This repository has been archived by the owner on Apr 18, 2024. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathbasic data analysis.py
124 lines (106 loc) · 2.97 KB
/
basic data analysis.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
import matplotlib.pyplot as plt
from math import sqrt
import numpy as np
#####變數宣告（？？？#####
sumx=0
sumy=0
sumx2=0
sumy2=0
sumxy=0
pix=1
xlist=[]
ylist=[]
#####輸入資料#####
mode=int(input("輸入 1 為一維分析、2 為二維分析："))
if mode==1:
print("----------")
print("一維數據分析")
print("----------")
n=int(input("輸入資料筆數："))
print("開始輸入資料")
for i in range(0,n):
x=float(input("第"+str(i+1)+"筆："))
xlist.append(x)
#####計算總和、平方合、相乘合#####
sumx += float(x)
pix*=x
sumx2+= float(x)**2
xlist.sort()
print(" ")
print(" ")
print(xlist)
#####中位數#####
if len(xlist)%2 == 0:
print("中位數：",(xlist[int(len(xlist)/2)]+xlist[int(len(xlist)/2-1)])/2)
else:
print("中位數",xlist[int(len(xlist)/2-0.5)])
#####平均#####
avgx=sumx/n
print("算數平均數：",round(avgx,4))
print("幾何平均數：",round(np.power(pix,1/n),4))
#####標準差#####
print("標準差：",round(sqrt(sumx2/n-avgx**2),4))
elif mode==2:
print("----------")
print("二維數據分析")
print("----------")
n=int(input("輸入資料筆數："))
print("輸入資料x及y，以空白分隔，例如「第1筆：37 53」")
for i in range(0,n):
x,y=input("第"+str(i+1)+"筆：").split()
x=float(x)
y=float(y)
xlist.append(x)
ylist.append(y)
#####計算總和、平方合、相乘合#####
sumx += float(x)
sumy += float(y)
sumx2+= float(x)**2
sumy2+= float(y)**2
sumxy+= float(x)*float(y)
#####找出極值、算平均、標準差、相關係數、斜率#####
max_x=float(max(xlist))
max_y=float(max(ylist))
min_x=float(min(xlist))
min_y=float(min(ylist))
avgx=sumx/n
avgy=sumy/n
sigmax=sqrt(sumx2/n-avgx**2)
sigmay=sqrt(sumy2/n-avgy**2)
r=(sumxy/n-avgx*avgy)/(sigmax*sigmay)
m=r*sigmay/sigmax
#####輸出結果#####
print(" ")
print(" ")
print("x資料：",xlist)
print("y資料：",ylist)
print("- - - - - - - - - - - - -")
print("平均數：(",round(avgx,4),",",round(avgy,4),")")
print("標準差：(",round(sigmax,4),",",round(sigmay,4),")")
print("- - - - - - - - - - - - -")
print("相關係數：",round(r,4),"；決定係數：",round(r**2,4))
print("--------------------------")
print("關閉圖表以利用回歸線預測資料")
#####製圖#####
plt.scatter(xlist,ylist)
z = np.polyfit(np.array(xlist), np.array(ylist), 1)
p = np.poly1d(z)
plt.plot(xlist, p(xlist))
func="y="+str(round(m,4))+"x+"+str(round(-m*avgx+avgy,4))
text="r^2="+str(round(r**2,4))
if m<0:
plt.text(0.7*max_x+0.3*min_x,0.9*max_y+0.1*min_y,func)
plt.text(0.7*max_x+0.3*min_x,0.85*max_y+0.15*min_y,text)
else:
plt.text(0.7*max_x+0.3*min_x,0.1*max_y+0.9*min_y,func)
plt.text(0.7*max_x+0.3*min_x,0.05*max_y+0.95*min_y,text)
plt.show()
#####迴歸線預測#####
print("- - - - - - - - - - - - -")
while True:
predx=float(input("輸入x值預測y值："))
print(round(m*(predx-avgx)+avgy,4))
else:
print("----------")
print("庭維數據分析")
print("----------")