numpy統計分析

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#計算鳶尾花的花瓣長度的最大值，均值，中值，均方差from sklearn.datasets import load_irisdata=load_iris()data_length=data.data[:,2]print("最大值:",np.max(data_length),"\n","平均值：",np.mean(data_length),"\n","中值:",np.median(data_length),"\n","均方差:",np.std(data_length))

#用np.random.normal()產生一個常態分佈的隨機數組，並顯示出來np.random.normal(3,5,100)import matplotlib.pyplot as pltmu=3sigma=5num=100rand_data=np.random.normal(mu,sigma,num)print(rand_data.shape,type(rand_data))count,bins,ignored=plt.hist(rand_data,30,normed=True)plt.plot(bins,1/(sigma*np.sqrt(2*np.pi))*np.exp(-(bins-mu)**2/(2*sigma**2)),linewidth=2,color=‘r‘)plt.show()

#用np.random.randn()產生一個常態分佈的隨機數組，並顯示出來n1=np.random.randn(4,4)mu=np.median(n1)sigma=np.std(n1)import matplotlib.pyplot as pltmu=np.median(n1)sigma=5num=np.std(n1)print(n1.shape,type(n1))count,bins,ignored=plt.hist(n1,15,normed=True)plt.plot(bins,1/(sigma*np.sqrt(2*np.pi))*np.exp(-(bins-mu)**2/(2*sigma**2)),linewidth=2,color=‘r‘)plt.show()

##顯示鳶尾花花瓣長度的常態分佈圖import matplotlib.pyplot as pltmu=np.median(data_length)sigma=np.std(data_length)num=data_length.shaperand_data=np.random.normal(mu,sigma,num)print(rand_data.shape,type(rand_data))count,bins,ignored=plt.hist(rand_data,30,normed=True)plt.plot(bins,1/(sigma*np.sqrt(2*np.pi))*np.exp(-(bins-mu)**2/(2*sigma**2)),linewidth=2,color=‘r‘)plt.show()

#鳶尾花花瓣的曲線圖，import matplotlib.pyplot as plty=np.linspace(0,150)x=data_lengthplt.plot(np.linspace(0,150,num=150),data_length,"g")plt.show()

#鳶尾花花瓣的散佈圖，import matplotlib.pyplot as plty=np.linspace(0,150)x=data_lengthplt.scatter(np.linspace(0,150,num=150),data_length,alpha=0.5,marker="+")plt.show()

numpy統計分析