# list 切片返回的是不原数据，对新数据的修改不会影响原数据
In [
45]: list1 = [
1,
2,
3,
4,
5]
In [
46]: list2 = list1[:
3]
In [
47]: list2
Out[
47]: [
1,
2,
3]
In [
49]: list2[
1] =
1999
# 原数据没变
In [
50]: list1
Out[
50]: [
1,
2,
3,
4,
5]
In [
51]: list2
Out[
51]: [
1,
1999,
3]
# 而 NumPy.ndarry 的切片返回的是原数据
In [
52]: arr = np.
array([
1,
2,
3,
4,
5])
In [
53]: arr
Out[
53]:
array([
1,
2,
3,
4,
5])
In [
54]: arr1 = arr[:
3]
In [
55]: arr1
Out[
55]:
array([
1,
2,
3])
In [
56]: arr1[
0] =
989
In [
57]: arr1
Out[
57]:
array([
989,
2,
3])
# 修改了原数据
In [
58]: arr
Out[
58]:
array([
989,
2,
3,
4,
5])
# 若希望得到原数据的副本， 可以用
copy()
In [
59]: arr2 = arr[:
3].
copy()
In [
60]: arr2
Out[
60]:
array([
989,
2,
3])
In [
61]: arr2[
1] =
99282
In [
62]: arr2
Out[
62]:
array([
989,
99282,
3])
# 原数据没被修改
In [
63]: arr
Out[
63]:
array([
989,
2,
3,
4,
5])
