diff --git a/IMG_0818.PNG b/IMG_0818.PNG
deleted file mode 100644
index aeb0c3d..0000000
Binary files a/IMG_0818.PNG and /dev/null differ
diff --git a/README.md b/README.md
deleted file mode 100644
index 35ab420..0000000
--- a/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-# zz01
-
diff --git a/new_house.py b/new_house.py
deleted file mode 100644
index fce6692..0000000
--- a/new_house.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# _*_ coding:utf-8 _*_
-import urllib.request
-import random
-from tqdm import tqdm
-from time import sleep
-import urllib
-from bs4 import BeautifulSoup
-import re
-import pandas as pd
-import os
-'''爬取新房数据存入excel'''
-# 数据请求构建
-def data_request(url):
-    user_agents = list({
-        'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60',
-        'Opera/8.0 (Windows NT 5.1; U; en)',
-        'Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50',
-        'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50',
-        'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0'})
-
-    headers = {
-        'Cookie': "otherid=a17973d936f83e36a1dce147d5401dbc; fang_hao123_layed=1; csrfToken=lcekq8-1Dcmxvkk8JatNlzZr; global_cookie=9t8q5c1pfexde98sri1xy12113pluq89t5t; g_sourcepage=xf_lp%5Elb_pc'; unique_cookie=U_9t8q5c1pfexde98sri1xy12113pluq89t5t*11",
-        'User-Agent': str(random.choice(user_agents)),
-        'Connection': 'keep-alive',
-
-    }
-    req = urllib.request.Request(url, headers=headers)
-    resp = urllib.request.urlopen(req)
-    content = resp.read().decode('utf-8')
-    return content
-# 数据解析
-def data_analysis(content):
-    html = content
-    soup = BeautifulSoup(content, 'html.parser')
-    return soup
-
-
-# 数据存储
-def data_storage(soup):
-    one_page_data = []
-    item_list = soup.select('#newhouse_loupan_list>ul>li')
-    for i in item_list:
-        # {'名称': None, '户型': None, '面积': None, '地址': None, '价格'}
-        item = {}
-        item['名称'] = i.select('.nlcd_name a')[0].text.strip()
-        n = len(''.join(i.select('.house_type')[0].text.split()).split('—'))
-        if n == 0:
-            item['户型'], item['面积'] = '', ''
-        elif n == 1:
-            item['户型'] = ''.join(i.select('.house_type')[0].text.split()).split('—')[0]
-            item['面积'] = ''
-        else:
-            item['户型'], item['面积'] = ''.join(i.select('.house_type')[0].text.split()).split('—')
-            item['面积']=item['面积'].replace('平米','')
-        item['行政区'] = i.select('.address span.sngrey')
-        if len(item['行政区'])==0:
-            item['行政区'] =''
-        else:
-            item['行政区'] =item['行政区'][0].text.replace('[','').replace(']','')
-        item['价格(元/㎡)'] = ''.join(re.findall(r'\d', i.select('.nhouse_price')[0].text))
-        one_page_data.append(item)
-    return one_page_data
-
-
-# 保存文件
-def data_files(all_page_data,save_path):
-    df = pd.DataFrame(all_page_data)
-    df.to_excel(save_path)
-
-# 主函数
-if __name__ == '__main__':
-    new_all_data=[]
-    page=25
-    for i in tqdm(range(1,page+1)):
-        sleep(1)
-        url=f'https://cs.newhouse.fang.com/house/s/b9{i}/'
-        content=data_request(url)
-        soup = data_analysis(content)
-        one_page_data = data_storage(soup)
-        new_all_data.extend(one_page_data)
-    data_files(new_all_data, './房天下新房数据.xlsx')
-    # 打开文件
-    os.startfile(r"房天下新房数据.xlsx")
-
diff --git a/new_house_clean.py b/new_house_clean.py
deleted file mode 100644
index dfb46d0..0000000
--- a/new_house_clean.py
+++ /dev/null
@@ -1,43 +0,0 @@
-'''清洗房天下新房数据'''
-import os
-import pandas as pd
-# 读取数据
-df1 = pd.read_excel(r'./房天下新房数据.xlsx', index_col=0)
-# 清洗行政区\t\n
-df1['行政区'] = df1['行政区'].str.replace('\t', '').str.replace('\n', '')
-
-# 处理户型字段
-df2 = df1.dropna(subset=['户型'])
-# 索引：户型list
-hxdict = dict(df2['户型'].str.split('/'))
-for i, vlist in hxdict.items():
-    for v in vlist:
-        df1.loc[i, v] = '有'
-col_list = df1.columns.tolist()[5:]
-for col in col_list:
-    df1.loc[df1[col].isna(), col] = '无'
-# 处理面积字段
-df3 = df1.dropna(subset=['面积'])
-# 索引：面积list
-mjlist = dict(df3['面积'].str.split('~'))
-for i, vlist in mjlist.items():
-    # 只有一个数据，最大最小面积一样
-    if len(vlist) == 1:
-        df1.loc[i, '最小面积(㎡)'] = vlist[0]
-        df1.loc[i, '最大面积(㎡)'] = vlist[0]
-    else:
-        df1.loc[i, '最小面积(㎡)'] = vlist[0]
-        df1.loc[i, '最大面积(㎡)'] = vlist[1]
-
-
-# 重排columns顺序
-df4 = df1.loc[:, ['名称', '一居', '二居', '三居', '四居', '五居', '五居以上', '最小面积(㎡)', '最大面积(㎡)', '行政区', '价格(元/㎡)']]
-# 清除存在空值的行
-df4=df4.dropna()
-# 重排索引
-df4= df4.reset_index(drop=True)
-
-# # 存入清洗后的数据
-df4.to_excel(r'./房天下新房数据（清洗后）.xlsx')
-# 打开文件
-os.startfile(r"房天下新房数据（清洗后）.xlsx")
\ No newline at end of file
diff --git a/new_house_visualization.py b/new_house_visualization.py
deleted file mode 100644
index aeac561..0000000
--- a/new_house_visualization.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#导包
-import pandas as pd
-import matplotlib.pyplot as plt
-# 正确显示
-plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
-plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
-df=pd.read_excel('./房天下新房数据（清洗后）.xlsx',index_col=0)
-df1=df.dropna(subset=['行政区','价格(元/㎡)'])
-# 根据行政区分组取平均
-df1=df.groupby('行政区').mean(numeric_only=True)
-
-# 行政区平均新房单价分析
-plt.title('行政区平均新房单价分析')
-df1['价格(元/㎡)'].plot.bar(color='#CCCCFF')
-plt.ylabel('价格(元/㎡)')
-for i,v in enumerate(df1['价格(元/㎡)']):
-    plt.text(i-0.35,v+300,round(v))
-plt.xticks(rotation=60)
-'''
-岳麓区的平均新房单价是最高的，浏阳的平均新房单价是最低的
-'''
-plt.savefig('./新房数据展示1.jpg')
-
-# 新房户型占比分析
-plt.figure(figsize=(10,10))
-plt.title('新房户型占比分析')
-df2=df
-df2[df2['一居']=='有'].count()
-a1=df2.一居.value_counts()['有']
-a2=df2.二居.value_counts()['有']
-a3=df2.三居.value_counts()['有']
-a4=df2.四居.value_counts()['有']
-a5=df2.五居.value_counts()['有']
-colors=['#FFECE5','#E5CCFF','#CCCCFF','#CCFFE5','#FFE5CC']
-plt.pie([a1,a2,a3,a4,a5],autopct='%.1f%%',colors=colors,labels=['一居','二居','三居','四居','五居'])
-plt.legend()
-plt.ylabel('')
-plt.savefig('./新房数据展示2.jpg')
-
-# 新房数量占比分析
-plt.figure(figsize=(10,10))
-df2=df.dropna(subset=['行政区'])
-df2=df.groupby('行政区').count()
-plt.title('新房数量占比分析')
-colors=['#FFECE5','#E5CCFF','#CCCCFF','#CCFFE5','#FFE5CC','#e6e6fa','#9cc3eb','#d0eb9b',"#e6cfe6","#d1fff8"]
-df2['名称'].plot.pie(autopct='%.1f%%',colors=colors)
-plt.legend()
-plt.ylabel('')
-plt.savefig('./新房数据展示3.jpg')
diff --git a/ui.py b/ui.py
deleted file mode 100644
index f1b2492..0000000
--- a/ui.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import tkinter as tk
-import os
-from tkinter import messagebox
-import ttkbootstrap as ttk
-
-#执行new_house.py
-def open_new_house():
-    os.system('new_house.py')
-# 执行new_house_clean.py
-def open_new_house_clean():
-    os.system('new_house_clean.py')
-# 执行new_house_visualization.py
-def open_new_house_visualization():
-    os.system('new_house_visualization.py')
-    messagebox.showinfo('提醒', '可视化完成！可以点击主页面下面按钮查看图像')
-def open_analyse_1():
-    os.startfile(f"新房数据展示1.jpg")
-def open_analyse_2():
-    os.startfile(f"新房数据展示2.jpg")
-def open_analyse_3():
-    os.startfile(f"新房数据展示3.jpg")
-
-# 窗口对象+美化
-window = ttk.Window()
-style = ttk.Style("minty")
-window.geometry("500x350+400+250")
-window.title("房天下长沙新房爬取")
-# 图片
-canvas = tk.Canvas(window, height=150, width=500)
-image_file = tk.PhotoImage(file=r"./IMG_0818.PNG")
-image = canvas.create_image(50,20, anchor='nw', image=image_file)
-canvas.pack(side='top')
-
-# 开始爬虫
-b1=tk.Button(window)
-b1['text']="1.开始爬虫"
-b1['command']=open_new_house
-b1.place(x=110, y=150)
-# 清洗数据
-b2=tk.Button(window)
-b2['text']="2.清洗数据"
-b2['command']=open_new_house_clean
-b2.place(x=210, y=150)
-# 可视化
-b3=tk.Button(window)
-b3['text']="3.开始可视化"
-b3['command']=open_new_house_visualization
-b3.place(x=310, y=150)
-
-b4=tk.Button(window)
-b4['text']="3.1查看行政区平均新房单价分析图像"
-b4['command']=open_analyse_1
-b4.place(x=130, y=190)
-
-b5=tk.Button(window)
-b5['text']="3.2查看新房户型占比分析图像"
-b5['command']=open_analyse_2
-b5.place(x=130, y=230)
-
-b6=tk.Button(window)
-b6['text']="3.3查看新房数量占比分析图像"
-b6['command']=open_analyse_3
-b6.place(x=130, y=270)
-# 进入消息循环
-window.mainloop()
\ No newline at end of file