python求用户输入数据的绝对值 spearman相关系数的检验？
spearman相关系数的检验？
Python的公式：r,pstats.spearmanr（X1,X2）结果为:r:相关系数，p：p_value功能：是两个数据集之间关系单调性的非参数度量，Spearman相关性不题中两个数据集是标准正态分布的。（检验2个变量之间的相关性）r:这个相关系数在-1和1之间变化，0意思是没有相关性。相关系数的绝对值约接近1，相关性越高，p:p值查阅地来表示不查找系统产生具备Spearman相关性的数据集的概率（简单通俗的说，那就是2个变量不相关的概率，总体上，若2个变量的相关系数越高，则P值会低些较高）。p值并不完全可靠，但相对于大于1500左右的数据集很有可能是合理不的。例子：r,pstats.spearmanr([1,2,3,4,5],[5,6,7,8,7])(1234321)x2nnp.random.randn(100,2)y2nnp.random.randn(100,2)stats.spearmanr(x2n)#最终(0.059969996999699973,0.55338590803773591)stats.spearmanr(x2n[:,0],x2n[:,1])#最终(0.059969996999699973,0.55338590803773591)rho,pvalstats.spearmanr(x2n,y2n)#最后gtgtgtrhoarray([[1.,0.05997,0.18569457,0.06258626],[0.05997,1.,0.110003,0.02534653],[0.18569457,0.110003,1.,0.03488749],[0.06258626,0.02534653,0.03488749,1.]])gtgtgtpvalarray([[0.,0.55338591,0.06435364,0.53617935],[0.55338591,0.,0.27592895,0.80234077],[0.06435364,0.27592895,0.,0.73039992],[0.53617935,0.80234077,0.73039992,0.]])gtgtgtrho,pvalstats.spearmanr(x2n.T, y2n.T,axis1)gtgtgtrhoarray([[1.,0.05997,0.18569457,0.06258626],[0.05997,1.,0.110003,0.02534653],[0.18569457,0.110003,1.,0.03488749],[0.06258626,0.02534653,0.03488749,1.]])stats.spearmanr(x2n,y2n,axisNone)#总体的相关性：(0.10816770419260482,0.1273562188027364)stats.spearmanr(x2n.ravel(),y2n.ravel())#总体的相关性：(0.10816770419260482,0.1273562188027364)xintnp.random.randint(10,size(100,2))stats.spearmanr(xint)#(0.052760927029710199,0.60213045837062351)
fab在python中的意思？
?请看：fabs()方法赶往数字的绝对值，如math.fabs(-10)直接返回10.0.
?语法：
importmath
math.fabs(x)
?参数：x-数值表达式
?返回值：赶往数字的绝对值.
?实例：
#!/usr/bin/python
#-*-coding:UTF-8-*-
importmath
#再导入数学模块
print#34math.fabs(-45.17):#34,math.fabs(-45.17)
print#34math.fabs(100.12):#34,math.fabs(100.12)
print#34math.fabs(100.72):#34,math.fabs(100.72)
print#34math.fabs(119L):#34,math.fabs(119L)
print#34math.fabs(math.pi):#34,math.fabs(math.pi)
作为输出结果：
math.fabs(-45.17):45.17
math.fabs(100.12):100.12
math.fabs(100.72):100.72
math.fabs(119L):119.0
math.fabs(math.pi):3.14159265359
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