数据的CSV文件存取
保存文件：savetxt()方法保存文件
csv文件格式：
np.savetxt(frame,array,fmt='%.18e',delimiter=None)
eg：
In [1]: import numpy as np
In [2]: a = np.arange(100).reshape(5,20)
In [3]: np.savetxt('a.csv', a, fmt='%d', delimiter=',')
读取文件： loadtxt()方法读取文件
eg:
In [5]: b = np.loadtxt('a1.csv', delimiter=',')
In [6]: b
np.random的随机数函数
eg:
In [18]: a = np.random.rand(3,4,5)
In [19]: a
Out[19]:
array([[[ 0.97845512, 0.90466706, 0.92576248, 0.77775142, 0.84334893],
[ 0.39599821, 0.31917683, 0.7961439 , 0.01324569, 0.97660396],
[ 0.5049603 , 0.80952265, 0.67359257, 0.89334316, 0.94496225],
[ 0.04840473, 0.04665257, 0.20956817, 0.62255095, 0.36600489]],
[[ 0.58059326, 0.28464266, 0.23596248, 0.16677631, 0.86467069],
[ 0.14691968, 0.60863245, 0.71725038, 0.69206766, 0.18301705],
[ 0.73197901, 0.99051723, 0.10489076, 0.33979432, 0.0354286 ],
[ 0.73696453, 0.48268632, 0.99294233, 0.06285961, 0.93090147]],
[[ 0.07853777, 0.827061 , 0.66325364, 0.52289669, 0.96894828],
[ 0.41912388, 0.01883408, 0.80978245, 0.93082898, 0.98095581],
[ 0.58614214, 0.55996867, 0.37734444, 0.79280598, 0.03626233],
[ 0.233132 , 0.22514788, 0.32245147, 0.13739658, 0.18866422]]])
eg:
In [28]: a = np.random.randint(100,200,(3,4))
In [29]: a
Out[29]:
array([[116, 111, 154, 188],
[162, 133, 172, 178],
[149, 151, 154, 177]])
NumPy统计函数
np.random的统计函数 ：
函数 说明
sum(a,axis = None) 根据给定轴axis计算数值a相关元素之和，axis整数或元组
mean(a,sxis = None) 根据给定轴axis计算数值a相关元素期望，axis整数或元组
average(a,axis = None,weight = None)根据给定轴axis计算数值a相关元素加权平均值
std(a,axis =None) 根据给定轴axis计算数值a相关元素标准差
var(a,axis = None) 跟据给定轴axis计算数值a相关元素方差
函数 说明
min(a) max(a) 计算数组a中元素的最小值，最大值
argmin(a)argmax(a) 计算数组a中元素的最小值，最大值的降一维后的下标
unravel_index(index,shape) 转换多维下标
ptp(a) 计算数组a中元素的最小值，最大值的差
median(a) 计算数组a中元素的中位数
eg:
In [47]: a = np.arange(15).reshape(3,5)
In [48]: a
Out[48]:
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
In [49]: np.sum(a)
Out[49]: 105
In [50]: np.mean(a,axis=1) # 2. = (0+5+10)/3
Out[50]: array([ 2., 7., 12.])
