一维数组的切片访问
numpy 中的一维数组的切片方法与 python 内置的list 切片类似.
Details:
1. ndarray[start:stop:step] # means start from start, stop at stop, step by step
2. ndarray[start:stop] # means start from start, stop at stop, step by 1
3. ndarray[start:] # means start from start, stop at the end, step by 1
4. ndarray[:stop] # means start from the beginning, stop at stop, step by 1
5. ndarray[:] # means start from the beginning, stop at the end, step by 1
6. ndarray[start:stop:step, start:stop:step] # means 2-dimensional slicing
Note:
start is from 0
start is inclusive, stop is exclusive
step could be negative, which means reverse order
下面是例子
先构建1个1维数组:
arr = np.arange(10) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
logger.info(f"arr: {
arr}") # [0 1 2 3 4 5 6 7 8 9]
- 如果stop index > 数组长度, 则只会返回 start: last index + 1
logger.info(f"arr[1:100]: {
arr[1:100]}") # [1 2 3 4 5 6 7 8 9] even stop > len(arr)
- 正常[1:5] 则返回 index 为 1 到 4的元素, 就是 第2 到 第5 的元素
logger.info(f"arr[1:5]: {
arr[1:5]}") # [1 2 3 4]
- [1:5:2] 则返回 index 为 1 到 4的元素内, 每2个选1个,因为step = 2, 所以就是第2 到第4的元素了
logger.info(f"arr[1:5:2]: {
arr[1:5:2]}") # [1 3]
- [1:] 没有指定stop 和 step, 则stop = last index + 1 (9 + 1) , step = 1 返回第2 个到 第10个元素 (index from 1 to 9)
logger.info(f"arr[1:]: {
arr[1:]}") # [1 2 3 4 5 6 7 8 9]
- [:5] 没有指定start 和 step, 则stop = 0 , step = 1 返回第1 到 第5 个元素 (index from 0 to 4)
logger.info(f"arr[:5]: {
arr[:5]}") # [0 1 2 3 4]
- [:] 返回所有元素, 等于[0: len(arr):1]
logger.info(f"arr[:]: {
arr[:]}") # [0 1 2 3 4 5 6 7 8 9]
- [::-1] 返回所有元素, 反向倒置, 因为step = -1
logger.info(f"arr[::-1]: {
arr[::-1]}") # [9 8 7 6 5 4 3 2 1 0]
- [5:2:-1] 返回 从 index 5 到 index 3(不包含2) 中, 反向相反, 每隔1个的元素
logger.info(f"arr[5:1:-2]: {
arr[5:1:-2]}") # [5 3]
- [5:2] 返回空数组, 因为start > stop 且step > 0
logger.info(f"arr[5:1]: {
arr[5:1]}") # [] # empty array because start > stop
二维/多维数组的切片访问
规则, 其实是基于1维数组的
arr[0轴上的切片, 1轴上的切片…, n-1轴上的切片]
例子:
# 2-dimensional slicing
""" [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] """
arr = np.arange(12).reshape(3, 4)
logger.info(f"arr: {
arr}")
logger.info(f"arr[1:3, 1:3]: {
arr[1:3, 1:3]}") # means row 1, 2, column 1, 2 # [[ 5 6] [ 9 10]]
logger.info(f"arr[1:3, :]: {
arr[1:3, :]}") # means row 1, 2, all columns # [[ 4 5 6 7] [ 8 9 10 11]]
logger.info(f"arr[:, 1:3]: {
arr[:, 1:3]}") # means all rows, column 1, 2 # [[ 1 2] [ 5 6] [ 9 10]]
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