关于Numpy Array的使用技巧整理

1. 数组的扩展：repeat & tile

repeat方法：实现按元素复制扩展

输入：(需要扩展的array)，repeats向量，轴向axis(用于多维array情形)

输出：扩展后的array，需要赋值才能保存，并不修改原array本身

关于repeats向量的使用 ：

若长度为1，则每个元素复制repeats次

若长度为array.shape[axis]，则array[i]复制repeats[i]次

若长度与array.shape[axis]不等则报错

>>>import numpy as np

>>>a = np.arange(5)

>>>a

array([0, 1, 2, 3, 4])

>>>a.repeat(2)

array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])

>>>np.repeat(a,2)

array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4])

>>>a.repeat(range(5))

array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])

>>>a.repeat(range(4))

Traceback (most recent call last):

File "", line 1, in

a.repeat(range(4))

ValueError: operands could not be broadcast together with shape (5,) (4,)

对于多维array的情况

如果不指定axis，则系统自动将array转换成一维数组，然后根据repeats进行复制

如果指定了axis，则在对应维度下，将下一维度当做一个元素根据repeats进行复制

应当保证repeats维度为1，并且len(repeats)==array.shape[axis]

>>>b = np.arange(5) + np.arange(5).reshape(5,1)

>>>b

array([[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8]])

>>>b.repeat(2)

array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 2, 2, 3,

3, 4, 4, 5, 5, 6, 6, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 4, 4, 5, 5, 6, 6,

7, 7, 8, 8])

>>>b.repeat(2,axis=1)

array([[0, 0, 1, 1, 2, 2, 3, 3, 4, 4],

[1, 1, 2, 2, 3, 3, 4, 4, 5, 5],

[2, 2, 3, 3, 4, 4, 5, 5, 6, 6],

[3, 3, 4, 4, 5, 5, 6, 6, 7, 7],

[4, 4, 5, 5, 6, 6, 7, 7, 8, 8]])

>>>b.repeat(2,axis=0)

array([[0, 1, 2, 3, 4],

[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8],

[4, 5, 6, 7, 8]])

>>>b.repeat(range(5),axis=0)

array([[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[3, 4, 5, 6, 7],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8],

[4, 5, 6, 7, 8],

[4, 5, 6, 7, 8],

[4, 5, 6, 7, 8]])

tile方法(注意：此方法与repeat不同，只能用np.tile()方式调用)

输入：数组array，复制方式数组reps

输出：对整个数组进行复制操作后的结果数组

依旧需要赋值，否则不会保存

关于reps的详细说明(array为多维情形)：

当reps维度小于array的维度时，默认从低到高的顺序对数组进行复制

相当于在reps左面补上1，将维度补齐

维度补齐以后，对应维度下，以数组为一个单位，进行扩展，具体见下面例子

>>>c=b.reshape(1,5,5)

>>>c

array([[[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8]]])

>>>c.shape

(1L, 5L, 5L)

>>>np.tile(c,2)

array([[[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],

[1, 2, 3, 4, 5, 1, 2, 3, 4, 5],

[2, 3, 4, 5, 6, 2, 3, 4, 5, 6],

[3, 4, 5, 6, 7, 3, 4, 5, 6, 7],

[4, 5, 6, 7, 8, 4, 5, 6, 7, 8]]])

>>>np.tile(c,(1,1,2))

array([[[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],

[1, 2, 3, 4, 5, 1, 2, 3, 4, 5],

[2, 3, 4, 5, 6, 2, 3, 4, 5, 6],

[3, 4, 5, 6, 7, 3, 4, 5, 6, 7],

[4, 5, 6, 7, 8, 4, 5, 6, 7, 8]]])

>>>np.tile(c,(1,2))

array([[[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],

[1, 2, 3, 4, 5, 1, 2, 3, 4, 5],

[2, 3, 4, 5, 6, 2, 3, 4, 5, 6],

[3, 4, 5, 6, 7, 3, 4, 5, 6, 7],

[4, 5, 6, 7, 8, 4, 5, 6, 7, 8]]])

>>>np.tile(c,(1,2,1))

array([[[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8],

[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8]]])

>>>np.tile(c,(2,1,1))

array([[[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8]],

[[0, 1, 2, 3, 4],

[1, 2, 3, 4, 5],

[2, 3, 4, 5, 6],

[3, 4, 5, 6, 7],

[4, 5, 6, 7, 8]]])

未完待续