numpy_2索引切片与迭代

RJ_Hwang

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2022-05-06

import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(x[2])  # 3

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
print(x[2])  # [21 22 23 24 25]
print(x[2][1])  # 22
print(x[2, 1])  # 22

3
[21 22 23 24 25]
22
22

 import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(x[0:2])  # [1 2]
#用下标0~5,以2为步长选取数组
print(x[1:5:2])  # [2 4]
print(x[2:])  # [3 4 5 6 7 8]
print(x[:2])  # [1 2]
print(x[-2:])  # [7 8]
print(x[:-2])  # [1 2 3 4 5 6]
print(x[:])  # [1 2 3 4 5 6 7 8]
#利用负数下标翻转数组
print(x[::-1])  # [8 7 6 5 4 3 2 1]

[1 2]
[2 4]
[3 4 5 6 7 8]
[1 2]
[7 8]
[1 2 3 4 5 6]
[1 2 3 4 5 6 7 8]
[8 7 6 5 4 3 2 1]

# 对二维数组切片
import numpy as np

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
print(x[0:2])
# [[11 12 13 14 15]
#  [16 17 18 19 20]]

print(x[1:5:2])
# [[16 17 18 19 20]
#  [26 27 28 29 30]]

print(x[2:])
# [[21 22 23 24 25]
#  [26 27 28 29 30]
#  [31 32 33 34 35]]

print(x[:2])
# [[11 12 13 14 15]
#  [16 17 18 19 20]]

print(x[-2:])
# [[26 27 28 29 30]
#  [31 32 33 34 35]]

print(x[:-2])
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]]

print(x[:])
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]
#  [26 27 28 29 30]
#  [31 32 33 34 35]]

print(x[2, :])  # [21 22 23 24 25]
print(x[:, 2])  # [13 18 23 28 33]
print(x[0, 1:4])  # [12 13 14]
print(x[1:4, 0])  # [16 21 26]
print(x[1:3, 2:4])
# [[18 19]
#  [23 24]]

print(x[:, :])
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]
#  [26 27 28 29 30]
#  [31 32 33 34 35]]

print(x[::2, ::2])
# [[11 13 15]
#  [21 23 25]
#  [31 33 35]]

print(x[::-1, :])
# [[31 32 33 34 35]
#  [26 27 28 29 30]
#  [21 22 23 24 25]
#  [16 17 18 19 20]
#  [11 12 13 14 15]]

print(x[:, ::-1])
# [[15 14 13 12 11]
#  [20 19 18 17 16]
#  [25 24 23 22 21]
#  [30 29 28 27 26]
#  [35 34 33 32 31]]

[[11 12 13 14 15]
 [16 17 18 19 20]]
[[16 17 18 19 20]
 [26 27 28 29 30]]
[[21 22 23 24 25]
 [26 27 28 29 30]
 [31 32 33 34 35]]
[[11 12 13 14 15]
 [16 17 18 19 20]]
[[26 27 28 29 30]
 [31 32 33 34 35]]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [21 22 23 24 25]]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [21 22 23 24 25]
 [26 27 28 29 30]
 [31 32 33 34 35]]
[21 22 23 24 25]
[13 18 23 28 33]
[12 13 14]
[16 21 26]
[[18 19]
 [23 24]]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [21 22 23 24 25]
 [26 27 28 29 30]
 [31 32 33 34 35]]
[[11 13 15]
 [21 23 25]
 [31 33 35]]
[[31 32 33 34 35]
 [26 27 28 29 30]
 [21 22 23 24 25]
 [16 17 18 19 20]
 [11 12 13 14 15]]
[[15 14 13 12 11]
 [20 19 18 17 16]
 [25 24 23 22 21]
 [30 29 28 27 26]
 [35 34 33 32 31]]

import numpy as np

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
print(x)
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]
#  [26 27 28 29 30]
#  [31 32 33 34 35]]

x[0::2, 1::3] = 0
print(x)
# [[11  0 13 14  0]
#  [16 17 18 19 20]
#  [21  0 23 24  0]
#  [26 27 28 29 30]
#  [31  0 33 34  0]]

[[11 12 13 14 15]
 [16 17 18 19 20]
 [21 22 23 24 25]
 [26 27 28 29 30]
 [31 32 33 34 35]]
[[11  0 13 14  0]
 [16 17 18 19 20]
 [21  0 23 24  0]
 [26 27 28 29 30]
 [31  0 33 34  0]]

x = np.random.randint(1, 100, [2, 2, 3])
print(x)
# [[[ 5 64 75]
#   [57 27 31]]
# 
#  [[68 85  3]
#   [93 26 25]]]

print(x[1, ...])
# [[68 85  3]
#  [93 26 25]]

print(x[..., 2])
# [[75 31]
#  [ 3 25]]

[[[82 24  7]
  [31 27 97]]

 [[64 18 33]
  [93 90 61]]]
[[64 18 33]
 [93 90 61]]
[[ 7 97]
 [33 61]]

import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
r = [0, 1, 2]
print(x[r])
# [1 2 3]

r = [0, 1, -1]
print(x[r])
# [1 2 8]

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])

r = [0, 1, 2]
print(x[r])
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]]

r = [0, 1, -1]
print(x[r])

# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [31 32 33 34 35]]

r = [0, 1, 2]
c = [2, 3, 4]
y = x[r, c]
print(y)
# [13 19 25]

[1 2 3]
[1 2 8]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [21 22 23 24 25]]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [31 32 33 34 35]]
[13 19 25]

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
r = np.array([[0, 1], [3, 4]])
print(x[r])
# [[1 2]
#  [4 5]]

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])

r = np.array([[0, 1], [3, 4]])
print(x[r])
# [[[11 12 13 14 15]
#   [16 17 18 19 20]]
#
#  [[26 27 28 29 30]
#   [31 32 33 34 35]]]

# 获取了 5X5 数组中的四个角的元素。
# 行索引是 [0,0] 和 [4,4]，而列索引是 [0,4] 和 [0,4]。
r = np.array([[0, 0], [4, 4]])
c = np.array([[0, 4], [0, 4]])
y = x[r, c]
print(y)
# [[11 15]
#  [31 35]]

[[1 2]
 [4 5]]
[[[11 12 13 14 15]
  [16 17 18 19 20]]

 [[26 27 28 29 30]
  [31 32 33 34 35]]]
[[11 15]
 [31 35]]

# 可以借助切片:与整数数组组合
import numpy as np

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])

y = x[0:3, [1, 2, 2]]
print(y)
# [[12 13 13]
#  [17 18 18]
#  [22 23 23]]

[[12 13 13]
 [17 18 18]
 [22 23 23]]

# numpy.take()

import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
r = [0, 1, 2]
print(np.take(x, r))
# [1 2 3]

r = [0, 1, -1]
print(np.take(x, r))
# [1 2 8]

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])

r = [0, 1, 2]
print(np.take(x, r, axis=0))
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [21 22 23 24 25]]

r = [0, 1, -1]
print(np.take(x, r, axis=0))
# [[11 12 13 14 15]
#  [16 17 18 19 20]
#  [31 32 33 34 35]]

r = [0, 1, 2]
c = [2, 3, 4]
y = np.take(x, [r, c])
print(y)
# [[11 12 13]
#  [13 14 15]]

[1 2 3]
[1 2 8]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [21 22 23 24 25]]
[[11 12 13 14 15]
 [16 17 18 19 20]
 [31 32 33 34 35]]
[[11 12 13]
 [13 14 15]]

# 布尔索引
import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = x > 5
print(y)
# [False False False False False  True  True  True]
print(x[x > 5])
# [6 7 8]

x = np.array([np.nan, 1, 2, np.nan, 3, 4, 5])
y = np.logical_not(np.isnan(x))
print(y)
print(x[y])
# [1. 2. 3. 4. 5.]

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
y = x > 25
print(y)
# [[False False False False False]
#  [False False False False False]
#  [False False False False False]
#  [ True  True  True  True  True]
#  [ True  True  True  True  True]]
print(x[x > 25])
# [26 27 28 29 30 31 32 33 34 35]

[False False False False False  True  True  True]
[6 7 8]
[False  True  True False  True  True  True]
[1. 2. 3. 4. 5.]
[[False False False False False]
 [False False False False False]
 [False False False False False]
 [ True  True  True  True  True]
 [ True  True  True  True  True]]
[26 27 28 29 30 31 32 33 34 35]

import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(0, 2 * np.pi, 50)
y = np.sin(x)
print(len(x))  # 50
plt.plot(x, y)

mask = y >= 0
print(len(x[mask]))  # 25
print(mask)
'''
[ True  True  True  True  True  True  True  True  True  True  True  True
  True  True  True  True  True  True  True  True  True  True  True  True
  True False False False False False False False False False False False
 False False False False False False False False False False False False
 False False]
'''
plt.plot(x[mask], y[mask], 'bo')

mask = np.logical_and(y >= 0, x <= np.pi / 2)
print(mask)
'''
[ True  True  True  True  True  True  True  True  True  True  True  True
  True False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False]
'''

plt.plot(x[mask], y[mask], 'go')
plt.show()
# 蓝色点（在图中还包括绿点，但绿点掩盖了蓝色点），显示值 大于0 的所有点。绿色点表示值 大于0 且 小于0.5π 的所有点。

50
25
[ True  True  True  True  True  True  True  True  True  True  True  True
  True  True  True  True  True  True  True  True  True  True  True  True
  True False False False False False False False False False False False
 False False False False False False False False False False False False
 False False]
[ True  True  True  True  True  True  True  True  True  True  True  True
  True False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False]

在这里插入图片描述

# 数组迭代
import numpy as np

x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])

y = np.apply_along_axis(np.sum, 0, x)
print(y)  # [105 110 115 120 125]
y = np.apply_along_axis(np.sum, 1, x)
print(y)  # [ 65  90 115 140 165]

y = np.apply_along_axis(np.mean, 0, x)
print(y)  # [21. 22. 23. 24. 25.]
y = np.apply_along_axis(np.mean, 1, x)
print(y)  # [13. 18. 23. 28. 33.]


def my_func(x):
    return (x[0] + x[-1]) * 0.5


y = np.apply_along_axis(my_func, 0, x)
print(y)  # [21. 22. 23. 24. 25.]
y = np.apply_along_axis(my_func, 1, x)
print(y)  # [13. 18. 23. 28. 33.]

[105 110 115 120 125]
[ 65  90 115 140 165]
[21. 22. 23. 24. 25.]
[13. 18. 23. 28. 33.]
[21. 22. 23. 24. 25.]
[13. 18. 23. 28. 33.]

11+12+13+14+15

65

# 交换二维数组中的两列
import numpy as np

arr = np.arange(9).reshape(3, 3)
print(arr)
# 利用数组索引实现
x = arr[:, [2, 1, 0]]
print(x)

[[0 1 2]
 [3 4 5]
 [6 7 8]]
[[2 1 0]
 [5 4 3]
 [8 7 6]]

# 交换第一行和第二行
y = arr[[1, 0, 2], :]
print(y)

[[3 4 5]
 [0 1 2]
 [6 7 8]]

# 反转二维数组的行
z = arr[::-1, :]
print(z)

[[6 7 8]
 [3 4 5]
 [0 1 2]]

# 反转二维数组的列
w = arr[:, ::-1]

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