python中二维数组到三维数组的转换

很抱歉问这个问题，如果已经问过的话，但是在我的例子中，我有一个大小为3000000x50的特殊矩阵，我想将其拆分为300个大小为10000x50的矩阵。我试过了，但不起作用

>>>import numpy as np
>>>data=np.random.randn(3000000,50)
>>>D=np.matrix.conjugate(data)
>>>ts=50
>>>ts=int(ts)       #number of time series that we have from our data
>>>lw=1e4
>>>lw=int(lw)    #length of each window 
>>>l=len(data)/lw   #l is number of windows
>>>l=np.floor(l)
>>>l=int(l)
#Dc is used to seperate each time series in l windows
>>>Dc=np.zeros((l,lw,ts))
>>>for i in range(l):
    Dc[i][0:lw-1][0:ts-1]=D[(lw)*(i):(lw*(i+1))-1][0:ts-1]

您正在查找（将数组垂直（按行）拆分为多个子数组）-

样本运行-

In [56]: data
Out[56]: 
array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
       [ 0.40789713,  0.36018843,  0.41731607,  0.17348898],
       [ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
       [ 0.52407036,  0.89913995,  0.59097535,  0.38376443],
       [ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
       [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])

In [57]: np.vsplit(data,3)
Out[57]: 
[array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
        [ 0.40789713,  0.36018843,  0.41731607,  0.17348898]]),
 array([[ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
        [ 0.52407036,  0.89913995,  0.59097535,  0.38376443]]),
 array([[ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
        [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])]

In [68]: data
Out[68]: 
array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
       [ 0.40789713,  0.36018843,  0.41731607,  0.17348898],
       [ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
       [ 0.52407036,  0.89913995,  0.59097535,  0.38376443],
       [ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
       [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])

In [69]: data.reshape(3,-1,data.shape[1])
Out[69]: 
array([[[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
        [ 0.40789713,  0.36018843,  0.41731607,  0.17348898]],

       [[ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
        [ 0.52407036,  0.89913995,  0.59097535,  0.38376443]],

       [[ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
        [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]]])

---

根据您将如何使用输出，您可以将2D输入阵列重塑为沿第一轴长度

300

的3D阵列，这在性能和内存方面必须更高效。Memorywise它必须是自由的，因为

重塑

只创建numpy数组的一个视图。执行工作将是：-

data.reshape(300,-1,data.shape[1])

样本运行-

In [56]: data
Out[56]: 
array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
       [ 0.40789713,  0.36018843,  0.41731607,  0.17348898],
       [ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
       [ 0.52407036,  0.89913995,  0.59097535,  0.38376443],
       [ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
       [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])

In [57]: np.vsplit(data,3)
Out[57]: 
[array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
        [ 0.40789713,  0.36018843,  0.41731607,  0.17348898]]),
 array([[ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
        [ 0.52407036,  0.89913995,  0.59097535,  0.38376443]]),
 array([[ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
        [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])]

In [68]: data
Out[68]: 
array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
       [ 0.40789713,  0.36018843,  0.41731607,  0.17348898],
       [ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
       [ 0.52407036,  0.89913995,  0.59097535,  0.38376443],
       [ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
       [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])

In [69]: data.reshape(3,-1,data.shape[1])
Out[69]: 
array([[[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
        [ 0.40789713,  0.36018843,  0.41731607,  0.17348898]],

       [[ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
        [ 0.52407036,  0.89913995,  0.59097535,  0.38376443]],

       [[ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
        [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]]])

这里有一些运行时测试来检查性能，比较实际拆分和重塑-

In [72]: data = np.random.rand(6000,40)

In [73]: %timeit np.vsplit(data,300)
100 loops, best of 3: 7.05 ms per loop

In [74]: %timeit data.reshape(300,-1,data.shape[1])
1000000 loops, best of 3: 1.08 µs per loop

---

如果您的初始数组已正确排序，并且您希望将数组拆分为300个矩阵“框”，那么您只需要对marix进行以下重新定义

import numpy as np
data = np.random.randn(3000000,50)
newData = data.reshape(300,10000,50) # This is as [300,10000,50] array

print newData[0,...] # Show the first matrix, 1 of 300

---

为什么不使用

np.split（）

：

new\u array=np.split（D，300）

读者请使用

重塑

部分而不是

（v）split

@askewchan完全同意这一点！谢谢你的回答，这对我帮助很大。比较测试非常有帮助。vsplit需要一个无余数的均匀分割。所有子体的大小必须相同