上传文件至 q2_1_crawler

q2_1_crawler/ii.py

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+import requests
+from bs4 import BeautifulSoup as bs
+import json
+
+url = 'https://exam.detr.top/exam-b/movies'
+headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/149.0.0.0 Safari/537.36 Edg/149.0.0.0',
+    'Referer':'https://exam.detr.top/exam-b/movies'}
+req = requests.get(url, headers=headers)
+req.encoding="utf-8"
+
+data=[]
+
+soup=bs(req.text,"html.parser")
+# print(soup)
+#id, title, director, year, rating, duration, genre, actors-count
+
+items=soup.select('.item-row')
+# movie_list=[]
+for i in range(len(items)):
+    id=items[i].find("td",class_="item-id").get_text()
+    title=items[i].find("td",class_="item-title").get_text().strip()
+    director=items[i].find("td",class_="item-director").get_text().strip()
+    year=items[i].find("td",class_="item-year").get_text()
+    rating=items[i].find("td",class_="item-rating").get_text()
+    duration=items[i].find("td",class_="item-duration").get_text()
+    genre=items[i].find("td",class_="item-genre").get_text()
+    actors_count=items[i].find("td",class_="item-actors-count").get_text()
+
+    data.append({
+        "id":id,
+        "title":title,
+        "director":director,
+        "year":year,
+        "rating":rating,
+        "duration":duration,
+        "genre":genre,
+        "actors_count":actors_count
+        
+    })
+
+print(data)
+
+# for tr in item:
+#     tds=tr.find_all("td")
+#     tds=list(tds)
+#     # print(tds)
+#     if len(tds)<8:
+#         continue
+#     movie={
+#         "id":tds[0].get_text(strip=True),
+#         "title":tds[1].get_text(strip=True),
+#         "director":tds[2].get_text(strip=True),
+#         "year":tds[3].get_text(strip=True),
+#         "rating":tds[4].get_text(strip=True),
+#         "duration":tds[5].get_text(strip=True),
+#         "genre":tds[6].get_text(strip=True),
+#         "actors_count":tds[7].get_text(strip=True)
+#         }
+#     movie_list.append(movie)
+# print(movie_list)
+    
+
+with open('movie.json', 'w', encoding='utf-8') as f:
+    json.dump(data, f, ensure_ascii=False, indent=2)
+
+with open("movie.html","w",encoding='utf-8') as f:
+     json.dump(data, f, ensure_ascii=False, indent=2)
+    
+
+
+
+
+# for i in range(len(items)):
+#     rank=i+1
+#     title=items[i].find("span",class_="title").get_text()
+#     actors=items[i].find("div",class_="bd").get_text().strip()
+#     try:
+#        actors=actors.split("主演：")[1].split("\n")[0]
+#     except:
+#        actors="无"
+#     quote=items[i].find("p",class_="quote").get_text().strip()
+
+#     data.append({
+#         "rank":rank,
+#         "title":title,
+#         "actors":actors,
+#         "quote":quote
+#     })

q2_1_crawler/q2_2.py

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+# ① 找出评分最高和最低的电影，打印电影名 + 评分。
+# ② 统计各类型的电影数量，用字典格式输出。
+# ③ 统计各导演的电影数量，用字典格式输出。
+# ④ 统计 2020 年（含）以后上映的电影数量。
+
+import json
+
+
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+   
+
+sort_movie=sorted(data,key=lambda x:x["rating"])
+min=sort_movie[0]
+max=sort_movie[-1]
+print("评分最低的电影",min["title"],min["rating"])
+print("评分最高的电影",max["title"],max["rating"])
+
+genre_shu={}
+for g in data:
+    ge=g["genre"]
+    if ge in genre_shu:
+        genre_shu[ge]+=1
+    else:
+        genre_shu[ge]=1
+print("各类型的电影数量",genre_shu)
+
+director_shu={}
+for d in data:
+    di=d["director"]
+    if di in director_shu:
+        director_shu[di]+=1
+    else:
+        director_shu[di]=1
+print("各导演的电影数量",director_shu)
+
+a=0
+for y in data:
+    if int(y["year"]) >= 2020:
+        a+=1
+print("2020 年（含）以后上映的电影数量",a)

q2_1_crawler/sandian.py

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+import matplotlib.pyplot as plt
+import json
+
+rating=[]
+duration=[]
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+    for i in data:
+        rating.append(i["rating"])
+        duration.append(i["duration"])
+plt.figure(figsize=(12, 8))
+plt.scatter(duration, rating,
+            c='red',
+            s=80,                      # 点的大小
+            alpha=0.6,                 # 透明度
+            edgecolors='white')         # 点的边框
+plt.title('时长与评分关系散点图', fontsize=14)
+plt.xlabel('时长', fontsize=12)
+plt.ylabel('评分', fontsize=12)
+plt.grid(True, linestyle='--', alpha=0.5)
+plt.show()
+plt.savefig("q4_2_scatter.png",dpi=150,format='png')
+       

q2_1_crawler/zhifang_rating.py

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+import matplotlib.pyplot as plt
+import json
+
+rating=[]
+
+with open('movie.json', 'r', encoding='utf-8') as f:
+    data=json.load(f)
+    # print(data)
+    for i in data:
+        rating.append(i["rating"])
+
+plt.figure(figsize=(12,8))
+plt.hist(rating,                     # 数据
+         bins=10,                    # 分成几个柱子
+         color='#3498DB',            # 颜色
+         edgecolor='white')          # 柱子边框颜色
+plt.title('评分分布', fontsize=14)
+plt.xlabel('评分', fontsize=13)
+plt.grid(True, linestyle='--', alpha=0.5, axis='y')
+plt.show()
+plt.savefig("q4_3a_hist.png",dpi=150,format='png')