引言

主要介绍 NumPy 数组索引 Index，切片 Slicing，子数组，更多 Python 进阶系列文章，请参考 Python 进阶学习 玩转数据系列

内容提要：

1. 索引，切片，子数组概要
2. 索引 Indexing
3. 连续切片和跨步切片 Slicing and striding
4. 子数组 Subarray: View vs. Copy

索引，切片，子数组概要:

索引 Indexing:

1. Index 是一个整型数
2. Index 从 0 开始
3. 负数的 index 也是可以的
4. Index 引用用中括号 [ ]
5. 某个维度 index 的范围值：
   正数: np.size (a, axis = axis_value) - 1
   负数: -np.size (a, axis = axis_value)
6. 多维数组: 用逗号分开每个维度的 index

Slicing and Striding:

1. [ start : stop : step ]: 用方括号 [ ] 表示
2. start, stop and step 切片访问某个特定维度的数组
3. 负数值也是可以的
4. 取值范围和 index 一样
5. [ start, stop ): 注意 stop 是不包含的
6. 多维数组的分片: 用逗号分开每个维度的 index

Subarray: View vs. Copy:
view subarray 会改变主 array
copy subarray 不会改变主 array

索引 Indexing

Indexing 索引主要提供随机访问数组的单个元素

index的重点：

1. index: 从 0 开始
2. 0 ≤ index ≤ np.size (a) - 1
3. 负数 index is ok
   –index ≡ np.size (a) — index

index 范围：

举例：

import numpy as nparray_1_d = np.arange(1, 5)
array_2_d = np.random.randint (0, 100, (3, 4))
# Right-most element of the 2-d array
row = np.size (array_2_d, axis=0) - 1 
col = np.size (array_2_d, axis=1) - 1 right_most = array_2_d[row, col]print("array_1_d: {}\nfirst element array_1_d[0]: {}\nlast element array_1_d[-1]: {} ".format(array_1_d, array_1_d[0], array_1_d[-1]))
print("array_2_d:\n{} \nfirst element array_2_d[0][0]: {}\nlast element array_2_d[-1][-1]: {} ".format(array_2_d, array_2_d[0][0], array_2_d[-1][-1]))
print("last element array_2_d[row, col]: {}".format(right_most))

输出：

array_1_d: [1 2 3 4]
first element array_1_d[0]: 1
last element array_1_d[-1]: 4
array_2_d:
[[15 53 42 77][48 38 67 53][12 84 98 17]]
first element array_2_d[0][0]: 15
last element array_2_d[-1][-1]: 17
last element array_2_d[row, col]: 17

连续切片和跨步切片 Slicing and striding

分片访问数组的元素

Slicing 连续切片
注意：end_index是不包含的
subarray = array [ start_index : end_index ]
np.size (subarray) = end_index – start_index


连续切片举例：

import numpy as nparray_1 = np.arange(0,5)
array_2 = np.arange(5, 12)
array_join = np.concatenate([array_1, array_2])
subarray_1 = array_join[1:5]array_2_d = np.reshape (array_join, (3,4))
subarray_2 = array_2_d[:2, :3]
first_column = array_2_d[:, 0]
thrid_row = array_2_d[2, :]print("array_1:{}\n".format(array_1))
print("array_2:{}\n".format(array_2))
print("array_join:{}\n".format(array_join))
print("sub array of array_join array_join[1:5]:{}\n".format(subarray_1))print("array_2_d:{}\n".format(array_2_d))
print("sub array of array_2_d array_2_d[:2, :3]:{}\n".format(subarray_2))
print("the first column of array_2_d array_2_d[:, 0]{}\n".format(first_column))
print("the third row of array_2_d array_2_d[2, :]:{}\n".format(thrid_row))

输出：

array_1:[0 1 2 3 4]array_2:[ 5  6  7  8  9 10 11]array_join:[ 0  1  2  3  4  5  6  7  8  9 10 11]sub array of array_join array_join[1:5]:[1 2 3 4]array_2_d:[[ 0  1  2  3][ 4  5  6  7][ 8  9 10 11]]sub array of array_2_d array_2_d[:2, :3]:[[0 1 2][4 5 6]]the first column of array_2_d array_2_d[:, 0][0 4 8]the third row of array_2_d array_2_d[2, :]:[ 8  9 10 11]

一维数组跨步不连续切片：
subarray = array [ start_index : end_index : stride ]

多维数组跨步不连续切片：
subarray = m [ start_0 : end_0 : stride_0, start_1 : end_1 : stride_1 ]

跨步不连续切片举例：

import numpy as nparray_1 = np.arange(0,12)
array_2_d = np.reshape (array_1, (3,4))# every other element
subarray_1_every_other = array_1[::2]
# every other element, starting at index 1
subarray_1_every_other_start_from_1 = array_1[1::2]
# reversed array
subarray_1_reversed = array_1[::-1]# reversing all dimensions
subarray_2_d_reversed = array_2_d[::-1, ::-1]print("array_1:{}\n".format(array_1))
print("subarray_1_every_other -> array_1[::2]:{}\n".format(subarray_1_every_other))
print("subarray_1_every_other_start_from_1 -> array_1[1::2]:{}\n".format(subarray_1_every_other_start_from_1))
print("subarray_1_reversed -> array_1[::-1]:{}\n".format(subarray_1_reversed))print("array_2_d:{}\n".format(array_2_d))
print("subarray_2_d_reversed -> array_2_d[::-1, ::-1]:\n{}".format(subarray_2_d_reversed)) 

输出：

array_1:[ 0  1  2  3  4  5  6  7  8  9 10 11]subarray_1_every_other -> array_1[::2]:[ 0  2  4  6  8 10]subarray_1_every_other_start_from_1 -> array_1[1::2]:[ 1  3  5  7  9 11]subarray_1_reversed -> array_1[::-1]:[11 10  9  8  7  6  5  4  3  2  1  0]array_2_d:[[ 0  1  2  3][ 4  5  6  7][ 8  9 10 11]]subarray_2_d_reversed -> array_2_d[::-1, ::-1]:
[[11 10  9  8][ 7  6  5  4][ 3  2  1  0]]

子数组 Subarray: View vs. Copy

1. Subarray 是一个视图不是拷贝
2. 改变 subarray, 将会影响到 主 array
3. 用 np.copy() 方法创建一个主 array 的拷贝，这样改变subarray 的值就不会影响到主 array

举例：

import numpy as npa = np.arange (12)
ma = np.reshape (a, (3,4))
print("main array:\n{}\n".format(ma))# a view not a copy
sma = ma [ : 2, : 2]
print("sub view array -> ma [ : 2, : 2]:\n{}\n".format(sma))
# changing the view, changes the original array
sma [0, 0] = 100
print("the changed sub view array -> sma [0, 0] = 100:\n{}\n".format(sma))
print("the changed main array:\n{}\n".format(ma))# create a copy of the subarray
cma = np.copy (sma)
print("sub copy array -> np.copy (sma):\n{}\n".format(cma))
# changing the copy, do not change the original array
cma [1, 1] = 1000
print("the changed sub copy array:\n{}\n".format(cma))
print("main array not changed:\n{}\n".format(ma))

输出：

main array:
[[ 0  1  2  3][ 4  5  6  7][ 8  9 10 11]]sub view array -> ma [ : 2, : 2]:
[[0 1][4 5]]the changed sub view array -> sma [0, 0] = 100:
[[100   1][  4   5]]the changed main array:
[[100   1   2   3][  4   5   6   7][  8   9  10  11]]sub copy array -> np.copy (sma):
[[100   1][  4   5]]the changed sub copy array:
[[ 100    1][   4 1000]]main array not changed:
[[100   1   2   3][  4   5   6   7][  8   9  10  11]]