test1

basic data analysis.py

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+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("----------")