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Commit da69b37

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用 Python 来了解一下《安家》
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‎anjia/__init__.py

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‎anjia/actor.py

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import pandas as pd, jieba, matplotlib.pyplot as plt
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csv_data = pd.read_csv('data.csv')
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roles = {'姑姑':0, '房似锦':0, '王子':0, '闪闪':0, '老油条':0, '楼山关':0, '鱼化龙':0}
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names = list(roles.keys())
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for name in names:
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jieba.add_word(name)
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for row in csv_data['comments']:
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row = str(row)
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for name in names:
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count = row.count(name)
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roles[name] += count
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plt.figure(figsize=(8, 5))
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# 数据
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plt.bar(list(roles.keys()), list(roles.values()), width=0.5, label='提及次数', color=['g', 'r', 'dodgerblue', 'c', 'm', 'y', 'aquamarine'])
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# 设置数字标签
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for a, b in zip(list(roles.keys()), list(roles.values())):
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plt.text(a, b, b, ha='center', va='bottom', fontsize=13, color='black')
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plt.title('角色被提及次数柱状图')
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plt.xticks(rotation=270)
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plt.tick_params(labelsize=10)
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plt.ylim(0, 30)
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plt.legend(loc='upper right')
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plt.show()

‎anjia/anjia.png

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‎anjia/bg.jpg

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‎anjia/cloud.py

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from wordcloud import WordCloud
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import numpy as np, jieba
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from PIL import Image
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def jieba_():
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# 打开评论数据文件
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content = open('comment.csv', 'rb').read()
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# jieba 分词
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word_list = jieba.cut(content)
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words = []
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# 过滤掉的词
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remove_words = ['以及', '不会', '一些', '那个', '只有',
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'不过', '东西', '这个', '所有', '这么',
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'但是', '全片', '一点', '一部', '一个',
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'什么', '虽然', '一切', '样子', '一样',
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'只能', '不是', '一种', '这个', '为了']
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for word in word_list:
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if word not in remove_words:
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words.append(word)
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global word_cloud
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# 用逗号隔开词语
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word_cloud = ','.join(words)
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def cloud():
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# 打开词云背景图
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cloud_mask = np.array(Image.open('bg.jpg'))
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# 定义词云的一些属性
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wc = WordCloud(
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# 背景图分割颜色为白色
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background_color='white',
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# 背景图样
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mask=cloud_mask,
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# 显示最大词数
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max_words=100,
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# 显示中文
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font_path='./fonts/simhei.ttf',
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# 最大尺寸
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max_font_size=80
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)
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global word_cloud
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# 词云函数
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x = wc.generate(word_cloud)
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# 生成词云图片
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image = x.to_image()
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# 展示词云图片
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image.show()
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# 保存词云图片
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wc.to_file('anjia.png')
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jieba_()
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cloud()

‎anjia/comment.csv

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‎anjia/comment.py

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import pandas as pd, matplotlib.pyplot as plt
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csv_data = pd.read_csv('data.csv')
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df = pd.DataFrame(csv_data)
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df_gp = df.groupby(['time']).size()
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values = df_gp.values.tolist()
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index = df_gp.index.tolist()
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# 设置画布大小
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plt.figure(figsize=(10, 6))
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# 数据
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# plt.plot(index, values, label='weight changes', linewidth=3, color='r', marker='o',
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# markerfacecolor='blue', markersize=20)
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plt.plot(index, values, label='评论数')
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# 设置数字标签
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for a, b in zip(index, values):
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plt.text(a, b, b, ha='center', va='bottom', fontsize=13, color='black')
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plt.title('评论数随时间变化折线图')
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# plt.xlabel('日期')
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# plt.ylabel('评论数')
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plt.xticks(rotation=330)
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plt.tick_params(labelsize=10)
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plt.ylim(0, 200)
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plt.legend(loc='upper right')
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plt.show()
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‎anjia/data.csv

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‎anjia/spd.py

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import requests, time, random, pandas as pd
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from lxml import etree
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def spider():
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url = 'https://accounts.douban.com/j/mobile/login/basic'
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headers = {"User-Agent": 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)'}
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# 安家评论网址,为了动态翻页,start 后加了格式化数字,短评页面有 20 条数据,每页增加 20 条
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url_comment = 'https://movie.douban.com/subject/30482003/comments?start=%d&limit=20&sort=new_score&status=P'
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data = {
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'ck': '',
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'name': '自己的用户',
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'password': '自己的密码',
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'remember': 'false',
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'ticket': ''
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}
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session = requests.session()
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session.post(url=url, headers=headers, data=data)
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# 初始化 4 个 list 分别存用户名、评星、时间、评论文字
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users = []
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stars = []
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times = []
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content = []
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# 抓取 500 条,每页 20 条,这也是豆瓣给的上限
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for i in range(0, 500, 20):
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# 获取 HTML
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data = session.get(url_comment % i, headers=headers)
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# 状态码 200 表是成功
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print('第', i, '页', '状态码:',data.status_code)
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# 暂停 0-1 秒时间,防止IP被封
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time.sleep(random.random())
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# 解析 HTML
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selector = etree.HTML(data.text)
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# 用 xpath 获取单页所有评论
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comments = selector.xpath('//div[@class="comment"]')
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# 遍历所有评论,获取详细信息
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for comment in comments:
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# 获取用户名
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user = comment.xpath('.//h3/span[2]/a/text()')[0]
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# 获取评星
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star = comment.xpath('.//h3/span[2]/span[2]/@class')[0][7:8]
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# 获取时间
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date_time = comment.xpath('.//h3/span[2]/span[3]/@title')
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# 有的时间为空,需要判断下
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if len(date_time) != 0:
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date_time = date_time[0]
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date_time = date_time[:10]
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else:
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date_time = None
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# 获取评论文字
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comment_text = comment.xpath('.//p/span/text()')[0].strip()
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# 添加所有信息到列表
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users.append(user)
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stars.append(star)
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times.append(date_time)
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content.append(comment_text)
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# 用字典包装
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comment_dic = {'user': users, 'star': stars, 'time': times, 'comments': content}
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# 转换成 DataFrame 格式
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comment_df = pd.DataFrame(comment_dic)
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# 保存数据
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comment_df.to_csv('data.csv')
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# 将评论单独再保存下来
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comment_df['comments'].to_csv('comment.csv', index=False)
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spider()

‎anjia/star.py

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import pandas as pd, numpy as np, matplotlib.pyplot as plt
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csv_data = pd.read_csv('data.csv')
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df_time = csv_data.groupby(['time']).size()
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df_star = csv_data.groupby(['star']).size()
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index = df_time.index.tolist()
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value = [0] * len(index)
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# 生成字典
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dic = dict(zip(index, value))
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# rows = df.loc[df['time'] == '2020年03月05日', 'star']
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# list = list(map(int, rows.values.tolist()))
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# avg = np.mean(list)
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# print(list)
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# print(avg)
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for k, v in dic.items():
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stars = csv_data.loc[csv_data['time'] == str(k), 'star']
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# 平均值
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avg = np.mean(list(map(int, stars.values.tolist())))
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dic[k] = round(avg ,2)
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# 设置画布大小
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plt.figure(figsize=(9, 6))
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# 数据
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plt.plot(list(dic.keys()), list(dic.values()), label='星级', color='red', marker='o')
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plt.title('星级随时间变化折线图')
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plt.xticks(rotation=330)
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plt.tick_params(labelsize=10)
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plt.ylim(0, 5)
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plt.legend(loc='upper right')
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plt.show()
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