self.get_data_and_add()
53 self.pie.render('每小时产生评论饼图.html')
54
55 def run(self):
56 self.draw()
57
58 if __name__ == "__main__":
59 app = drawPie()
60 app.run()5，全局数据内，各天产生评论的饼图。
1 """
2 评论时间内，每天产生的评论统计饼图
3 """
4 from pyecharts.charts import Pie
5 from pyecharts.globals import ThemeType
6 import pyecharts.options as opts
7 import csv
8
9 class drawPie():
10 """
11 每天评论总数饼图
12 """
13 def __init__(self):
14 self.pie = Pie(init_opts=opts.InitOpts(width='1700px', height='750px', theme=ThemeType.VINTAGE))
15
16 def set_global(self):
17 # 设置全局属性
18 self.pie.set_global_opts(
19 title_opts=opts.TitleOpts(
20 title='每天的评论数量统计',
21 title_textstyle_opts=opts.TextStyleOpts(font_size=18),
22 ),
23 legend_opts=opts.LegendOpts(
24 pos_left='20%',
25 pos_top='100px',
26 ),
27 )
28 # 设置标签属性
29 self.pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c}"))
30
31 def get_data_and_add(self):
32 # 提取出标签
33 with open('../dataProcess/dateprocessed.csv', mode='r', encoding='utf-8-sig') as f:
34 reader = csv.reader(f)
35 lab = [str(row[0]) for row in reader]
36 print(lab)
37
38 # 提取数据
39 with open('../dataProcess/dateprocessed.csv', mode='r', encoding='utf-8-sig') as f:
40 reader = csv.reader(f)
41 num = [float(row[1]) for row in reader]
42 print(num)
43
44 # 添加数据
45 self.pie.add(series_name='饼图', center=[280, 370], data_pair=[list(z) for z in zip(lab, num)]) # 饼图
46 self.pie.add(series_name='环图', center=[845, 380], data_pair=[list(z) for z in zip(lab, num)], radius=['40%', '75%']) # 环图
47 self.pie.add(series_name='南丁格尔图', center=[1350, 380], data_pair=[list(z) for z in zip(lab, num)], rosetype='radius') # 南丁格尔图
48
49 def draw(self):
50 self.set_global() # 设置全局属性
51 self.get_data_and_add() # 添加
52 self.pie.render('每天评论数量饼图.html')
53
54 def run(self):
55 self.draw()
56
57 if __name__ == "__main__":
58 app = drawPie()
59 app.run()6，全局数据内，观看时间区间，即凌晨，上午，下午，晚上产生评论的饼图。这样也可以分析出，哪个时间段人数评论最活跃。
1 """
2 制作观看时间区间评论统计饼图，也就是上午看的还是下午看的之类的
3 """
4 import csv
5 from pyecharts.charts import Pie
6 from pyecharts.globals import ThemeType
7 import pyecharts.options as opts
8
9 # 先把时间读出来
10 with open('../dataProcess/timedata.csv', mode='r', encoding='utf-8-sig') as f:
11 reader = csv.reader(f)
12 timeInt = [int(row[1].strip('')[0:2]) for row in reader]
13 print(timeInt)
14
15 # 建立集合，取出唯一的
16 timeSet = set(timeInt)
17 timeList = [] #
18 for item in timeSet:
19 timeList.append((item, timeInt.count(item)))
20 timeList.sort()
21 print(timeList)
22
23 # 将timeList写入到新文档
24 with open('time2.csv', mode='w+', encoding='utf-8-sig', newline='') as f:
25 csvWriter = csv.writer(f, delimiter=',')
26 for item in timeList:
27 csvWriter.writerow(item)
28
29 # 开始分组
30 n = 4 # 分成四组
31 m = int(len(timeList)/n) # 就是6
32 timeList2 = [] # 建立空分组
33 for item in range(0, len(timeList), m): #
34 timeList2.append(item)
35 print(timeList2) # [0, 6, 12, 18]
36 print('凌晨:', timeList2[0])
37 print('上午:', timeList2[1])
38 print('下午:', timeList2[2])
39 print('晚上:', timeList2[3])
40
41 # 读取刚刚建的csv文档
42 with open('time2.csv', mode='r', encoding='utf-8-sig') as f:
43 reader = csv.reader(f)
44 y1 = [int(row[1]) for row in reader]
45 # print(y1)
46
47 n = 6
48 groups = [y1[i:i+n] for i in range(0, len(y1), n)]
49 print(groups)
50
51 x = ['凌晨', '上午', '下午', '晚上']
52 y1 = []
53 for y1 in groups:
54 numSum = 0
55 for groups in y1:
56 numSum += groups
57
58 strName = '点'
59 num = y1
60 lab = x
61
62 (
63 Pie(init_opts=opts.InitOpts(width='1500px', height='450px', theme=ThemeType.LIGHT))
64 .set_global_opts(
65 title_opts=opts.TitleOpts(
66 title='雪中悍刀行观看时间区间评论统计',
67 title_textstyle_opts=opts.TextStyleOpts(font_size=28),
68 ),
69 legend_opts=opts.LegendOpts(
70 pos_left='10%',
71 pos_top='8%',
72 )
73 )
74 .add(series_name='', center=[260, 270], data_pair=[(j, i) for i, j in zip(num, lab)]) # 饼图
75 .add(series_name='', center=[1230, 270], data_pair=[(j, i) for i, j in zip(num, lab)], radius=['40%', '75%'])
76 .add(series_name='', center=[750, 270], data_pair=[(j, i) for i, j in zip(num, lab)], rosetype='radius')
77 ).render('观看时间评论统计.html')7，演员提及占比饼图。
1 """
2 主演提及饼图
3 """
4 from pyecharts.charts import Pie
5 from pyecharts.globals import ThemeType
6 import pyecharts.options as opts
7 import csv
8
9 # f = open('../spider/雪中悍刀行评论内容.txt', 'r', encoding='utf-8') # 这是数据源，也就是想生成词云的数据
10 # words = f.read() # 读取文件
11 # f.close() # 关闭文件，其实用with就好，但是懒得改了
12 with open('../spider/雪中悍刀行评论内容.txt', mode='r', encoding='utf-8-sig') as f:
13 words = f.read()
14 print(words)
15
16 actors = ["张若昀","李庚希","胡军"]
17 counts = [
18 float(words.count('张若昀')),
19 float(words.count('李庚希')),
20 float(words.count('胡军')),
21 ]
22 print(counts)
23 num = counts
24 lab = actors
25
26 (
27 Pie(init_opts=opts.InitOpts(width='1650px', height='550px', theme=ThemeType.LIGHT))
28 .set_global_opts(
29 title_opts=opts.TitleOpts(
30 title='雪中悍刀行演员提及占比',
31 title_textstyle_opts=opts.TextStyleOpts(font_size=28),
32 ),
33 legend_opts=opts.LegendOpts(
34 pos_left='8%',
35 pos_top='8%',
36 )
37 )
38 .add(series_name='', center=[280, 270], data_pair=[(i, j) for i, j in zip(lab, num)]) # 第一个参数是标签，第二个是值
39 .add(series_name='', center=[800, 270], data_pair=[list(z) for z in zip(lab, num)], radius=['40%', '70%'])
40 .add(series_name='', center=[1300, 270], data_pair=[list(z) for z in zip(lab, num)], rosetype='radius')
41 ).render('演员提及饼图.html')8，评论内容情感分析。分值在0,1之间，越接近1表示越评论内容越积极，反之则越消极。
1 """
2 评论内容情感分析
3 """
4
5 import numpy as np
6 from snownlp import SnowNLP
7 import matplotlib.pyplot as plt
8
9 with open('../spider/雪中悍刀行评论内容.txt', mode='r', encoding='utf-8') as f:
10 lis = f.readlines()
11
12 sentimentlist = []
13
14 for i in lis:
15 s = SnowNLP(i)
16 print(s.sentiments)
17 sentimentlist.append(s.sentiments)
18
19 plt.hist(sentimentlist, bins=np.arange(0, 1, 0.01), facecolor='g')
20 plt.xlabel('Sentiments Probability')
21 plt.ylabel('Quantity')
22 plt.title('Analysis of Sentiments')
23 plt.show()好像都忘记放效果图了，最后放一个评论内容情感分析的效果图。
从图中大致看出，消极评论的人还是占多数，因为咱天朝大多键盘侠，有些人根本就是一点都不尊重社会化生产劳动下产生的成果。也可以利用上面的数据，做一个线性回归。做完之后就可以做一个可视化大屏。 - Pyecharts绘制精美图标并做成可视化大屏 - Python。
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