diff --git a/ACM/DongDong认亲戚.py b/ACM/DongDong认亲戚.py
deleted file mode 100644
index f28dfbf..0000000
--- a/ACM/DongDong认亲戚.py
+++ /dev/null
@@ -1,2 +0,0 @@
-a=[0]*int(2e4)
-print(len(a))
\ No newline at end of file
diff --git a/ACM/World Finals.py b/ACM/World Finals.py
deleted file mode 100644
index 70c2a32..0000000
--- a/ACM/World Finals.py	
+++ /dev/null
@@ -1,54 +0,0 @@
-from functools import cmp_to_key
-c=[[],[]]
-string=[[],[]]
-for i in range(2):
-    a = eval(input())
-    while a>0:
-        s=input().split()
-        s[1],s[2]=eval(s[1]),eval(s[2])
-        c[i].append(s)
-        string[i].append(s[0])
-        a-=1
-def cmp(a,b):
-    if a[1]>b[1]:
-        return -1
-    elif a[1]==b[1]:
-        if a[2]<b[2] :
-            return -1
-        else:
-            return 1
-    else :
-        return 1
-c[0]=sorted(c[0],key=cmp_to_key(cmp))
-# print(c[0])
-c[1]=sorted(c[1],key=cmp_to_key(cmp))
-# print(c[1])
-# m='123'
-# n='123'
-# print(m==n)
-st="lzr010506"
-s=set(string[0])&set(string[1])
-# for i in c[0]:
-#     for j in c[1]:
-#         if i[0] == j[0] :
-#             s.append(j[0])
-# print(s)
-ans1=0
-ans2=0
-i=0
-while c[0][i][0] != st:
-    # print(c[0][i][0])
-    if c[0][i][0] in s:
-        ans1+=1
-    i+=1
-ans1= i+1-ans1
-i=0
-while c[1][i][0]!=st:
-    # print(c[1][i][0])
-    if c[1][i][0] in s:
-        ans2+=1
-    i+=1
-ans2=i+1-ans2
-# print(ans1,ans2)
-print(min(ans1,ans2))
-# print(c[0],'\n',c[1])
\ No newline at end of file
diff --git a/ACM/[NOIP2004]合并果子.py b/ACM/[NOIP2004]合并果子.py
deleted file mode 100644
index b5b1429..0000000
--- a/ACM/[NOIP2004]合并果子.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import heapq
-import ctypes
-a=int(input())
-b=input().split()
-for i in range(len(b)):
-    b[i]=int(b[i])
-heapq.heapify(b)
-# print(b)
-sum=0
-while len(b) != 1:
-    c,d=heapq.nsmallest(2,b)
-    # heapq.heappop(b)
-    # heapq.heappop(b)
-    b.remove(c)
-    b.remove(d)
-    sum+=c+d
-    heapq.heappush(b,c+d)
-print(sum)
\ No newline at end of file
diff --git a/ACM/值周.py b/ACM/值周.py
deleted file mode 100644
index 1c1aefa..0000000
--- a/ACM/值周.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import functools
-a,b =map(int, input().split(' '))
-nums=[]
-for i in range(b):
-    m,n=map(int,input().split(' '))
-    if m>n:continue;
-    nums.append([m,n])
-def cmp(a,b):#升序
-    if(a[0]>b[0]):
-        return 1
-    else :
-        return -1#不变
-nums=sorted(nums,key=functools.cmp_to_key(cmp))
-# for i in nums:
-#     print(i)
-sum = 0
-start,end=nums[0][0],nums[0][1]
-count=1
-while count<len(nums):
-    if nums[count][0]>=start and nums[count][0]<=end:
-        if end<=nums[count][1]:
-            end=nums[count][1]
-    else :
-        sum+=end-start+1
-        start=nums[count][0];end=nums[count][1]
-    count+=1
-sum+=end-start+1
-print(a-sum+1)
\ No newline at end of file
diff --git a/ACM/校门外的树.py b/ACM/校门外的树.py
deleted file mode 100644
index b9b44d6..0000000
--- a/ACM/校门外的树.py
+++ /dev/null
@@ -1,7 +0,0 @@
-a,b =map(int, input().split(' '))
-nums=[]
-for i in range(b):
-    m,n=map(int,input().split(' '))
-    nums.extend(range(m,n+1))
-nums=set(nums)
-print(a+1-len(nums))
\ No newline at end of file
diff --git a/盘荣博/数据可视化/week1.py b/盘荣博/数据可视化/week1.py
index e69de29..66aa09d 100644
--- a/盘荣博/数据可视化/week1.py
+++ b/盘荣博/数据可视化/week1.py
@@ -0,0 +1,68 @@
+import pandas as pd
+<<<<<<< HEAD
+import numpy as np
+from  scipy.stats import zscore
+from sklearn.decomposition import PCA
+from  scipy.stats import zscore
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import ylabel
+df = pd.read_excel("棉花产量论文作业的数据.xlsx")
+# plt.plot(df["年份"],df["单产"])
+plt.rcParams['font.sans-serif']="SimHei"
+# plt.rcParams['size'] =10
+# plt.ylabel('单产')
+# plt.xlabel('年份')
+
+# print(df)
+d = df.to_numpy()[:,1:]
+print(d)
+plt.subplot(4,1,1)
+plt.scatter(d[:,:1],d[:,1:2],c='r')
+ylabel('原始数据'),plt.title("单产和种子费用的关系")
+#公式调用标准化，遵守标准正态分布
+data = zscore(d)
+print(data)
+plt.subplot(4,1,2)
+plt.scatter(data[:,:1],data[:,1:2],c='b',)
+ylabel('zscore')
+
+print(d.max(axis=0))
+print(d.std(axis=0))
+print(d.mean(axis=0))
+#手写标准正态分布
+data1=(d-d.mean(axis=0))/d.std(axis=0)
+print(data1)
+plt.subplot(4,1,3)
+plt.scatter(data1[:,:1],data1[:,1:2],c='y')
+ylabel('手写标准正态分布')
+
+data2=(d-d.min(axis=0))/(d.max(axis=0)-d.min(axis=0))
+plt.subplot(4,1,4)
+plt.scatter(data2[:,:1],data2[:,1:2],c='g')
+plt.xlabel('压缩到0~1')
+print(data==data1)
+
+
+# plt.savefig("shuju.jpg",dpi=2000)
+# plt.show()
+md= PCA().fit(data)
+cf = np.cov(data.T)#求协方差矩阵
+print(cf)
+c, d= np.linalg.eig(cf)
+print("特征值：\n",c)
+print(md.explained_variance_)
+e=c/c.sum()
+# for _ in range(len(e)):
+#     if(_!=0):
+#         e[_]+=e[_-1]
+print('贡献率：')
+print(e)
+print(md.explained_variance_ratio_)
+print('特征向量：')
+print(d.T)
+print(md.components_)
+print(md.components_-d.T<=0.1)
+
+plt.savefig("shuju.jpg",dpi=2000)
+plt.show()
+