《西虹市首富》文章相關程式碼分享

• 2019 年 10 月 8 日
• 筆記

本文詳細程式碼如下：

"""  Created on Sun Jul 29 09:35:03 2018    @author: dell  """  ## 調用要使用的包  import json  import random  import requests  import time  import pandas as pd  import os  from pyecharts import Bar,Geo,Line,Overlap  import jieba  from scipy.misc import imread  # 這是一個處理影像的函數  from wordcloud import WordCloud, ImageColorGenerator  import matplotlib.pyplot as plt  from collections import Counter  os.chdir('D:/爬蟲/西紅柿')    ## 設置headers和cookie  header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win32; x32; rv:54.0) Gecko/20100101 Firefox/54.0',  'Connection': 'keep-alive'}  cookies ='v=3; iuuid=1A6E888B4A4B29B16FBA1299108DBE9CDCB327A9713C232B36E4DB4FF222CF03; webp=true; ci=1%2C%E5%8C%97%E4%BA%AC; __guid=26581345.3954606544145667000.1530879049181.8303; _lxsdk_cuid=1646f808301c8-0a4e19f5421593-5d4e211f-100200-1646f808302c8; _lxsdk=1A6E888B4A4B29B16FBA1299108DBE9CDCB327A9713C232B36E4DB4FF222CF03; monitor_count=1; _lxsdk_s=16472ee89ec-de2-f91-ed0%7C%7C5; __mta=189118996.1530879050545.1530936763555.1530937843742.18'  cookie = {}  for line in cookies.split(';'):      name, value = cookies.strip().split('=', 1)      cookie[name] = value    ## 爬取數據，每次理論上可以爬取1.5W調數據，存在大量重複數據，需要多次執行，最後統一去重  tomato = pd.DataFrame(columns=['date','score','city','comment','nick'])  for i in range(0, 1000):      j = random.randint(1,1000)      print(str(i)+' '+str(j))      try:          time.sleep(2)          url= 'http://m.maoyan.com/mmdb/comments/movie/1212592.json?_v_=yes&offset=' + str(j)          html = requests.get(url=url, cookies=cookie, headers=header).content          data = json.loads(html.decode('utf-8'))['cmts']          for item in data:              tomato = tomato.append({'date':item['time'].split(' ')[0],'city':item['cityName'],                                      'score':item['score'],'comment':item['content'],                                      'nick':item['nick']},ignore_index=True)            tomato.to_excel('西虹市首富.xlsx',index=False)      except:          continue    ## 可以直接讀取我們已經爬到的數據進行分析  tomato_com = pd.read_excel('西虹市首富.xlsx')  grouped = tomato_com.groupby(['city'])  grouped_pct = grouped['score']    ## 全國熱力圖  city_com = grouped_pct.agg(['mean','count'])  city_com.reset_index(inplace=True)  city_com['mean'] = round(city_com['mean'],2)  data=[(city_com['city'][i],city_com['count'][i]) for i in range(0,city_com.shape[0])]  geo = Geo('《西虹市首富》全國熱力圖', title_color="#fff",            title_pos="center", width=1200,height=600, background_color='#404a59')  attr, value = geo.cast(data)  geo.add("", attr, value, type="heatmap", visual_range=[0, 200],          visual_text_color="#fff", symbol_size=10, is_visualmap=True,          is_roam=False)  geo.render('西虹市首富全國熱力圖.html')    ## 主要城市評論數與評分  city_main = city_com.sort_values('count',ascending=False)[0:20]  attr = city_main['city']  v1=city_main['count']  v2=city_main['mean']  line = Line("主要城市評分")  line.add("城市", attr, v2, is_stack=True,xaxis_rotate=30,yaxis_min=4.2,           mark_point=['min','max'],xaxis_interval=0,line_color='lightblue',           line_width=4,mark_point_textcolor='black',mark_point_color='lightblue',           is_splitline_show=False)    bar = Bar("主要城市評論數")  bar.add("城市", attr, v1, is_stack=True,xaxis_rotate=30,yaxis_min=4.2,           xaxis_interval =0,is_splitline_show=False)  overlap = Overlap()  # 默認不新增 x y 軸，並且 x y 軸的索引都為 0  overlap.add(bar)  overlap.add(line, yaxis_index=1, is_add_yaxis=True)  overlap.render('主要城市評論數_平均分.html')      ## 主要城市評分降序  city_score = city_main.sort_values('mean',ascending=False)[0:20]  attr = city_score['city']  v1=city_score['mean']  line = Line("主要城市評分")  line.add("城市", attr, v1, is_stack=True,xaxis_rotate=30,yaxis_min=4.2,           mark_point=['min','max'],xaxis_interval=0,line_color='lightblue',           line_width=4,mark_point_textcolor='black',mark_point_color='lightblue',           is_splitline_show=False)  line.render('主要城市評分.html')    ## 主要城市評分全國分布  city_score_area = city_com.sort_values('count',ascending=False)[0:30]  city_score_area.reset_index(inplace=True)  data=[(city_score_area['city'][i],city_score_area['mean'][i]) for i in range(0,        city_score_area.shape[0])]  geo = Geo('《西虹市首富》全國主要城市打分圖', title_color="#fff",            title_pos="center", width=1200,height=600, background_color='#404a59')  attr, value = geo.cast(data)  geo.add("", attr, value, visual_range=[4.4, 4.8],          visual_text_color="#fff", symbol_size=15, is_visualmap=True,          is_roam=False)  geo.render('西虹市首富全國主要城市打分圖.html')    ## 前三天票房對比  piaofang = pd.read_excel('票房.xlsx')  attr1 = piaofang[piaofang['film']=='西虹市首富']['day']  v1= piaofang[piaofang['film']=='西虹市首富']['money']  attr2 = piaofang[piaofang['film']=='羞羞的鐵拳']['day']  v2= piaofang[piaofang['film']=='羞羞的鐵拳']['money']  line = Line("前三天票房對比")  line.add("西紅柿首富", attr1, v1, is_stack=True)  line.add("羞羞的鐵拳", attr2, v2, is_stack=True)  line.render('前三天票房對比.html')    ## 繪製詞雲  tomato_str =  ' '.join(tomato_com['comment'])  words_list = []  word_generator = jieba.cut_for_search(tomato_str)  for word in word_generator:      words_list.append(word)  words_list = [k for k in words_list if len(k)>1]  back_color = imread('西紅柿.jpg')  # 解析該圖片  wc = WordCloud(background_color='white',  # 背景顏色                 max_words=200,  # 最大詞數                 mask=back_color,  # 以該參數值作圖繪製詞雲，這個參數不為空時，width和height會被忽略                 max_font_size=300,  # 顯示字體的最大值                 font_path="C:/Windows/Fonts/STFANGSO.ttf",  # 解決顯示口字型亂碼問題，可進入C:/Windows/Fonts/目錄更換字體                 random_state=42,  # 為每個詞返回一個PIL顏色                 )  tomato_count = Counter(words_list)  wc.generate_from_frequencies(tomato_count)  # 基於彩色影像生成相應彩色  image_colors = ImageColorGenerator(back_color)  # 繪製結果  plt.figure()  plt.imshow(wc.recolor(color_func=image_colors))  plt.axis('off')

如果大家從這裡直接複製程式碼不太方便，請關注「數據森麟」公眾號，在公眾號後台直接回復「西紅柿」或者「西虹市」，會有詳細的程式碼和數據、包括圖片地址。也歡迎大家留言，分享你對《西虹市首富》電影或者我們文章的看法。

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