diff --git a/python1111.py b/python1111.py
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+++ b/python1111.py
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+import pandas
+import requests  #发送请求
+import pandas as pd
+import numpy as np#导入matplotlib库，绘制图像
+import matplotlib.pyplot as plt#读取数据集到phone_data中
+import seaborn as sns
+import jieba
+from collections import Counter
+from wordcloud import WordCloud
+ 
+url_dict = {
+	'全站': 'https://api.bilibili.com/x/web-interface/ranking/v2?rid=0&type=all',
+	'音乐': 'https://api.bilibili.com/x/web-interface/ranking/v2?rid=3&type=all',
+	'舞蹈': 'https://api.bilibili.com/x/web-interface/ranking/v2?rid=129&type=all',
+	'生活': 'https://api.bilibili.com/x/web-interface/ranking/v2?rid=160&type=all',
+	'美食': 'https://api.bilibili.com/x/web-interface/ranking/v2?rid=211&type=all',
+}
+ 
+headers = {
+    "upgrade-insecure-requests": "1",
+    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:125.0) Gecko/20100101 Firefox/125.0",
+    "Referer": "https://www.bilibili.com/v/popular/rank/all/",
+}
+ 
+for i in url_dict.items():
+	url = i[1]  # url地址
+	tab_name = i[0]  # tab页名称
+	title_list = []
+	play_cnt_list = []  # 播放数
+	danmu_cnt_list = []  # 弹幕数
+	coin_cnt_list = []  # 投币数
+	like_cnt_list = []  # 点赞数
+	share_cnt_list = []  # 分享数
+	favorite_cnt_list = []  # 收藏数
+	author_list = []
+	video_url = []
+	try:
+		r = requests.get(url, headers=headers)
+		print(r.status_code)#获取目标数据所在数据并转成字典类型
+		json_data = r.json()
+		list_data = json_data['data']['list']
+ 
+		for data in list_data:
+			title_list.append(data['title'])
+			play_cnt_list.append(data['stat']['view'])
+			danmu_cnt_list.append(data['stat']['danmaku'])
+			coin_cnt_list.append(data['stat']['coin'])
+			like_cnt_list.append(data['stat']['like'])
+			# dislike_cnt_list.append(data['stat']['dislike'])
+			share_cnt_list.append(data['stat']['share'])
+			favorite_cnt_list.append(data['stat']['favorite'])
+			author_list.append(data['owner']['name'])
+			# score_list.append(data['score'])
+			video_url.append('https://www.bilibili.com/video/' + data['bvid'])
+			# print('*' * 10)
+	except Exception as e:
+		print("爬取失败:{}".format(str(e)))
+	#创建dataframe保存数据
+	df = pd.DataFrame(
+		{'视频标题': title_list,
+		 '视频地址': video_url,
+		 '作者': author_list,
+		 '播放数': play_cnt_list,
+		 '弹幕数': danmu_cnt_list,
+		 '投币数': coin_cnt_list,
+		 '点赞数': like_cnt_list,
+		 '分享数': share_cnt_list,
+		 '收藏数': favorite_cnt_list,
+		 })
+	#print(df.head())
+	#将数据保存到本地
+	df.to_csv('B站TOP100-{}.csv'.format(tab_name), index=False,encoding='utf_8_sig')  # utf_8_sig修复乱码问题
+	print('写入成功: ' + 'B站TOP100-{}.csv'.format(tab_name))
+data_meishi=pd.read_csv('B站TOP100-美食.csv')
+data_quanzhan=pd.read_csv('B站TOP100-全站.csv')
+data_shenghuo=pd.read_csv('B站TOP100-生活.csv')
+data_wudao=pd.read_csv('B站TOP100-舞蹈.csv')
+data_yinyue=pd.read_csv('B站TOP100-音乐.csv')
+ 
+plt.rcParams['font.family'] = 'SimHei'
+
+#弹幕数与播放数的散点图
+plt.figure(figsize=(8, 6))   # 调整图像大小：确认图像没有被显示在可视区域外,尝试设置图像的大小利用plt.figure(figsize=(,))
+sns.scatterplot(x='弹幕数', y='播放数', data=data_quanzhan)
+plt.show() # 调用 plt.show()显示结果
+
+numeric_data = data_quanzhan.select_dtypes(include=['number'])
+cor = numeric_data.corr(method='spearman')
+ 
+sns.heatmap(cor,
+            annot=True,  # 显示相关系数的数据
+            center=0.5,  # 居中
+            fmt='.2f',  # 只显示两位小数
+            linewidth=0.5,  # 设置每个单元格的距离
+            linecolor='blue',  # 设置间距线的颜色
+            vmin=0, vmax=1,  # 设置数值最小值和最大值
+            xticklabels=True, yticklabels=True,  # 显示x轴和y轴
+            square=True,  # 每个方格都是正方形
+            cbar=True,  # 绘制颜色条
+            cmap='coolwarm_r',  # 设置热力图颜色
+            )
+plt.show() #显示图片
+
+mean_list = [data_quanzhan['播放数'].mean(),data_wudao['播放数'].mean(),data_shenghuo['播放数'].mean(),data_meishi['播放数'].mean(),data_yinyue['播放数'].mean()]
+name_list=['全站','舞蹈','生活','美食','音乐']
+plt.bar(name_list,mean_list)
+plt.ylabel('播放数')
+plt.show()
+
+var_list = [data_quanzhan['播放数'].var(),data_wudao['播放数'].var(),data_shenghuo['播放数'].var(),data_meishi['播放数'].var(),data_yinyue['播放数'].var()]
+plt.bar(name_list,var_list)
+plt.ylabel('播放数方差')
+
+# 文本预处理
+data_quanzhan['视频标题'] = data_quanzhan['视频标题'].apply(lambda x: ' '.join(jieba.lcut(x))) # 分词
+# 读取停用词
+with open('./停用词表.txt', 'r', encoding='utf-8') as f:
+    stop_words = f.read().split('\n')
+    data_quanzhan['视频标题'] = data_quanzhan['视频标题'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop_words])) # 去除停用词
+# 统计单词出现频率
+words = []
+for comment in data_quanzhan['视频标题']:
+    words += comment.split()
+    word_count = Counter(words).most_common(100)
+# 转成字典
+dct = {k:v for k, v in word_count}
+print(dct)
+# 生成词云图
+font_path = r'C:\Windows\Fonts\msyh.ttc' # 指定微软雅黑字体路径
+wordcloud = WordCloud(width=800, height=600, background_color='white',font_path=font_path).generate_from_frequencies(dct)
+plt.imshow(wordcloud, interpolation='bilinear')
+plt.axis('off')
+plt.show()
+
+def panduan(x):
+    if x in data_meishi['作者'].tolist():
+        return '美食'
+    elif x in data_shenghuo['作者'].tolist():
+        return '生活'
+    elif x in data_yinyue['作者'].tolist():
+        return '音乐'
+    elif x in data_wudao['作者'].tolist():
+        return '舞蹈'
+    else:
+        return '其他'
+data_quanzhan['类别'] = data_quanzhan['作者'].apply(panduan)
+data_pie = data_quanzhan['类别'].value_counts()
+plt.pie(data_pie.values,labels=data_pie.index,autopct="%3.1f%%") 
+plt.show()