1.组合（stack）：
#coding=utf-8
import numpy as np
# stack different arrays
a = np.floor(10*np.random.random((2,2)))
print a
'''
[[4. 4.]
[7. 2.]]
'''
b = np.floor(10*np.random.random((2,2)))
print b
'''
[[4. 7.]
[9. 0.]]
'''
print np.vstack((a,b)) # 将a和b纵向组合在一起
'''
[[4. 4.]
[7. 2.]
[4. 7.]
[9. 0.]]
'''
print np.hstack((a,b)) #将a和b横向组合在一起
'''
[[4. 4. 4. 7.]
[7. 2. 9. 0.]]
'''注意：行向组合（hstack）得保障行数一样；纵向组合（stack）得保障列数一样。
#coding=utf-8
import numpy as np
# stack different arrays
a = np.floor(10*np.random.random((2,2)))
print a
'''
[[6. 0.]
[9. 8.]]
'''
b = np.floor(10*np.random.random((2,3)))
print b
'''
[[9. 0. 5.]
[6. 8. 3.]]
'''
# 函数column_stack以列将一维数组合成二维数组，它等同与vstack对一维数组
print np.column_stack((a,b)) # # With 2D arrays
'''
[[6. 0. 9. 0. 5.]
[9. 8. 6. 8. 3.]]
'''2.数组增加维度：
#coding=utf-8
import numpy as np
a = np.array([4.,2.])
print a.ndim #1
b = np.array([2.,8.])
c=a[:,np.newaxis] # This allows to have a 2D columns vector
print c
'''
[[4.]
[2.]]
'''
print c.ndim #2#coding=utf-8
import numpy as np
a = np.array([4.,2.])
print a.ndim #1
b = np.array([2.,8.])
c=a[:,np.newaxis] # This allows to have a 2D columns vector
print c
'''
[[4.]
[2.]]
'''
print c.ndim #2
print np.column_stack((a[:,np.newaxis],b[:,np.newaxis]))
'''
[[4. 2.]
[2. 8.]]
'''
print np.vstack((a[:,np.newaxis],b[:,np.newaxis])) #The behavior of vstack is different
'''
[[4.]
[2.]
[2.]
[8.]]
'''3.分割。使用hsplit你能将数组沿着它的水平轴分割，或者指定返回相同形状数组的个数，或者指定在哪些列后发生分割:
#coding=utf-8
import numpy as np
# split an array into several pieces
a = np.floor(10*np.random.random((2,12)))
print a
'''
[[7. 8. 9. 0. 1. 3. 6. 9. 6. 4. 8. 3.]
[4. 1. 8. 6. 8. 6. 8. 6. 5. 2. 6. 8.]]
'''
print np.hsplit(a,3) # split into 3
'''
[array([[7., 8., 9., 0.],
[4., 1., 8., 6.]]), array([[1., 3., 6., 9.],
[8., 6., 8., 6.]]), array([[6., 4., 8., 3.],
[5., 2., 6., 8.]])]
'''
print np.hsplit(a,4)
'''
[array([[7., 8., 9.],
[4., 1., 8.]]), array([[0., 1., 3.],
[6., 8., 6.]]), array([[6., 9., 6.],
[8., 6., 5.]]), array([[4., 8., 3.],
[2., 6., 8.]])]
'''
print np.hsplit(a,(3,4)) # split a after the third and the fourth column
'''
[array([[7., 8., 9.],
[4., 1., 8.]]), array([[0.],
[6.]]), array([[1., 3., 6., 9., 6., 4., 8., 3.],
[8., 6., 8., 6., 5., 2., 6., 8.]])]
'''
# vsplit沿着纵向的轴分割，array split允许指定沿哪个轴分割4.拷贝
（1）完全不拷贝：简单的赋值不拷贝数据对象或其数据
#coding=utf-8
import numpy as np
# 1.totally not copied
a = np.arange(12) #[ 0 1 2 3 4 5 6 7 8 9 10 11]
print a
b = a
print b #[ 0 1 2 3 4 5 6 7 8 9 10 11]
print b is a #True
b.shape = 3,4 # change the shape of a
print a.shape #(3, 4)
print a
'''
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
'''（2）视图view和浅复制：不同的数组对象分享同一个数据，视图方法创造一个新的数组对象指向同一数据
#coding=utf-8
import numpy as np
a = np.arange(12) #[ 0 1 2 3 4 5 6 7 8 9 10 11]
print a
b = a
#print b is a
b.shape = 3,4 # change the shape of a
print a
# 2.view and shadow copy
# 不同的数组对象分享同一个数据。视图方法创造一个新的数组对象指向同一数据,数据其实是存在源数组的
c = a.view()
print 'c:',c
print c is a #False
print c.base is a # c is a vie of the data owned by a #True
print c.flags.owndata # judge,c has no data #False
c.shape = 2,6
print a.shape # chage c's shape but a's shape goesnbt change #(3,4)
c[0,4] = 1234
print a # a's data changes
'''
[[ 0 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
'''
# 切片数组返回它的一个视图：
s = a[:,1:3] # spaces added for clarity; could also be written "s = a[:,1:3]"
s[:] = 10 # # s[:] is a view of s. Note the difference between s=10 and s[:]=10
print a
'''
[[ 0 10 10 3]
[1234 10 10 7]
[ 8 10 10 11]]
'''（3）深复制：完全赋值数据及其数据
#coding=utf-8
import numpy as np
a = np.arange(12) #[ 0 1 2 3 4 5 6 7 8 9 10 11]
print a
d = a.copy() # a new array object with new data is created
print d #[ 0 1 2 3 4 5 6 7 8 9 10 11]
print d is a #False
print d.base is a # d doesn't share anything with a #False
d[0] = 9999
print a #[ 0 1 2 3 4 5 6 7 8 9 10 11]
print d #[9999 1 2 3 4 5 6 7 8 9 10 11]5.函数
'''
# 1.创建数组
arange, array, copy, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r, zeros, zeros_like
# 2.转化
astype, atleast ld, atleast 2d, atleast 3d, mat
# 3.操作
array split, column stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, item, newsxis,ravel, repeat, reshape, resize, squeeze,swapaxes, take,transpose
# 4.询问
all, any, nonzero, where
# 5.排序
argmax, argmin, argsort, max, min, ptp, searchsorted, sort
# 6.运算
choose, compress, cumprod, cumsum, inner, fill, imag, prod, putmask, real, sum
# 7.基本统计
cov, mean, std, var
# 8.基本线性代数
cross, dot, outer, svd, vdot
'''